Electricity-heat-hydrogen comprehensive energy optimization model establishment method based on improved multi-objective hedera helix optimization algorithm
By constructing an electro-thermal-hydrogen coupling model and employing an improved multi-objective ivy optimization algorithm, the multi-objective optimization problem of the electro-thermal-hydrogen multi-energy system was solved. This enabled system-level collaborative optimization and intelligent management of salt cavern hydrogen storage in scenarios with a high proportion of renewable energy access, thereby improving the system's flexibility and stability.
Patent Information
- Application Number
- CN202510994358.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-18
- Publication Date
- 2025-11-14
AI Technical Summary
Existing technologies lack sufficient multi-objective high-precision optimization for electric-thermal-hydrogen multi-energy systems, deep integration of salt cavern hydrogen storage and system scheduling, and convergence and diversity of optimization algorithms, making it difficult to meet the complex optimization needs of scenarios with a high proportion of renewable energy access.
An electro-thermal-hydrogen coupling model was constructed, which includes photovoltaic power generation, wind power generation, electrolyzers, hydrogen fuel cells, and salt cavern hydrogen storage. An improved multi-objective Ivy optimization algorithm was adopted, and Pareto dominance relationship and congestion distance mechanism were introduced to establish a multi-objective optimization model. Combined with an intelligent hydrogen storage management system, multi-energy flow collaborative optimization and dynamic scheduling were realized.
It improves the practical applicability and optimization depth of the system scheduling model, enhances the scheduling intelligence and algorithm robustness of the system in complex operating scenarios, improves the system's dynamic response to external disturbances and resource allocation efficiency, and ensures the safety and stability of the salt cavern hydrogen storage system.
Smart Images
Figure CN120955728A_ABST
Abstract
Description
Technical Field
[0001] This involves the field of integrated energy systems and hydrogen energy storage, specifically the construction of a mixed integer nonlinear programming model that considers network losses, voltage deviations, the full life cycle cost of energy storage, and reactive power compensation costs. Background Technology
[0002] With the advancement of "dual carbon" (carbon reduction and emission reduction), building efficient and low-carbon integrated energy systems (IES) has become an important direction for the development of future energy systems. Integrated energy systems improve energy utilization efficiency and promote the deep integration and large-scale consumption of renewable energy through the coordinated and optimized allocation and dispatch of multiple energy forms such as electricity, heat, gas, and cooling. Currently, the proportion of new energy sources such as wind power and photovoltaic power connected to the grid continues to rise, but due to their high volatility and uncertainty, they are prone to problems such as grid dispatch difficulties and wind and solar curtailment, limiting their further development within the system.
[0003] Against this backdrop, hydrogen energy storage, with its advantages of high energy density, long storage cycle, and flexible conversion, has become a key technology for supporting the integration of high-proportion renewable energy and improving system flexibility. Related research is increasingly integrating hydrogen energy systems with grid dispatch, attempting to solve the problems of power redundancy and insufficient energy storage through "electricity-hydrogen conversion." For example, some studies have constructed electrolysis-to-fuel cell cycle models to achieve peak-valley power regulation and deep utilization of new energy sources; other studies have combined compressed gaseous hydrogen storage or liquid hydrogen storage systems to explore energy regulation strategies across multiple time scales.
[0004] However, existing research still has the following shortcomings: First, there is insufficient multi-objective collaborative optimization capability. Most studies focus on a single objective (such as economic efficiency or carbon emissions), neglecting the comprehensive trade-off between multiple dimensions such as grid operation safety (such as voltage stability and reactive power compensation), system losses, and energy storage lifetime, making it difficult to meet the complex optimization needs of scenarios with a high proportion of renewable energy access.
[0005] Second, the granularity of hydrogen energy storage modeling is relatively coarse. Existing methods often fail to accurately depict the dynamic energy conversion relationship between hydrogen production by electrolysis, hydrogen storage, and fuel cells. In particular, they lack in-depth descriptions of the engineering feasibility and control characteristics of large-scale geological hydrogen storage methods such as salt cavern hydrogen storage in practical applications.
[0006] Third, the optimization algorithms have poor adaptability. When faced with the highly nonlinear, strongly constrained, and multi-objective characteristics of electro-thermal-hydrogen multi-energy coupled systems, traditional algorithms (such as particle swarm optimization and genetic algorithms) are prone to getting trapped in local optima, with limited solution set diversity and global convergence performance, and cannot support the intelligent and efficient requirements of system-level scheduling decisions.
[0007] In summary, existing technologies have shortcomings such as failing to achieve multi-objective high-precision optimization of electro-thermal-hydrogen multi-energy systems, failing to deeply integrate salt cavern hydrogen storage with system scheduling, and having insufficient convergence and diversity of optimization algorithms. Summary of the Invention
[0008] To address the shortcomings of existing technologies, such as failure to achieve multi-objective high-precision optimization of electro-thermal-hydrogen multi-energy systems, deep integration of salt cavern hydrogen storage and system scheduling, and insufficient convergence and diversity of optimization algorithms, the technical solution provided by this invention is as follows: A method for establishing an integrated energy optimization model for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm includes: The steps to construct an electro-thermal-hydrogen coupling model that includes photovoltaic power generation, wind power generation, electrolyzers, hydrogen fuel cells and salt cavern hydrogen storage, and output time-series power data of preset energy forms; Based on the time-series power data, a multi-objective optimization model is established with grid loss, voltage deviation, reactive power compensation cost and energy storage system life cycle cost as objectives, and the steps of outputting the optimization solution model containing multiple objective functions and constraints are described. The steps involve taking the optimization solution model as input, using an improved multi-objective ivy optimization algorithm that incorporates Pareto dominance relations and crowding distance mechanisms to solve the problem, and outputting the Pareto optimal solution set.
[0009] Furthermore, a preferred embodiment is provided, in which the photovoltaic power generation model considers the coupling relationship between light intensity and temperature, and the wind power generation model considers the dynamic adjustment of wind speed zoning and wind energy utilization coefficient.
[0010] Furthermore, a preferred embodiment is provided, wherein the multi-objective optimization model includes power balance constraints, heat supply and demand constraints, and hydrogen mass continuity constraints, and is provided with energy storage device operating boundaries and waste heat recovery efficiency constraints.
[0011] Furthermore, a preferred embodiment is provided in which the improved multi-objective Ivy optimization algorithm hierarchically divides the solution set through non-dominated sorting and uses crowding distance to maintain the uniformity of solution distribution.
[0012] It also provides a device for establishing an integrated energy optimization model for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm, including: A module is constructed that includes an electro-thermal-hydrogen coupling model encompassing photovoltaic power generation, wind power generation, electrolyzers, hydrogen fuel cells, and salt cavern hydrogen storage, and outputs time-series power data for preset energy forms. Based on the time-series power data, a multi-objective optimization model is established with the objectives of grid loss, voltage deviation, reactive power compensation cost and energy storage system life cycle cost as objectives, and the output module contains an optimization solution model containing multiple objective functions and constraints. The module takes the optimization model as input, uses an improved multi-objective ivy optimization algorithm that incorporates Pareto dominance and crowding distance mechanisms to solve the problem, and outputs the Pareto optimal solution set.
[0013] It also provides a comprehensive energy optimization method for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm, including: Steps for collecting data on renewable energy sources; Based on the method described, the steps to obtain the Pareto optimal solution are as follows: The steps involved in constructing an intelligent hydrogen storage management system based on the Pareto optimal solution to control the hydrogen storage state in salt caverns.
[0014] It also provides an integrated energy optimization device for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm, including: A module for collecting data from renewable energy sources; Based on the method described, a module is used to obtain the Pareto optimal solution; A smart hydrogen storage management system is constructed based on the Pareto optimal solution, which is a module that controls the hydrogen storage status in salt caverns.
[0015] A computer storage medium is also provided for storing a computer program, which, when read by the computer, executes the method.
[0016] A computer is also provided, including a processor and a storage medium, wherein the computer executes the method when the processor reads a computer program stored in the storage medium.
[0017] A computer program product is also provided, which, when executed, implements the method described.
[0018] Compared with the prior art, the advantages of the technical solution provided by the present invention are as follows: This invention constructs a multi-objective optimization model for electricity-thermal-hydrogen energy storage capacity, introducing multi-dimensional indicators such as grid losses, voltage deviation, reactive power compensation costs, and energy storage lifecycle costs as optimization objectives. This enables the system scheduling model to comprehensively reflect the integrated performance requirements in actual operation. Compared to existing optimization models that primarily focus on single economic or energy efficiency indicators, this approach significantly improves the model's practical applicability and optimization depth, achieving system-level collaborative optimization under multi-objective constraints.
[0019] This invention employs an improved Multi-Objective Ivy League Optimization (MO-IVYA) algorithm. By introducing Pareto dominance relations and a diversity maintenance mechanism, it addresses the problems of traditional algorithms easily getting trapped in local optima and exhibiting poor solution set distribution when dealing with multi-objective high-dimensional nonlinear problems. Simulation results show that this algorithm outperforms commonly used methods such as particle swarm optimization and NSGA-II in terms of solution set diversity and convergence accuracy, enhancing the scheduling intelligence and algorithm robustness of the electro-thermal-hydrogen multi-energy system under complex operating scenarios.
[0020] This invention improves the accuracy of describing the operational status of large-scale, long-term hydrogen storage technology by integrating a salt cavern hydrogen storage model and incorporating geological structure capacity modeling and dynamic pressure management mechanisms into the model. Compared with traditional gaseous or liquid hydrogen storage models, this scheme can support larger-capacity cross-cycle hydrogen storage scheduling and effectively control the operating pressure and temperature range, enhancing the system's flexibility and stability under conditions of high-proportion renewable energy integration.
[0021] This invention designs an intelligent management system for underground salt caverns. Through real-time data acquisition from multiple sensors, Kalman filter fusion modeling, leakage risk quantification, and an anomaly response closed-loop mechanism, it achieves status monitoring, risk warning, and automatic maintenance of underground hydrogen storage facilities. Compared to existing passive monitoring methods that rely on manual inspections and single threshold alarms, this system offers higher safety and operational reliability, ensuring the long-term stable operation of the salt cavern hydrogen storage system.
[0022] This invention proposes a multi-timescale power allocation strategy, establishing charging triggering and discharging control mechanisms, and deeply coupling them with an optimization model. This enables the system to maintain coordinated operation even under scenarios involving fluctuations in renewable energy output and drastic load changes. Compared to traditional fixed-timescale scheduling mechanisms, this strategy significantly improves the system's dynamic response to external disturbances and resource allocation efficiency, thereby optimizing the utilization level of wind and solar energy.
[0023] It is applicable to the planning and scheduling optimization of integrated energy systems of electricity, heat and hydrogen under the background of high proportion of renewable energy access. Attached Figure Description
[0024] Figure 1 The system block diagram of the model; Figure 2 Flowchart of a smart monitoring system for hydrogen storage in salt caverns; Figure 3 Here is the logic diagram for power generation dispatch; Figure 4 This is a logic diagram for hydrogen energy dispatching; Figure 5 This is a logic diagram for thermal energy utilization. Detailed Implementation
[0025] To make the advantages and benefits of the technical solution provided by the present invention clearer, the technical solution provided by the present invention will now be described in further detail with reference to the accompanying drawings, specifically: Implementation Method 1: This implementation method provides a method for establishing an integrated energy optimization model for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm, including: The steps to construct an electro-thermal-hydrogen coupling model that includes photovoltaic power generation, wind power generation, electrolyzers, hydrogen fuel cells and salt cavern hydrogen storage, and output time-series power data of preset energy forms; Based on the time-series power data, a multi-objective optimization model is established with grid loss, voltage deviation, reactive power compensation cost and energy storage system life cycle cost as objectives, and the steps of outputting the optimization solution model containing multiple objective functions and constraints are described. The steps involve taking the optimization solution model as input, using an improved multi-objective ivy optimization algorithm that incorporates Pareto dominance relations and crowding distance mechanisms to solve the problem, and outputting the Pareto optimal solution set.
[0026] In the aforementioned electro-thermal-hydrogen coupling model, the photovoltaic power generation model considers the coupling relationship between light intensity and temperature, while the wind power generation model considers wind speed zoning and dynamic adjustment of wind energy utilization coefficient.
[0027] The multi-objective optimization model includes constraints on power balance, heat supply and demand, and hydrogen quality continuity, and also includes constraints on the operating boundary of energy storage equipment and waste heat recovery efficiency.
[0028] The improved multi-objective Ivy optimization algorithm hierarchically divides the solution set through non-dominated sorting and uses crowding distance to maintain the uniformity of solution distribution.
[0029] It also provides a comprehensive energy optimization method for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm, including: Steps for collecting data on renewable energy sources; Based on the method described, the steps to obtain the Pareto optimal solution are as follows: The steps involved in constructing an intelligent hydrogen storage management system based on the Pareto optimal solution to control the hydrogen storage state in salt caverns.
[0030] Implementation Method Two: This implementation method is a further detailed description of the technical solution provided in Implementation Method One, specifically: include: Modeling an electric-thermal-hydrogen energy storage system: This system is an integrated multi-energy coupling and complementary system combining renewable energy sources. It includes photovoltaic power generation models, wind power generation models, electrolyzer models, salt cavern hydrogen storage models, and hydrogen fuel cell models. The system consists of coordinated electrical energy cycles, thermal energy cycles, and hydrogen energy cycles. Inputting raw data such as sunlight, wind speed, and load, it is fed into the power generation scheduling logic, hydrogen energy scheduling logic, and thermal energy utilization logic loops to ensure normal system operation. Under different loads and energy fluctuations, it flexibly allocates power generation, energy storage, hydrogen energy, and thermal energy to ensure the coordinated operation of each component.
[0031] Establish a multi-objective optimization model for the capacity of electric-thermal-hydrogen energy storage: This model constructs a multi-objective optimization framework by coordinating the multi-energy flow coupling characteristics of the electric, thermal, and hydrogen energy storage systems, aiming to achieve synergistic optimization of power grid economy, stability, and environmental protection. The objective functions mainly include: minimizing network losses, minimizing voltage amplitude deviation, minimizing the installation cost of reactive power compensation equipment, and minimizing the total life-cycle cost of the energy storage system. Based on the law of conservation of energy, a triple constraint is established, consisting of the power balance equation, the heat network supply and demand equation, and the hydrogen mass continuity equation. Simultaneously, constraints on the power system, the dynamic constraints of the energy storage system, and the waste heat recovery efficiency constraints are also constructed.
[0032] Constructing an electric-thermal-hydrogen energy storage operation strategy and solving for multi-objective optimization: A multi-timescale dynamic power allocation strategy is proposed, which sets up a charging triggering mechanism and a discharging control mechanism. At the same time, the multi-objective ivy optimization algorithm is improved to dynamically balance the global exploration and local exploitation capabilities.
[0033] Includes the following steps: The first step is to construct a multi-source renewable energy power generation model: This step aims to obtain the available electrical energy input in the system, serving as the foundational data for subsequent scheduling and energy storage calculations. First, a photovoltaic (PV) power generation model is established, considering the nonlinear coupling relationship between ambient temperature and solar irradiance. Factors such as photoelectric conversion efficiency, PV panel area, and incident angle correction are integrated to obtain the PV output power under different meteorological conditions. Next, a wind power generation model is established, considering factors such as wind speed variations, air density, turbine structure, and control strategies. Wind speed regions are divided, and wind power output characteristic curves are established. The output of this step is time-series data of PV and wind power, which serves as the system's power generation-side input.
[0034] The second step is to establish a hydrogen energy storage and conversion model: This step, based on obtaining the output power of the renewable energy source, further simulates the energy conversion process of the hydrogen energy system. First, an electrolyzer model is constructed, considering the impact of factors such as current density, membrane material, and electrode reactions on electrolysis efficiency, and the hydrogen production per unit of electricity is calculated. Then, a hydrogen fuel cell model is established, comprehensively considering the theoretical open-circuit voltage, polarization loss, and concentration loss of the cell, determining its output voltage characteristics to evaluate the hydrogen-to-electricity efficiency. Next, in conjunction with hydrogen storage methods, a salt cavern hydrogen storage model is introduced, establishing the relationship between cavern capacity and geological structure, and setting upper and lower limits for safe pressure. By simulating factors such as salt rock deformation and pressure regulation, the hydrogen storage state is dynamically managed. The output of this step is time-series data on hydrogen production, storage, and energy recovery, providing support for energy balance calculations.
[0035] The third step is to construct a multi-energy flow integrated coupling model: Based on the acquired power input and hydrogen energy system energy change data, a multi-energy flow collaborative model of electricity, heat, and hydrogen is constructed. This model includes the power balance equation of the power grid, the thermal supply and demand equation, and the hydrogen mass conservation equation. By modeling at a unified time scale, the dynamic coupling relationships between multi-energy flow nodes are integrated. Constraints on energy storage device charging and discharging, thermal energy utilization efficiency, energy conversion paths, and physical boundary conditions are set to form a closed-loop system model. The output of this step is a complete constrained model used for scheduling optimization calculations.
[0036] The fourth step is to construct a multi-objective optimization model: Based on the aforementioned coupled model, the optimization objectives and constraints are further clarified, and a mixed-integer nonlinear programming model is established. The objective function includes four parts: minimizing network losses, minimizing voltage deviation, minimizing the cost of reactive power compensation equipment, and minimizing the total life-cycle cost of the energy storage system. System energy balance constraints, grid physical constraints, energy storage operating state constraints, and economic constraints are introduced into the model to ensure physical and operational feasibility during the optimization process. The output of this step is an optimization mathematical model that can be solved by intelligent algorithms.
[0037] Step 5: Design a multi-timescale operation strategy: After establishing the optimization model, to improve the dynamic adaptability of the system, a multi-timescale energy allocation strategy is set. For different timescales (e.g., minutes, hours, and days), charging trigger thresholds, discharging control boundaries, and hydrogen and thermal energy scheduling priority rules are formulated to enable the system to respond quickly to external disturbances and enhance scheduling flexibility. The output of this step is the operating control parameters of the adapted model.
[0038] Step 6: Introduce an improved multi-objective ivy optimization algorithm to solve the model: To effectively solve the aforementioned complex nonlinear multi-objective model, this implementation introduces an improved multi-objective ivy optimization algorithm. This algorithm employs Pareto dominance relations to hierarchically divide the solutions, prioritizing the retention of non-dominated solutions to avoid getting trapped in local optima. Simultaneously, it introduces crowding distance as a diversity maintenance mechanism to ensure a uniform distribution of the solution set. An external archive structure is set up to store historical best solutions, and the solution space is dynamically expanded through vine generation and search strategies to improve the algorithm's global exploration capability and convergence speed. The output of this step is a complete Pareto front solution set, providing a reference for the scheduling strategy.
[0039] Step 7: Implement the intelligent salt cavern hydrogen storage management system. After generating the system scheduling plan, an intelligent management platform was constructed, taking into account the unique characteristics of underground salt cavern hydrogen storage. A multi-sensor monitoring network (including pressure, temperature, and hydrogen concentration) was deployed to collect real-time underground reservoir operation data. Data fusion and state estimation algorithms were then used to reconstruct a three-dimensional operational status map of the reservoir. A risk warning mechanism was established to determine the reservoir's safety level in real time based on indicators such as pressure fluctuations and leakage indices, triggering automatic inspection and anomaly response processes to achieve closed-loop safety management. The output of this step is the hydrogen storage safety status assessment and maintenance measures during system operation.
[0040] Implementation Method 3: Combination Figure 1-5 This embodiment describes the technical solution provided above in further detail through specific examples. Specifically: The construction of a multi-objective optimization system model for electric-thermal-hydrogen energy storage capacity mainly includes the following parts: 1. Photovoltaic power generation model Considering the nonlinear coupling effect between temperature and irradiance, the output power of the photovoltaic array can be modeled as follows:
[0041] in, The output power of the photovoltaic array (W); Photoelectric conversion efficiency (%) The effective area of the photovoltaic panel (m²) 2 ) Real-time irradiance (W / m 2 ); This is the temperature coefficient (typically -0.0045 / ℃). The real-time temperature of the solar cell (°C); The standard test temperature is (usually 25°C). This is the incident angle correction factor; 1. Angle (°) between sunlight and the normal to the photovoltaic panel. 2. Wind Power Generation Model Based on the aerodynamic characteristics of the wind turbine and the pitch control strategy, the output power model is improved as follows: in, The output power of the wind turbine (W); Real-time wind speed (m / s); The cut-in wind speed is the minimum wind speed at which the fan starts (usually 3-4 m / s). Rated wind speed, which is the wind speed at which the fan reaches its maximum power (usually 12-15 m / s). The cut-off wind speed is the wind speed for fan shutdown protection (usually 25-30 m / s). air density ( ); The area swept by the wind turbine ( ); The wind energy utilization coefficient (related to the tip speed ratio λ and the blade pitch angle β, typically 0.35-0.45). It is the turbulence attenuation factor (typically 0.05-0.15). This is the overspeed protection attenuation coefficient (generally 0.01-0.03).
[0042] 3. Electrolytic cell model Considering overpotential and thermodynamic losses, electrolysis efficiency Revised to:
[0043] in, Electrolysis efficiency (%) This is due to the high enthalpy change of hydrogen (typically 285.8 KJ / mol); The Gibbs free energy change is typically 237.2 kJ / mol. For operating current density (typically 2000-6000 A / m) 2 ); The limiting current density of the membrane material (typically 8000-12000 A / m) 2 ); This is the activation overpotential correction factor (typically 0.03-0.05). For exchange current density (typically 100-500 A / m) 2 4. Salt cavern hydrogen storage model (1) Technical advantages of hydrogen storage in salt caverns Salt cavern hydrogen storage is a technology that utilizes caverns formed by the dissolution of underground salt rock layers to store hydrogen. Salt rock has low permeability, high plasticity, and self-healing properties, which can effectively prevent gas leakage, making it an ideal geological structure for hydrogen storage. Its advantages include: (1) High sealing performance: The permeability of salt rock is lower than that of salt rock. (1) Almost no gas escapes; (2) Stability: The salt layer can withstand high pressure (usually designed pressure is 1000 ℃). (3) Scalability potential: The volume of a single salt cavern can reach 1000 cubic meters. It supports large-scale energy storage. As a core technology for large-scale, long-term energy storage, salt cavern hydrogen storage has significant advantages in many aspects compared with high-pressure gaseous, liquid, and solid hydrogen storage methods, as detailed in Table 1, which compares hydrogen storage methods.
[0044]
[0045] (2) Operating principle of salt cavern hydrogen storage 1) Geological structure and capacity modeling Salt caverns are formed by the dissolution of salt layers through the injection of fresh water; their volume is calculated using a modified ellipsoidal model.
[0046] in, The maximum diameter of the cavern; The height of the cavern; Salt rock creep correction factor ( This reflects the impact of long-term geological deformation.
[0047] 2) Dynamic pressure management Salt cavern pressure needs to be maintained within a safe range. To prevent structural instability: ,
[0048] in, ; ; These are the depth of the salt cave neck, the depth of the cave center, and the zero-pressure reference depth, respectively.
[0049] (3) Intelligent management of hydrogen storage in salt caverns 1) Logical flow of the intelligent management system This system achieves efficient management of underground reservoirs through real-time monitoring and intelligent response mechanisms. The core process is divided into four stages: data acquisition, analysis and early warning, anomaly handling, and closed-loop verification, forming a complete cycle of autonomous optimization. First, the system continuously acquires key reservoir parameters (such as pressure and displacement) through a distributed sensor network and transmits them to the central control unit. After data cleaning and fusion, the data is synchronously updated to the dynamic simulation model, mapping the three-dimensional state of the reservoir in real time. Second, based on preset safety thresholds and prediction algorithms, the system automatically assesses the risk level: if the threshold is exceeded, a high-risk warning is immediately triggered, and an emergency response is initiated; if the trend is abnormal, the monitoring frequency is increased, and potential risks are marked; conversely, if the data is normal, routine monitoring continues. Third, after a high-risk warning is triggered, the system automatically dispatches detection equipment to the target area and confirms the anomaly type through multi-dimensional scanning: if the anomaly supports automatic repair, dedicated equipment is called to perform standardized maintenance operations; if the anomaly requires manual intervention, a location is generated and pushed to maintenance personnel, along with on-site data and operation instructions. Finally, after the handling is completed, the system re-collects parameters and compares them with the repair target: if the verification is successful, the warning is lifted, and the simulation model is synchronously updated; if the expected results are not achieved, the anomaly diagnosis process is restarted. See the attached flowchart for details. Figure 2 Flowchart of intelligent monitoring system for hydrogen storage in salt caverns.
[0050] 2) Intelligent Management System Design A real-time monitoring system is built by deploying a network of pressure, temperature, and gas concentration sensors. (a) Multi-source data fusion: Deploy a network of pressure, temperature, and gas concentration sensors to construct a real-time state estimation model based on Kalman filtering:
[0051] in, It is a state vector (pressure, temperature, etc.). For observation data, This is the Kalman gain matrix.
[0052] (b) Leakage risk quantification: Based on the gas diffusion equation and threshold comparison, a leakage risk index is defined. :
[0053] in, For sensor parameters, The weighting coefficients, determined by the entropy weighting method, trigger an emergency response when a threshold is reached. 5. Hydrogen Fuel Cell Model Based on the Butler-Volmer equation and the Ohmic polarization effect, the output voltage model is improved as follows:
[0054] in, The output voltage of a single battery (V); This is the theoretical open-circuit voltage (usually 1.23V). The activation overpotential (V); This is an ohmic overpotential, caused by the resistance of the electrolyte (V); This is a concentration overpotential, caused by the reactant concentration gradient (V); The gas constant (usually 1) ); This refers to the battery operating temperature (typically 323–353 K). It is the Faraday constant (typically 96485 C / mol); It is the temperature sensitivity coefficient (typically between 0.001 and 0.003 V / ℃). This is a reference temperature (typically 298K).
[0055] The above description of several specific embodiments further details the technical solution provided by the present invention in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the above-described specific embodiments are not intended to limit the present invention. Any reasonable modifications and improvements to the present invention, combinations of embodiments, and equivalent substitutions based on the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for establishing an integrated energy optimization model for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm, including: The steps to construct an electro-thermal-hydrogen coupling model that includes photovoltaic power generation, wind power generation, electrolyzers, hydrogen fuel cells and salt cavern hydrogen storage, and output time-series power data of preset energy forms; Based on the time-series power data, a multi-objective optimization model is established with grid loss, voltage deviation, reactive power compensation cost and energy storage system life cycle cost as objectives, and the steps of outputting the optimization solution model containing multiple objective functions and constraints are described. The steps involve taking the optimization solution model as input, using an improved multi-objective ivy optimization algorithm that incorporates Pareto dominance relations and crowding distance mechanisms to solve the problem, and outputting the Pareto optimal solution set.
2. The method for establishing an integrated energy optimization model for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm as described in claim 1, wherein in the electricity-heat-hydrogen coupling model, the photovoltaic power generation model considers the coupling relationship between light intensity and temperature, and the wind power generation model considers the dynamic adjustment of wind speed zoning and wind energy utilization coefficient.
3. The method for establishing an integrated energy optimization model for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm as described in claim 1, wherein the multi-objective optimization model includes electrical power balance constraints, heat supply and demand constraints, and hydrogen quality continuity constraints, and is provided with energy storage device operating boundaries and waste heat recovery efficiency constraints.
4. The method for establishing an integrated energy optimization model for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm according to claim 1, wherein the improved multi-objective ivy optimization algorithm hierarchically divides the solution set through non-dominated sorting and maintains the uniformity of solution distribution by using crowding distance.
5. A device for establishing an integrated energy optimization model for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm, comprising: A module is constructed that includes an electro-thermal-hydrogen coupling model encompassing photovoltaic power generation, wind power generation, electrolyzers, hydrogen fuel cells, and salt cavern hydrogen storage, and outputs time-series power data for preset energy forms. Based on the time-series power data, a multi-objective optimization model is established with the objectives of grid loss, voltage deviation, reactive power compensation cost and energy storage system life cycle cost as objectives, and the output module contains an optimization solution model containing multiple objective functions and constraints. The module takes the optimization model as input, uses an improved multi-objective ivy optimization algorithm that incorporates Pareto dominance and crowding distance mechanisms to solve the problem, and outputs the Pareto optimal solution set.
6. A method for integrated energy optimization of electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm, characterized in that: include: Steps for collecting data on renewable energy sources; The steps for obtaining the Pareto optimal solution based on the method described in claim 1; The steps involved in constructing an intelligent hydrogen storage management system based on the Pareto optimal solution to control the hydrogen storage state in salt caverns.
7. An integrated energy optimization device for electricity, heat, and hydrogen based on an improved multi-objective ivy optimization algorithm, characterized in that, include: A module for collecting data from renewable energy sources; A module for obtaining the Pareto optimal solution based on the method described in claim 1; A smart hydrogen storage management system is constructed based on the Pareto optimal solution, which is a module that controls the hydrogen storage status in salt caverns.
8. A computer storage medium for storing computer programs, characterized in that, When the computer program is read by the computer, the computer executes the method of claim 1.
9. A computer, comprising a processor and a storage medium, characterized in that, When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 1.
10. A computer program product, as a computer program, is characterized by: When the computer program is executed, it implements the method of claim 1.