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

Abstract

The application discloses a kind of multi-station energy system collaborative optimization scheduling method and system considering hydrogen energy interaction, and it is related to hydrogen energy technology field.The method includes the following steps: obtaining multi-station energy system historical operation data, and building station internal energy equipment operation scheduling model;According to the hydrogen energy transport interaction relationship between multi-station, establish the hydrogen energy dynamic transport interaction model between stations;Considering the uncertainty of renewable energy output and hydrogen energy transport process, the station internal energy equipment operation scheduling model and the hydrogen energy dynamic transport interaction model between stations are optimized, and the hydrogen energy multi-station robust interval collaborative operation optimization model is established;Solve the hydrogen energy multi-station robust interval collaborative operation optimization model, obtain the multi-station collaborative operation strategy to schedule multi-station energy system.The application can overcome the limitation of single scene hydrogen and the uncertainty in the process of multi-station collaboration, realize the multi-energy collaborative optimization and hydrogen energy sharing scheduling between stations.

Description

Technical Field

[0001] This invention relates to the field of hydrogen energy technology, and in particular to a method and system for collaborative optimization scheduling of multi-site energy systems that takes into account hydrogen energy interaction. Background Technology

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

[0003] Multi-energy flow interaction and multi-site coordination mechanisms effectively promote the efficient use of energy. On the one hand, multi-energy flow systems significantly improve the comprehensive utilization rate of energy through complementary conversion of different energy forms; on the other hand, relying on the unique resource endowments of each region, efficient energy allocation and scheduling are achieved among multiple sites. However, the current coordinated optimization scheduling of multi-site energy systems still faces some challenges. First, existing sites mostly rely on large power grids for limited electricity exchange. Hydrogen energy, as an ideal interconnection medium with mobility and large-scale storage characteristics, has not yet fully explored its potential for flexible hydrogen energy interaction through hydrogen tube trailers. Second, existing hydrogen energy interaction is mainly limited to a single scenario between hydrogen production plants and hydrogen refueling stations, lacking deep integration with diverse hydrogen use scenarios such as hydrogen energy parks and hydrogen energy communities, which to some extent limits the overall efficiency and operational flexibility of the energy system. In addition, the uncertainties in renewable energy output within sites and hydrogen transportation between sites also pose challenges to system coordinated optimization. Therefore, in-depth research on the coordinated optimization scheduling method of multi-site energy systems considering hydrogen energy interaction under the influence of multiple uncertain factors has important theoretical value and practical significance.

[0004] Existing collaborative optimization scheduling of multi-park energy systems largely relies on limited power exchange through the main power grid. For example, Chinese invention patent CN110112728A, published on August 9, 2019, describes a cooperative game theory method for multi-park microgrids considering the robust uncertainty of wind power. This method considers the power interaction between microgrids and the uncertainty of wind power output, achieving collaborative optimization among multi-park microgrids. However, this patent does not address hydrogen energy in its collaborative optimization scheduling of multi-park energy systems. Chinese invention patent CN114091913A, published on February 25, 2022, describes a low-carbon economic scheduling method for multi-park integrated energy systems considering heating networks and P2G, taking into account the impact of hydrogen production via electricity on the collaborative optimization scheduling of multi-park integrated energy systems. Chinese invention patent CN118153991A, published on June 7, 2024, describes a resilience assessment method for multi-microgrid systems considering hydrogen transportation during typhoons, taking into account the positive role of hydrogen transportation in enhancing the resilience of multi-microgrid systems. The aforementioned multi-park energy system includes hydrogen, but the interaction of hydrogen energy is mainly limited to the single scenario of hydrogen production at hydrogen production plants and hydrogen consumption at hydrogen refueling stations. It lacks deep integration with diverse hydrogen consumption scenarios such as hydrogen energy communities, and it 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-site energy system, and achieve collaborative optimization and sharing of hydrogen energy among sites, has become a technical problem that needs to be solved by existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this invention is to provide a collaborative optimization scheduling method and system for multi-site energy systems that takes into account hydrogen energy interaction. By establishing a collaborative optimization scheduling model for multi-energy complementary conversion within hydrogen energy sites and hydrogen energy transportation interaction between multiple sites, this invention constructs a probability distribution of uncertain scenarios for renewable energy output and an uncertain interval for hydrogen long-tube trailer transportation, thus more comprehensively reflecting the impact of uncertain factors in renewable energy output and hydrogen long-tube trailer transportation on the optimal scheduling of multi-site hydrogen energy systems.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0008] The first aspect of this invention provides a collaborative optimization scheduling method for multi-site energy systems that takes into account hydrogen energy interaction, comprising the following steps:

[0009] Acquire historical operating data of energy systems at multiple power stations, generate a set of renewable energy output scenarios, and build an operation scheduling model for multiple energy devices within the power station by combining equipment operating parameters;

[0010] Based on the interaction relationship of hydrogen energy transportation among multiple stations, a dynamic interaction model of hydrogen energy transportation among stations is established;

[0011] Considering the uncertainties in renewable energy output and hydrogen transportation, a robust inter-station collaborative operation optimization model for hydrogen energy is established by coordinating the operation scheduling model of multi-energy equipment within the station with the dynamic hydrogen transportation interaction model between stations.

[0012] The robust inter-regional collaborative operation optimization model of hydrogen energy multi-stations is solved to obtain the multi-station collaborative operation strategy, and the multi-station energy system is scheduled using the multi-station collaborative operation strategy.

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

[0014] Furthermore, energy sources include hydrogen, electricity, heat, and cold energy.

[0015] Furthermore, the specific steps for building an operational model of the energy equipment within the power station are as follows:

[0016] Modeling is performed separately for different energy storage devices and energy conversion devices;

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

[0018] Furthermore, the dynamic hydrogen transportation interaction model between stations includes a hydrogen tube trailer state transition model and a hydrogen energy transmission interaction model. The hydrogen tube trailer state transition model is used to represent the hydrogen storage capacity during the hydrogen energy dispatch process, while the hydrogen energy transmission interaction model is used to represent the time and route conditions during the hydrogen energy dispatch process.

[0019] Furthermore, considering the uncertainties in renewable energy output and hydrogen transportation, the specific steps for collaborative optimization of the energy equipment operation scheduling model within the power station and the dynamic hydrogen transportation interaction model between power stations are as follows:

[0020] The uncertainties in renewable energy output and hydrogen transportation are analyzed, and the probability distribution of uncertain scenarios for renewable energy output and the uncertainty interval for hydrogen tube trailer transportation are constructed.

[0021] By integrating the operation and scheduling models of energy equipment within multiple power stations and the dynamic transportation interaction model of hydrogen energy between power stations, and incorporating the probability distribution of uncertain scenarios for renewable energy output and the uncertain interval of hydrogen long-tube trailer transportation, a robust interval collaborative operation optimization model for hydrogen energy multiple power stations is obtained.

[0022] Furthermore, the specific steps for solving the robust inter-station collaborative operation optimization model for hydrogen energy multi-site are as follows:

[0023] Linearization and transformation of the optimization model for robust inter-regional collaborative operation of hydrogen energy multi-station based on the big M method and strong duality theory;

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

[0025] A second aspect of the present invention provides a multi-site energy system collaborative optimization scheduling system considering hydrogen energy interaction, comprising:

[0026] The data acquisition module is configured to acquire historical operating data of energy systems at multiple power stations, generate a set of renewable energy output scenarios, and build an operation scheduling model for energy equipment within the power station by combining equipment operating parameters.

[0027] The interaction relationship determination module is configured to establish a dynamic hydrogen energy transportation interaction model between multiple stations based on the hydrogen energy transportation interaction relationship between them.

[0028] The operation optimization module is configured to consider the uncertainties of renewable energy output and hydrogen transportation process, and to collaboratively optimize the operation scheduling model of energy equipment within the station and the dynamic hydrogen transportation interaction model between stations, and establish a robust inter-station collaborative operation optimization model for hydrogen energy.

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

[0030] A third aspect of the present invention provides a medium on which a program is stored, which, when executed by a processor, implements the steps of the multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in the first aspect of the present invention.

[0031] A fourth aspect of the present invention provides an apparatus including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in the first aspect of the present invention.

[0032] The above one or more technical solutions have the following beneficial effects:

[0033] This invention discloses a collaborative optimization scheduling method and system for multi-site energy systems that takes hydrogen energy interaction into account. It can realize the efficient interaction of multiple energy flows and the coordinated operation of multiple energy devices in multi-site energy systems. Through the complementarity and conversion of multiple energy forms such as electricity, hydrogen, heat, and cooling, as well as the sharing and collaborative scheduling of hydrogen energy between different sites, various energy devices such as electrolyzers, fuel cells, and hydrogen storage tanks can flexibly switch working states according to the needs of the sites, forming an overall coordinated operation mode.

[0034] This invention can enhance the system's adaptability to uncertainties. By constructing an uncertainty model for renewable energy output and hydrogen transportation, it fully reflects the fluctuation factors in actual operation, improves the system's robustness in the face of uncertain conditions, effectively reduces potential operational risks, and ensures the system's stability under various changing conditions.

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

[0036] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0037] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0038] Figure 1 This is a flowchart of the multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction in Embodiment 1 of the present invention;

[0039] Figure 2 This is a schematic diagram of the multi-site energy system structure that incorporates hydrogen energy interaction in Embodiment 1 of the present invention. Detailed Implementation

[0040] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0041] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0042] Example 1:

[0043] Embodiment 1 of the present invention provides a collaborative optimization scheduling method for multi-site energy systems that takes into account hydrogen energy interaction, such as... Figure 1 As shown, it includes the following steps:

[0044] Step 1: Obtain historical operating data of energy systems at multiple power stations, generate a set of renewable energy output scenarios, and build an operation and scheduling model for energy equipment within the power stations by combining equipment operating parameters.

[0045] Step 1.1: Obtain historical operational data of energy systems from multiple power plants to generate a set of renewable energy output scenarios. The operational model of energy equipment within the power plants includes a set of energy storage and energy conversion equipment. Energy sources include hydrogen, electricity, heat, and cooling.

[0046] Step 1.2: Based on the set of renewable energy output scenarios and combined with equipment operating parameters, build an operation and scheduling model for energy equipment within the power station.

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

[0048] In one specific implementation, an operational model of the power station energy system, incorporating multiple energy forms such as hydrogen, electricity, heat, and cooling, is constructed. This model models energy conversion equipment within the hydrogen power station, including electrolyzers, fuel cells, gas turbines, and absorption chillers, as well as energy storage equipment such as hydrogen storage tanks, enabling complementary and coordinated operation of multiple energy forms. Considering the variable operating conditions of each energy conversion device, piecewise linearization is performed using a convex combination approach.

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

[0050]

[0051] in, and These are the input electrical power and the output hydrogen power, respectively. Let w be the x and y coordinates of the segment points. and These are the continuous auxiliary variables and binary auxiliary variables introduced for each segment point.

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

[0053]

[0054]

[0055] in, Let represent the hydrogen storage state at time t at the gas station (n). and These represent the hydrogen storage and release amounts at time t at station n, respectively. and These represent the hydrogen energy interaction between the station at time t (n) and the hydrogen tube trailer. and These represent the output and input hydrogen quantities of vehicle d from depot n at time t, respectively. and These represent the minimum and maximum hydrogen storage states of station n, respectively. and These represent the initial and final hydrogen storage states of station n, respectively. and These are the minimum and maximum hydrogen storage capacities, respectively. and These represent the minimum and maximum hydrogen release amounts, respectively. and These are binary state variables representing hydrogen storage and release, respectively.

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

[0057] In one specific implementation, the balance of hydrogen, electricity, heat, and cold energy flows is achieved based on the energy equipment situation within the site:

[0058]

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

[0060] Step 2: Based on the hydrogen energy transportation interaction relationships between multiple stations, establish a dynamic hydrogen energy transportation interaction model between stations. The transportation architecture of the multi-station energy system is as follows: Figure 2 As shown, the facilities include hydrogen refueling stations, hydrogen energy parks, and renewable energy hydrogen production facilities, which are transported between the various facilities via hydrogen tube trailers. In this embodiment, facility 1 is designated as a renewable energy hydrogen production facility, facilities 2 and 5 as hydrogen energy communities, facilities 3 and 6 as hydrogen refueling stations, and facility 4 as a hydrogen energy park.

[0061] In one specific implementation, the energy equipment operation and scheduling model within the station includes a hydrogen long-tube trailer state transition model and a hydrogen energy transmission interaction model. The hydrogen long-tube trailer state transition model represents the hydrogen storage capacity during the hydrogen energy scheduling process, while the hydrogen energy transmission interaction model represents the time and route conditions during the hydrogen energy scheduling process. The hydrogen energy transmission interaction model is divided into a time constraint part and a state-route correspondence part.

[0062] The state transition models for each hydrogen tube trailer are as follows:

[0063]

[0064] in, Let represent the hydrogen storage state of the vehicle at time t, denoted by d. and These represent the initial and final states of hydrogen storage for vehicle d, respectively. and The minimum and maximum hydrogen storage states of vehicle d, and Let z be the binary state variables, representing the output and input of vehicle d from depot n at time t. t,d ,n Let be the binary state variable representing the interaction between vehicle d and station n at time t.

[0065] The time constraint part of the hydrogen energy transport interaction model is:

[0066]

[0067] Among them, T t,d,n Let t be the time interval between when vehicle d interacts at depot n and the last interaction. Let z be the transport time for vehicle d from station m to station n. t,d ,n Let be the binary state variable representing the interaction between vehicle d and depot n at time t. and These represent the service time and dwell time of vehicle d at station n, respectively. Let t be the time of interaction between vehicle d and hydrogen at depot n. Let be the set of vehicles that interact with the hydrogen station at time t.

[0068] The relationship between states and routes is as follows: While route I can be determined from state z, an auxiliary variable z′ needs to be introduced to specifically represent it.

[0069]

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

[0071] Where, z′ t,d,n To be with z t,d,n The corresponding binary state auxiliary variable, z′, represents the interaction between vehicle d and depot n at time t. int,d,2 and z′ fin,d,2 These represent the initial and final states of vehicle d and station 2, respectively.

[0072] Step 3: Considering the uncertainties in renewable energy output and hydrogen transportation, the operation scheduling model of energy equipment within the station and the dynamic transportation interaction model of hydrogen between stations are jointly optimized to establish a robust inter-station collaborative operation optimization model for hydrogen energy.

[0073] Step 3.1: Analyze the uncertainties in renewable energy output and hydrogen transportation processes, and construct the probability distribution of uncertain scenarios for renewable energy output and the uncertainty interval for hydrogen tube trailer transportation.

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

[0075] In this embodiment, renewable energy output is uncertain due to its inherent natural fluctuations (originating from 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 these uncertainties in renewable energy output, a probability distribution of uncertain renewable energy output scenarios and an uncertain range for hydrogen energy transportation are constructed.

[0076] The probability distribution of uncertain renewable energy output scenarios is as follows:

[0077]

[0078] in, Let p be the empirical probability of scenario s. s Let N be the actual probability of scenario s. s Where N is the number of scenes, θ1 is the number of samples of historical source data, and θ2 is the number of historical data. ∞ α represents the allowable deviation values ​​under 1-norm and ∞-norm constraints, respectively, and β represents the confidence level.

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

[0080]

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

[0082] The uncertainty range for hydrogen transportation is:

[0083]

[0084]

[0085] in, This refers to the speed range of vehicle d. It is the desired driving speed. and These represent the maximum and minimum deviations from the desired driving speed, respectively. and v d These are the maximum and minimum driving speeds, respectively. and These are auxiliary continuous variables.

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

[0087]

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

[0089]

[0090] Where, x s and y s Let c and d be the binary and continuous optimization variables in scenario s, respectively, and let v be the parameters corresponding to the optimization variables. d Let d be the speed of vehicle d.

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

[0092] In one specific implementation, the objective function of the robust inter-station collaborative operation optimization model for hydrogen energy multi-site is:

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

[0094]

[0095]

[0096] The total operating cost J includes the energy purchase cost J. E Operation and maintenance costs J OM Environmental costs J Env and hydrogen transportation costs J Tra , and These represent the grid purchase and sale prices and the amount of electricity generated at station n at time t. and These are the online gas price and volume. and These represent the unit power maintenance cost and operating power of energy conversion equipment and energy storage equipment, respectively, ρ co For carbon cost, αelec and α gas These are the carbon content per unit power of electricity and natural gas, respectively. Let be the binary state variable for vehicle d traveling from depot m to depot n at time t. Let m be the transport distance from station m to station n.

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

[0098] Step 4.1: Linearization and transformation of the robust inter-station collaborative operation optimization model for hydrogen energy multi-site 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, it transforms the nonlinear problem into a linear one, helping to find an initial feasible solution using the simplex method.

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

[0100] The linearized form of formula (23) is:

[0101]

[0102] The linearized form of formula (25) is:

[0103]

[0104] Where M is a maximal value introduced; after linearization, formula (31) becomes:

[0105]

[0106] The linearized form of formula (37) is:

[0107]

[0108] The linearized form of formula (26) is:

[0109]

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

[0111]

[0112] In this embodiment, the Optimistic model is the optimal operating model under the worst transportation conditions, i.e., the most congested transportation conditions; the Optimistic model is the optimal operating model under the most ideal transportation conditions, i.e., the most unobstructed transportation conditions.

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

[0114]

[0115] Step 4.2: Solve the outer optimization problem and the inner optimization problem in the objective function separately by decomposition.

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

[0117]

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

[0119] Example 2:

[0120] Embodiment 2 of the present invention provides a multi-site energy system collaborative optimization scheduling system that considers hydrogen energy interaction, comprising:

[0121] The data acquisition module is configured to acquire historical operating data of energy systems at multiple power stations to generate a set of renewable energy output scenarios, and to build an operation and scheduling model for energy equipment within the power station based on the set of renewable energy output scenarios.

[0122] The interaction relationship determination module is configured to establish a dynamic hydrogen energy transportation interaction model between stations based on the energy equipment operation scheduling model within the station and the transportation interaction relationship between each piece of equipment.

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

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

[0125] Example 3:

[0126] Embodiment 3 of the present invention provides a medium on which a program is stored. When the program is executed by a processor, it implements the steps in the multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in Embodiment 1 of the present invention.

[0127] Example 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, it implements the steps in the multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in Embodiment 1 of the present invention.

[0129] The steps and methods involved in Examples 2, 3 and 4 above correspond to those in Example 1. For specific implementation details, please refer to the relevant description section of Example 1.

[0130] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0131] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this 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 without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for collaborative optimization scheduling of multi-site energy systems considering hydrogen energy interaction, characterized in that, Includes the following steps: Acquire historical operation data of energy systems at multiple power stations, generate a set of renewable energy output scenarios, and build an operation scheduling model for energy equipment within the power station by combining equipment operation parameters; Based on the interaction relationships of hydrogen energy transportation among multiple stations, a dynamic hydrogen energy transportation interaction model is established. The stations include hydrogen refueling stations, hydrogen energy parks, and renewable energy production facilities. Hydrogen is transported between stations via hydrogen tubular trailers. The energy equipment operation and scheduling model within each station includes a hydrogen tubular trailer state transition model and a hydrogen energy transmission interaction model. The hydrogen tubular trailer state transition model represents the hydrogen storage capacity during the hydrogen energy scheduling process, while the hydrogen energy transmission interaction model represents the time and route conditions during the hydrogen energy scheduling process. The hydrogen energy transmission interaction model is divided into a time constraint part and a state-route correspondence part. The state transition models for each hydrogen tube trailer are as follows: ， ， ， ， ， in, For vehicles time hydrogen storage state, and vehicles The initial and final states of hydrogen storage. and vehicle The minimum and maximum hydrogen storage states, and They are time points vehicle From the station The output and input binary state variables, For a moment vehicle With station Interactive binary state variables, The time constraint part of the hydrogen energy transport interaction model is: ， ， ， in, For a moment vehicle At the station The time interval between the last interaction and the current interaction. For vehicles From the station Arrival Station The delivery time For a moment vehicle With station Interactive binary state variables, and vehicles At the station Service hours and stay time, For a moment Vehicle With station The time of hydrogen interaction, For a moment With station A collection of hydrogen-powered vehicles The correspondence between states and routes is as follows: State With route Relationships, through state Determine the route , Introduction Auxiliary variables To explain in detail: ， ， ， ， in, To and corresponding time vehicle With station Interactive binary state auxiliary variables, and vehicles The initial and final states of Station II; Considering the uncertainties in renewable energy output and hydrogen transportation, a collaborative optimization model is implemented for the operation and scheduling of energy equipment within power plants and the dynamic hydrogen transportation interaction model between power plants. A robust multi-power plant collaborative operation optimization model is established. The uncertainties in renewable energy output and hydrogen transportation are analyzed, and probability distributions for uncertain renewable energy output scenarios and uncertain intervals for hydrogen tube trailer transportation are constructed to describe the uncertainties in both processes. The probability distribution for uncertain renewable energy output scenarios is as follows: ， ， ， ， in, For the scene The empirical probability, For the scene The actual probability, For the number of scenes, The number of samples for the historical data of source load. and They are respectively 1-norm and - Allowable deviation value under norm constraint For confidence level, The probability distribution of uncertain scenarios can be further represented as: ， in, The uncertainty set corresponding to the probability distribution of an uncertain scenario. The uncertainty range for hydrogen transportation is: ， ， ， in, It is a vehicle The driving speed range, It is the desired driving speed. and These represent the maximum and minimum deviations from the desired driving speed, respectively. and These are the maximum and minimum driving speeds, respectively. and These are auxiliary continuous variables, make The uncertainty interval for hydrogen energy transportation can be further expressed as: ， ， ， After considering the uncertainties in renewable energy output and hydrogen transportation, the optimization objective is expressed as: ， in, and Scenes Bivariate optimization variables and continuous optimization variables and These are the parameters corresponding to the optimization variables. For vehicles driving speed, By integrating the energy equipment operation and scheduling model within the power station and the dynamic hydrogen transportation interaction model between power stations, and incorporating the probability distribution of uncertain renewable energy output scenarios and the uncertain interval of hydrogen tube trailer transportation, a robust interval collaborative operation optimization model for multiple hydrogen power stations is obtained. The objective function of the robust interval collaborative operation optimization model for multiple hydrogen power stations is: ， ， ， ， ， Of which, total operating cost Includes energy purchase costs Operation and maintenance costs Environmental costs and hydrogen transportation costs , , and , They are time points station The electricity purchase and sale prices and volume of the power grid. and These are the online gas price and volume. , and , These are the unit power maintenance cost and operating power of energy conversion equipment and energy storage equipment, respectively. For carbon costs, and These are the carbon content per unit power of electricity and natural gas, respectively. For a moment vehicle From the station Arrival Station binary state variables, For the station Arrival Station The transportation distance; The robust inter-regional collaborative operation optimization model of hydrogen energy multi-stations is solved to obtain the multi-station collaborative operation strategy, and the multi-station energy system is scheduled using the multi-station collaborative operation strategy.

2. The multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in claim 1, characterized in that, The operating model of energy equipment within the station includes the operating models of energy storage equipment and energy conversion equipment.

3. The multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in claim 1, characterized in that, Energy sources include hydrogen, electricity, heat, and cold energy.

4. The multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in claim 2, characterized in that, The specific steps for building an operational model of energy equipment within the power station are as follows: Modeling is performed separately for different energy storage devices and energy conversion devices; Based on the balance between different energy sources and the interaction between energy equipment within the power station, an operation and scheduling model for energy equipment within the power station is established.

5. The multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in claim 1, characterized in that, The specific steps for solving the robust inter-regional collaborative operation optimization model for multiple hydrogen energy stations are as follows: Linearization and transformation of the optimization model for robust inter-regional collaborative operation of hydrogen energy multi-station based on the big M method and strong duality theory; The outer and inner optimization problems in the objective function are solved separately by decomposition.

6. A collaborative optimization scheduling system for the multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in any one of claims 1-5, characterized in that, include: The data acquisition module is configured to acquire historical operating data of energy systems at multiple power stations, generate a set of renewable energy output scenarios, and build an operation scheduling model for energy equipment within the power station by combining equipment operating parameters. The interaction relationship determination module is configured to establish a dynamic hydrogen energy transportation interaction model between multiple stations based on the hydrogen energy transportation interaction relationship between them. The operation optimization module is configured to consider the uncertainties of renewable energy output and hydrogen transportation process, and to collaboratively optimize the operation scheduling model of energy equipment within the station and the dynamic hydrogen transportation interaction model between stations, and establish a robust inter-station collaborative operation optimization model for hydrogen energy. The collaborative scheduling module is configured to solve the robust interval collaborative operation optimization model of hydrogen energy multi-site stations, obtain the multi-site collaborative operation strategy, and use the multi-site collaborative operation strategy to schedule the energy system of multi-site stations.

7. A computer-readable storage medium, characterized in that, The device stores multiple instructions, which are adapted to be loaded and executed by the processor of the terminal device using the multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in any one of claims 1-5.

8. A terminal device, characterized in that, The invention includes a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; and the computer-readable storage medium is used to store multiple instructions, which are adapted to be loaded by the processor and executed by the processor to implement the multi-site energy system collaborative optimization scheduling method considering hydrogen energy interaction as described in any one of claims 1-5.

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