Multi-station energy system collaborative optimization scheduling method and system considering hydrogen energy interaction

Abstract

The invention discloses a multi-station energy system collaborative optimization scheduling method and system considering hydrogen energy interaction, and relates to the technical field of hydrogen energy. The method comprises the steps that historical operation data of a multi-station energy system are acquired, and an in-station energy equipment operation scheduling model is built; establishing an inter-station hydrogen energy dynamic transportation interaction model according to a hydrogen energy transportation interaction relationship among multiple stations; considering uncertainty of renewable energy output and a hydrogen energy transportation process, performing collaborative optimization on an in-station energy equipment operation scheduling model and an inter-station hydrogen energy dynamic transportation interaction model, and establishing a hydrogen energy multi-station robust interval collaborative operation optimization model; and solving the hydrogen energy multi-station robust interval collaborative operation optimization model to obtain a multi-station collaborative operation strategy to schedule a multi-station energy system. According to the invention, hydrogen use limitation in a single scene and uncertainty in a multi-station cooperation process can be overcome, and multi-energy cooperation optimization and hydrogen energy sharing scheduling between stations are realized.

Description

Technical Field

[0001] The present invention relates to the field of hydrogen energy technology, and in particular to a method and system for collaborative optimization scheduling of a multi-station energy system taking hydrogen energy interaction into account. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The multi-energy flow interaction and multi-station coordination mechanism effectively promote the efficient use of energy. On the one hand, the multi-energy flow system significantly improves the comprehensive utilization rate of energy through the complementary conversion of different energy forms; on the other hand, relying on the unique resource endowment of each region, the efficient configuration and scheduling of energy is achieved between multiple stations. However, the current coordinated optimization and scheduling of multi-station energy systems still faces some challenges. First, the existing stations mostly rely on large power grids for limited power exchange. As an ideal interconnected medium with mobility and large-scale storage characteristics, hydrogen energy has not yet been fully explored in its potential to achieve flexible hydrogen energy interaction through hydrogen long-tube trailers; secondly, the existing hydrogen energy interaction is mainly limited to a single scenario between hydrogen production sites and hydrogen refueling stations, lacking deep integration with multiple hydrogen use scenarios such as hydrogen energy parks and hydrogen energy communities, which to a certain extent limits the overall efficiency and operational flexibility of the energy system. In addition, the uncertainty of renewable energy output within the station and hydrogen energy transportation between stations also brings challenges to the coordinated optimization of the system. Therefore, in-depth research on the coordinated optimization and scheduling method of multi-station energy systems taking into account hydrogen energy interaction under the influence of multiple uncertain factors has important theoretical value and practical significance.

[0004] The existing coordinated optimization and dispatching of multi-park energy systems mostly rely on the large power grid for limited power exchange. For example, the Chinese invention patent with publication number CN110112728A and publication date August 9, 2019, a multi-park microgrid cooperative game method considering the robust uncertainty of wind power, takes into account the interaction of power between microgrids and the uncertainty of wind power output, and realizes the coordinated optimization between multi-park microgrids. However, hydrogen energy is not involved in the coordinated optimization and dispatching of multi-park energy systems mentioned in the patent. The Chinese invention patent with publication number CN114091913A and publication date February 25, 2022 considers the low-carbon economic dispatching method of heat network and P2G multi-park integrated energy system, and considers the impact of electric hydrogen production on the coordinated optimization and dispatching of multi-park integrated energy systems; the Chinese invention patent with publication number CN118153991A and publication date June 7, 2024, a resilience assessment method for multi-microgrid systems taking into account hydrogen transportation under typhoons, takes into account the positive role of hydrogen energy transportation in enhancing the resilience of multi-microgrid systems. The multi-park energy system mentioned above includes hydrogen, but the interaction of hydrogen energy is mainly limited to the single scenario of hydrogen production at hydrogen production sites and hydrogen use at hydrogen refueling stations. It lacks deep integration with multiple hydrogen use scenarios such as hydrogen energy communities, and lacks consideration of the uncertainties in renewable energy output within the park and hydrogen transportation between parks.

[0005] In summary, how to overcome the limitations of hydrogen use in a single scenario and the uncertainties in the operation process in a multi-station energy system and achieve coordinated optimization, scheduling and sharing of hydrogen energy among stations has become a technical problem that needs to be urgently solved by existing technologies. Summary of the invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method and system for collaborative optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction. By establishing a collaborative optimization and scheduling model for multi-energy complementary conversion within a hydrogen energy station and hydrogen energy transportation interaction between multiple stations, a probability distribution of uncertain scenarios of renewable energy output and uncertainty intervals of hydrogen long-tube trailer transportation are constructed, which more comprehensively reflects the impact of uncertainty factors in renewable energy output and hydrogen long-tube trailer transportation on the optimal scheduling of multi-station hydrogen energy systems.

[0007] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0008] A first aspect of the present invention provides a method for collaborative optimization and scheduling of a multi-station energy system taking into account hydrogen energy interaction, comprising the following steps:

[0009] Obtain historical operation data of energy systems in multiple stations, generate a set of renewable energy output scenarios, and build a multi-energy equipment operation scheduling model in the station based on equipment operation parameters;

[0010] According to the interactive relationship between hydrogen transport between multiple stations, a dynamic hydrogen transport interactive model between stations is established;

[0011] Considering the uncertainty of renewable energy output and hydrogen transportation process, the operation scheduling model of multi-energy equipment in the station and the dynamic transportation interaction model of hydrogen between stations are coordinated and optimized to establish a robust interval coordinated operation optimization model for hydrogen multi-stations.

[0012] The robust interval coordinated operation optimization model of hydrogen energy multi-stations is solved to obtain the multi-station coordinated operation strategy, and the multi-station coordinated operation strategy is used to dispatch the multi-station energy system.

[0013] Furthermore, the energy equipment operation model within the station includes energy storage equipment and energy conversion equipment operation models.

[0014] Furthermore, energy includes hydrogen energy, electrical energy, thermal energy and cold energy.

[0015] Furthermore, the specific steps to build the energy equipment operation model in the station are:

[0016] Model different energy storage devices and energy conversion devices separately;

[0017] Based on the balance relationship between different energy sources and the interaction of energy equipment within the site, an operation and scheduling model for energy equipment within the site is constructed.

[0018] Furthermore, the dynamic hydrogen transportation interaction model between stations includes a hydrogen long-tube trailer state transition model and a hydrogen energy transmission interaction model, wherein the hydrogen long-tube trailer state transition model is used to represent the hydrogen storage situation during the hydrogen energy scheduling process, and the hydrogen energy transmission interaction model is used to represent the time and route situation during the hydrogen energy scheduling process.

[0019] Furthermore, considering the uncertainty of renewable energy output and hydrogen energy transportation process, the specific steps for collaboratively optimizing the energy equipment operation scheduling model within the station and the dynamic hydrogen energy transportation interaction model between stations are as follows:

[0020] Analyze the uncertainty of renewable energy output and hydrogen transportation process, and construct the probability distribution of renewable energy output uncertainty scenarios and the uncertainty interval of hydrogen long-tube trailer transportation;

[0021] The energy equipment operation and scheduling models within multiple stations and the dynamic hydrogen transportation interaction model between stations are integrated, and the probability distribution of uncertain scenarios of renewable energy output and the uncertain intervals of hydrogen long-tube trailer transportation are incorporated to obtain a robust interval collaborative operation optimization model for hydrogen energy multiple stations.

[0022] Furthermore, the specific steps for solving the robust interval coordinated operation optimization model of hydrogen energy multi-stations are as follows:

[0023] Based on the big M method and strong duality theory, the robust interval coordinated operation optimization model of hydrogen energy multi-station is linearized and transformed;

[0024] The outer optimization problem and the inner optimization problem in the objective function are solved separately by decomposition.

[0025] The second aspect of the present invention provides a multi-station energy system collaborative optimization scheduling system taking into account hydrogen energy interaction, comprising:

[0026] The data acquisition module is configured to acquire the historical operation data of the energy systems of multiple stations, generate a set of renewable energy output scenarios, and build an operation and scheduling model for energy equipment in the station in combination with the equipment operation parameters;

[0027] An interaction relationship determination module is configured to establish an interaction model of hydrogen energy dynamic transportation among multiple stations according to the interaction relationship of hydrogen energy transportation among multiple stations;

[0028] The operation optimization module is configured to consider the uncertainty of renewable energy output and hydrogen energy transportation process, coordinately optimize the energy equipment operation scheduling model within the station and the hydrogen energy dynamic transportation interaction model between stations, and establish a hydrogen energy multi-station robust interval coordinated operation optimization model;

[0029] The collaborative scheduling module is configured to solve the hydrogen energy multi-station robust interval collaborative operation optimization model, obtain the multi-station collaborative operation strategy, and use the multi-station collaborative operation strategy to schedule the multi-station energy system.

[0030] The third aspect of the present invention provides a medium having a program stored thereon, which, when executed by a processor, implements the steps in the method for collaborative optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction as described in the first aspect of the present invention.

[0031] The fourth aspect of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the method for collaborative optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction as described in the first aspect of the present invention are implemented.

[0032] One or more of the above technical solutions have the following beneficial effects:

[0033] The present invention discloses a method and system for collaborative optimization and scheduling of a multi-station energy system taking into account hydrogen energy interaction, which can realize efficient interaction of multiple energy flows and coordinated operation of multiple energy equipment in the multi-station energy system. Through the complementarity and conversion of multiple energy forms such as electricity, hydrogen, heat, and cold, as well as hydrogen energy sharing and collaborative scheduling between different stations, various energy equipment such as electrolyzers, fuel cells, hydrogen storage tanks, etc. can flexibly switch working states according to station needs, forming an overall coordinated operation mode.

[0034] The present invention can enhance the system's adaptability to uncertain factors. By constructing uncertainty models for renewable energy output and hydrogen transportation, it can fully reflect the fluctuation factors in actual operation, improve the robustness of the system when facing uncertain conditions, effectively reduce potential operating risks, and ensure the stability of the system under various changing conditions.

[0035] The present invention can improve the overall resource allocation efficiency and operational flexibility of the system. Through the hydrogen energy interaction mechanism of the hydrogen long-tube trailer, it can realize the comprehensive utilization and coordinated optimization of energy forms across stations. While considering the economic, environmental and transportation costs of the system operation, it can optimize resource allocation and improve overall efficiency.

[0036] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0038] Figure 1 This is a flow chart of a method for collaborative optimization and scheduling of a multi-station energy system taking into account hydrogen energy interaction in Embodiment 1 of the present invention;

[0039] Figure 2 This is a schematic diagram of the structure of a multi-station energy system taking into account hydrogen energy interaction in Example 1 of the present invention. DETAILED DESCRIPTION

[0040] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0041] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or their combinations;

[0042] Embodiment 1:

[0043] Embodiment 1 of the present invention provides a method for collaborative optimization and scheduling of a multi-station energy system taking into account hydrogen energy interaction, such as Figure 1 As shown, the following steps are included:

[0044] Step 1: Obtain historical operation data of energy systems in multiple stations, generate a set of renewable energy output scenarios, and build an operation and scheduling model for energy equipment within the station based on equipment operation parameters.

[0045] Step 1.1: Obtain the historical operation data of the energy system of multiple stations and generate a set of renewable energy output scenarios. The energy equipment operation model in the station includes a set of energy storage equipment and energy conversion equipment. Energy includes hydrogen energy, electrical energy, thermal energy and cold energy.

[0046] Step 1.2: Based on the renewable energy output scenario set and combined with the equipment operating parameters, build an energy equipment operation scheduling model within the site.

[0047] Step 1.2.1: Model different energy storage devices and energy conversion devices separately.

[0048] In a specific implementation, a site energy system operation model containing multiple energy forms such as hydrogen, electricity, heat, and cold is built, and energy conversion equipment such as electrolyzers, fuel cells, gas turbines, and absorption refrigerators in hydrogen energy sites, as well as energy storage equipment such as hydrogen storage tanks, are modeled to achieve complementary and coordinated operation of multiple energy forms. Considering the variable operating characteristics of each energy conversion equipment, piecewise linearization is performed using a convex combination method.

[0049] Taking the electrolyzer as an example, its energy conversion model is:

[0050]

[0051] in, and are the input electrical power and output hydrogen power, are the horizontal and vertical coordinates of w segment points, and Continuous auxiliary variables and binary auxiliary variables are introduced for each segmentation point respectively.

[0052] Taking hydrogen storage tanks as an example, the state transition model of energy storage equipment at each station is:

[0053]

[0054]

[0055] in, is the hydrogen storage state at the station at time t, and are the storage and release amounts of hydrogen at the station at time t, and They are the hydrogen energy interaction between the station at time n and the hydrogen long tube trailer, and are the output and input hydrogen quantities of vehicle d from station n at time t, and are the minimum and maximum hydrogen storage states of station n, and are the initial and final hydrogen storage states of station n, respectively; and are the minimum and maximum hydrogen storage capacities, and are the minimum and maximum hydrogen release amounts, and are binary state variables for hydrogen storage and release respectively.

[0056] Step 1.2.2: Based on the balance relationship between different energy sources and the interaction of energy equipment in the station, build an operation and scheduling model for energy equipment in the station.

[0057] In a specific implementation, based on the energy equipment conditions in the station, the balance of hydrogen, electricity, heat and cold energy flows is met:

[0058]

[0059] Among them, ED is water electrolysis equipment, BHP is industrial by-product hydrogen, CHP is gas turbine, EC is electric refrigerator, GB is gas boiler, AC is absorption refrigerator, H is hydrogen energy, P is electric energy, and Q is heat (cold) energy.

[0060] Step 2: Based on the interaction relationship between hydrogen energy transportation among multiple stations, establish a dynamic hydrogen energy transportation interaction model among stations. Figure 2 As shown, the stations include hydrogen refueling stations, hydrogen energy parks, renewable energy hydrogen production, etc., and are transported between the stations by hydrogen long tube trailers. In this embodiment, No. 1 is set as renewable energy hydrogen production, No. 2 and No. 5 are hydrogen energy communities, No. 3 and No. 6 are hydrogen refueling stations, and No. 4 is a hydrogen energy park.

[0061] In a specific implementation, the energy equipment operation scheduling model in the station includes a hydrogen tube trailer state transition model and a hydrogen energy transmission interaction model, wherein the hydrogen tube trailer state transition model is used to represent the hydrogen storage situation during the hydrogen energy scheduling process, and the hydrogen energy transmission interaction model is used to represent the time and route situation during the hydrogen energy scheduling process. The hydrogen energy transmission interaction model is divided into a time constraint part and a state and route correspondence part.

[0062] The state transition model of each hydrogen long tube trailer is:

[0063]

[0064] in, is the hydrogen storage state of the vehicle at time t, and are the initial and final states of hydrogen storage of vehicle d, and The minimum and maximum hydrogen storage states of vehicle d, and are the binary state variables of the output and input of vehicle d from station n at time t, respectively, t,d ,n is the binary state variable of the interaction between vehicle d and station n at time t.

[0065] The time constraint part in the hydrogen energy transmission interaction model is:

[0066]

[0067] Among them, T t,d,n is the time interval between the last interaction of vehicle d at station n at time t, is the transportation time of vehicle d from station m to station n, z t,d ,n is the binary state variable of the interaction between vehicle d and station n at time t, and are the service time and detention time of vehicle d at station n, is the time of hydrogen interaction between vehicle d and station n at time t, is the set of vehicles interacting with station n at time t.

[0068] The corresponding relationship between state and route is as follows: the relationship between state z and route I. Although route I can be determined by state z, an auxiliary variable z′ of z needs to be introduced to specifically represent it:

[0069]

[0070] z′ int,d,2 =1,z′ fin,d,2 =1 (27).

[0071] Among them, z′ t,d,n For z t,d,n The corresponding binary state auxiliary variable z′ of the interaction between vehicle d and station n at time t int,d,2 and z′ fin,d,2 are the initial and final states of vehicle d and station 2 respectively.

[0072] Step 3: Considering the uncertainty of renewable energy output and hydrogen energy transportation process, the energy equipment operation scheduling model within the station and the hydrogen energy dynamic transportation interaction model between stations are collaboratively optimized to establish a hydrogen energy multi-station robust interval collaborative operation optimization model.

[0073] Step 3.1: Analyze the uncertainty of renewable energy output and hydrogen transportation process, and construct the probability distribution of renewable energy output uncertainty scenarios and the uncertainty interval of hydrogen long-tube trailer transportation.

[0074] In a specific implementation, a probability distribution of renewable energy output uncertainty scenarios and a hydrogen energy transportation uncertainty interval are constructed to describe the uncertainty of renewable energy output and hydrogen energy transportation processes.

[0075] In this embodiment, the output of renewable energy is uncertain due to its inherent natural volatility (caused by unstable changes in natural conditions such as wind speed and light intensity) and the errors in the prediction model (involving the accuracy of model parameters, data quality, and the impact on the simplification of complex dynamic processes). Based on the uncertainty of these renewable energy outputs, the probability distribution of renewable energy output uncertainty scenarios and the uncertainty interval of hydrogen energy transportation are constructed.

[0076] The probability distribution of renewable energy output uncertainty scenario is:

[0077]

[0078] in, is the empirical probability of scenario s, p s is the actual probability of scene s, N s is the number of scenarios, N is the number of samples of historical data of source and load, θ1 and θ ∞ are the allowable deviation values ​​under 1-norm and ∞-norm constraints respectively, and β is the confidence level.

[0079] The probability distribution of uncertain scenarios can be further expressed as:

[0080]

[0081] in, is the uncertainty set corresponding to the probability distribution of uncertain scenarios.

[0082] The uncertainty range for hydrogen transport is:

[0083]

[0084]

[0085] in, is the driving speed range of vehicle d, is the expected driving speed, and are the maximum and minimum values ​​of deviation from the expected driving speed, respectively. and v d are the maximum and minimum driving speeds, respectively. and are auxiliary continuous variables.

[0086] Let ξ d =1 / v d , the uncertainty interval of hydrogen energy transportation can be further expressed as:

[0087]

[0088] After considering the uncertainty of renewable energy output and hydrogen transportation process, the optimization objective can be expressed as:

[0089]

[0090] Among them, x s and s are the binary optimization variables and continuous optimization variables in scenario s, c and d are the parameters corresponding to the optimization variables, v d is the speed of vehicle d.

[0091] Step 3.2: Integrate the energy equipment operation scheduling model within the station and the hydrogen energy dynamic transportation interaction model between stations, incorporate the probability distribution of renewable energy output uncertainty scenarios and the uncertainty interval of hydrogen long-tube trailer transportation, and obtain the hydrogen energy multi-station robust interval collaborative operation optimization model.

[0092] In a specific implementation, the objective function of the hydrogen energy multi-station robust interval collaborative operation optimization model is:

[0093] minJ=(J E +J OM )+J Env +J Tra (40),

[0094]

[0095]

[0096] Among them, the total operating cost J includes the energy purchase cost J E , operation and maintenance costsJ OM 、Environmental cost Env and hydrogen transportation costJ Tra , and are the power purchase and sales prices and power consumption of station n at time t, and are the price and volume of gas purchased from the gas network, and are the unit power maintenance cost and operating power of energy conversion equipment and energy storage equipment, respectively, co is the carbon cost, αelec and α gas are the carbon content of electricity and gas per unit power, is the binary state variable of vehicle d from station m to station n at time t, is the transportation distance from station m to station n.

[0097] Step 4: Solve the hydrogen energy multi-station robust interval coordinated operation optimization model to obtain the multi-station coordinated operation strategy, and use the multi-station coordinated operation strategy to dispatch the multi-station energy system.

[0098] Step 4.1: Linearize and transform the robust interval coordinated operation optimization model of hydrogen energy multi-station based on the big M method and strong duality theory. The big M method is a method for solving nonlinear programming problems. By introducing a binary state variable and a sufficiently large positive number M, the nonlinear problem is transformed into a linear problem, which helps to find the initial feasible solution in the simplex method.

[0099] In a specific implementation, formula (23) of the time constraint part and formulas (25) and (26) of the state and route correspondence part in the hydrogen energy transmission interaction model, formula (32) of the renewable energy output uncertainty set, and formula (21) after considering transportation uncertainty are all nonlinear and therefore need to be linearized.

[0100] Formula (23) can be linearized to:

[0101]

[0102] Formula (25) can be linearized to:

[0103]

[0104] Where M is a very large value introduced; formula (31) is linearized to:

[0105]

[0106] Formula (37) can be linearized to:

[0107]

[0108] Formula (26) can be linearized to:

[0109]

[0110] Since formula (48) contains interval variables By decomposing the inner interval optimization model of the objective function (48), the model is transformed into two robust optimization models:

[0111]

[0112] Among them, the Optimistic model is an optimistic model, and the Pessimistic model is a pessimistic model. In this embodiment, the pessimistic model is the optimal operation optimization model under the worst transportation conditions, that is, the most congested transportation conditions; the optimistic model is the optimal operation optimization model under the most ideal transportation conditions, that is, the most unobstructed transportation conditions.

[0113] "Min-max" is a two-layer structure, the outer layer is min, which means minimizing the operating cost, and the inner layer is max, which means maximizing the transportation time. Formula (51) is a robust optimization in the form of "min-max", which is difficult to solve directly. Since the inner layer is convex, it can be converted into a mixed integer linear optimization problem in the form of "min" based on the strong duality theory, which is easy to solve. After the conversion, formulas (50) and (51) can be uniformly expressed as:

[0114]

[0115] Step 4.2: Use decomposition to solve the outer optimization problem and inner optimization problem in the objective function respectively.

[0116] Since the outer optimization problem and the inner optimization problem in the objective function are relatively independent, the optimization objective is further expressed as:

[0117]

[0118] in, is the optimal solution for the corresponding scenario s; the inner and outer layer problems can be solved separately, and the robust optimization in the form of "min-max" in the objective function can be easily solved.

[0119] Embodiment 2:

[0120] Embodiment 2 of the present invention provides a multi-station energy system collaborative optimization scheduling system taking into account hydrogen energy interaction, including:

[0121] A data acquisition module is configured to acquire historical operation data of energy systems of multiple stations to generate a renewable energy output scenario set, and to build an energy equipment operation scheduling model within the station according to the renewable energy output scenario set;

[0122] An interaction relationship determination module is configured to establish an inter-station hydrogen energy dynamic transportation interaction model based on an energy equipment operation scheduling model within the station and a transportation interaction relationship between each device;

[0123] The operation optimization module is configured to perform collaborative operation optimization on the hydrogen energy dynamic transportation interaction model between stations based on the uncertainty of renewable energy output and hydrogen energy transportation process, and obtain a hydrogen energy multi-station robust interval collaborative operation optimization model;

[0124] The collaborative scheduling module is configured to solve the hydrogen energy multi-station robust interval collaborative operation optimization model, obtain the multi-station collaborative operation strategy, and use the multi-station collaborative operation strategy to schedule the multi-station energy system.

[0125] Embodiment three:

[0126] Embodiment 3 of the present invention provides a medium on which a program is stored. When the program is executed by a processor, the steps in the method for collaborative optimization and scheduling of a multi-station energy system taking into account hydrogen energy interaction as described in Embodiment 1 of the present invention are implemented.

[0127] Embodiment 4:

[0128] Embodiment 4 of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for collaborative optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction as described in Embodiment 1 of the present invention are implemented.

[0129] The steps involved in the above embodiments 2, 3 and 4 correspond to the method embodiment 1. For the specific implementation methods, please refer to the relevant description part of embodiment 1.

[0130] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0131] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for coordinated optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction, characterized in that: The following steps are involved: Obtain historical operation data of energy systems in multiple stations, generate a set of renewable energy output scenarios, and build an operation and scheduling model for energy equipment within the station based on equipment operation parameters; According to the interactive relationship between hydrogen transport between multiple stations, a dynamic hydrogen transport interactive model between stations is established; Considering the uncertainty of renewable energy output and hydrogen energy transportation process, the energy equipment operation scheduling model within the station and the hydrogen energy dynamic transportation interaction model between stations are coordinated and optimized, and a hydrogen energy multi-station robust interval coordinated operation optimization model is established; The robust interval coordinated operation optimization model of hydrogen energy multi-stations is solved to obtain the multi-station coordinated operation strategy, and the multi-station coordinated operation strategy is used to dispatch the multi-station energy system.

2. The method for coordinated optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction as claimed in claim 1, characterized in that: The energy equipment operation model within the station includes the energy storage equipment and energy conversion equipment operation models.

3. The method for coordinated optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction as claimed in claim 1, characterized in that: Energy sources include hydrogen energy, electrical energy, thermal energy and cold energy.

4. The method for coordinated optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction as claimed in claim 2, characterized in that: The specific steps to build the energy equipment operation model in the station are: Model different energy storage devices and energy conversion devices separately; Based on the balance relationship between different energy sources and the interaction of energy equipment in the station, an operation and scheduling model for energy equipment in the station is established.

5. The method for coordinated optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction as claimed in claim 1, characterized in that: The dynamic hydrogen transportation interaction model between stations includes a hydrogen long-tube trailer state transition model and a hydrogen transmission interaction model. The hydrogen long-tube trailer state transition model is used to represent the hydrogen storage situation during the hydrogen energy scheduling process, and the hydrogen energy transmission interaction model is used to represent the time and route situation during the hydrogen energy scheduling process.

6. The method for coordinated optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction as claimed in claim 5, characterized in that: Considering the uncertainty of renewable energy output and hydrogen energy transportation process, the specific steps for collaborative optimization of the energy equipment operation scheduling model within the station and the dynamic hydrogen energy transportation interaction model between stations are as follows: Analyze the uncertainty of renewable energy output and hydrogen transportation process, and construct the probability distribution of renewable energy output uncertainty scenarios and the uncertainty interval of hydrogen long-tube trailer transportation; The energy equipment operation and scheduling models within multiple stations and the dynamic hydrogen transportation interaction model between stations are integrated, and the probability distribution of uncertain scenarios of renewable energy output and the uncertain intervals of hydrogen long-tube trailer transportation are incorporated to obtain a robust interval collaborative operation optimization model for hydrogen energy multiple stations.

7. The method for coordinated optimization and scheduling of multi-station energy systems taking into account hydrogen energy interaction as claimed in claim 1, characterized in that: The specific steps for solving the robust interval coordinated operation optimization model of hydrogen energy multi-stations are as follows: Based on the big M method and strong duality theory, the robust interval coordinated operation optimization model of hydrogen energy multi-station is linearized and transformed; The outer optimization problem and the inner optimization problem in the objective function are solved separately by decomposition.

8. A multi-station energy system collaborative optimization scheduling system taking into account hydrogen energy interaction, characterized in that: include: The data acquisition module is configured to acquire the historical operation data of the energy systems of multiple stations, generate a set of renewable energy output scenarios, and build an operation and scheduling model for energy equipment in the station in combination with the equipment operation parameters; An interaction relationship determination module is configured to establish an interaction model of hydrogen energy dynamic transportation among multiple stations according to the interaction relationship of hydrogen energy transportation among multiple stations; The operation optimization module is configured to consider the uncertainty of renewable energy output and hydrogen energy transportation process, coordinately optimize the energy equipment operation scheduling model within the station and the hydrogen energy dynamic transportation interaction model between stations, and establish a hydrogen energy multi-station robust interval coordinated operation optimization model; The collaborative scheduling module is configured to solve the hydrogen energy multi-station robust interval collaborative operation optimization model, obtain the multi-station collaborative operation strategy, and use the multi-station collaborative operation strategy to schedule the multi-station energy system.

9. A computer-readable storage medium, characterized in that: A plurality of instructions are stored therein, and the instructions are suitable for being loaded by a processor of a terminal device and executing the collaborative optimization scheduling method for a multi-station energy system taking into account hydrogen energy interaction as described in any one of claims 1-7.

10. A terminal device, characterized in that: It includes a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the multi-station energy system collaborative optimization scheduling method taking into account hydrogen energy interaction as described in any one of claims 1-7.

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