Geothermal-fused hydrogen-doped natural gas park comprehensive energy system planning method and system

Abstract

The invention provides a terrestrial heat fused hydrogen-doped natural gas park comprehensive energy system planning method and system. The method comprises the following steps: establishing a system model of a terrestrial heat-fused hydrogen-doped natural gas park comprehensive energy system, and determining constraint conditions of the system model; defining a multi-objective optimization function set on the basis of the demand load of the integrated energy system; solving the multi-objective optimization function set based on an NSGA-II algorithm and the constraint condition to obtain a non-dominated solution set; and performing multi-target sorting on the non-dominated solution set to screen a target planning scheme. Through complementation and gradient utilization of multiple energy sources such as electric energy, hydrogen energy, geothermal energy and natural gas, efficient conversion and collaborative optimization of the energy sources are achieved, energy waste is reduced, the energy utilization efficiency is remarkably improved, the total cost of the comprehensive energy system can be reduced through equipment optimal configuration based on model design with the minimum annual total cost and the optimal carbon emission, and the energy utilization efficiency is improved. And carbon emission is reduced.

Description

Technical Field

[0001] The present application belongs to the technical field of integrated energy system planning, and relates to a method and system for planning an integrated energy system for a hydrogen-blended natural gas park integrating geothermal energy. Background Art

[0002] Against the backdrop of my country's continued advancement of its "dual carbon" strategic goals and the accelerated transformation of the energy system towards low-carbonization, exploring multi-energy complementary paths that couple renewable energy sources such as photovoltaics, wind power, geothermal energy with hydrogen energy and natural gas has become the core direction of the park's distributed integrated energy system planning.

[0003] The hydrogen-blended natural gas system deeply integrates renewable energy sources such as solar energy. Using electrolyzers to produce hydrogen from water, the system blends the resulting hydrogen with natural gas, significantly reducing the system's reliance on pure natural gas and significantly reducing carbon emissions. Furthermore, the system's integration of multiple energy devices and technologies allows it to efficiently meet the diverse energy load demands of industrial parks.

[0004] Geothermal energy, with its high stability and low carbon emissions, has become a vital addition to the integrated energy systems of industrial parks. This system delivers clean heating and reduces CO2 emissions. Furthermore, the synergistic application of geothermal energy with photovoltaics and energy storage further enhances system flexibility, enabling cross-time regulation of heating supply and mitigating the cost pressures associated with peak-valley electricity price fluctuations.

[0005] Traditional integrated energy system design optimization methods mostly focus on a single energy type and lack overall coordination of multiple energy flows such as photovoltaics, wind power, hydrogen energy, and energy storage.

[0006] Renewable energy is affected by natural factors such as weather and seasons, energy supply is unstable, and the intermittent nature of distributed energy exacerbates the uncertainty of the overall system.

[0007] The existing distributed integrated energy system of hydrogen-blended natural gas parks does not take into account the hydrogen produced by renewable energy such as solar energy and wind power generation in the hydrogen-blended natural gas formed. It lacks consideration of the hydrogen blending ratio when hydrogen produced by renewable energy such as solar energy is blended with natural gas. It cannot effectively solve the uneconomical and environmentally unfriendly problems of the operation of the multi-energy complementary integrated energy system in industrial parks. The energy utilization rate is low, resulting in high operating costs. Summary of the Invention

[0008] The present application provides a planning method and system for a comprehensive energy system of a hydrogen-blended natural gas park that integrates geothermal energy, which is used to solve the technical problem that the distributed comprehensive energy system of the hydrogen-blended natural gas park lacks consideration of the hydrogen blending ratio when hydrogen is produced from renewable energy such as solar energy and blended with natural gas, and cannot effectively solve the uneconomical and environmentally unfriendly problems of the operation of the multi-energy complementary comprehensive energy system in the industrial park.

[0009] In a first aspect, the present application provides a method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park. The method comprises: Establishing a system model of a geothermal integrated hydrogen-blended natural gas park integrated energy system, and determining constraints of the system model, including: defining equipment parameters and meteorological parameters of the integrated energy system, obtaining load demand data of each subsystem in the integrated energy system, establishing a system model of the integrated energy system based on the equipment parameters, the meteorological parameters, and the load demand data of each subsystem, and determining constraints of the integrated energy system based on an energy balance principle and an output constraint principle of a photovoltaic power generation system; Based on the load demand data of each subsystem, respectively calculating the cost of each subsystem and the system capacity and power of each subsystem, and defining a multi-objective optimization function set based on the cost of each subsystem and the system capacity and power of each subsystem; Solving the multi-objective optimization function set based on the NSGA-II algorithm and the constraints to obtain a non-dominated solution set, including: randomly generating an initial set containing a plurality of solutions; processing the solutions based on the objective optimization function set, the constraints, and the constraint penalty terms to obtain the objective function values ​​and constraint violation degrees of the solutions; performing fast non-dominated sorting on the solutions based on the objective function values ​​and constraint violation degrees to divide the solutions into multiple levels, and screening the solutions in the same non-dominated level by calculating the congestion degree to obtain parent solutions; performing a loop iteration process with the parent solutions as initial input, the loop iteration process including: screening the parent solutions by tournament selection, performing crossover and mutation operations on the screened parent solutions to generate child solutions, calculating the objective function values ​​and constraint violation degrees of the child solutions, and merging the parent solutions and the child solutions to generate a new generation set; performing fast non-dominated sorting and congestion calculation operations on the solutions in the new generation set to obtain the parent solution; and after the loop iteration reaches a maximum number of iterations, taking the solutions in the first non-dominated level in the set as the non-dominated solution set; The non-dominated solution set is subjected to multi-objective sorting to screen the target planning scheme, including: adopting a linear weighted method to perform multi-objective sorting on the non-dominated solution set, and screening the target planning scheme based on the sorting result.

[0010] In an implementation of the first aspect, the subsystem includes: a photovoltaic power generation system, a wind power generation system, a hydrogen-blended natural gas power generation system, a geothermal heating system, a water electrolysis hydrogen production system, a battery energy storage system, and a hydrogen energy storage system.

[0011] In an implementation of the first aspect, the model of the photovoltaic power generation system is expressed as: ; in, is the real-time power of the photovoltaic power generation system, is the photovoltaic system power, is the photovoltaic module conversion efficiency, is the real-time solar radiation intensity, is the solar radiation intensity under standard conditions; The model of the wind power generation system is expressed as: ; in, is the real-time power of the wind power generation system, is the air density, is the wind wheel swept area, is the real-time wind speed, is the wind turbine efficiency; The model of the hydrogen-blended natural gas power generation system is expressed as: ; ; in, is the real-time power of the hydrogen-blended natural gas power generation system, is the gas turbine efficiency, is the real-time thermal power, is the fuel flow rate, The lower calorific value of the mixed fuel, For combustion efficiency; The model of the geothermal heating system is expressed as: ; in, is the real-time heating power of the geothermal heating system, is the geothermal fluid mass flow rate, is the specific heat capacity, is the temperature difference; The model of the water electrolysis hydrogen production system is expressed as: ; in, The real-time amount of hydrogen produced by the water electrolysis hydrogen production system, is the efficiency of hydrogen production by water electrolysis, is the electrolytic cell power; The model of the battery energy storage system is expressed as: ; in, is the real-time energy storage energy of the battery energy storage system, is the battery energy storage capacity, 、 is the charge and discharge efficiency coefficient of the battery energy storage system; The model of the hydrogen energy storage system is expressed as: ; in, is the real-time energy storage capacity of the hydrogen energy storage system, 、 is the charge and discharge efficiency coefficient of the hydrogen energy storage system.

[0012] In an implementation of the first aspect, the constraints of the system model include: an energy balance condition and a photovoltaic power generation system new energy output constraint condition; wherein the energy balance condition includes: a power demand balance condition and a heat demand balance condition; the power demand balance condition is expressed as: ; in, is the peak power demanded by the power load, is the power requirement of the water electrolysis hydrogen production system, The power consumption on the grid side; The heat demand balance condition is expressed as: ; in, For heating needs; The new energy output constraint condition of the photovoltaic power generation system is expressed as: ; ; ; ; ; in, is the rated power of the photovoltaic power generation system, is the rated power of the wind power generation system, is the rated wind speed.

[0013] In an implementation of the first aspect, the target optimization function set includes: a first optimization objective function and a second optimization objective function; wherein the expression of the first optimization objective function is: ; in, is the system investment cost, System operation and maintenance costs, The cost of purchasing electricity, For gas costs; The expression of the second optimization objective function is: ; in, 、 are the capacity and power of photovoltaic power generation system respectively, 、 are the capacity and power of wind power generation system respectively, 、 are the capacity and power of geothermal heating system respectively, 、 are the capacity and power of hydrogen-blended natural gas power generation system, 、 are the capacity and power of the water electrolysis hydrogen production system respectively.

[0014] In an implementation of the first aspect, the constraint penalty term is expressed as: ; in, is the initial set, is the penalty coefficient, is a constrained inequality.

[0015] In an implementation of the first aspect, the non-dominated solution set is subjected to multi-objective sorting to screen the target planning scheme, including: using a linear weighted method to perform multi-objective sorting on the non-dominated solution set; and screening the target planning scheme based on the sorting result.

[0016] In a second aspect, the present application provides a comprehensive energy system planning system for a geothermal-integrated hydrogen-blended natural gas park. The system includes: a model building module configured to establish a system model of a geothermal integrated hydrogen-blended natural gas park integrated energy system and determine constraints of the system model, including: defining equipment parameters and meteorological parameters of the integrated energy system, obtaining load demand data of each subsystem in the integrated energy system, establishing the system model of the integrated energy system based on the equipment parameters, the meteorological parameters, and the load demand data of each subsystem, and determining the constraints of the integrated energy system based on an energy balance principle and an output constraint principle of a photovoltaic power generation system; an optimization function generation module configured to calculate the cost of each subsystem and the system capacity and power of each subsystem based on the load demand data of each subsystem, and define a multi-objective optimization function set based on the cost of each subsystem and the system capacity and power of each subsystem; The optimization function solving module is configured to solve the multi-objective optimization function set based on the NSGA-II algorithm and the constraints to obtain a non-dominated solution set, including: randomly generating an initial set containing a plurality of solutions; processing the solutions based on the objective optimization function set, the constraints and the constraint penalty terms to obtain the objective function values ​​and constraint violation degrees of the solutions; performing fast non-dominated sorting on the solutions based on the objective function values ​​and constraint violation degrees to divide the solutions into multiple levels, and screening the solutions within the same non-dominated level through congestion calculation to obtain parent solutions; performing a loop iteration process with the parent solutions as initial input, the loop iteration process including: screening the parent solutions through tournament selection, performing crossover and mutation operations on the screened parent solutions to generate child solutions, calculating the objective function values ​​and constraint violation degrees of the child solutions, and merging the parent solutions and the child solutions to generate a new generation set; performing fast non-dominated sorting and congestion calculation operations on the solutions within the new generation set to obtain the parent solution; After the loop iteration reaches a maximum number of iterations, the solution of the first non-dominated level in the set is used as a non-dominated solution set; The screening module is configured to perform multi-objective sorting on the non-dominated solution set to screen the target planning scheme, including: using a linear weighted method to perform multi-objective sorting on the non-dominated solution set, and screening the target planning scheme based on the sorting result.

[0017] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, the memory being used to store a computer program; the processor is communicatively connected to the memory, and when the computer program is called, the method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park as described in any one of the first aspects of the present application is executed.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park as described in any one of the first aspects of the present application.

[0019] As described above, the method and system for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park described in this application have the following beneficial effects: This application focuses on the field of design and optimization technology for distributed integrated energy systems in industrial parks, and innovatively proposes a method and system for optimizing distributed integrated energy systems in industrial parks. This application uses the balance of supply and demand of multiple energy sources, new energy output, energy storage capacity, and carbon emissions as constraints, takes industrial park energy demand load data as the basic input, combines design parameters with meteorological data, introduces new energy installed capacity, electrical energy storage capacity, hydrogen energy storage capacity, and hydrogen blending ratio as key decision variables, constructs an integrated energy system optimization model, and clarifies the objective function and constraint processing. This application takes minimizing annual total costs and carbon emissions as its core objectives. It uses an algorithm to calculate the objective function and output a Pareto solution set. It then performs multi-objective sorting on the Pareto solution set, accurately screening the optimal solution corresponding to the decision variables. It then obtains the power parameters of each device under the optimal solution state, allowing for flexible adjustment of the hydrogen energy usage ratio based on different operating conditions, maximizing both energy efficiency and environmental benefits, and providing a scientific solution for the efficient and green operation of the park's integrated energy system. This application achieves efficient energy conversion and coordinated optimization through the complementary and cascaded utilization of multiple energy sources such as electricity, hydrogen, geothermal energy, and natural gas, reduces energy waste, and significantly improves energy utilization efficiency. Through multi-energy coupling and flexible regulation, it can effectively absorb fluctuating renewable energy sources such as wind power and photovoltaics. Combined with the optimization of energy storage capacity configuration, it can smooth out fluctuations in renewable energy output and improve system stability and cleanliness. This application is based on a model design with minimal annual total cost and optimal carbon emissions. Through optimized equipment configuration, it can reduce the total cost of the integrated energy system and reduce carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Shown is a scene diagram of an embodiment of the method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park described in an embodiment of the present application.

[0021] Figure 2 Shown is a flow chart of the method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park as described in an embodiment of the present application.

[0022] Figure 3 Shown is a flowchart of obtaining a non-dominated solution set based on the NSGA-II algorithm described in an embodiment of the present application.

[0023] Figure 4 Shown is a flow chart of a method for planning an integrated energy system for a geothermal-integrated hydrogen-blended natural gas park as described in another embodiment of the present application.

[0024] Figure 5 Shown is a structural schematic diagram of the integrated energy system planning system for the geothermal-integrated hydrogen-blended natural gas park described in an embodiment of the present application.

[0025] Figure 6 Shown is a structural schematic diagram of an electronic device described in an embodiment of the present application.

[0026] Component number description 11 cell phone 12 tablet 13 laptop 5 Comprehensive energy system planning system for hydrogen-blended natural gas parks integrating geothermal energy 51 Model building module 52 Optimization function generation module 53 Optimization function solving module 54 Filter module 6 electronic devices 61 processor 62 Memory 621 operating system 622 app 63 Network interface 64 bus system 65 User Interface S21~S24 step S231~S235 step S41~S48, S461~S464 step DETAILED DESCRIPTION

[0027] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0028] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0029] The Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm is an evolutionary-based multi-objective optimization algorithm designed to solve the challenge of simultaneously optimizing multiple conflicting objectives. Its core goal is to find a set of efficient solutions, known as the Pareto frontier, that cannot be completely surpassed by other solutions in all objectives. The core concept is to gradually approach the true Pareto optimal set of solutions by simulating the process of natural selection, combining genetic variation with an elite retention strategy.

[0030] The integrated energy system planning method for a geothermal-integrated hydrogen-blended natural gas park of the present application can be applied to the electronic device shown in FIG1 . The electronic devices described in the present application may include a mobile phone 11 with wireless charging function, a tablet computer 12, a laptop computer 13, a wearable device, an in-vehicle device, an augmented reality (AR) / virtual reality (VR) device, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc. The embodiments of the present application do not impose any restrictions on the specific type of electronic device.

[0031] For example, the electronic device may communicate with a network and other devices via wireless communications. The wireless communications may use any communication standard or protocol, including but not limited to Global System of Mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), BT, GNSS, WLAN, NFC, FM, and / or IR technology.

[0032] The following will describe in detail the principles and implementation methods of the integrated energy system planning method and system for a geothermal integrated hydrogen-blended natural gas park described in the embodiment of the present application in conjunction with the accompanying drawings, so that those skilled in the art can understand the integrated energy system planning method and system for a geothermal integrated hydrogen-blended natural gas park described in the embodiment without the need for creative work.

[0033] To facilitate understanding of the embodiments of this application, first Figure 2 Detailed description. Figure 2 As shown, the integrated energy system planning method for integrating geothermal energy and hydrogen-blended natural gas described in the embodiment of the present application includes the following steps S21 to S24.

[0034] Step S21: Establish a system model of an integrated energy system integrating geothermal energy and hydrogen-doped natural gas, and determine the constraints of the system model.

[0035] Specifically, the parameters of the integrated energy system, including equipment parameters and meteorological parameters, are defined, the corresponding load demand data is obtained, and the system model of each subsystem is established based on the equipment parameters, meteorological parameters and load demand parameters, and the constraints of the system model are determined.

[0036] The subsystems of the integrated energy system include: photovoltaic power generation system, wind power generation system, hydrogen-blended natural gas power generation system, geothermal heating system, water electrolysis hydrogen production system, battery energy storage system and hydrogen energy storage system.

[0037] Among them, the model of photovoltaic power generation system is expressed as: ; in, is the real-time power of the photovoltaic power generation system, is the photovoltaic system power, is the photovoltaic module conversion efficiency, is the real-time solar radiation intensity, is the solar radiation intensity under standard conditions; The model of the wind power generation system is expressed as: ; in, is the real-time power of the wind power generation system, is the air density, is the wind wheel swept area, is the real-time wind speed, is the wind turbine efficiency; The model of hydrogen-blended natural gas power generation system is expressed as: ; ; in, is the real-time power of the hydrogen-blended natural gas power generation system, is the gas turbine efficiency, is the real-time thermal power, is the fuel flow rate, The lower calorific value of the mixed fuel, For combustion efficiency; The model of the geothermal heating system is expressed as: ; in, The real-time heating power of the geothermal heating system. is the geothermal fluid mass flow rate, is the specific heat capacity, is the temperature difference; The model of the water electrolysis hydrogen production system is expressed as: ; in, The real-time amount of hydrogen produced by the water electrolysis hydrogen production system, is the efficiency of hydrogen production by water electrolysis, is the electrolytic cell power; The model of the battery energy storage system is expressed as: ; in, Real-time energy storage for battery energy storage systems, is the battery energy storage capacity, 、 is the charge and discharge efficiency coefficient of the battery energy storage system; The model of the hydrogen energy storage system is expressed as: ; in, Real-time energy storage for hydrogen energy storage systems, 、 is the charge and discharge efficiency coefficient of the hydrogen energy storage system.

[0038] The constraints of the system model include: energy balance conditions and new energy output constraints of the photovoltaic power generation system; among them, the energy balance conditions include: power demand balance conditions and heat demand balance conditions.

[0039] Among them, the power demand balance condition is expressed as: ; in, is the peak power demanded by the power load, is the power requirement of the water electrolysis hydrogen production system, The power consumption on the grid side; The heat demand balance condition is expressed as: ; in, For heating needs; The new energy output constraint condition of photovoltaic power generation system is expressed as: ; ; ; ; ; in, is the rated power of the photovoltaic power generation system, is the rated power of the wind power generation system, is the rated wind speed.

[0040] Step S22: Based on the demand load of the integrated energy system, define a multi-objective optimization function set.

[0041] Specifically, based on constraints such as the balance of supply and demand of various energy sources, new energy output, energy storage capacity, and carbon emissions, the energy demand load data of the park is used as the basic input, and the design parameters and meteorological data are combined to determine the multi-objective optimization function set.

[0042] The multi-objective optimization function set includes: a first optimization objective function and a second optimization objective function; wherein, The expression of the first optimization objective function is: ; in, is the system investment cost, System operation and maintenance costs, The cost of purchasing electricity, For gas costs; The expression of the second optimization objective function is: ; in, 、 are the capacity and power of photovoltaic power generation system respectively, 、 are the capacity and power of wind power generation system respectively, 、 are the capacity and power of geothermal heating system respectively, 、 are the capacity and power of hydrogen-blended natural gas power generation system, 、 are the capacity and power of the water electrolysis hydrogen production system respectively.

[0043] Step S23: Solve the multi-objective optimization function set based on the NSGA-II algorithm and the constraint conditions to obtain a non-dominated solution set.

[0044] like Figure 3 As shown, the target optimization function set is solved based on the NSGA-II algorithm and the constraint conditions to obtain a non-dominated solution set, including the following steps S231 to S235.

[0045] Step S231: Randomly generate an initial set containing several solutions.

[0046] Specifically, an initial set containing N solutions is generated randomly or by a specific method.

[0047] Step S232: Process the solution based on the multi-objective optimization function set, constraint conditions, and constraint penalty items to obtain the objective function value and constraint violation degree of the solution.

[0048] Among them, the expression of the constraint penalty term is: ; in, For the collection, is the penalty coefficient, is a constrained inequality.

[0049] For the solutions in the initial set, the multi-objective optimization function set and the constraint penalty term are respectively calculated to obtain the first objective function value, the second objective function value and the constraint violation degree corresponding to the solution.

[0050] Step S233: Perform fast non-dominated sorting on the solutions based on the objective function value and the constraint violation degree to divide the solutions into multiple levels, and filter the solutions in the same non-dominated level by congestion calculation to obtain the parent solution.

[0051] Based on the constraint dominance principle, the constraint violation degrees between the schemes are compared, and the schemes with a constraint violation degree of 0 are subjected to Pareto dominance comparison to divide the schemes in the set into multiple levels, where the first level is the non-dominated level.

[0052] The distribution density between solutions at the same level is calculated separately, and solutions with high congestion are selected for retention. All solutions retained at all levels are parent solutions.

[0053] Step S234: Execute a loop iteration process with the parent solution as the initial input.

[0054] The loop iteration process is: S2341. Screening parent solutions through tournament selection, and performing crossover and mutation operations on the screened parent solutions to generate child solutions; S2342. Calculate the objective function value and constraint violation degree of the offspring solution, and merge the parent solution and the offspring solution to generate a new generation set; S2343. Perform fast non-dominated sorting and congestion calculation operations on the solutions in the new generation set to obtain the parent solution.

[0055] Specifically, a binary tournament selection algorithm is used to screen all parent solutions, and preset parent solutions are randomly selected from all parent solutions to form a tournament. The objective function values ​​of each parent solution in the tournament are compared, and the parent solution with the highest objective function value is retained.

[0056] The binary tournament selection algorithm is repeatedly executed until a sufficient number of parent solutions are retained, wherein the retained parent solutions are the screened parent solutions.

[0057] The retained parent solutions are subjected to crossover and mutation to generate offspring solutions.

[0058] Execute the above step S232 on the generated offspring solution to complete the adaptive processing of the offspring solution, so as to obtain the corresponding first objective function value, second objective function value and constraint violation degree of the offspring solution, and then merge the parent solution and the offspring solution to obtain a new generation set.

[0059] Continue to execute steps S2341 to S2343 for the new generation set.

[0060] Step S235: After the loop iteration reaches the maximum number of iterations, the solution of the first non-dominated level in the set is taken as the non-dominated solution set.

[0061] Specifically, a fast non-dominated sorting operation is performed on the final set generated when the loop iterations reach the maximum number, so as to divide the solutions in the final set into multiple levels, where the first level is a non-dominated level, and the solutions in the first level constitute a non-dominated solution set.

[0062] Step S24: perform multi-objective sorting on the non-dominated solution set to screen the target planning scheme.

[0063] Specifically, a linear weighted method is used to perform multi-objective sorting on the non-dominated solution set; and the target planning scheme is screened based on the sorting results.

[0064] The linear weighted formula is: ; in, , 、 are weight coefficients, Indicates cost priority, Indicates environmental protection priority.

[0065] See also Figure 4 , which shows a flow chart of a method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park according to another embodiment of the present application. Figure 4 As shown, the method includes: Step S41, establishing a system model of the integrated energy system: defining the parameters of the integrated energy system, including equipment parameters and meteorological parameters, obtaining corresponding load demand data, and establishing system models of each system based on the equipment parameters, meteorological parameters and load demand parameters, and determining the constraints of the system model.

[0066] Step S42, define and generate a multi-objective optimization function set: based on constraints such as energy supply and demand balance, new energy output, energy storage capacity, carbon emissions, etc., use the park energy demand load data as the basic input, combine design parameters and meteorological data, and determine the multi-objective optimization function set.

[0067] Step S43: generating an initial set: generating an initial set containing N solutions randomly or by a specific method.

[0068] Step S44, fitness calculation: processing the solution based on the multi-objective optimization function set, constraint conditions and constraint penalty items to obtain the solution's objective function value and constraint violation degree.

[0069] Step S45, screening parent solutions: performing fast non-dominated sorting on the solutions based on the objective function value and constraint violation degree to divide the solutions into multiple levels, and screening solutions within the same non-dominated level through congestion calculation to obtain parent solutions.

[0070] Step S46, loop iterative calculation: Execute loop iterative process with the parent solution as initial input. It includes: Step S461, offspring solution generation: screening parent solutions through tournament selection, and performing crossover and mutation operations on the screened parent solutions to generate offspring solutions; Step S462, new generation set generation: calculate the objective function value and constraint violation degree of the offspring solution, and merge the parent solution and the offspring solution to generate a new generation set; Step S463, screening of the new generation parent solutions: performing fast non-dominated sorting and congestion calculation operations on the solutions in the new generation set to obtain the parent solutions; Step S464: Determine whether the number of iterations has reached the maximum. If so, jump to step S47; otherwise, jump to step S461.

[0071] Step S47 , generating a non-dominated solution set: taking the first non-dominated level solution in the set as the non-dominated solution set.

[0072] Step S48, target planning scheme screening: use linear weighted method to perform multi-objective sorting on the non-dominated solution set; and screen the target planning scheme based on the sorting result.

[0073] The protection scope of the integrated energy system planning method for a geothermal integrated hydrogen-blended natural gas park in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, subtracting, or replacing steps in the existing technology based on the principles of the present application are included in the protection scope of the present application.

[0074] The embodiments of the present application also provide a geothermal integrated hydrogen-blended natural gas park comprehensive energy system planning system, which can implement the geothermal integrated hydrogen-blended natural gas park comprehensive energy system planning method described in the present application. However, the implementation device of the geothermal integrated hydrogen-blended natural gas park comprehensive energy system planning method described in the present application includes but is not limited to the structure of the geothermal integrated hydrogen-blended natural gas park comprehensive energy system planning system listed in the present embodiment. All structural deformations and replacements of the existing technology made according to the principles of the present application are included in the protection scope of the present application.

[0075] like Figure 5 As shown, this embodiment provides a geothermal integrated hydrogen-blended natural gas park integrated energy system planning system 5, including: The model building module 51 is configured to build a system model of a geothermal integrated hydrogen-blended natural gas park integrated energy system and determine the constraints of the system model; an optimization function generating module 52 configured to define a multi-objective optimization function set based on the demand load of the integrated energy system; an optimization function solving module 53, configured to solve the multi-objective optimization function set based on the NSGA-II algorithm and the constraint conditions to obtain a non-dominated solution set; The screening module 54 is configured to perform multi-objective sorting on the non-dominated solution set to screen target planning solutions.

[0076] Since the specific implementation of this embodiment corresponds to the aforementioned method embodiment, the same details will not be repeated here.

[0077] It should be understood that the division of the modules described above is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a single physical entity or physically separated. Furthermore, these modules may be implemented entirely as software invoked by a processing element, or entirely as hardware. Alternatively, some modules may be implemented as software invoked by a processing element, while others may be implemented as hardware. For example, module x may be a separate processing element, or integrated into a chip of the aforementioned device. Furthermore, it may be stored in the form of program code in the memory of the aforementioned device, invoked by a processing element of the aforementioned device to perform the functions of module x. The implementation of other modules is similar. Furthermore, these modules may be fully or partially integrated or implemented independently. The processing element described herein may be an integrated circuit with signal processing capabilities. During implementation, the steps of the above method or the modules described above may be performed by hardware integrated logic circuits within the processor element or by software instructions.

[0078] The present application also provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park provided in an embodiment of the present invention.

[0079] In this application, any combination of one or more storage media may be used. The storage medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM, ROM, an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0080] The embodiments of the present application may also provide a computer program product, which includes one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the process or function described in the embodiments of the present application is generated in whole or in part. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0081] When the computer program product is executed by a computer, the computer executes the method described in the above method embodiment. The computer program product can be a software installation package. When the above method is needed, the computer program product can be downloaded and executed on the computer.

[0082] This application also provides an electronic device, see Figure 6 , which is a schematic diagram of the structure of an electronic device 6 in one embodiment of the present application. Figure 6 As shown, the electronic device 6 includes: at least one processor 61, a memory 62, at least one network interface 63 and a user interface 65. The various components in the electronic device 6 are coupled together via a bus system 64. It is understood that the bus system 64 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 64 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 6 In the text, various buses are labeled as bus systems.

[0083] The user interface 65 may include a display, keyboard, mouse, trackball, click gun, keys, buttons, touch pad, touch screen, or the like.

[0084] It will be appreciated that the memory 62 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM) or a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable categories of memories.

[0085] The memory 62 in the embodiment of the present application is used to store various categories of data to support the operation of the electronic device 6. Examples of these data include: any executable program for operating on the electronic device 6, such as an operating system 621 and an application 622; the operating system 621 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 622 can include various applications, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. The integrated energy system planning method for the integrated geothermal hydrogen-blended natural gas park provided in the embodiment of the present application can be included in the application 622.

[0086] The method disclosed in the above embodiment of the present application can be applied to the processor 61, or implemented by the processor 61. The processor 61 may be an integrated circuit chip with signal processing capabilities. During the implementation process, the steps of the above method can be completed by the hardware integrated logic circuit in the processor 61 or instructions in the form of software. The above processor 61 can be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 61 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure. The general-purpose processor 61 can be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in conjunction with the embodiment of the present application can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory. The processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0087] In an exemplary embodiment, the electronic device 6 may be one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), and complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0088] The descriptions of the processes or structures corresponding to the above figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0089] In summary, the geothermal integrated hydrogen-blended natural gas park integrated energy system planning method and system described in this application, compared with the existing technology, focuses on the field of design optimization technology of distributed integrated energy system in the park, and innovatively proposes a distributed integrated energy system optimization method and system for the park; with the balance of supply and demand of multiple energy sources, new energy output, energy storage capacity, and carbon emissions as constraints, the energy demand load data of the park is used as the basic input, and combined with design parameters and meteorological data, new energy installed capacity, electric energy storage capacity, hydrogen energy storage capacity, and hydrogen blending ratio are introduced as key decision variables to construct an integrated energy system optimization model and clarify the objective function and constraint processing; with the minimization of annual total cost and carbon emissions as the core goals, the objective function is calculated through an algorithm, and the Pareto solution set is output, and then the Pareto solution is analyzed. The solution set implements multi-objective sorting, accurately screens out the optimal solution corresponding to the decision variables, and then obtains the power parameters of each device in the optimal solution state, so as to flexibly adjust the proportion of hydrogen energy use according to different operating conditions, achieve dual maximization of energy efficiency and environmental protection benefits, and provide a scientific solution for the efficient and green operation of the park's integrated energy system; through the complementary and cascade utilization of multiple energy sources such as electricity, hydrogen, geothermal energy, and natural gas, it realizes efficient energy conversion and coordinated optimization, reduces energy waste, and significantly improves energy utilization efficiency; through multi-energy coupling and flexible adjustment, it can effectively absorb fluctuating renewable energy sources such as wind power and photovoltaics; combined with the optimization of energy storage capacity configuration, it can smooth out the fluctuation of renewable energy output and improve the stability and cleanliness level of the system; based on the model design with the minimum annual total cost and the optimal carbon emission, through the optimized configuration of equipment, it can reduce the total cost of the integrated energy system and reduce carbon emissions. Therefore, this application effectively overcomes the various shortcomings in the existing technology and has a high industrial utilization value.

[0090] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A method for planning an integrated energy system for a geothermal-integrated hydrogen-blended natural gas park, characterized in that: include: Establishing a system model of a geothermal integrated hydrogen-blended natural gas park integrated energy system, and determining constraints of the system model, including: defining equipment parameters and meteorological parameters of the integrated energy system, obtaining load demand data of each subsystem in the integrated energy system, establishing a system model of the integrated energy system based on the equipment parameters, the meteorological parameters, and the load demand data of each subsystem, and determining constraints of the integrated energy system based on the energy balance principle and the new energy output constraint principle of the photovoltaic power generation system; Based on the load demand data of each subsystem, respectively calculating the cost of each subsystem and the system capacity and power of each subsystem, and defining a multi-objective optimization function set based on the cost of each subsystem and the system capacity and power of each subsystem; Solving the multi-objective optimization function set based on the NSGA-II algorithm and the constraints to obtain a non-dominated solution set, including: randomly generating an initial set containing a plurality of solutions; processing the solutions based on the objective optimization function set, the constraints and the constraint penalty terms to obtain the objective function values ​​and constraint violation degrees of the solutions; performing fast non-dominated sorting on the solutions based on the objective function values ​​and constraint violation degrees to divide the solutions into multiple levels, and screening the solutions in the same non-dominated level by calculating the congestion degree to obtain parent solutions; performing a loop iteration process with the parent solutions as initial input, the loop iteration process including: screening the parent solutions by tournament selection, performing crossover and mutation operations on the screened parent solutions to generate child solutions, calculating the objective function values ​​and constraint violation degrees of the child solutions, and merging the parent solutions and the child solutions to generate a new generation set; performing fast non-dominated sorting and congestion calculation operations on the solutions in the new generation set to obtain the parent solution; after the loop iteration reaches a maximum number of iterations, taking the solutions in the first non-dominated level in the set as the non-dominated solution set; The non-dominated solution set is subjected to multi-objective sorting to screen the target planning scheme, including: adopting a linear weighted method to perform multi-objective sorting on the non-dominated solution set, and screening the target planning scheme based on the sorting result.

2. The method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park according to claim 1, characterized in that: The subsystems include: a photovoltaic power generation system, a wind power generation system, a hydrogen-blended natural gas power generation system, a geothermal heating system, a water electrolysis hydrogen production system, a battery energy storage system and a hydrogen energy storage system.

3. The method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park according to claim 2, characterized in that: The model of the photovoltaic power generation system is expressed as: ； in, is the real-time power of the photovoltaic power generation system, is the photovoltaic system power, is the photovoltaic module conversion efficiency, is the real-time solar radiation intensity, is the solar radiation intensity under standard conditions; The model of the wind power generation system is expressed as: ； in, is the real-time power of the wind power generation system, is the air density, is the wind wheel swept area, is the real-time wind speed, is the wind turbine efficiency; The model of the hydrogen-blended natural gas power generation system is expressed as: ； ； in, is the real-time power of the hydrogen-blended natural gas power generation system, is the gas turbine efficiency, is the real-time thermal power, is the fuel flow rate, The lower calorific value of the mixed fuel, For combustion efficiency; The model of the geothermal heating system is expressed as: ； in, is the real-time heating power of the geothermal heating system, is the geothermal fluid mass flow rate, is the specific heat capacity, is the temperature difference; The model of the water electrolysis hydrogen production system is expressed as: ； in, The real-time amount of hydrogen produced by the water electrolysis hydrogen production system, is the efficiency of hydrogen production by water electrolysis, is the electrolytic cell power; The model of the battery energy storage system is expressed as: ； in, is the real-time energy storage energy of the battery energy storage system, is the battery energy storage capacity, 、 is the charge and discharge efficiency coefficient of the battery energy storage system; The model of the hydrogen energy storage system is expressed as: ； in, is the real-time energy storage capacity of the hydrogen energy storage system, 、 is the charge and discharge efficiency coefficient of the hydrogen energy storage system.

4. The method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park according to claim 2, characterized in that: The constraints of the system model include: energy balance conditions and photovoltaic power generation system new energy output constraints; wherein, the energy balance conditions include: power demand balance conditions and heat demand balance conditions; The power demand balance condition is expressed as: ； in, is the peak power demanded by the power load, is the power requirement of the water electrolysis hydrogen production system, The power consumption on the grid side; The heat demand balance condition is expressed as: ； in, For heating needs; The new energy output constraint condition of the photovoltaic power generation system is expressed as: ； ； ； ； ； in, is the rated power of the photovoltaic power generation system, is the rated power of the wind power generation system, is the rated wind speed.

5. The method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park according to claim 1, characterized in that: The target optimization function set includes: a first optimization target function and a second optimization target function; wherein, The expression of the first optimization objective function is: ； in, is the system investment cost, System operation and maintenance costs, The cost of purchasing electricity, For gas costs; The expression of the second optimization objective function is: ； in, 、 are the capacity and power of photovoltaic power generation system respectively, 、 are the capacity and power of wind power generation system respectively, 、 are the capacity and power of geothermal heating system respectively, 、 are the capacity and power of hydrogen-blended natural gas power generation system, 、 are the capacity and power of the water electrolysis hydrogen production system respectively.

6. The method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park according to claim 1, characterized in that: The expression of the constraint penalty term is: ； in, is the initial set, is the penalty coefficient, is a constrained inequality.

7. The method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park according to claim 1, characterized in that: The non-dominated solution set is subjected to multi-objective sorting to screen the target planning scheme, including: adopting a linear weighted method to perform multi-objective sorting on the non-dominated solution set; and screening the target planning scheme based on the sorting result.

8. A comprehensive energy system planning system for a geothermal-integrated hydrogen-blended natural gas park, characterized in that: include: a model building module configured to establish a system model of a geothermal integrated hydrogen-blended natural gas park integrated energy system and determine constraints of the system model, including: defining equipment parameters and meteorological parameters of the integrated energy system, obtaining load demand data of each subsystem in the integrated energy system, establishing the system model of the integrated energy system based on the equipment parameters, the meteorological parameters, and the load demand data of each subsystem, and determining the constraints of the integrated energy system based on an energy balance principle and an output constraint principle of a photovoltaic power generation system; an optimization function generation module configured to calculate the cost of each subsystem and the system capacity and power of each subsystem based on the load demand data of each subsystem, and define a multi-objective optimization function set based on the cost of each subsystem and the system capacity and power of each subsystem; The optimization function solving module is configured to solve the multi-objective optimization function set based on the NSGA-II algorithm and the constraints to obtain a non-dominated solution set, including: randomly generating an initial set containing a plurality of solutions; processing the solutions based on the objective optimization function set, the constraints and the constraint penalty terms to obtain the objective function values ​​and constraint violation degrees of the solutions; performing fast non-dominated sorting on the solutions based on the objective function values ​​and constraint violation degrees to divide the solutions into multiple levels, and screening the solutions within the same non-dominated level through congestion calculation to obtain parent solutions; performing a loop iteration process with the parent solutions as initial input, the loop iteration process including: screening the parent solutions through tournament selection, performing crossover and mutation operations on the screened parent solutions to generate child solutions, calculating the objective function values ​​and constraint violation degrees of the child solutions, and merging the parent solutions and the child solutions to generate a new generation set; performing fast non-dominated sorting and congestion calculation operations on the solutions within the new generation set to obtain the parent solution; After the loop iteration reaches a maximum number of iterations, the solution of the first non-dominated level in the set is used as a non-dominated solution set; The screening module is configured to perform multi-objective sorting on the non-dominated solution set to screen the target planning scheme, including: using a linear weighted method to perform multi-objective sorting on the non-dominated solution set, and screening the target planning scheme based on the sorting result.

9. An electronic device, characterized in that: The electronic device comprises: a memory storing a computer program; A processor is communicatively connected to the memory and executes the method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park according to any one of claims 1 to 7 when calling each of the computer programs.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for planning a comprehensive energy system for a geothermal-integrated hydrogen-blended natural gas park according to any one of claims 1 to 7 is implemented.

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