A low-carbon operation optimization method for an integrated energy system considering hydrogen energy utilization

Abstract

The application belongs to the technical field of IES application of electric hydrogen coupling, and discloses a low-carbon operation optimization method of a comprehensive energy system considering hydrogen energy utilization, comprising the following steps: constructing an IES considering electric, heat, gas and hydrogen multi-energy coupling, the IES comprising a photovoltaic power generation device model, an energy conversion equipment model and an energy storage equipment model; simulating electric vehicle driving behavior data based on a Monte Carlo sampling simulation method, and establishing an electric vehicle orderly charging and discharging model for describing two-way interaction between a vehicle and a network in the IES; and generating a source and load uncertainty scenario set containing multiple typical scenarios and their distribution probabilities based on the probability distribution of prediction errors in view of the prediction uncertainty of renewable energy output and various types of loads in the IES. The method effectively solves the problem that in the prior art, under the double fluctuation of renewable energy and loads, it is difficult to realize the low-carbon, economic and stable operation collaborative optimization of the electric hydrogen coupling comprehensive energy system.

Description

Technical Field

[0001] This invention belongs to the field of IES application technology of electro-hydrogen coupling, and specifically relates to a low-carbon operation optimization method for a comprehensive energy system that takes hydrogen energy utilization into account. Background Technology

[0002] In the current global energy landscape, fossil fuel resources are becoming increasingly depleted, and carbon dioxide emissions continue to rise, posing a serious threat to the sustainability and security of the energy structure. Promoting the transformation of energy production and consumption patterns has become an inevitable requirement for supporting sustainable social development.

[0003] However, the uncertainties and volatility caused by the grid integration of renewable energy significantly increase the difficulty of system operation and regulation. How to effectively improve the capacity for renewable energy absorption and achieve efficient energy utilization and carbon emission reduction targets is one of the core challenges in optimizing the operation of the current power system. With the expansion of renewable energy generation and the gradual tightening of carbon constraints, hydrogen energy, as a clean energy source, is developing rapidly. How to construct a low-carbon energy system with hydrogen energy as its hub, thereby promoting the restructuring of the global energy system and alleviating environmental pressure, has become an important issue. Secondly, integrated energy systems (IES) coupled with electricity and hydrogen exhibit significant fluctuations on both the supply and demand sides, posing challenges to the precise control of the output of operating units. Uncertainties and power fluctuations in the system can adversely affect safe and stable operation. Therefore, how to fully utilize and dispatch various flexible resources to suppress the negative impact of fluctuations on system performance and enhance the adaptability of dispatch strategies to the uncertainties of renewable energy and load has become another critical issue that urgently needs to be addressed. Therefore, existing technologies face the challenge of achieving synergistic optimization of low-carbon, economical, and stable operation of integrated energy systems coupled with electricity and hydrogen under the dual fluctuations of renewable energy and load. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a method for optimizing the low-carbon operation of an integrated energy system that takes into account hydrogen energy utilization. This method solves the problem in existing technologies where it is difficult to achieve coordinated optimization of low-carbon, economical, and stable operation of integrated energy systems with electro-hydrogen coupling under the dual fluctuations of renewable energy and load.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for optimizing the low-carbon operation of a comprehensive energy system that takes into account hydrogen energy utilization includes the following steps: Construct an IES that considers the coupling of multiple energy sources including electricity, heat, gas, and hydrogen. The IES includes a photovoltaic power generation device model, an energy conversion equipment model, and an energy storage equipment model. Based on the Monte Carlo sampling simulation method, electric vehicle driving behavior data is simulated, and an orderly charging and discharging model of electric vehicles is established in IES to describe the two-way interaction between vehicles and the network. To address the prediction uncertainties of renewable energy output and various loads in IES, a set of source-load uncertainty scenarios, including multiple typical scenarios and their distribution probabilities, is generated based on the probability distribution of prediction errors. Based on the tiered carbon trading mechanism, a total operating cost function is constructed to evaluate the economic efficiency and low carbon emissions of IES. The total operating cost function is used to accumulate operation and maintenance costs, curtailment costs, carbon emission costs, and energy purchase costs to obtain the total operating cost. By integrating the IES, the electric vehicle orderly charging and discharging model, the source-load uncertainty scenario set, and the total operating cost function, a day-ahead low-carbon stochastic optimization model with minimizing the total operating cost as the day-ahead optimization objective is formed, and the day-ahead optimal operating plan of the output of each model in the IES is obtained by solving the model. Based on the day-ahead optimal operating plan and taking into account the characteristic that the accuracy of source load prediction increases as the prediction time scale decreases, an intraday low-carbon operation optimization model is established. The adjustment cost of each model in the IES compared to the day-ahead is introduced into the intraday model. The intraday optimization objective is to minimize the total operating cost and the total cost of the sum of each adjustment amount, and the intraday optimal operating plan of the output of each model in the IES is obtained.

[0006] Furthermore, the energy conversion equipment models include electrolyzer models, fuel cell models, electric boiler models, gas turbine models, gas boiler models, and methane reactor models; Energy storage equipment models include electric storage device models, thermal storage device models, gas storage device models, and hydrogen storage device models.

[0007] Furthermore, based on the Monte Carlo sampling simulation method to simulate electric vehicle driving behavior data, an ordered charging and discharging model of electric vehicles to describe the bidirectional interaction between the vehicle and the network is established in IES, specifically including the following steps: Based on Monte Carlo sampling simulation, the driving behavior of each electric vehicle is randomly sampled to obtain the charging start time, charging end time, and daily mileage, where: The charging start time and charging end time are generated by sampling from a preset normal distribution, and the charging start time is guaranteed to be earlier than the charging end time. Daily mileage is generated by sampling from a preset log-normal distribution; Based on the daily mileage and the battery parameters of the electric vehicle, the initial state of charge of the electric vehicle is calculated, and the specific expression is as follows: in, For the first electric vehicles The initial state of charge during the time period; For the first The expected amount of electricity a vehicle can achieve when it finishes charging; For the battery capacity of electric vehicles; For the first The mileage traveled by the electric vehicle; Electricity consumption per 100 kilometers for electric vehicles; For the first The time it takes for an electric vehicle to begin charging; An ordered charging and discharging model for electric vehicles is established to describe the bidirectional interaction between vehicles and the network. The specific expression is as follows: in, and The first electric vehicles Time period and State of charge over a period of time; and They are respectively Time period The charging and discharging power of an electric vehicle; and These refer to the charging efficiency and discharging efficiency of electric vehicles, respectively. This refers to the upper limit of the charging / discharging power of electric vehicles. and They are respectively Time period The charging and discharging state variables of an electric vehicle; for Total charging power / total discharging power of electric vehicles participating in the scheduling in the time-period system; for The number of electric vehicles connected to the system during a given time period; and The first The start and end times of charging for each electric vehicle.

[0008] Furthermore, a set of source load uncertainty scenarios, containing multiple typical scenarios and their probability distributions, is generated, specifically including the following steps: To address the prediction uncertainties of photovoltaic output and electricity, heat, gas, and hydrogen loads, it is assumed that photovoltaic output and the output of various loads follow a normal distribution within the fluctuation range of the predicted values. An initial scenario set is randomly generated, and a scenario reduction method is used to extract a source-load uncertainty scenario set, including multiple typical scenarios and their distribution probabilities, from the initial scenario set.

[0009] Furthermore, the carbon emission cost is calculated based on the difference between the actual carbon emissions and the carbon emission allowance, combined with a predefined tiered carbon price range. The specific formula for calculating actual carbon emissions is as follows: in, This represents the actual carbon emissions of the IES; Carbon intensity per unit of purchased electrical energy; Carbon emission intensity per unit of natural gas power input for a gas turbine unit; The coefficient for CO2 absorbed to produce one unit of CH4, ,in This refers to the amount of CO2 required per unit of CH4. The calorific value of natural gas; The specific formula for calculating carbon emission allowances is as follows: in, Carbon emission allowances for IES; , and These are the carbon emission allowances per unit of electricity purchased from the external power grid, and the carbon emission allowances per unit of electricity and heat output from gas turbine units; for Purchased electrical energy power during the specified time period; Thermoelectric conversion coefficient; The scheduling period; Based on the predefined tiered carbon price ranges in the tiered carbon trading mechanism, the formula for calculating carbon emission costs is as follows: in, Cost of carbon emissions; The base price for tiered carbon trading; Price growth rate; This refers to the carbon emission range.

[0010] Furthermore, the specific expression for the day-ahead optimization objective in the day-ahead low-carbon stochastic optimization model is as follows: in, These are the IES scenarios in the recent low-carbon stochastic optimization model. Down Total operating costs, maintenance costs, curtailment costs, carbon emission costs, and energy purchase costs for each time period; These are the unit operation and maintenance costs for electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors, respectively. In the scene respectively Down Input power of time-lapse electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors; Unit cost of curtailment; In the scene respectively Down Forecasted and planned photovoltaic power output for each time period; In the scene respectively Down Purchased electricity, purchased natural gas, and purchased hydrogen power during the specified time period; Hydrogen has a low calorific value; Time-of-use pricing; These are the prices of natural gas and hydrogen, respectively.

[0011] Furthermore, the intraday optimization objective is to minimize the total operating cost plus the sum of all adjustments, as shown in the following expression: in, In the intraday low-carbon operation optimization model, IES in Total cost for the period, operation and maintenance cost, curtailment cost, carbon emission cost, energy purchase cost, and adjustment cost of each model in IES compared to the previous day; This represents the total number of time periods within the current day's scheduling cycle. These are the unit adjustment costs for electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors, respectively. These include electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors. The amount of adjustment compared to the previous time scale; , , , , , These are, respectively, electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors on the day-ahead timescale. t Input power during the time period; , , , , , These are electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors, respectively, on an intraday timescale. t Input power during a given time period.

[0012] A storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to implement a low-carbon operation optimization method for an integrated energy system that takes hydrogen energy utilization into account.

[0013] The beneficial effects of this invention are: This invention constructs an Energy System Integrated with Electricity, Heat, Gas, and Hydrogen (IES) that incorporates multi-energy coupling, and establishes an orderly charging and discharging model for electric vehicles based on Monte Carlo sampling simulation, effectively integrating the flexibility of electric vehicles as a schedulable resource. By generating a set of source-load uncertainty scenarios containing multiple typical scenarios and their distribution probabilities, day-ahead optimization can fully consider the predictive uncertainty of renewable energy output and load, thereby enhancing the robustness of the operation plan. Based on a tiered carbon trading mechanism, a total operating cost function is constructed, directly linking carbon emission costs with economic operating costs, guiding the system to proactively reduce carbon emissions during the optimization process. By constructing a day-ahead low-carbon stochastic optimization model and solving it to obtain the day-ahead optimal operation plan for the output of each model in the IES, and further introducing the intraday adjustment cost of each model in the IES compared to the day-ahead, an intraday low-carbon operation optimization model is established, solving it to obtain the intraday optimal operation plan for the output of each model in the IES. In a more precise intraday stage, smooth correction and fine adjustment of the day-ahead optimal operation plan are achieved, effectively solving the problem in existing technologies where it is difficult to achieve coordinated optimization of low-carbon, economic, and stable operation of integrated energy systems with electricity-hydrogen coupling under the dual fluctuations of renewable energy and load. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the overall process of the present invention; Figure 2 This is a schematic diagram of the IES framework structure of the present invention; Figure 3 This is a schematic diagram of the predicted electrical, thermal, gas loads and photovoltaic output in an embodiment of the present invention. Detailed Implementation

[0016] 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.

[0017] like Figures 1 to 3 As shown, a method for optimizing the low-carbon operation of a comprehensive energy system that takes into account hydrogen energy utilization includes the following steps: Construct an IES that considers the coupling of multiple energy sources including electricity, heat, gas, and hydrogen. The IES includes a photovoltaic power generation device model, an energy conversion equipment model, and an energy storage equipment model. Based on the Monte Carlo sampling simulation method, electric vehicle driving behavior data is simulated, and an orderly charging and discharging model of electric vehicles is established in IES to describe the two-way interaction between vehicles and the network. To address the prediction uncertainties of renewable energy output and various loads in IES, a set of source-load uncertainty scenarios, including multiple typical scenarios and their distribution probabilities, is generated based on the probability distribution of prediction errors. Based on the tiered carbon trading mechanism, a total operating cost function is constructed to evaluate the economic efficiency and low carbon emissions of IES. The total operating cost function is used to accumulate operation and maintenance costs, curtailment costs, carbon emission costs, and energy purchase costs to obtain the total operating cost. By integrating the IES, the electric vehicle orderly charging and discharging model, the source-load uncertainty scenario set, and the total operating cost function, a day-ahead low-carbon stochastic optimization model with minimizing the total operating cost as the day-ahead optimization objective is formed, and the day-ahead optimal operating plan of the output of each model in the IES is obtained by solving the model. Based on the day-ahead optimal operating plan and taking into account the characteristic that the accuracy of source load prediction increases as the prediction time scale decreases, an intraday low-carbon operation optimization model is established. The adjustment cost of each model in the IES compared to the day-ahead is introduced into the intraday model. The intraday optimization objective is to minimize the total operating cost and the total cost of the sum of each adjustment amount, and the intraday optimal operating plan of the output of each model in the IES is obtained.

[0018] Energy conversion equipment models include electrolyzer (ELZ) model, fuel cell (HFC) model, electric boiler (EB) model, gas turbine (GT) model, gas boiler (GB) model, and methane reactor (MR) model; Energy storage device models include electric storage (ES) models, thermal storage (TS) models, gas storage (GS) models, and hydrogen storage (HS) models; The mathematical expression for the photovoltaic power generation device model is as follows: in, for The power output of photovoltaic power generation devices during a given period; The rated power of the photovoltaic power generation device; and They are respectively Light intensity during the time period and reference light intensity under standard test conditions; The power temperature coefficient; and These are ambient temperature and reference temperature, respectively. The mathematical expression for the electrolyzer (ELZ) model is as follows: in, and They are respectively Input and output power of ELZ during the time period; For ELZ conversion efficiency; The mathematical expression for the fuel cell (HFC) model is as follows: in, , and They are respectively The input, output electrical, and output thermal power of the HFC during the specified time period; and These are the electrical, thermal, and overall conversion efficiencies of HFC, respectively. The mathematical expression for the electric boiler (EB) model is as follows: in, and They are respectively Input and output power of the time period EB; For HFC conversion efficiency; The mathematical expression for the gas turbine (GT) model is as follows: in, , and They are respectively Input, output electrical, and output thermal power of time period GT; and These are the electrical, thermal, and overall conversion efficiencies of GT, respectively. The mathematical expression for the gas-fired boiler (GB) model is as follows: in, and They are respectively Input and output power of GB during the time period; Conversion efficiency for GB; The mathematical expression for the methane reactor (MR) model is as follows: in, and They are respectively Input and output power of MR during the time period; The conversion efficiency of MR; The mathematical expression for the energy storage device (ES) model is as follows: in, and ES respectively Time period and Energy storage capacity during a given period; and These are the charging and discharging efficiencies of the ES, respectively. and They are respectively The charging and discharging power of ES during the time period; For scheduling time scale; The mathematical expression for the thermal storage device (TS) model is as follows: in, and TS respectively Time period and Heat storage during a given period; and These are the charging and discharging efficiencies of TS, respectively. and They are respectively The charging and discharging power of time period TS; For scheduling time scale; The mathematical expression for the gas storage device (GS) model is as follows: in, and GS respectively Time period and Gas storage capacity for a given period of time; and These are the charge and discharge efficiencies of GS, respectively. and They are respectively The charging and discharging power of GS during the time period; For scheduling time scale; The mathematical expression for the hydrogen storage device (HS) model is as follows: in, and HS respectively Time period and Hydrogen storage capacity over a given period of time; and These are the charge and discharge efficiencies of HS, respectively. and They are respectively The charging and discharging power of the HS at any given time; The scheduling time scale.

[0019] Based on Monte Carlo sampling simulation data of electric vehicle driving behavior, an ordered charging and discharging model of electric vehicles describing the two-way interaction between the vehicle and the network is established in IES. The specific steps include: Based on Monte Carlo sampling simulation, the driving behavior of each electric vehicle is randomly sampled to obtain the charging start time, charging end time, and daily mileage, where: The charging start time and charging end time are generated by sampling from a preset normal distribution, and the charging start time is guaranteed to be earlier than the charging end time. The time probability density function for when an electric vehicle begins charging is shown below: in, , The probability density function This indicates the moment when EVs begin charging.

[0020] The probability density function for the end of charging for an electric vehicle is shown below: in, , The probability density function Indicates the time when EVs finish charging; Daily mileage is generated by sampling from a preset log-normal distribution; Based on data from the U.S. Department of Transportation's survey of private vehicles across the United States, data analysis shows that the daily mileage of EVs approximately follows a log-normal distribution: in, , The probability density function Indicates the daily mileage of EVs; Based on the daily mileage and the battery parameters of the electric vehicle, the initial state of charge of the electric vehicle is calculated, and the specific expression is as follows: in, For the first electric vehicles The initial state of charge during the time period; For the first The expected amount of electricity a vehicle can achieve when it finishes charging; For the battery capacity of electric vehicles; For the first The mileage traveled by the electric vehicle; Electricity consumption per 100 kilometers for electric vehicles; For the first The time it takes for an electric vehicle to begin charging; An ordered charging and discharging model for electric vehicles is established to describe the bidirectional interaction between vehicles and the network. The specific expression is as follows: in, and The first electric vehicles Time period and State of charge over a period of time; and They are respectively Time period The charging and discharging power of an electric vehicle; and These refer to the charging efficiency and discharging efficiency of electric vehicles, respectively. This refers to the upper limit of the charging / discharging power of electric vehicles. and They are respectively Time period The charging and discharging state variables of an electric vehicle; for Total charging power / total discharging power of electric vehicles participating in the scheduling in the time-period system; for The number of electric vehicles connected to the system during a given time period; and The first The start and end times of charging for each electric vehicle.

[0021] Generating a set of source load uncertainty scenarios that includes multiple typical scenarios and their probability distributions involves the following steps: To address the prediction uncertainties of photovoltaic output and electricity, heat, gas, and hydrogen loads, it is assumed that photovoltaic output and the output of various loads follow a normal distribution within the fluctuation range of the predicted values. An initial scenario set containing M scenarios to represent historical data of photovoltaic and various loads is randomly generated. A scenario reduction method is used to extract K discrete typical scenarios and their distribution probabilities from the initial scenario set, which together constitute the source-load uncertainty scenario set.

[0022] Carbon emission costs are calculated based on the difference between actual carbon emissions and carbon emission allowances, combined with predefined tiered carbon price ranges. The specific formula for calculating actual carbon emissions is as follows: in, This represents the actual carbon emissions of the IES; Carbon intensity per unit of purchased electrical energy; Carbon emission intensity per unit of natural gas power input for a gas turbine unit; The coefficient for CO2 absorbed to produce one unit of CH4, ,in This refers to the amount of CO2 required per unit of CH4. The calorific value of natural gas; The specific formula for calculating carbon emission allowances is as follows: in, Carbon emission allowances for IES; , and These are the carbon emission allowances per unit of electricity purchased from the external power grid, and the carbon emission allowances per unit of electricity and heat output from gas turbine units; for Purchased electrical energy power during the specified time period; Thermoelectric conversion coefficient; The scheduling period; Based on the predefined tiered carbon price ranges in the tiered carbon trading mechanism, the formula for calculating carbon emission costs is as follows: in, Cost of carbon emissions; The base price for tiered carbon trading; Price growth rate; This refers to the carbon emission range.

[0023] The constraints of the IES low-carbon stochastic optimization operation model at the current time scale include power balance constraints, energy conversion equipment operation constraints, energy storage equipment operation constraints, ELZ output and HS input balance constraints, network transmission power constraints, photovoltaic output constraints, and electric vehicle capacity constraints. The mathematical expression for the power balance constraint is as follows: in,, In the scene respectively Down Forecasted electricity, heat, and gas loads for the specified time period; In the scene respectively Down Charge / discharge power for ES, TS, GS, and HS time periods; In the scene Down Output electrical / thermal power of GT / HFC during the time period; In the scene Down Input power of GT / HFC during the time period; In the scene respectively Down Power supplied by externally purchased electricity, natural gas, and hydrogen during specific time periods; In the scene Down The actual photovoltaic output consumed during the period; In the scene respectively Down Input / output power of ELZ, EB, GB, and MR during the time period; In the scene Down Charge / discharge power of EVs during a given period; The mathematical expression for the operating constraints of energy conversion equipment is as follows: in, In the scene Down Operating status variables for time periods ELZ / HFC / EB / GT / GB / MR; These are the upper and lower limits of capacity for ELZ / HFC / EB / GT / GB / MR, respectively. Output power fluctuation limits for EL / EB / GB / MR / HFC / GT; The mathematical expression for the operating constraints of energy storage devices is as follows: in, and ES / TS / GS / HS in different scenarios Down Time period and The storage capacity of electricity, heat, gas, and hydrogen during a given time period; and ES / TS / GS / HS in different scenarios The charging and discharging efficiency is reduced; In the scene Down Time period ES / TS / GS / HS charge / discharge state variables; ES / TS / GS / HS in different scenarios Lower and lower limits of charge / discharge power; Capacity upper and lower limits for ES / TS / GS / HS respectively; ES / TS / GS / HS in different scenarios The initial and final storage capacities of electricity, heat, gas, and hydrogen at the end of the next scheduling cycle; For scheduling time scale; The scheduling period; The mathematical expression for the network transmission power constraint is as follows: in, In the scene Down Maximum capacity for externally purchased electricity, natural gas, and hydrogen during specific time periods; The mathematical expression for the photovoltaic output constraint is as follows: in, In the scene Down The actual photovoltaic power generation absorbed by the system during the time period; In the scene Down The amount of light discarded during a given period; In the scene Down The projected power output of photovoltaic power generation devices for the specified time period; The mathematical expression for the capacity constraint of electric vehicles is as follows: in, and These are the upper and lower limits of the state of charge of the EV, respectively. In the scene Down Time period The state of charge of an EV; For the first The minimum expected charge level that an EV needs to reach after charging is complete; and The first EVs in the scenario The start and end times of charging are indicated below. In the scene Down The number of EVs participating in scheduling during a given time period; This represents the total number of EVs connected to the IES. The specific expression for the day-ahead optimization objective in the current low-carbon stochastic optimization model is as follows: in, These are the IES scenarios in the recent low-carbon stochastic optimization model. Down Total operating costs, maintenance costs, curtailment costs, carbon emission costs, and energy purchase costs for each time period; These are the unit operation and maintenance costs for electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors, respectively. In the scene respectively Down Input power of time-lapse electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors; Unit cost of curtailment; In the scene respectively Down Forecasted and planned photovoltaic power output for each time period; In the scene respectively Down Purchased electricity, purchased natural gas, and purchased hydrogen power during the specified time period; Hydrogen has a low calorific value; Time-of-use pricing; These are the prices of natural gas and hydrogen, respectively. The day-ahead low-carbon stochastic optimization operation model of the IES, which takes into account hydrogen energy utilization, is solved using solvers (such as gurobi, cplex, etc.) to obtain the day-ahead optimal operation plan that minimizes the total operating cost, including the output of energy conversion equipment and energy storage equipment.

[0024] Similar to the daytime IES low-carbon stochastic optimization phase, the power balance constraints, energy conversion equipment operation constraints, energy storage equipment operation constraints, and network transmission power constraints in the intraday phase are basically the same as those in the daytime low-carbon operation optimization model. However, the change in time scale needs to be considered, and the time scale-related constraints need to be adjusted. The specific mathematical expressions are as follows: in, On intraday and day-ahead timescales, respectively Operating status variables for time periods ELZ / HFC / EB / GT / GB / MR; They are respectively in Output power of ELZ / EB / GB / MR during the time period; In order to be in Output power of HFC / GT during the time period.

[0025] The intraday optimization objective is to minimize the total operating cost plus the sum of all adjustments, as shown in the following expression: in, In the intraday low-carbon operation optimization model, IES in Total cost for the period, operation and maintenance cost, curtailment cost, carbon emission cost, energy purchase cost, and adjustment cost of each model in IES compared to the previous day; This represents the total number of time periods within the current day's scheduling cycle. These are the unit adjustment costs for electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors, respectively. These include electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors. The amount of adjustment compared to the previous time scale; , , , , , These are, respectively, electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors on the day-ahead timescale. t Input power during the time period; , , , , , These are electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors, respectively, on an intraday timescale. t Input power during the time period; The daily optimization objective is to minimize the total operating cost plus the sum of all adjustments. Solvers (such as gurobi, cplex, etc.) are used to solve the daily low-carbon operation optimization model.

[0026] A storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to implement a low-carbon operation optimization method for an integrated energy system that takes hydrogen energy utilization into account.

[0027] In this embodiment, taking an IES (Environmentally Integrated System) that couples multiple energy sources including electricity, heat, gas, and hydrogen as an example, we introduce a multi-timescale low-carbon operation optimization method for IES that takes hydrogen energy utilization into account; specifically, it includes the following steps: A schematic diagram of the IES framework for multi-energy coupling of electricity, heat, gas, and hydrogen is shown below. Figure 2 As shown in the figure, the predicted electricity, heat, gas loads, and photovoltaic output are as follows: Figure 3 As shown in the figure. Photovoltaic output and various loads are set to fluctuate within 15% and 3% of their day-ahead forecasts, respectively, and follow a normal distribution with an expected value of 0 and a standard deviation of 0.15. 1000 scenarios are randomly generated to represent historical data for photovoltaic and various loads. A probability-based scenario reduction method is used to obtain 10 discrete typical scenarios and their probability distribution values ​​from these 1000 historical data samples. The main parameters of the energy conversion equipment and energy storage equipment in the IES are shown in Tables 1 and 2, respectively. Table 1 Main parameters of energy conversion equipment Table 2 Energy Storage Equipment Parameters Based on the IES model, carbon trading mechanism model, and EV orderly charge and discharge model that take into account the coupling of multiple energy sources including electricity, heat, gas, and hydrogen; considering power balance constraints, energy conversion equipment operation constraints, energy storage equipment operation constraints, network transmission power constraints, and EV capacity constraints; and also considering environmental benefits and reducing carbon dioxide emissions to construct the total operating cost function; Based on the uncertainty of source load prediction, a large number of historical scenarios are generated. Typical scenarios are obtained by using scenario reduction methods. A day-ahead stochastic optimization model is constructed. Combining the characteristic that the accuracy of data prediction increases step by step as the time scale decreases, optimization is carried out on a shorter time scale.

[0028] To verify the effectiveness of the method proposed in this invention, comparative examples are provided, in which: Option 1: IES current-day low-carbon deterministic low-carbon optimization operation model; Option 2: IES day-ahead low-carbon stochastic optimization operation model considering source-load uncertainty; Option 3: Based on Option 2, further consider the intraday timescale low-carbon optimization operation model; Table 3 shows the cost comparison results of the three options: Table 3 Comparison of scheduling results under various schemes in low-carbon operation optimization on a week-scale. As shown in Table 3, the deterministic model does not consider the uncertainties of multi-energy loads and photovoltaics in the day-ahead phase, making day-ahead decisions too risky. In the intraday phase, due to prediction errors, more corrections are needed, leading to larger fluctuations in electricity, gas, and hydrogen purchases and higher costs. The stochastic optimization model considers uncertainty and describes uncertain information more accurately, thus offering greater economic flexibility in the intraday correction phase.

[0029] Considering the refined utilization of hydrogen energy and the bidirectional interaction between EVs and the grid, IES (Enhanced Energy Storage Systems) exhibit more significant advantages and development potential compared to traditional IES. By coupling hydrogen energy into IES, ELZ (Elastic Energy Storage Zone) can convert surplus electricity in the system into hydrogen energy during peak photovoltaic output periods and store it in HS (High-Speed ​​Electricity Storage), thereby reducing resource waste and improving photovoltaic utilization efficiency. The bidirectional interaction mechanism between EVs and the grid enables flexible energy exchange between EVs and the system: charging during periods of abundant renewable energy output and feeding back electricity to the system when photovoltaic output is insufficient and system energy demand is high. This helps enhance the absorption capacity of renewable energy, reduce curtailment, maintain system supply and demand balance, effectively smooth load fluctuations, reduce peak-valley load differences, and achieve peak shaving and valley filling functions. In addition, hydrogen storage has significant advantages in long-term energy storage, indicating its broad development potential in future long-term electrical energy storage.

[0030] Analyzing Scheme 3, although the operation and maintenance costs are slightly higher than day-ahead costs, the costs of curtailment, carbon emissions, and energy purchases are significantly lower. Day-ahead and intraday coordinated optimization helps to absorb renewable energy and reduce carbon emissions. Due to the continuously decreasing time scale, the scheduling plan is more refined, thus the scheduling scheme is more in line with reality. A comparison of various costs also reveals that multi-timescale scheduling strategies can effectively ensure greater stability and safety during operation while reducing the overall energy system's scheduling costs.

[0031] Therefore, under the multi-timescale scheduling strategy framework, the system can more fully consider the uncertainties of renewable energy output and various types of loads. Through more accurate source-load forecasting information, the system can dynamically optimize unit output plans, effectively suppressing fluctuations on both the supply and demand sides. The scheduling results generated under this strategy have higher reliability and rationality, and the output plans of various operating equipment are more closely aligned with actual operating conditions, helping to improve the accuracy of unit output control and form a more refined scheduling scheme, thereby ensuring stable system operation. EVs, by adjusting their charging and discharging power, can fully leverage their rapid response and flexible adjustment capabilities, effectively mitigating forecast deviations in photovoltaic output and load on ultra-short-term timescales. This alleviates the power regulation pressure on energy storage devices and energy conversion devices in the short and even ultra-short term, improving the reliability, safety, and stability of system operation.

[0032] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0033] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for optimizing the low-carbon operation of a comprehensive energy system considering hydrogen energy utilization, characterized in that, Includes the following steps: Construct an IES that considers the coupling of multiple energy sources including electricity, heat, gas, and hydrogen. The IES includes a photovoltaic power generation device model, an energy conversion equipment model, and an energy storage equipment model. Based on the Monte Carlo sampling simulation method, electric vehicle driving behavior data is simulated, and an orderly charging and discharging model of electric vehicles is established in IES to describe the two-way interaction between vehicles and the network. To address the prediction uncertainties of renewable energy output and various loads in IES, a set of source-load uncertainty scenarios, including multiple typical scenarios and their distribution probabilities, is generated based on the probability distribution of prediction errors. Based on the tiered carbon trading mechanism, a total operating cost function is constructed to evaluate the economic efficiency and low carbon emissions of IES. The total operating cost function is used to accumulate operation and maintenance costs, curtailment costs, carbon emission costs, and energy purchase costs to obtain the total operating cost. By integrating the IES, the electric vehicle orderly charging and discharging model, the source-load uncertainty scenario set, and the total operating cost function, a day-ahead low-carbon stochastic optimization model with minimizing the total operating cost as the day-ahead optimization objective is formed, and the day-ahead optimal operating plan of the output of each model in the IES is obtained by solving the model. Based on the day-ahead optimal operating plan and taking into account the characteristic that the accuracy of source load prediction increases as the prediction time scale decreases, an intraday low-carbon operation optimization model is established. The adjustment cost of each model in the IES compared to the day-ahead is introduced into the intraday model. The intraday optimization objective is to minimize the total operating cost and the total cost of the sum of each adjustment amount, and the intraday optimal operating plan of the output of each model in the IES is obtained.

2. The method for optimizing the low-carbon operation of a comprehensive energy system considering hydrogen energy utilization according to claim 1, characterized in that, The energy conversion equipment models include electrolyzer models, fuel cell models, electric boiler models, gas turbine models, gas boiler models, and methane reactor models; Energy storage equipment models include electric storage device models, thermal storage device models, gas storage device models, and hydrogen storage device models.

3. The method for optimizing the low-carbon operation of a comprehensive energy system considering hydrogen energy utilization according to claim 1, characterized in that, Based on Monte Carlo sampling simulation data of electric vehicle driving behavior, an ordered charging and discharging model of electric vehicles describing the two-way interaction between the vehicle and the network is established in IES. The specific steps include: Based on Monte Carlo sampling simulation, the driving behavior of each electric vehicle is randomly sampled to obtain the charging start time, charging end time, and daily mileage, where: The charging start time and charging end time are generated by sampling from a preset normal distribution, and the charging start time is guaranteed to be earlier than the charging end time. Daily mileage is generated by sampling from a preset log-normal distribution; Based on the daily mileage and the battery parameters of the electric vehicle, the initial state of charge of the electric vehicle is calculated, and the specific expression is as follows: in, For the first electric vehicles The initial state of charge during the time period; For the first The expected amount of electricity a vehicle can achieve when it finishes charging; For the battery capacity of electric vehicles; For the first The mileage traveled by the electric vehicle; Electricity consumption per 100 kilometers for electric vehicles; For the first The time it takes for an electric vehicle to begin charging; An ordered charging and discharging model for electric vehicles is established to describe the bidirectional interaction between vehicles and the network. The specific expression is as follows: in, and The first electric vehicles Time period and State of charge over a period of time; and They are respectively Time period The charging and discharging power of an electric vehicle; and These refer to the charging efficiency and discharging efficiency of electric vehicles, respectively. This refers to the upper limit of the charging / discharging power of electric vehicles. and They are respectively Time period The charging and discharging state variables of an electric vehicle; for Total charging power / total discharging power of electric vehicles participating in the scheduling in the time-period system; for The number of electric vehicles connected to the system during a given time period; and The first The start and end times of charging for each electric vehicle.

4. The method for optimizing the low-carbon operation of a comprehensive energy system considering hydrogen energy utilization according to claim 1, characterized in that, Generating a set of source load uncertainty scenarios that includes multiple typical scenarios and their probability distributions involves the following steps: To address the prediction uncertainties of photovoltaic output and electricity, heat, gas, and hydrogen loads, it is assumed that photovoltaic output and the output of various loads follow a normal distribution within the fluctuation range of the predicted values. An initial scenario set is randomly generated, and a scenario reduction method is used to extract a source-load uncertainty scenario set, including multiple typical scenarios and their distribution probabilities, from the initial scenario set.

5. The method for optimizing the low-carbon operation of a comprehensive energy system considering hydrogen energy utilization according to claim 4, characterized in that, Carbon emission costs are calculated based on the difference between actual carbon emissions and carbon emission allowances, combined with predefined tiered carbon price ranges. The specific formula for calculating actual carbon emissions is as follows: in, This represents the actual carbon emissions of the IES; Carbon intensity per unit of purchased electrical energy; Carbon emission intensity per unit of natural gas power input for a gas turbine unit; The coefficient of CO2 absorbed to produce one unit of CH4 ,in This refers to the amount of CO2 required per unit of CH4. The calorific value of natural gas; The specific formula for calculating carbon emission allowances is as follows: in, Carbon emission allowances for IES; , and These are the carbon emission allowances per unit of electricity purchased from the external power grid, and the carbon emission allowances per unit of electricity and heat output from gas turbine units; for Purchased electrical energy power during the specified time period; Thermoelectric conversion coefficient; The scheduling period; Based on the predefined tiered carbon price ranges in the tiered carbon trading mechanism, the formula for calculating carbon emission costs is as follows: in, Cost of carbon emissions; The base price for tiered carbon trading; Price growth rate; This refers to the carbon emission range.

6. The method for optimizing the low-carbon operation of a comprehensive energy system considering hydrogen energy utilization according to claim 5, characterized in that, The specific expression for the day-ahead optimization objective in the current low-carbon stochastic optimization model is as follows: in, These are the IES scenarios in the recent low-carbon stochastic optimization model. Down Total operating costs, maintenance costs, curtailment costs, carbon emission costs, and energy purchase costs for each time period; These are the unit operation and maintenance costs for electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors, respectively. In the scene respectively Down Input power of time-lapse electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors; Unit cost of curtailment; In the scene respectively Down Forecasted and planned photovoltaic power output for each time period; In the scene respectively Down Purchased electricity, purchased natural gas, and purchased hydrogen power during the specified time period; Hydrogen has a low calorific value; Time-of-use pricing; These are the prices of natural gas and hydrogen, respectively.

7. The method for optimizing the low-carbon operation of a comprehensive energy system considering hydrogen energy utilization according to claim 6, characterized in that, The intraday optimization objective is to minimize the total operating cost plus the sum of all adjustments, as shown in the following expression: in, In the intraday low-carbon operation optimization model, IES in Total cost for the period, operation and maintenance cost, curtailment cost, carbon emission cost, energy purchase cost, and adjustment cost of each model in IES compared to the previous day; This represents the total number of time periods within the current day's scheduling cycle. These are the unit adjustment costs for electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors, respectively. These include electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors. The amount of adjustment compared to the previous time scale; , , , , , These are, respectively, electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors on the day-ahead timescale. t Input power during the time period; , , , , , These are electrolyzers, fuel cells, electric boilers, gas turbines, gas boilers, and methane reactors, respectively, on an intraday timescale. t Input power during a given time period.

8. A storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the low-carbon operation optimization method for an integrated energy system taking into account hydrogen energy utilization as described in any one of claims 1 to 7.

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