Comprehensive energy system capacity configuration method and device considering electricity-hydrogen energy market

Abstract

The invention belongs to the field of comprehensive energy system capacity configuration, and particularly discloses a comprehensive energy system capacity configuration method and device considering an electricity-hydrogen energy market. The method comprises the steps of performing correlation analysis according to historical annual wind speed and illumination radiation intensity data of a target area where the integrated energy system is located, and determining power transaction data of the integrated energy system in a target time period in each season; solving the double-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in each target time period, and determining the current optimal capacity configuration information of the integrated energy system; the double-layer capacity planning model comprises an outer-layer optimization model used for optimizing the annual total cost, the carbon dioxide emission amount and the wind-solar energy redundancy rate of the comprehensive energy system and an inner-layer optimization model used for optimizing the electricity-hydrogen energy income of the comprehensive energy system. According to the invention, the reliability, economical efficiency and environmental protection performance of the operation of the comprehensive energy system can be effectively improved.

Description

Technical Field

[0001] The present application belongs to the technical field of integrated energy system capacity configuration, and more specifically, to a method and device for integrated energy system capacity configuration taking into account the electric hydrogen energy market. Background Art

[0002] The global trend of reducing carbon emissions is accelerating, and the energy structure is changing from centralized and extensive to distributed and green energy systems. Integrated energy systems have emerged as an innovative distributed energy architecture that can achieve gradient utilization of energy, effectively improve energy utilization and significantly reduce carbon emissions.

[0003] In order to further reduce carbon emissions and promote the clean development of integrated energy systems, the integration of renewable energy is increasing, but this trend has also caused some problems. On the one hand, due to the intermittent and volatile characteristics of renewable energy output, energy utilization is low and the phenomenon of wind and solar power abandonment is becoming increasingly serious; on the other hand, the load and renewable energy output show significant seasonal imbalance characteristics. In order to solve the above problems, energy storage systems have been widely developed.

[0004] The optimal configuration of the capacity of the integrated energy system is the key to its planning and design. At present, in the existing research on the configuration of integrated energy storage, there are problems such as single consideration factors and rough calculation methods, which leads to poor system robustness, low reliability, economy and environmental protection of system operation.

[0005] Therefore, how to better achieve the optimal configuration of the capacity of the integrated energy system has become a technical problem that needs to be urgently solved in the industry. Summary of the invention

[0006] In view of the defects of the prior art, the purpose of this application is to achieve the optimal configuration of the capacity of the integrated energy system, aiming to solve the problems in the existing research on the integrated energy storage configuration, such as single consideration factors and rough calculation methods, resulting in poor system robustness, low reliability, economy and environmental protection of the system operation.

[0007] To achieve the above objectives, in a first aspect, the present application provides a comprehensive energy system capacity configuration method considering the electric hydrogen energy market, comprising: Performing correlation analysis based on historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located, and determining the power trading data of the integrated energy system in the target period of each season; Solving the two-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in each of the target time periods to determine the current optimal capacity configuration information of the integrated energy system; The two-layer capacity planning model includes an outer optimization model for optimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system and an inner optimization model for optimizing the electric hydrogen energy benefits of the integrated energy system.

[0008] Optionally, before solving the two-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in each target time period to determine the current optimal capacity configuration information of the integrated energy system, the method includes: The outer optimization model is established based on the annual total cost of the integrated energy system, the carbon dioxide emissions and the wind and solar energy redundancy rate; the annual total cost is determined based on the annual investment cost, equipment replacement cost and annual operating benefit of the integrated energy system; the carbon dioxide emissions are determined based on the carbon emission coefficient and the daily power purchase amount in each of the target time periods; the wind and solar energy redundancy rate is determined based on the daily power sales amount in each of the target time periods and the photovoltaic output and wind power output at different times; Based on the daily electricity sales revenue and hydrogen sales revenue in each of the target time periods, establishing the inner optimization model; The double-layer capacity planning model is obtained according to the outer-layer optimization model and the inner-layer optimization model.

[0009] Optionally, the outer optimization model is established based on the annual total cost of the integrated energy system, the carbon dioxide emissions and the wind and solar energy redundancy rate, including: Taking the annual total cost, the carbon dioxide emissions and the wind and solar energy redundancy rate as the minimum as the optimization goal, constructing a first objective function, and determining a first constraint condition based on the capacity range of each subsystem in the integrated energy system; The outer layer optimization model is established based on the first objective function and the first constraint condition.

[0010] Optionally, the inner optimization model is established based on the daily electricity sales revenue and hydrogen sales revenue in each target period, including: Taking the maximum sum of the daily electricity sales revenue and the hydrogen sales revenue in each target period as the optimization goal, constructing a second objective function, and determining a second constraint condition based on the power operation characteristics of each subsystem in the integrated energy system; The inner layer optimization model is established based on the second objective function and the second constraint condition.

[0011] Optionally, the performing of correlation analysis based on historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located to determine the power trading data of the integrated energy system in the target period of each season includes: Using the nonparametric kernel density estimation method, the marginal distribution functions of wind speed and light radiation intensity are determined using the historical annual wind speed and light radiation intensity data; A two-dimensional Frank-Copula function is used to generate a joint distribution function of wind speed and light radiation intensity using the marginal distribution functions of the wind speed and light radiation intensity; Inverse sampling is performed on the joint distribution function of the wind speed and light radiation intensity, and cluster analysis is performed based on the sampled joint distribution samples to determine the target time period in each season when the wind speed and light radiation intensity data meet the seasonal characteristic type; Obtain power transaction data of the integrated energy system during a target period in each season.

[0012] Optionally, solving the two-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in each of the target time periods to determine the current optimal capacity configuration information of the integrated energy system includes: Initializing the two-layer capacity planning model to determine initial capacity configuration information of the integrated energy system; Determine the maximum wind and solar power output based on the initial capacity configuration information of the integrated energy system and the power transaction data in each target time period; Solving the inner optimization model based on the maximum wind and solar power output to determine the output of each subsystem of the integrated energy system and the maximum electric hydrogen energy benefit; The particle swarm optimization algorithm is used to iteratively solve the outer optimization model according to the output of each subsystem of the integrated energy system and the maximum electric hydrogen energy benefit, so as to determine the current optimal capacity configuration information of the integrated energy system.

[0013] In a second aspect, the present application provides a comprehensive energy system capacity configuration device considering the electric hydrogen energy market, comprising: An analysis module, used to perform correlation analysis based on historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located, and determine the power trading data of the integrated energy system in the target period of each season; A configuration module, used to solve the double-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in each of the target time periods, and determine the current optimal capacity configuration information of the integrated energy system; The two-layer capacity planning model includes an outer optimization model for optimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system and an inner optimization model for optimizing the electric hydrogen energy benefits of the integrated energy system.

[0014] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0016] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0017] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0018] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art: The present application provides a method and device for capacity configuration of an integrated energy system taking into account the electric hydrogen energy market. By comprehensively considering the economy, environmental protection and stability of the system, an outer optimization model for optimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system and an inner optimization model for optimizing the electric hydrogen energy revenue in the integrated energy system are established, thereby constructing a multi-objective two-layer capacity planning model. At the same time, by considering the correlation between wind speed and light radiation intensity, the two-layer capacity planning model is solved using the power trading data of the integrated energy system in typical scenarios of each season, so as to obtain the current optimal capacity configuration information of the integrated energy system, which can effectively improve the system's absorption level of wind power and photovoltaic energy, improve the resource utilization and robustness of the integrated energy system, and thus effectively improve the reliability, economy and environmental protection of the system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a comprehensive energy system capacity configuration method considering the electric hydrogen energy market provided in an embodiment of the present application; Figure 2 It is a flowchart of a solution process of a two-layer capacity planning model for an integrated energy system provided in an embodiment of the present application; Figure 3 is a schematic diagram of the capacity configuration optimization result of the integrated energy system provided in the embodiment of the present application; Figure 4It is a structural schematic diagram of a comprehensive energy system capacity configuration device considering the electric hydrogen energy market provided in an embodiment of the present application; Figure 5 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0021] The terms "first" and "second" in the specification and claims of this application are used to distinguish different objects rather than to describe a specific order of objects. For example, the first objective function and the second objective function are used to distinguish different objective functions rather than to describe a specific order of objective functions.

[0022] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0023] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0024] Figure 1 is a flow chart of a comprehensive energy system capacity configuration method considering the electric hydrogen energy market provided in an embodiment of the present application, such as Figure 1 As shown, including: Step S1, performing correlation analysis based on the historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located, and determining the power trading data of the integrated energy system in the target period of each season; Step S2, using the power transaction data of the integrated energy system in each target period to solve the two-layer capacity planning model of the integrated energy system, and determine the current optimal capacity configuration information of the integrated energy system; The two-layer capacity planning model includes an outer optimization model for optimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system, and an inner optimization model for optimizing the electric and hydrogen energy benefits of the integrated energy system.

[0025] Specifically, the integrated energy system described in the embodiment of the present application refers to a total system composed of multiple types of electric energy systems, wherein the multiple types of electric energy systems may include a wind turbine system, a photovoltaic system, an electrolyzer system, a fuel cell system, a hydrogen storage tank system and a battery system.

[0026] The target time period described in the embodiment of the present application refers to a time period in each season in which the change characteristics of wind speed and light radiation intensity data are representative as the seasons change. It can also be described as a time period covered under a typical daily scenario of wind speed and light radiation intensity.

[0027] The power transaction data described in the embodiment of the present application refers to the power transaction data displayed by the integrated energy system in each target time period, including but not limited to the total amount of power purchased, revenue, total amount of power sold, etc.

[0028] The optimal capacity configuration information described in the embodiments of the present application can characterize the capacity information of the wind turbine system, photovoltaic system, electrolyzer system, fuel cell system, hydrogen storage tank system and battery system under the scenario where the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system are minimized and the electric hydrogen energy benefits are maximized.

[0029] In an embodiment of the present application, the two-layer capacity planning model of the integrated energy system includes an outer optimization model for optimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system and an inner optimization model for optimizing the electric hydrogen energy benefits of the integrated energy system.

[0030] Among them, it should be noted that the wind and solar energy redundancy rate refers to the ratio of the total electricity sales during the target period to the total output of the photovoltaic system and the wind power system; the electric hydrogen energy income refers to the income obtained by the integrated energy system through the sale of electricity and hydrogen.

[0031] Furthermore, in an embodiment of the present application, in step S1, a Copula function can be used to model the correlation between wind speed and light radiation intensity, and seasonal division can be performed based on the historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located. Then, a correlation analysis is performed on the historical wind speed and light radiation intensity data in each season to generate a typical daily scene of wind speed and light radiation intensity in each season, from which the target time period covered by the typical day is determined, and the typical daily scene data of wind speed and light radiation intensity can be used to calculate the power trading data of the integrated energy system in the target time period of each season.

[0032] Furthermore, in an embodiment of the present application, in step S2, an outer optimization model for optimizing the annual total cost of the integrated energy system, carbon dioxide emissions and wind and solar energy redundancy rate and an inner optimization model for optimizing the electric and hydrogen energy benefits of the integrated energy system are established in advance to jointly construct a two-layer capacity planning model.

[0033] Then, by using the CPLEX solver, the inner model of the integrated energy system is optimized according to the power trading data of the integrated energy system in each target period, and the output of each part of the integrated energy system and the typical daily income are obtained. Finally, according to the results obtained from the inner optimization model, the multi-objective particle swarm algorithm is used to solve the outer model of the integrated energy system, and finally the optimal capacity configuration information of the integrated energy system is determined.

[0034] The method for configuring the capacity of an integrated energy system taking into account the electric hydrogen energy market in the embodiment of the present application comprehensively considers the economy, environmental protection and stability of the system, establishes an outer optimization model for optimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system, and an inner optimization model for optimizing the electric hydrogen energy revenue in the integrated energy system, thereby constructing a multi-objective two-layer capacity planning model. At the same time, by considering the correlation between wind speed and light radiation intensity, the two-layer capacity planning model is solved using the power trading data of the integrated energy system in typical scenarios of each season, so as to obtain the current optimal capacity configuration information of the integrated energy system, which can effectively improve the system's absorption level of wind power and photovoltaic energy, improve the resource utilization and robustness of the integrated energy system, and thus effectively improve the reliability, economy and environmental protection of the system operation.

[0035] Based on the content of the above embodiment, as an optional embodiment, step S1, performing correlation analysis based on the historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located, and determining the power transaction data of the integrated energy system in the target period of each season, includes: The marginal distribution functions of wind speed and solar radiation intensity were determined by using the nonparametric kernel density estimation method and historical annual wind speed and solar radiation intensity data. The two-dimensional Frank-Copula function is used to generate the joint distribution function of wind speed and light radiation intensity using the marginal distribution functions of wind speed and light radiation intensity. The joint distribution function of wind speed and light radiation intensity is inversely sampled, and cluster analysis is performed based on the sampled joint distribution samples to generate target scene data of wind speed and light radiation intensity in each season; Based on the target scenario data of wind speed and light radiation intensity in each season, the power trading data of the integrated energy system in the target period of each season is determined.

[0036] Specifically, in the embodiment of the present application, the Copula function is used to model the correlation between wind speed and light radiation intensity, and the specific implementation method of finally determining the power transaction data of the integrated energy system in the target period of each season is as follows: Step 1.1, divide the historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located into four seasons: spring, summer, autumn and winter. Perform steps 1.2 to 1.6 on the historical wind speed and light radiation intensity data of each season to obtain four typical daily scenes of wind speed and light radiation intensity.

[0037] In step 1.2, the nonparametric kernel density estimation method is used to estimate the marginal distribution of wind speed and light radiation intensity. Kernel density estimation is a nonparametric estimation method. Assume that from the historical annual wind speed and light radiation intensity data The independent and identically distributed sample data drawn from , for The corresponding density function is an unknown function. , need to calculate the point The estimated value of the probability density function at , that is, the marginal distribution function of wind speed and light radiation intensity is expressed as: ; In the formula, is the sample size; is the window width; is the kernel function used in the kernel density estimation method.

[0038] In step 1.3, the Copula function can use a two-dimensional Frank-Copula function, and use the marginal distribution functions of the above wind speed and light radiation intensity to generate a joint distribution function of wind speed and light radiation intensity, where the expression of the Frank-Copula function is: ; In the formula, is the number of variables; is the marginal distribution function of a single variable; is the Copula connection function; for The joint distribution function of the variables.

[0039] in, It can be expressed as: ; In the formula, , are the marginal distribution functions of wind speed and light radiation intensity respectively; is the parameter of the two-dimensional Frank-Copula function, which can be estimated by the maximum likelihood method.

[0040] Step 1.4, reverse sampling is performed on the joint probability distribution function of the above wind speed and light radiation intensity, and the wind speed and light radiation intensity scene data corresponding to each time period are obtained based on the sampled joint distribution samples and the inverse transformation of the joint probability distribution function.

[0041] Step 1.5: Use the K-means clustering algorithm to reduce the scene data generated in step 1.4 to obtain K representative scenes. The specific steps are as follows: Step 1), randomly select a wind speed scenario from the wind speed scenario obtained in step 1.4. is the first cluster center of the wind speed data. At the same time, randomly select As the first cluster center of the light radiation intensity data; Step 2), for wind speed scene data, calculate each of them and the current existing cluster center The shortest distance between , for the scene data of light radiation intensity, calculate the cluster center of each The shortest distance between , and sum the squares of the shortest distances to obtain and ; Step 3), calculate the probability of the wind speed scene being selected as the next cluster center and the probability that the illumination radiation intensity scene is selected as the next cluster center , the corresponding calculation formula is as follows: ; ; Step 4), pick a random number M between [0,1] and subtract If the result is greater than 0, continue to subtract , until the result is not greater than 0, and the probability at this time The corresponding wind speed scene is recorded as the second cluster center, and the light radiation intensity is also obtained in the same way to obtain the second cluster center; Then, repeat steps 2) to 4) until you get K Cluster centers, that is K representative scenarios and their corresponding probabilities, where the probability satisfies , .

[0042] Step 1.6: Perform weighted summation on the K representative scenes obtained in step 1.5 above to obtain a typical day scene data of wind speed and light radiation intensity, that is, to obtain the scene data of wind speed and light radiation intensity during the target period. This process can be expressed as: ; ; Finally, based on the typical daily scene data of four wind speeds and light radiation intensities generated corresponding to the four seasons, the relevant existing power system calculation formulas can be used to determine the power trading data of the target time period covered by the integrated energy system under the typical day scene in each season, including the total amount of electricity purchased, the total amount of electricity sold, the revenue and other data on the typical day.

[0043] The method of the embodiment of the present application establishes a joint probability distribution function of wind speed and light radiation intensity by adopting a two-dimensional Frank-Copula function, and generates a typical daily scene that takes into account the correlation between wind speed and light radiation intensity through cluster analysis. It can truly reflect the wind power fluctuation and temporal and spatial correlation, which is beneficial to reducing the wind power abandonment rate and the solar power abandonment rate, improving the system's wind power and photovoltaic absorption level, and enhancing the capacity configuration optimization effect of the integrated energy system.

[0044] Based on the content of the above embodiment, as an optional embodiment, in step S2, before solving the two-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in the target period and determining the current optimal capacity configuration information of the integrated energy system, the following steps are included: An outer optimization model is established based on the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system; the annual total cost is determined based on the annual investment cost, equipment replacement cost and annual operating benefits of the integrated energy system; carbon dioxide emissions are determined based on the carbon emission coefficient and the daily electricity purchase amount in each target period; the wind and solar energy redundancy rate is determined based on the daily electricity sales in each target period and the photovoltaic output and wind power output at different times; An inner optimization model is established based on the daily electricity sales revenue and hydrogen sales revenue in each target period; According to the outer optimization model and the inner optimization model, a two-layer capacity planning model is obtained.

[0045] Specifically, in an embodiment of the present application, in step S2, the two-layer capacity planning model of the integrated energy system is solved using the power trading data of the integrated energy system in the target period. Before determining the current optimal capacity configuration information of the integrated energy system, a two-layer capacity planning model of the integrated energy system needs to be constructed.

[0046] It should be noted that the two-layer capacity planning model of the integrated energy system that takes into account economy, environmental protection and stability is a multi-objective optimization problem. In the outer optimization model, goal 1 is to minimize the annual total cost, goal 2 is to minimize carbon dioxide emissions, and goal 3 is to minimize the wind and solar energy redundancy rate. In the inner optimization model, the goal is to maximize the system operation benefits.

[0047] Firstly, based on the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system, the outer optimization model of the two-layer capacity planning model is established.

[0048] Based on the content of the above embodiment, as an optional embodiment, an outer optimization model is established based on the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system, including: Taking the minimization of annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate as the optimization goals, the first objective function is constructed, and the first constraint condition is determined based on the capacity range of each subsystem in the integrated energy system; Based on the first objective function and the first constraint condition, an outer optimization model is established.

[0049] Specifically, the first objective function described in the embodiment of the present application refers to the objective function of the outer optimization model, which takes minimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system as the optimization goal.

[0050] The first constraint condition described in the embodiment of the present application refers to the constraint condition in the iterative solution process of the outer optimization model.

[0051] In the embodiment of the present application, the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate are minimized as the optimization goals, a first objective function is constructed, and based on the capacity range of each subsystem in the integrated energy system, a first constraint condition is determined, and its specific implementation method is as follows: The first objective function can be constructed according to the following formula, wherein the decision variables are the capacities corresponding to the wind turbine system, the photovoltaic system, the battery system, the electrolyzer system, the hydrogen storage tank system, and the fuel cell system.

[0052] ; ; ; in, ; ; ; In the formula, Annual investment cost; Equipment replacement costs; represents the investment recovery factor; Annual operating benefit; , , , , , They represent the unit capacity investment costs of the wind turbine system, photovoltaic system, electrolyzer system, fuel cell system, hydrogen storage tank system and battery system respectively; , , , , , Respectively represent the capacities of the wind turbine system, photovoltaic system, electrolyzer system, fuel cell system, hydrogen storage tank system and battery system; , , , , , They represent the replacement costs of the wind turbine system, photovoltaic system, electrolyzer system, fuel cell system, hydrogen storage tank system and battery system respectively; number of typical days; Indicates a typical day The corresponding number of days; Indicates a typical day The following income; unit penalty costs for insufficient power; Indicates a typical day The total amount of electricity purchased under unit penalty cost of excess electricity; Indicates a typical day The total amount of electricity sold under Operation and maintenance costs; Indicates the indirect carbon emission coefficient generated by purchasing electricity from the grid; , Indicates a typical day Down The output of photovoltaic system and wind power system at each moment.

[0053] The first constraint can be expressed as follows: ; In the formula, , , , , and Respectively represent the minimum values ​​of the capacities of the wind turbine system, photovoltaic system, electrolyzer system, hydrogen storage tank system, fuel cell system and battery system; , , , , and They are the maximum capacities of the wind turbine system, photovoltaic system, electrolyzer system, hydrogen storage tank system, fuel cell system and battery system respectively. Therefore, in an embodiment of the present application, based on the above-mentioned first objective function and the first constraint condition, an outer optimization model in the two-layer capacity planning model of the integrated energy system is constructed.

[0054] The method of the embodiment of the present application takes the minimization of annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate as the optimization goals, and uses the capacity range of each subsystem in the integrated energy system as a constraint condition to construct an outer optimization model of double-layer optimization, thereby ensuring that the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system are minimized, improving the resource utilization of the system, and being conducive to achieving the economy, environmental protection and reliability of the operation of the integrated energy system.

[0055] Next, in an embodiment of the present application, an inner optimization model of the double-layer capacity planning model is established based on the daily electricity sales revenue and hydrogen sales revenue in each target period.

[0056] Based on the content of the above embodiment, as an optional embodiment, an inner optimization model is established based on the daily electricity sales revenue and hydrogen sales revenue in each target period, including: Taking the maximum sum of daily electricity sales revenue and hydrogen sales revenue in each target period as the optimization goal, the second objective function is constructed, and the second constraint condition is determined based on the power operation characteristics of each subsystem in the integrated energy system; Based on the second objective function and the second constraint condition, an inner optimization model is established.

[0057] Specifically, the second objective function described in the embodiment of the present application is the objective function of the inner optimization model, which takes the maximum sum of daily electricity sales revenue and hydrogen sales revenue in each target period as the optimization goal.

[0058] The second constraint condition described in the embodiment of the present application is a constraint condition in the iterative solution process of the inner optimization model, which may include the hydrogen storage capacity constraint in the hydrogen storage tank, the charge state constraint of the battery, and the output constraint of each system.

[0059] In the embodiment of the present application, the maximum sum of the daily electricity sales revenue and hydrogen sales revenue in each target period is taken as the optimization goal, the second objective function is constructed, and the second constraint condition is determined based on the power operation characteristics of each subsystem in the integrated energy system. The specific implementation method is as follows: The second objective function can be constructed according to the following formula: ; ; ; ; In the formula, Indicates a typical day The proceeds from the sale of electricity; Indicates a typical day Proceeds from the sale of hydrogen; Indicates a typical day The penalty cost of curtailing wind and solar power; and Respectively, on a typical day Down The price of electricity purchased and sold at any given moment; In a typical day Down The price of hydrogen sold at any given moment; Indicates a typical day Down Amount of hydrogen sold at any given moment; It represents the penalty cost for unit wind and solar power abandonment; and Represents a typical day Down The amount of abandoned solar power and wind power at the moment.

[0060] The second constraint can be expressed as: ; In the formula, , The minimum and maximum capacity of hydrogen storage in the hydrogen tank system; typical day Down The amount of hydrogen stored in the hydrogen storage tank system at any given moment; typical day Down The amount of hydrogen produced by the electrolyzer system at the moment; typical day Down The amount of hydrogen consumed by the fuel cell system at any given moment; Indicates the efficiency of electricity-to-hydrogen conversion; typical day Down The electrolyzer system output at all times; Indicates the efficiency of the electrolyzer system; Indicates the efficiency of the electrolyzer system; typical day Down The fuel cell system outputs at all times; Indicates the efficiency of hydrogen-to-electricity conversion; Indicates the efficiency of the fuel cell system; Indicates the efficiency of the fuel cell system; typical day Down The state of charge of the battery system at all times; , The minimum and maximum state of charge of the battery; Indicates the self-discharge rate of the battery system; Indicates the charging efficiency of the battery system; Indicates the discharge efficiency of the battery system; typical day Down Discharge power of the battery system at all times; typical day Down The charging power of the battery system at all times; load; typical day The maximum output of the downstream wind power system; typical day The maximum output of the photovoltaic system; Maximum output of fuel cell system; Maximum output of electrolyzer system; , Indicates the maximum discharge and charge power of the battery system.

[0061] It can be understood that the power operation characteristics described in the embodiments of the present application refer to the characteristic parameters of each subsystem in the integrated energy system during operation, including the amount of hydrogen stored in the hydrogen storage tank system, the amount of hydrogen produced by the electrolyzer system, the amount of hydrogen consumed by the fuel cell system, the efficiency of electricity to hydrogen, the output of the electrolyzer system, the efficiency of the electrolyzer system, the output of the fuel cell system, etc.

[0062] Therefore, in an embodiment of the present application, based on the above-mentioned second objective function and second constraint condition, an inner optimization model of the two-layer capacity planning model of the integrated energy system is constructed.

[0063] The method of the embodiment of the present application, by taking the maximum sum of daily electricity sales revenue and hydrogen sales revenue in the target period of each season as the optimization goal, mobilizes the joint optimization of the output of each subsystem, ensures that the electric hydrogen energy revenue of the system energy storage configuration is maximized, reduces the system's wind and solar power abandonment, and further improves the economy and reliability of the operation of the integrated energy system; at the same time, by considering the integrated energy market of the electricity market and the hydrogen market, for the hydrogen energy storage system, not only its energy storage function is considered, but also the transaction of the hydrogen it produces with the hydrogen market is considered, which can effectively solve the problem of high existing hydrogen energy storage costs.

[0064] Furthermore, by combining the above outer optimization model and inner optimization model, a two-layer capacity planning model for the integrated energy system can be obtained.

[0065] The method of the embodiment of the present application establishes a multi-objective optimized two-layer capacity planning model with the goal of minimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate and maximizing the electric hydrogen energy revenue, and utilizes the optimal solution to be transferred between the inner and outer layer models to achieve mutual exchange of two-layer information, which can effectively output the optimal capacity configuration plan of the system, thereby improving the economy, environmental protection and stability of the system operation.

[0066] Based on the content of the above embodiment, as an optional embodiment, step S2, using the power transaction data of the integrated energy system in each target period to solve the two-layer capacity planning model of the integrated energy system, to determine the current optimal capacity configuration information of the integrated energy system, includes: Initialize the two-layer capacity planning model to determine the initial capacity configuration information of the integrated energy system; Determine the maximum wind and solar output based on the initial capacity configuration information of the integrated energy system and the power trading data in each target period; Solve the inner optimization model based on the maximum wind and solar output to determine the output of each subsystem of the integrated energy system and the maximum electric hydrogen energy benefit; Using the particle swarm optimization algorithm, the outer optimization model is iteratively solved according to the output of each subsystem of the integrated energy system and the maximum electric hydrogen energy benefit to determine the current optimal capacity configuration information of the integrated energy system.

[0067] Specifically, in an embodiment of the present application, in the process of solving the two-layer capacity planning model of the integrated energy system, the two-layer capacity planning model is first randomly initialized to determine the initial capacity configuration information of the integrated energy system, and the initial capacity configuration result of the outer optimization model can be obtained, including the initial capacity values ​​of the wind turbine system, photovoltaic system, electrolyzer system, hydrogen storage tank system, fuel cell system and battery system.

[0068] Furthermore, in the embodiment of the present application, the power transaction data in each target period of each season is calculated based on the typical daily scene data obtained in the aforementioned step S1. And according to the typical daily scene data in each season, based on the initial capacity configuration result of the above outer optimization model, the maximum wind and solar output of the system is calculated.

[0069] Among them, the maximum wind and solar output can be calculated according to the following calculation formula, namely: ; ; In the formula, Indicates real-time wind speed; Indicates the cut-in wind speed; Indicates rated wind speed; Indicates the cut-out wind speed; Indicates the real-time lighting radiation intensity; Indicates the intensity of light radiation under standard conditions; Represents the temperature coefficient of the photovoltaic panel; Indicates the real-time temperature of the photovoltaic panel; Indicates the temperature of the photovoltaic panel under standard conditions.

[0070] Furthermore, the maximum wind and solar power output of the system is input into the inner optimization model of the two-layer capacity planning model. Based on the objective function and constraints of the inner optimization model, the CPLEX solver is used to optimize the inner optimization model to obtain the output of each system in the integrated energy system, as well as the revenue from electricity and hydrogen sales under typical daily scenarios, that is, the maximum electric hydrogen energy revenue. Then, the obtained maximum electric hydrogen energy revenue and the output of each system are passed to the outer optimization model, and based on the power trading data under the target period of each season, the multi-objective particle swarm optimization algorithm is used to iteratively solve the outer optimization model, and finally the current optimal capacity configuration information of the integrated energy system is obtained. Finally, the obtained optimal capacity configuration information can be passed to the inner optimization model to realize the mutual exchange of two-layer information.

[0071] Figure 2 is a flow chart of the solution process of the two-layer capacity planning model for the integrated energy system provided in the embodiment of the present application, such as Figure 2 As shown, in an embodiment of the present application, the implementation process of solving the outer optimization model based on the multi-objective particle swarm optimization algorithm includes: randomly initializing particles, that is, the initial capacity of the wind turbine system, photovoltaic system, electrolyzer system, fuel cell system, hydrogen storage tank system and battery system; then, evaluating each particle to obtain the global optimal particle; updating the position and speed of each particle according to the set outer constraint conditions; then, inputting the capacity configuration information corresponding to each particle into the inner optimization model, combining the typical daily scene data of each season generated in the aforementioned step S1, and calculating the maximum wind and solar output of the system, and then based on the objective function and inner constraint conditions of the inner optimization model, that is, the second objective function and the second constraint conditions, the CPLEX solver is used to optimize and solve the inner optimization model to obtain the output of each system of the integrated energy system and the maximum electric hydrogen energy benefit.

[0072] Furthermore, the obtained maximum electric hydrogen energy revenue and the output of each system are passed to the outer optimization model, and the first objective function of the outer optimization model is used as the fitness function. The obtained power trading data, maximum electric hydrogen energy revenue and the output of each system in each seasonal target period are substituted into the fitness function to calculate the function fitness value of each particle, and then the optimal position of each particle is updated, and then the historical optimal position of the group is updated. The constraints are continuously judged and solved iteratively until it is determined that the capacity configuration information corresponding to the optimal particle meets the constraints, and finally the current optimal capacity configuration information of the system corresponding to the optimal particle can be obtained.

[0073] Figure 3 is a schematic diagram of the comprehensive energy system capacity configuration optimization result provided by the embodiment of the present application, such as Figure 3 As shown, in an embodiment of the present application, through the above-mentioned multi-objective two-level planning iterative solution process of the integrated energy system, a capacity configuration scheme that satisfies the system's minimum annual total cost, minimum carbon dioxide emissions, and minimum wind and solar energy redundancy rate (i.e., minimum energy redundancy rate) can eventually be found, that is, the system's current optimal capacity configuration information can be obtained.

[0074] The method of the embodiment of the present application, by considering the correlation between wind speed and light radiation intensity, combines the particle swarm optimization algorithm to analyze and solve the problems planned by the optimization models of each layer of the double-layer capacity planning model, thereby ensuring the accuracy and convergence of the model solution results, ensuring that the optimal capacity configuration plan of the system is obtained, and improving the effect of capacity configuration optimization of the integrated energy system.

[0075] The following is a description of the comprehensive energy system capacity configuration device considering the electric hydrogen energy market provided in the present application. The comprehensive energy system capacity configuration device considering the electric hydrogen energy market described below and the comprehensive energy system capacity configuration method considering the electric hydrogen energy market described above can be referenced to each other.

[0076] Figure 4 is a schematic diagram of the structure of a comprehensive energy system capacity configuration device considering the electric hydrogen energy market provided in an embodiment of the present application, such as Figure 4 As shown, including: The analysis module 10 is used to perform correlation analysis based on the historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located, and determine the power transaction data of the integrated energy system in the target period of each season; The configuration module 20 is used to solve the double-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in each target period, and determine the current optimal capacity configuration information of the integrated energy system; The two-layer capacity planning model includes an outer optimization model for optimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system, and an inner optimization model for optimizing the electric and hydrogen energy benefits of the integrated energy system.

[0077] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0078] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0079] The integrated energy system capacity configuration device considering the electric hydrogen energy market in the embodiment of the present application establishes an outer optimization model for optimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system and an inner optimization model for optimizing the electric hydrogen energy revenue in the integrated energy system by comprehensively considering the economy, environmental protection and stability of the system, thereby constructing a multi-objective two-layer capacity planning model. At the same time, by considering the correlation between wind speed and light radiation intensity, the two-layer capacity planning model is solved using the power trading data of the integrated energy system in typical scenarios of each season, so as to obtain the current optimal capacity configuration information of the integrated energy system, which can effectively improve the system's absorption level of wind power and photovoltaic energy, improve the resource utilization and robustness of the integrated energy system, and thus effectively improve the reliability, economy and environmental protection of the system operation.

[0080] Based on the method in the above embodiment, the embodiment of the present application provides an electronic device, such as Figure 5 As shown, the electronic device may include: a processor (Processor) 510, a communication interface (Communications Interface) 520, a memory (Memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logic instructions in the memory 530 to execute the method in the above embodiment.

[0081] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0082] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0083] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0084] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0085] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0086] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0087] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0088] It should be understood that expressions such as "including" and "may include" that may be used in the present application indicate the existence of the disclosed functions, operations, or constituent elements, and do not limit one or more additional functions, operations, and constituent elements. In the present application, terms such as "including" and / or "having" may be interpreted as indicating specific characteristics, numbers, operations, constituent elements, components, or combinations thereof, but may not be interpreted as excluding the existence or possibility of adding one or more other characteristics, numbers, operations, constituent elements, components, or combinations thereof.

[0089] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for configuring the capacity of an integrated energy system considering the electric hydrogen energy market, characterized in that: include: Conduct correlation analysis based on historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located, and determine the power trading data of the integrated energy system in the target period of each season; Solving the two-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in each of the target time periods to determine the current optimal capacity configuration information of the integrated energy system; The two-layer capacity planning model includes an outer optimization model for optimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system and an inner optimization model for optimizing the electric hydrogen energy benefits of the integrated energy system.

2. The method for configuring the capacity of an integrated energy system considering the electric hydrogen energy market according to claim 1 is characterized in that: Before solving the two-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in each target time period to determine the current optimal capacity configuration information of the integrated energy system, the method includes: The outer optimization model is established based on the annual total cost of the integrated energy system, the carbon dioxide emissions and the wind and solar energy redundancy rate; the annual total cost is determined based on the annual investment cost, equipment replacement cost and annual operating benefit of the integrated energy system; the carbon dioxide emissions are determined based on the carbon emission coefficient and the daily power purchase amount in each of the target time periods; the wind and solar energy redundancy rate is determined based on the daily power sales amount in each of the target time periods and the photovoltaic output and wind power output at different times; Establishing the inner optimization model based on the daily electricity sales revenue and hydrogen sales revenue in each of the target time periods; The double-layer capacity planning model is obtained according to the outer-layer optimization model and the inner-layer optimization model.

3. The method for configuring the capacity of an integrated energy system considering the electric hydrogen energy market according to claim 2 is characterized in that: The outer optimization model is established based on the annual total cost of the integrated energy system, the carbon dioxide emissions and the wind and solar energy redundancy rate, including: Taking the annual total cost, the carbon dioxide emissions and the wind and solar energy redundancy rate as the minimum as the optimization goal, constructing a first objective function, and determining a first constraint condition based on the capacity range of each subsystem in the integrated energy system; The outer layer optimization model is established based on the first objective function and the first constraint condition.

4. The method for configuring the capacity of an integrated energy system considering the electric hydrogen energy market according to claim 2 is characterized in that: The inner optimization model is established based on the daily electricity sales revenue and hydrogen sales revenue in each target period, including: Taking the maximum sum of the daily electricity sales revenue and the hydrogen sales revenue in each target period as the optimization goal, constructing a second objective function, and determining a second constraint condition based on the power operation characteristics of each subsystem in the integrated energy system; The inner layer optimization model is established based on the second objective function and the second constraint condition.

5. The method for configuring the capacity of an integrated energy system considering the electric hydrogen energy market according to any one of claims 1 to 4, characterized in that: The correlation analysis is performed based on the historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located to determine the power trading data of the integrated energy system in the target period of each season, including: Using the nonparametric kernel density estimation method, the marginal distribution functions of wind speed and light radiation intensity are determined using the historical annual wind speed and light radiation intensity data; A two-dimensional Frank-Copula function is used to generate a joint distribution function of wind speed and light radiation intensity using the marginal distribution functions of the wind speed and light radiation intensity; Inverse sampling is performed on the joint distribution function of the wind speed and light radiation intensity, and cluster analysis is performed based on the sampled joint distribution samples to determine the target time period in each season when the wind speed and light radiation intensity data meet the seasonal characteristic type; Obtain power transaction data of the integrated energy system during a target period in each season.

6. The method for configuring the capacity of an integrated energy system considering the electric hydrogen energy market according to any one of claims 1 to 4, characterized in that: The method of solving the two-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in each target time period to determine the current optimal capacity configuration information of the integrated energy system includes: Initializing the two-layer capacity planning model to determine initial capacity configuration information of the integrated energy system; Determine the maximum wind and solar power output based on the initial capacity configuration information of the integrated energy system and the power transaction data in each target time period; Solving the inner optimization model based on the maximum wind and solar power output to determine the output of each subsystem of the integrated energy system and the maximum electric hydrogen energy benefit; The particle swarm optimization algorithm is used to iteratively solve the outer optimization model according to the output of each subsystem of the integrated energy system and the maximum electric hydrogen energy benefit, so as to determine the current optimal capacity configuration information of the integrated energy system.

7. A comprehensive energy system capacity configuration device considering the electric hydrogen energy market, characterized in that: include: An analysis module, used to perform correlation analysis based on historical annual wind speed and light radiation intensity data of the target area where the integrated energy system is located, and determine the power trading data of the integrated energy system in the target period of each season; A configuration module, used to solve the double-layer capacity planning model of the integrated energy system by using the power transaction data of the integrated energy system in each of the target time periods, and determine the current optimal capacity configuration information of the integrated energy system; The two-layer capacity planning model includes an outer optimization model for optimizing the annual total cost, carbon dioxide emissions and wind and solar energy redundancy rate of the integrated energy system and an inner optimization model for optimizing the electric hydrogen energy benefits of the integrated energy system.

8. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that When the computer program product runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 6.

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