Geothermal energy and hydrogen energy comprehensive energy collaborative planning method based on double-layer optimization model

Abstract

The invention discloses a geothermal energy and hydrogen energy comprehensive energy collaborative planning method based on a double-layer optimization model, and particularly relates to the technical field of energy collaborative planning. Based on the basic data, a double-layer optimization model is constructed, an upper-layer planning model takes energy system full-life-cycle total cost minimization as a first objective function, and a lower-layer operation model takes annual operation cost minimization of the energy system as a second objective function; a KKT conditional mathematical method is adopted, the double-layer optimization model is converted into a solvable single-layer linear programming model, and an optimal programming capacity configuration scheme of the terrestrial heat and hydrogen energy equipment is obtained; and outputting an optimal planning capacity configuration scheme. According to the method, through data processing and parameter acquisition, construction of a geothermal and hydrogen energy collaborative planning double-layer optimization model, model solving and scheme generation, and collaborative planning scheme output, the problems that planning and operation coupling is insufficient, and a geothermal and hydrogen energy collaborative mechanism cannot be modeled finely are solved.

Description

Technical Field

[0001] This invention relates to the field of energy synergy planning technology, and more specifically, to a method for integrated energy synergy planning of geothermal and hydrogen energy based on a two-layer optimization model. Background Technology

[0002] As the global energy structure transitions towards cleaner and lower-carbon energy, geothermal energy and hydrogen energy, as two important forms of renewable energy, have received widespread attention. Geothermal energy is characterized by high stability and sustainability, making it suitable as a baseload power source; hydrogen energy, on the other hand, boasts advantages such as high energy density and convenient storage and transportation, making it a valuable energy carrier and backup power source. Coordinating the planning and operation of both is expected to improve the economic efficiency, reliability, and low-carbon level of regional energy systems.

[0003] Existing methods for handling the synergistic characteristics of geothermal and hydrogen energy often employ fixed ratios or empirical coefficients for capacity matching, failing to fully utilize the stability of geothermal power generation to support the flexible operation of electrolytic hydrogen production, and also neglecting the optimization role of hydrogen storage systems in cross-time energy transfer. Regarding model solving, existing methods frequently employ heuristic algorithms or simplified linearization, making it difficult to guarantee optimal solutions, or resulting in low efficiency under complex constraints. However, the aforementioned existing technologies still have several technical shortcomings in practical applications: First, the coupling between planning and operation is insufficient, resulting in poor economic performance of the planning results in actual operation; second, the effective overall planning of the entire life cycle cost is not achieved, and the investment cost and operating cost are optimized separately; third, the model solution method is not rigorous enough, making it difficult to guarantee the overall optimization of the system; and fourth, the geothermal and hydrogen energy synergy mechanism has not been modeled in a refined manner, and the synergistic potential of the system has not been fully realized.

[0004] Therefore, there is an urgent need for a comprehensive energy planning method that can deeply integrate planning and operation, achieve optimal life-cycle costs, and accurately characterize the synergistic characteristics of geothermal and hydrogen energy, so as to improve the overall economy, reliability and synergistic efficiency of the system. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, this invention provides a method for integrated energy planning of geothermal energy and hydrogen energy based on a two-layer optimization model, which solves the problems mentioned in the background art through the following scheme.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for integrated energy planning of geothermal energy and hydrogen energy based on a two-layer optimization model, the method comprising: S1: Data processing and parameter acquisition, acquiring basic energy system data for the target area, including geographic resource data, load data, energy price data, equipment parameters, and constraint parameters; S2: Construct a two-layer optimization model for geothermal hydrogen energy collaborative planning. Based on the aforementioned basic data, construct a two-layer optimization model that couples the upper-layer planning model with the lower-layer operation model. S201: The upper-level planning model takes minimizing the total cost of the energy system throughout its entire life cycle as its first objective function, and its decision variables are the planned configuration capacity of geothermal power generation capacity, electrolysis hydrogen production capacity, and hydrogen storage capacity. S202: The lower-level operation model takes minimizing the annual operating cost of the energy system under the planned configuration capacity as the second objective function, and its decision variables are the inland thermal power generation output, electrolysis hydrogen production power, and hydrogen storage equipment charging and discharging power in each time period; S203: Upper-level planning model and lower-level operation model, a cost-coupled two-level optimization model is established based on the first objective function and the second objective function; S3: Model solving and scheme generation. Using a mathematical method based on KKT conditions, the two-layer optimization model is transformed into a solvable single-layer mixed integer linear programming model. The model is then solved using an optimization solver to obtain the optimal planning capacity configuration scheme and corresponding optimal operation strategy for geothermal and hydrogen energy equipment. S4: Output the collaborative planning scheme, outputting the optimal planning capacity configuration scheme obtained from S3. The scheme includes the collaborative configuration results of the total geothermal power generation capacity, the rated power of electrolysis hydrogen production, the capacity of hydrogen storage equipment, and the capacity of hydrogen power generation equipment.

[0007] The technical effects and advantages of this invention are as follows: 1. By constructing a two-layer optimization structure that couples an upper-layer planning model with a lower-layer operation model, this invention can simultaneously optimize at two levels: equipment capacity configuration and system real-time operation. The upper-layer model determines the equipment capacity with the goal of minimizing the total cost over the entire lifecycle, while the lower-layer model optimizes the operation strategy under a given capacity and feeds back the operation cost to the upper layer, forming a closed-loop optimization. 2. This invention integrates geothermal power generation with multiple processes such as electrolytic hydrogen production, hydrogen storage, and hydrogen power generation into a unified optimization framework. Through capacity matching constraints and operational coordination strategies, it achieves efficient spatiotemporal coupling of geothermal energy and hydrogen energy. Geothermal energy serves as a stable base load power source to support electrolytic hydrogen production, while hydrogen energy serves as an energy storage medium to achieve peak shaving and valley filling. The two complement each other, enhancing the system's flexibility and reliability, and improving the overall absorption rate and utilization efficiency of clean energy. 3. This invention employs a mathematical method based on KKT conditions to transform a complex two-level optimization model into a solvable single-level mixed integer linear programming model. The complementary relaxation conditions are linearized using the Big M method. This method significantly reduces the solution complexity while ensuring model accuracy. It can be solved efficiently using existing mature optimization solvers and is suitable for large-scale, multi-period energy system planning problems, thus improving the engineering applicability of the method. 4. This invention supports the integration and processing of multi-source heterogeneous data, including geographic resource data, time-series load data, market price information, equipment technical parameters, etc., and can flexibly handle various physical constraints, security constraints and economic constraints. Through hierarchical modeling and coupled feedback mechanisms, the system can achieve the most economically optimal planning scheme while meeting various practical constraints, thus enhancing the adaptability and reliability of the method in practical regional energy systems. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the overall planning method structure of the present invention.

[0009] Figure 2 This is a schematic diagram of the data processing and parameter acquisition structure of S1 of the present invention.

[0010] Figure 3 This is a schematic diagram of the two-layer optimization model structure for the construction of geothermal hydrogen energy collaborative planning in S2 of the present invention.

[0011] Figure 4 This is a schematic diagram of the model solving and scheme generation structure of S3 in this invention.

[0012] Figure 5 This is a schematic diagram of the output collaborative planning scheme structure of S4 in this invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Please see Figure 1 - Figure 5 As shown, this embodiment of the invention provides a method for integrated energy planning of geothermal energy and hydrogen energy based on a two-layer optimization model. The method includes: S1: Data processing and parameter acquisition, acquiring basic energy system data for the target area, including geographic resource data, load data, energy price data, equipment parameters, and constraint parameters; S2: Construct a two-layer optimization model for geothermal hydrogen energy collaborative planning. Based on the aforementioned basic data, construct a two-layer optimization model that couples the upper-layer planning model with the lower-layer operation model. S201: The upper-level planning model takes minimizing the total cost of the energy system throughout its entire life cycle as its first objective function, and its decision variables are the planned configuration capacity of geothermal power generation capacity, electrolysis hydrogen production capacity, and hydrogen storage capacity. S202: The lower-level operation model takes minimizing the annual operating cost of the energy system under the planned configuration capacity as the second objective function, and its decision variables are the inland thermal power generation output, electrolysis hydrogen production power, and hydrogen storage equipment charging and discharging power in each time period; S203: Upper-level planning model and lower-level operation model, a cost-coupled two-level optimization model is established based on the first objective function and the second objective function; S3: Model solving and scheme generation. Using a mathematical method based on KKT conditions, the two-layer optimization model is transformed into a solvable single-layer mixed integer linear programming model. The model is then solved using an optimization solver to obtain the optimal planning capacity configuration scheme and corresponding optimal operation strategy for geothermal and hydrogen energy equipment. S4: Output the collaborative planning scheme, outputting the optimal planning capacity configuration scheme obtained from S3. The scheme includes the collaborative configuration results of the total geothermal power generation capacity, the rated power of electrolysis hydrogen production, the capacity of hydrogen storage equipment, and the capacity of hydrogen power generation equipment.

[0015] In S1, data processing and parameter acquisition are performed, including acquiring basic energy system data for the target area. This basic data includes geographic resource data, load data, energy price data, equipment parameters, and constraint parameters. To construct a complete, accurate, and uniformly formatted database of input parameters for a two-layer optimization model for geothermal-hydrogen energy collaborative planning; S101: Determine the target area and data requirements list Define geographic boundaries. In a Geographic Information System (GIS), define the precise boundaries of the planning area, such as an industrial park, city, or specific plot of land. Set the time scale, determine the planning period as 20 years, and the time scale of the running data as hourly; output the target area boundary vector file and time scale division, including peak, valley and normal time division: peak time 8:00-22:00, valley time 22:00-8:00 the next day.

[0016] Determine the data requirements list and priorities. Based on the needs of geothermal hydrogen energy collaborative planning, formulate a data requirements list, clarifying the priorities: P1 is mandatory and P2 is optional. P1 level: Geothermal resource parameters, power load data, economic and technical parameters of main equipment, power purchase and sale price, and constraint parameters; P2 level: hydrogen load data, hydrogen sales price, auxiliary equipment parameters.

[0017] S102: Multi-source data acquisition and collection Geographic resource data: Geothermal resource data of P1 level in the target area is obtained from field survey reports and regional energy planning white papers. The geothermal resource data includes geothermal gradient, thermal reservoir temperature, and geothermal fluid flow rate, and outputs the geothermal usable thermal power curve for the whole year.

[0018] Load data: Historical P1 level power load data, obtained hourly for at least one year from the regional power grid company or the target area's power management department; Predict P2 level hydrogen load data. Based on planned or known hydrogen energy users in the region, such as chemical plants and hydrogen refueling stations, predict hourly hydrogen demand curves. If there are no specific users, estimates can be made based on macro-planning targets.

[0019] Energy price data: P1 level power grid purchase and sale price data, obtained from the power trading market, including hourly electricity price data, i.e., purchase price and sale price; P2 level hydrogen energy price data, obtained from the target area industrial hydrogen market price of 35 yuan / kg, combined with the trend of hydrogen production cost by electrolysis, to predict future hydrogen energy prices.

[0020] Key equipment economic and technical parameters: P1-level equipment economic and technical parameters were obtained from equipment manufacturers, industry databases, and academic literature; geothermal generator sets: unit capacity investment cost, operation and maintenance cost, design power generation efficiency, and heat-to-power ratio; PEM electrolyzers: unit power investment cost, hydrogen production efficiency, rated power range, and load efficiency curve; underground hydrogen storage equipment: unit capacity investment cost, hydrogen charging / discharging power cost, and cycle efficiency; hydrogen power generation equipment: unit power investment cost, power generation efficiency, and operation and maintenance cost.

[0021] System constraint parameters: grid interaction constraints, maximum exchange power at the connection point with the external grid; safety and operation constraints, maximum ramp rate of equipment, minimum start-up and shutdown time, minimum / maximum storage capacity limit of hydrogen storage equipment.

[0022] S103: Data Cleaning and Formatting Time alignment unifies all time-series data on load, geothermal resources, and electricity prices to the same timestamp, and interpolation is used to supplement missing data; outlier handling identifies and corrects or removes abnormal peaks or valleys in load and price data; data normalization converts all data into the standard unit system required by the model.

[0023] S104: Construction and Verification of Key Equipment Parameter Library Cost parameterization models equipment investment costs as a function of capacity size, using piecewise linear functions to reflect economies of scale. Efficiency curve processing fits the partial load efficiency data of the equipment into a mathematical relationship so as to accurately calculate the energy consumption of partial load in the operating model; Economic conversion involves using the capital recovery factor formula to convert the initial investment cost and operation and maintenance cost coefficients of the equipment into the equivalent annual cost required by the upper-level model.

[0024] S105: Generate model input file The processed data is then used to generate a structured input file according to the model interface requirements. This file typically includes: Configuration file: A JSON file containing all device parameters and system constraint parameters; The time-series data file includes timestamps, electrical load, hydrogen load, geothermal available power, electricity purchase price, and electricity sales price; the output can be directly imported into the parameter dataset of the two-layer optimization model in S2, providing a foundation for subsequent optimization calculations.

[0025] In S2, a two-layer optimization model for geothermal hydrogen energy collaborative planning is constructed. Based on the aforementioned basic data, a two-layer optimization model coupling the upper-layer planning model and the lower-layer operation model is constructed. Based on the S1 time-series data file and parameter dataset, a unified variable naming convention is established, and specific variable definitions include: Upper-level decision variables include: geothermal power generation planning capacity. Electrolysis hydrogen production planned capacity Hydrogen storage equipment planned capacity ; The lower-level decision variables include: geothermal power output during time period t. Hydrogen production power during time period t Hydrogen charging power of hydrogen storage equipment during a given time period (t) Hydrogen release power of hydrogen storage equipment during a given time period (t) Power purchased by the power grid during the specified time period (t) Power sold by the power grid during a given time period (t) ; Lower-level state variables, remaining capacity of hydrogen storage equipment during time period t Intermediate variable: Hydrogen production from electrolysis during period t Hydrogen power generation output during a given time period (t) The above variables are non-negative continuous variables, which come from the S1 time series data file and parameter dataset.

[0026] S201: The upper-level planning model takes minimizing the total cost of the energy system throughout its entire life cycle as its first objective function, and its decision variables are the planned configuration capacity of geothermal power generation capacity, electrolysis hydrogen production capacity, and hydrogen storage capacity. The first objective function is the total lifecycle cost. Minimization includes: annual investment cost of equipment, annual maintenance cost, and annual operating cost fed back from the lower-level model; constructed based on S1 equipment parameters, investment cost, lifespan, and maintenance cost coefficients. , in This represents the annualized investment cost, which converts a one-time investment into an annualized cost. It is calculated using the Capital Recovery Factor (CRF), which is based on the S1 planning period n and the discount rate r. , in This represents the annual operation and maintenance cost, calculated based on the S1 equipment operation and maintenance cost coefficient, and included in the annual fixed expenditure: ,in Indicates the operation and maintenance cost coefficient of geothermal power generation units. Indicates the operation and maintenance cost coefficient of the electrolytic cell. This represents the operation and maintenance cost coefficient of hydrogen storage equipment; Indicates the unit investment cost of geothermal power generation units, Indicates the unit investment cost of the electrolytic cell, This indicates the unit investment cost of hydrogen storage equipment; in The annual operating cost is represented by the optimal annual operating cost obtained from the lower-level operating model, which is then substituted into the upper-level first objective function as a feedback variable. The upper-level model constraints, based on S1 geographic resource data, equipment parameters, and constraint parameters, construct boundary constraints for capacity planning. These boundary constraints include: Equipment capacity upper and lower limits constraints: , , , Indicates the maximum planable capacity of geothermal power generation. Indicates the maximum planarable capacity for hydrogen production via electrolysis. This indicates the maximum planarable capacity of the hydrogen storage equipment; Capacity matching constraints ensure the synergy between electrolysis hydrogen production capacity and geothermal power generation and hydrogen storage capacity: , ,in Indicates the maximum support coefficient of geothermal power generation for hydrogen electrolysis. Indicates maximum power purchase capacity, This indicates the matching coefficient between hydrogen storage capacity and electrolysis hydrogen production capacity. Indicates the maximum continuous hydrogen charging time; Output the mathematical expression of the upper-level planning model, including the first objective function and constraints, and clarify the reference paths of each parameter and S1 data.

[0027] S202: The lower-level operation model takes minimizing the annual operating cost of the energy system under the planned configuration capacity as the second objective function, and its decision variables are the inland thermal power generation output, electrolysis hydrogen production power, and hydrogen storage equipment charging and discharging power in each time period; The second objective function is to minimize the annual operating cost; S201 is the upper-level decision variable. Given the constraints, the objective is to minimize the variable costs during the annual operation, encompassing electricity purchase costs, operating loss costs, and load deficit penalty costs. The specific calculation formula is as follows: , in This represents the cost of electricity purchased from the power grid, calculated based on the peak, valley, and normal periods of S1, for each time period. , in Indicates the grid purchase price of electricity during time period t. Indicates the time step, with a step size of 1 hour.

[0028] This represents the operating loss cost, which takes into account energy losses during equipment operation and is calculated based on the efficiency parameter of equipment S1. , in Indicates the power generation capacity of hydrogen electrolysis within time period t. Indicates the hydrogen production efficiency of the electrolyzer, Indicates the efficiency of hydrogen power generation; Logically, the first term represents the cost of electrical energy loss in hydrogen electrolysis, i.e., the energy consumed from input electrical energy to effective hydrogen production. The second term represents the cost of energy loss in hydrogen power generation, i.e., the energy consumed from hydrogen release to effective power generation.

[0029] This represents the penalty cost for load shortfall. If the energy system's output cannot meet the load demand, the loss is calculated based on the penalty factor. , in Indicates the power load deficit during time period t. Indicates the power load during time period t. This represents the load deficit penalty coefficient; The lower-level model constraints, based on S1 device parameters, constraint parameters, and load data, construct physical and logical constraints at the operational level: Electricity supply and demand balance constraints: ; Equipment output constraints are based on the upper-level planned capacity. ; Hydrogen storage equipment charging and discharging power constraints: ; The output of the lower-level operating model mathematical expression includes the second objective function and constraints, clarifies the substitution method of the upper-level decision variables as constraints, and the relationship between each operating parameter and the S1 data.

[0030] S203: Upper-level planning model and lower-level operation model, a cost-coupled two-level optimization model is established based on the first objective function and the second objective function; Based on the closed-loop logic of upper-level planning and lower-level feedback, the interaction relationship between the two models is clarified, with the core coupling point being capacity constraint transmission and operating cost feedback: Forward propagation: Decision variables output by the upper-level model As a hard constraint, it is substituted into the equipment output constraint and hydrogen storage capacity constraint of the lower-level model to limit the boundary of the lower-level operation; Reverse feedback: Under given capacity constraints, the lower-level model solves for the optimal annual operating cost. As a key component of the total lifecycle cost of the upper layer, it inversely influences the optimization direction of upper layer capacity configuration; Further explanation is needed for the coupled two-level optimization model. Transforming the two-level model into a mathematically nested structure, the formal description of the explicit coupling relationship is as follows: .

[0031] The feasibility of coupling was verified based on parameters in the S1 basic data, such as a geothermal power generation capacity of 50MW, an electrolysis hydrogen production capacity of 30MW, and a hydrogen storage capacity of 1000m³. 3 Verify the consistency of the coupled logic: Substitute the upper-level capacity parameters into the lower-level model and check whether all operational constraints are met, such as whether the hydrogen storage capacity can accommodate the daily output of hydrogen produced by electrolysis. Solve the lower-level model to obtain the operating cost, substitute it into the upper-level model, and check the rationality of the total cost calculation, such as whether the operating cost ratio is within the industry's normal range of 30%-50%; Adjust the upper-level capacity parameters and observe whether the changing trend of the lower-level operating costs is logical. For example, increasing hydrogen storage capacity should reduce electricity purchase costs. Output the coupling mathematical expression of the two-level model and the coupling feasibility verification report.

[0032] In S3, model solving and scheme generation adopt a mathematical method based on KKT conditions to transform the two-layer optimization model into a solvable single-layer mixed integer linear programming model, and use an optimization solver to solve it to obtain the optimal planning capacity configuration scheme and corresponding optimal operation strategy for geothermal and hydrogen energy equipment. The two-layer optimization model based on S2 has a lower-level running model that is a convex linear programming (LP) model. The objective function and constraints are linear. According to the theory of convex optimization, its optimal solution satisfies the KKT conditions and is unique, providing a theoretical basis for the transformation of the two-layer model. The chosen solution tool is the industrial-grade optimization solver Gurobi 11.0, paired with Python 3.9 as the modeling platform, which supports efficient solutions for mixed-integer linear programming two-level optimization models. Import the basic data and model files. Import the S1 normalized dataset and the S2 normalized two-layer model document into the solver platform to establish the mapping relationship between variables and data, such as model variables. The investment cost of the geothermal generator set is related to the S1 equipment parameter table.

[0033] S301: Deriving the KKT conditions for the lower-level model The optimal solution of the lower-level model needs to satisfy the constraints of the lower-level model. All constraints are directly adopted from the S2 lower-level model. Only the decision variables are marked as the optimal solution to ensure the original feasibility. Dual feasibility conditions, based on the standard form of the original LP, the dual objective function is as follows: The dual constraints are determined by the coefficients of the original second objective function and the constraint conditions, for the lower-level objective function S2: , in Indicates operating loss cost, The penalty cost is represented by the coefficients of the objective function of the original variables, which are the inner product of the dual variables and the column vectors of the original constraint matrix, ensuring the feasibility of the dual solution. Linearization of complementary relaxation conditions: The product terms of variables in complementary relaxation conditions are nonlinear and need to be transformed into linear constraints to construct the MILP model. The conventional Big M method in this field is used for linearization, and the steps are as follows: Define binary variables Where k is the constraint number and t is the time period, used to identify whether the original constraint is active: if When, it indicates that the constraint is active. ;like When, it indicates that the constraint is inactive. ; S302: Constructing linear constraints based on binary variables, replacing product terms: for upper bound constraints Complementary relaxation conditions : , Indicates the large M value, take The upper limit, derived from S1 geothermal resource data, is 100MW, ensuring... hour ; To represent the large M value of the dual variable, take 10 times, to ensure hour ; For lower bound constraints Complementary relaxation conditions :

[0034] The remaining complementary relaxation conditions are linearized according to the above logic, and the large M values ​​are all determined based on the parameter boundaries of S1 and S2.

[0035] S303: Constructing a Single-Level Mixed Integer Linear Programming (MILP) Model The first objective function and constraints of the upper-level model S2 are integrated with the KKT constraints after linearization of S302 to form a unified single-level MILP model. The core logic is as follows: Based on upper-level decision variables Constructing a single-level MILP model with constraints on lower-level decision variables: Annual operating costs The variables have been directly factored into the objective function through the lower-level variables and do not need to be used as feedback variables; the mainstream MILP solver in this field is adopted, and commercial solvers that support large-scale constraints are preferred to improve the solution efficiency; S304: Solution Result Record Record all optimal variable values ​​output by the solver, categorize and store them by type, including the optimal values ​​of upper-level decision variables. That is, the optimal planned capacity of geothermal and hydrogen energy equipment; The optimal value of the lower-level decision variables Optimal value of the objective function: Total lifecycle cost Equivalent annual investment cost Annual maintenance costs Annual operating cost, output results include solver output logs, convergence judgment conclusions, and original data of optimal variable values.

[0036] S305: Extraction of Optimal Planning Capacity Configuration Scheme and Corresponding Optimal Operation Strategy Time series data are extracted from the optimal values ​​of lower-level decision variables and categorized by peak-valley-normal periods and seasons: Geothermal power generation operation strategy: The hourly output curves, such as 45MW at peak times and 45MW at off-peak times, are due to the stable geothermal output. Electrolysis hydrogen production operation strategy: The hourly power curve, such as 30MW at full capacity during off-peak hours and 6MW during peak hours, takes advantage of the low-priced electricity during off-peak hours; Hydrogen storage equipment operation strategy: The hourly capacity curve shows that hydrogen can be charged to 1200m³ during off-peak hours. 3 Hydrogen is released at its peak to 200m 3 , / The hydrogen charge / discharge power curves; Power grid interaction strategy: / The hourly power purchase and sales curve shows that 5MW of power is purchased during peak hours and 10MW of power is sold during off-peak hours. Load fulfillment status: Statistical results, such as an annual load deficit rate of 0.1%, meet the S1 constraint requirements.

[0037] In S4, the collaborative planning scheme is output, and the optimal planning capacity configuration scheme obtained in S3 is output. The scheme includes the collaborative configuration results of the total geothermal power generation capacity, the rated power of electrolysis hydrogen production, the capacity of hydrogen storage equipment, and the capacity of hydrogen power generation equipment.

[0038] S401: Define Output Boundaries and Core Objectives Define the scope of the solution output: Focus on the core equipment configuration from geothermal to hydrogen energy obtained from the S3 solution, and clarify the output boundary as equipment capacity configuration, collaborative logic description, and implementation support data; Align with core objectives: With technical feasibility, economic optimization, and collaborative efficiency as output objectives, ensure that the solution aligns with the objectives of the S2 model, minimizes the total lifecycle cost, and is consistent with the S3 solution results; Determine the target audience and purpose of the output: differentiate the different needs of the decision-making level, the project implementation level, and the regulatory level, and plan the types of output documents accordingly.

[0039] S402: Extraction and Coordination Verification of Core Configuration Indicators Extraction and quantification of four core indicators, extraction of total geothermal power generation capacity: direct extraction of S3. ,like Converted to 45MW, labeled with technology type, based on S1 equipment parameters, such as Organic Rankine Cycle (ORC) geothermal generator set; Electrolytic hydrogen production at rated power extraction: Extraction of S3 ,like Convert to 30MW, indicate the type of electrolyzer, based on S1 equipment parameters, such as alkaline electrolyzer; Hydrogen storage equipment capacity extraction: Extraction of S3 ,like The storage type is indicated based on the S1 device parameters, such as a 35MPa high-pressure gaseous hydrogen storage tank. Derivation and extraction of hydrogen power generation equipment capacity: The maximum output of hydrogen power generation equipment is constrained by the maximum hydrogen release power of hydrogen storage, combined with S3's... Hydrogen storage and dehydrogenation power; The synergy verification of the four indicators is based on the constraints of S2 and the coordination logic of S3 to verify the matching of the four indicators and ensure no conflicts. Capacity matching verification: The rated power of hydrogen production by electrolysis is less than or equal to the total capacity of geothermal power generation plus the maximum power purchase capacity (S1 constraint parameter): 30MW≤45MW+50MW; Hydrogen storage equipment capacity ≥ rated power of electrolytic hydrogen production × hydrogen production efficiency × 24h (S2 capacity matching constraint); The capacity of hydrogen power generation equipment ≤ maximum hydrogen storage and release power × power generation efficiency (S2 operating constraint) 10MW ≤ 22.22MW × 0.45; Operational Coordination Verification: Combining the optimal operation strategy of S3, verify whether the indicators can support the peak shaving and valley filling functions. For example, during valley hours, geothermal power generation is at full capacity of 45MW, electrolysis hydrogen production is at full capacity of 30MW, and the remaining 15MW can be sold. At peak times, the hydrogen storage equipment can release up to 10MW of hydrogen power generation, which, combined with geothermal power generation of 45MW, can meet peak load. Based on S1 load data, at 55MW, no electricity purchase is required or only a small amount of electricity needs to be purchased.

[0040] S403: Supplementary Solution Details and Implementation Support Based on the equipment parameters of S1 and the capacity indicators of S3, supplementary specific equipment selection suggestions and key technical parameters are provided to support engineering procurement: S1031: Installation layout recommendations should be based on the geographical resource data and geographical information of S1 to clarify the spatial layout of the equipment: Geothermal generator sets are located in areas with concentrated geothermal reservoirs within the target region, close to geothermal well locations, to shorten the geothermal fluid transportation distance; electrolysis hydrogen production equipment is located adjacent to the geothermal generator sets, sharing the cooling system to reduce power transmission losses; hydrogen storage equipment is located on the edge of industrial areas, at least 500m away from residential areas, meeting safety distance requirements; hydrogen power generation equipment is located close to load centers, such as the core area of ​​industrial parks, to reduce power transmission losses.

[0041] S4032: Description of collaborative operation logic, based on the optimal operation strategy of S3, extracting simplified operation rules for easy execution by operation and maintenance personnel: During off-peak hours from 22:00 to 8:00 the next day: geothermal energy is operating at full capacity, electrolytic hydrogen production is operating at full capacity, hydrogen storage equipment is being filled with hydrogen, and surplus electricity is being sold to the grid. During normal periods from 8:00 to 10:00 and from 14:00 to 18:00: geothermal energy is operating at full capacity, hydrogen production via electrolysis is operating at half load, hydrogen storage equipment maintains stable capacity, and electricity is purchased and sold as needed; Peak hours 10:00 to 14:00 and 18:00 to 22:00: Geothermal energy is operating at full capacity, electrolysis hydrogen production is shut down, hydrogen storage equipment releases hydrogen to generate electricity, making up for the load gap and reducing electricity purchases.

[0042] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for integrated energy planning of geothermal and hydrogen energy based on a two-layer optimization model, characterized in that, include: S1: Data processing and parameter acquisition, acquiring basic energy system data for the target area, including geographic resource data, load data, energy price data, equipment parameters, and constraint parameters; S2: Construct a two-layer optimization model for geothermal hydrogen energy collaborative planning. Based on the aforementioned basic data, construct a two-layer optimization model that couples the upper-layer planning model with the lower-layer operation model. S201: The upper-level planning model takes minimizing the total cost of the energy system throughout its entire life cycle as its first objective function, and its decision variables are the planned configuration capacity of geothermal power generation capacity, electrolysis hydrogen production capacity, and hydrogen storage capacity. S202: The lower-level operation model takes minimizing the annual operating cost of the energy system under the planned configuration capacity as the second objective function, and its decision variables are the inland thermal power generation output, electrolysis hydrogen production power, and hydrogen storage equipment charging and discharging power in each time period; S203: Upper-level planning model and lower-level operation model, a cost-coupled two-level optimization model is established based on the first objective function and the second objective function; S3: Model solving and scheme generation. Using a mathematical method based on KKT conditions, the two-layer optimization model is transformed into a solvable single-layer mixed integer linear programming model. The model is then solved using an optimization solver to obtain the optimal planning capacity configuration scheme and corresponding optimal operation strategy for geothermal and hydrogen energy equipment. S4: Output the collaborative planning scheme, outputting the optimal planning capacity configuration scheme obtained from S3. The scheme includes the collaborative configuration results of the total geothermal power generation capacity, the rated power of electrolysis hydrogen production, the capacity of hydrogen storage equipment, and the capacity of hydrogen power generation equipment.

2. The integrated energy planning method for geothermal and hydrogen energy based on a two-layer optimization model according to claim 1, characterized in that, S1 specifically includes: S101: Determine the target area and data requirement list, and clarify the geographical boundaries and time scale. The time scale includes hourly operational data scale. The data requirement list is divided into P1 level mandatory data and P2 level optional data according to priority. S102: Multi-source data acquisition, obtaining geothermal resource data from field survey reports and regional energy planning white papers, and obtaining at least one year's worth of hourly power load data from regional power grid companies or electricity management departments; S103: Data cleaning and formatting, unifying all time-series data to the same timestamp, using interpolation to supplement missing data, correcting or removing outliers, and converting data into a standard unit system; S104: Construct a key equipment parameter library, model equipment investment cost as a piecewise linear function of capacity scale, fit equipment partial load efficiency data into a mathematical relationship, and use the capital recovery factor formula to convert the initial investment cost and operation and maintenance cost coefficient of equipment into equivalent annual value cost. S105: Generate model input files, including a JSON configuration file containing equipment parameters and system constraint parameters, and a time-series data file containing timestamps, electrical load, hydrogen load, geothermal available power, and electricity purchase and sale prices.

3. The integrated energy planning method for geothermal and hydrogen energy based on a two-layer optimization model according to claim 1, characterized in that, S2 includes: S201: The upper-level planning model takes minimizing the total cost of the energy system's entire life cycle as its first objective function, and its decision variables are geothermal power generation planned capacity, electrolysis hydrogen production planned capacity, and hydrogen storage equipment planned capacity. S202: The lower-level operation model takes minimizing the annual operating cost of the energy system under the planned configuration capacity as the second objective function, and its decision variables are the inland thermal power generation output, electrolysis hydrogen production power, and hydrogen storage equipment charging and discharging power in each time period; S203: The upper-level planning model and the lower-level operation model are coupled through decision variables and cost functions. The planned configuration capacity of the upper-level model serves as the equipment capacity constraint of the lower-level model. The optimal annual operating cost obtained by the lower-level model is used as a key cost input and fed back to the total cost function of the upper level.

4. The integrated energy synergy planning method for geothermal and hydrogen energy based on a two-layer optimization model according to claim 3, characterized in that, The first objective function in S201 includes: Upper-level decision variables include geothermal power generation planning capacity. Electrolysis hydrogen production planned capacity Hydrogen storage equipment planned capacity ; Total lifecycle cost Minimizes the following: annual investment cost of equipment, annual maintenance cost, and annual operating cost fed back from the lower-level model. The annual operating cost is obtained by solving the lower-level operating model and fed back to the upper level.

5. The integrated energy planning method for geothermal and hydrogen energy based on a two-layer optimization model according to claim 3, characterized in that, The second objective function in S202 includes: The lower-level decision variables include geothermal power output during time period t. Hydrogen production power during time period t Hydrogen charging power of hydrogen storage equipment during a given time period (t) Hydrogen release power of hydrogen storage equipment during a given time period (t) Power purchased by the power grid during the specified time period (t) Power sold by the power grid during a given time period (t) ; Minimize annual operating costs S201 Upper-level decision variables Given the constraints, the objective is to minimize the variable costs during the annual operation, which include electricity purchase costs, operating loss costs, and load shortfall penalty costs.

6. The integrated energy planning method for geothermal and hydrogen energy based on a two-layer optimization model according to claim 3, characterized in that, The S203 upper-level planning model and lower-level operational model are coupled through decision variables and cost functions, including: The coupling relationship is that the decision variables output by the upper-level model are used as hard constraints and substituted into the equipment output constraints and hydrogen storage capacity constraints of the lower-level model. The optimal annual operating cost obtained by the lower-level model is fed back to the upper-level model as a component of the total life cycle cost.

7. The integrated energy planning method for geothermal and hydrogen energy based on a two-layer optimization model according to claim 1, characterized in that, The mathematical methods based on KKT conditions in S3 specifically include: S301: Derive the KKT conditions of the lower-level model, including the original feasibility conditions, dual feasibility conditions, and complementary relaxation conditions; S302: The Big M method is used to linearize the nonlinear variable product terms in the complementary relaxation conditions. Binary variables are defined to identify whether the original constraints are active. Linear constraints are constructed to replace the product terms. The Big M value is determined based on the parameter boundaries of S1 and S2. S303: Integrate the first objective function and constraints of the upper-level model with the linearized KKT constraints to form a single-level mixed integer linear programming model.

8. The integrated energy synergy planning method for geothermal and hydrogen energy based on a two-layer optimization model according to claim 1, characterized in that, S4 specifically includes: S401: Define the output scope as equipment capacity configuration, collaborative logic description, and implementation support data, aligning with the output goals of technical feasibility, economic optimization, and efficient collaboration. S402: Extract four core indicators and perform synergy verification. The four core indicators are the total capacity of geothermal power generation, the rated power of electrolysis hydrogen production, the capacity of hydrogen storage equipment, and the capacity of hydrogen power generation equipment. The synergy verification includes capacity matching verification and operation synergy verification. S403: Supplementary recommendations on equipment selection, installation layout, and collaborative operation logic.

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