Power carbon emission decision optimization model algorithm for hydrogen energy park

Abstract

The invention provides a hydrogen energy park electric power carbon emission decision optimization model algorithm for solving the problem that an existing park energy management model cannot accurately simulate and optimize the coupling relation between the special element hydrogen energy and an electric power system. The core lies in that a system dynamics model containing'hydrogen energy 'state variables is constructed, and the mutual conversion, storage and consumption processes of electric energy and hydrogen energy in the park are accurately described by defining three state variables of power storage, power generation and hydrogen energy and a plurality of flow variables of electrolysis, direct use, load and the like. According to the model, system dynamic behaviors under different strategies (such as water electrolysis hydrogen production load adjustment, energy storage scheduling optimization, renewable energy source introduction and the like) are simulated, and the influence of the system dynamic behaviors on park electric power carbon emission is quantitatively evaluated, so that data-driven low-carbon operation decision support is provided for park managers, and collaborative optimization of carbon emission and operation cost is realized.

Description

Technical Field

[0001] This invention relates to the field of energy system modeling and simulation technology, and in particular to an intelligent decision-making optimization model and method for electricity carbon emissions tailored to the characteristics of hydrogen energy industrial parks. Background Technology

[0002] With the advancement of the "dual-carbon" strategy, the construction of zero-carbon industrial parks has become a key measure. Hydrogen energy industrial parks, as special functional areas integrating hydrogen production, storage, transmission, and utilization, have energy systems that differ significantly from traditional industrial or commercial parks. They not only include conventional electricity loads but also large-scale, dispatchable, flexible loads such as water electrolysis for hydrogen production, as well as fuel cell power generation units for hydrogen storage, forming a coupled "electricity-hydrogen-electricity" system. Existing park energy management systems or online carbon emission monitoring platforms mostly focus on monitoring and improving the energy efficiency of traditional electricity loads, lacking systematic modeling and optimization of hydrogen energy as a crucial element.

[0003] In recent years, China has conducted in-depth research on carbon monitoring and emission reduction in power systems. This paper proposes a carbon emission reduction optimization technology based on the TS model to address the issue of low-carbon coordinated emission reduction in power systems. Based on the energy transmission characteristics during power system production and operation, a carbon emission flow topology model is constructed to clarify the total carbon emissions of nodes within the system. Then, from the perspectives of carbon capture technology and carbon trading mechanisms, the influencing factors of carbon-related costs are analyzed, and a carbon-related cost calculation scheme is generated (E-House & Yue Tonggengri, 2025). Through digital means, carbon emission data from the power industry is collected, analyzed, and processed, enabling precise monitoring of data at the regional, enterprise, and power plant levels. An ensemble learning-based carbon emission monitoring model is constructed. Simulation analysis verifies the model's advantages in improving the efficiency and accuracy of carbon emission monitoring and compares it with the shortcomings of traditional carbon emission monitoring methods (Rong Gaosheng et al., 2025). However, current research on the synergy between hydrogen and electricity is relatively scarce.

[0004] A search revealed several invention patents related to power system technologies and methods. One patent, with application number CN202411725889.X, is titled "A Novel Multi-Objective Cooperative Planning Method and System for Power Systems." This invention, based on power system state data, establishes constraints for dual-carbon objectives, renewable energy penetration rate, curtailment rate of solar and wind power, demand response, and energy storage capacity configuration. Based on the constraint correlations and synergistic correlations between these constraints, it generates source-side constraints, source-storage synergistic constraints, source-load-storage synergistic constraints, and source-grid-load-storage constraints. Finally, it establishes a source-grid-load-storage optimization model with the objective of minimizing the total cost of the power system. Another patent, with application number CN202211541109.7, is also mentioned. One invention, titled "A Carbon Neutrality Simulation Method and Terminal," calculates carbon emissions from the power system, fits and predicts the total carbon emissions and the total carbon emissions of the whole society, and establishes a proportional relationship between the calculated carbon emissions from the power system and the predicted total carbon emissions of the whole society. Based on this proportional relationship, it simulates the carbon neutrality route, which can more comprehensively and effectively determine the carbon neutrality route. Another patent, with application number CN202210120821.3, titled "A Provincial-Level Regional Dual-Carbon Comprehensive Analysis and Evaluation System," provides a provincial-level regional comprehensive analysis and evaluation system serving "dual carbon" by integrating data resources from all industries in the province, providing users with an information-based analysis tool for optimizing their "carbon peak and carbon neutrality" path.

[0005] Therefore, there is an urgent need in this field for a dedicated model and method that can accurately describe the electricity-hydrogen coupling relationship within hydrogen energy parks and can perform dynamic simulation and strategy optimization of electricity carbon emissions. Summary of the Invention

[0006] This invention provides a decision-making optimization model and method for carbon emissions in hydrogen energy parks, addressing the problem that existing technologies cannot accurately model and optimize carbon emissions for the unique "electricity-hydrogen" coupling system of hydrogen energy parks. Compared with existing technologies, this invention has the following significant advantages: (1) Accurately depicting the characteristics of hydrogen energy: For the first time, the "hydrogen energy" state variable and key flow such as "electrolysis" are systematically introduced into the park-level energy model, accurately describing the multiple roles of hydrogen energy as an energy carrier, energy storage medium and load, filling the model gap in this field; (2) Electricity-hydrogen synergistic optimization: The model can reveal the dynamic conversion and synergistic optimization mechanism between electricity and hydrogen energy. For example, through simulation, the contribution of "using wind and solar power to produce hydrogen" to improve the renewable energy consumption rate and reduce comprehensive carbon emissions can be quantified. (3) Strong forward-looking decision support: This model can not only optimize the current operation, but also be used to assess the long-term impact of forward-looking technologies (such as improving the efficiency of electrolyzers and increasing the scale of hydrogen storage) or policies (such as carbon tax) on the future carbon emissions and economic benefits of the park, providing a scientific basis for park planning and investment; (4) High practicality and operability: The model is based on mature system dynamics theory and is easy to implement and simulate on a computer. Users can quickly test different scenarios by adjusting parameters, and the results are output in the form of key performance indicators, which are intuitive and easy to understand, and can be directly used by park managers for operational decision-making. Attached Figure Description

[0007] Figure 1 System dynamics structure of the decision optimization model for carbon emissions from electricity in hydrogen energy parks Figure 2 Overall process of optimization method Figure 3 Comparison of total carbon emissions in the park based on simulations using three different strategies Detailed Implementation

[0008] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0009] This invention designs an optimization model algorithm for carbon emission decision-making in hydrogen energy industrial parks. This algorithm can simulate the daily operation of the park's power system, including power generation, storage, use, and loss. By adjusting the model parameters, strategies to reduce carbon emissions from the park's power system can be explored. The model considers the annualized unit price and cost of different components and can be used for economic analysis. The specific implementation steps are as follows: S1: Construct a system dynamics model, which includes three state variables: energy storage (energy status of the park's energy storage system), power generation (power generation status of the park's power generation system), and hydrogen (hydrogen storage status of the park). S2: Define the flow variables of the connection state variables. The flow variables include: electrolysis (the flow of hydrogen generated by consuming electricity to produce hydrogen through water electrolysis), direct use (the flow of electricity used directly for the park load without going through the energy storage stage), and load (the total load demand of the park's power system). S3: Define the auxiliary variables for the model. The auxiliary variables include: electrolysis efficiency, hydrogen loss, maximum hydrogen storage, power generation efficiency, and annual load. S4: Input the initial operating parameters of the target park into the system dynamics model and set the carbon emission optimization target; S5: Simulate the set energy dispatch strategy in the model. The energy dispatch strategy includes adjusting the start-up and shutdown and power of water electrolysis hydrogen production, controlling the charging and discharging strategy of hydrogen storage and electricity storage, and changing the energy structure on the power generation side. S6: Based on the simulation results, output the key performance indicators under different strategies. The key performance indicators include the total carbon emissions of the system, operating costs, hydrogen production and energy self-balance rate. S7: Based on key performance indicators, compare and select the optimal energy dispatch strategy to achieve the carbon emission optimization target; In step S1, the change in the "hydrogen" state variable is input by the "electrolysis" flow rate and output by "hydrogen storage loss" and "hydrogen energy usage flow rate"; the output power of the "power generation" state variable is allocated to the "direct use" flow rate, the power consumed by the "electrolysis" flow rate, and the power used to charge the "energy storage" state variable; auxiliary variables include the power loss rate, which is used to quantify the loss of power during transmission and distribution; the model considers the external power grid supply as a power generation source with a specific carbon emission factor, thereby including the indirect carbon emissions of purchased power in the calculation; In step S5, changing the energy structure on the power generation side includes: increasing the proportion of renewable energy power generation in the model parameters and simulating its impact on the increase of the "hydrogen" state variable through "electrolysis" flow, as well as its effect on reducing the total carbon emissions of the system; the specific strategy for controlling the charging and discharging of hydrogen storage and energy storage is as follows: during periods of low electricity prices or peak renewable energy power generation, the model prioritizes scheduling electricity for hydrogen production through water electrolysis and charging the energy storage system; during periods of high electricity prices or peak load, the model prioritizes scheduling the energy storage system to discharge and using the stored hydrogen energy to generate electricity, so as to reduce dependence on the high-carbon electricity of the external power grid. Example

[0010] This embodiment uses a hydrogen energy port park in Shanghai as the application object.

[0011] S1: Model Building The model was constructed in the system dynamics software Vensim. First, three state variables were created, named "Electricity Storage", "Electricity Generation", and "Hydrogen" respectively. Second, flow variables were established. The "Load" and "Electrolysis" flow rates connected "Electrolysis" to the outside of the system. The "Electrolysis" flow rate also served as the input to the "Hydrogen" state variable. The "Direct Use" flow rate connected "Electrolysis" to the load. The "Hydrogen Storage Loss" flow rate was set as the output of the "Hydrogen" state variable.

[0012] The charging and discharging flow rates are set to connect "power generation" and "energy storage", as well as "energy storage" and load; furthermore, auxiliary variables are set: electrolysis efficiency is assigned a value of 0.70 (assuming the current efficiency of the electrolyzer system), hydrogen loss rate is assigned a value of 0.001 (daily natural volatilization loss rate), maximum hydrogen storage is assigned a value of 5000 kg (based on the hydrogen storage facilities in the park), grid carbon emission factor is assigned a value of 0.581 kgCO2 / kWh (taking Shanghai as an example), and photovoltaic carbon emission factor is assigned a value of 0.050 kgCO2 / kWh.

[0013] Power generation = (PV installed capacity × sunshine duration) + purchased grid power (1) Total carbon emissions of the system = Σ (Energy generation of each power source × its carbon emission factor) (2) S2: Data Input and Scenario Setting We collected typical daily load curves, photovoltaic power generation forecast curves, and time-of-use electricity price data for a hydrogen energy port park. Two comparative scenarios were set: the baseline scenario operates according to the current model, with the electrolyzer operating during the nighttime off-peak electricity price period; the optimized scenario, based on model simulation results, involves the electrolyzer mainly operating during the midday peak photovoltaic power generation period, using zero-carbon electricity to produce hydrogen as much as possible.

[0014] S3: Simulation Execution and Result Analysis Input the data into the model, run the baseline scenario and the optimized scenario respectively, and record key indicators such as total electricity consumption, annualized total cost, and levelized cost of generation.

[0015] S4: Decision Support Based on the simulation results, park managers can decide to adjust the operation strategy of water electrolysis for hydrogen production, shifting the main operating time from nighttime to the peak photovoltaic period during the day. They can also consider increasing the photovoltaic installed capacity in future plans to further enhance the optimization effect on power generation costs.

Claims

1. An algorithm for optimizing the decision-making process for carbon emissions from electricity generation in hydrogen energy industrial parks, characterized in that... It can simulate the daily operation of the park's power system, including power generation, storage, use, and loss. By adjusting the model parameters, strategies to reduce the carbon emissions of the park's power system can be explored. The model considers the annualized unit price and cost of different components and can be used for economic analysis, including the following steps: S1: Construct a system dynamics model, which includes three state variables: energy storage (energy status of the park's energy storage system), power generation (power generation status of the park's power generation system), and hydrogen (hydrogen storage status of the park). S2: Define the flow variables of the connection state variables. The flow variables include: electrolysis (the flow of hydrogen generated by consuming electricity to produce hydrogen through water electrolysis), direct use (the flow of electricity used directly for the park load without going through the energy storage stage), and load (the total load demand of the park's power system). S3: Define the auxiliary variables for the model. The auxiliary variables include: electrolysis efficiency, hydrogen loss, maximum hydrogen storage, power generation efficiency, and annual load. S4: Input the initial operating parameters of the target park into the system dynamics model and set the carbon emission optimization target; S5: Simulate the set energy dispatch strategy in the model. The energy dispatch strategy includes adjusting the start-up and shutdown and power of water electrolysis hydrogen production, controlling the charging and discharging strategy of hydrogen storage and electricity storage, and changing the energy structure on the power generation side. S6: Based on the simulation results, output the key performance indicators under different strategies. The key performance indicators include the total carbon emissions of the system, operating costs, hydrogen production and energy self-balance rate. S7: Based on key performance indicators, compare and select the optimal energy dispatch strategy to achieve the carbon emission optimization target; In step S1, the change in the "hydrogen" state variable is input by the "electrolysis" flow rate and output by "hydrogen storage loss" and "hydrogen energy usage flow rate"; the output power of the "power generation" state variable is allocated to the "direct use" flow rate, the power consumed by the "electrolysis" flow rate, and the power used to charge the "energy storage" state variable; auxiliary variables include the power loss rate, which is used to quantify the loss of power during transmission and distribution; the model considers the external power grid supply as a power generation source with a specific carbon emission factor, thereby including the indirect carbon emissions of purchased power in the calculation; In step S5, changing the energy structure on the power generation side includes: increasing the proportion of renewable energy power generation in the model parameters and simulating its impact on the increase of the "hydrogen" state variable through "electrolysis" flow, as well as its effect on reducing the total carbon emissions of the system; the specific strategy for controlling the charging and discharging of hydrogen storage and energy storage is as follows: during periods of low electricity prices or peak renewable energy power generation, the model prioritizes scheduling electricity for hydrogen production through water electrolysis and charging the energy storage system; during periods of high electricity prices or peak load, the model prioritizes scheduling the energy storage system for discharging and using the stored hydrogen energy for power generation, so as to reduce dependence on the high-carbon electricity of the external power grid.

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