Carbon footprint optimization scheduling method and system of hydrogen-electricity cooperative power distribution network, electronic equipment and medium

Abstract

The invention relates to the technical field of low-carbon dispatching of a power system, in particular to a carbon footprint optimization dispatching method and system of a hydrogen-electricity collaborative power distribution network, electronic equipment and a medium. The method comprises the steps of firstly obtaining multi-source operation parameters and market data; constructing a carbon footprint tracking model for realizing dynamic allocation of node carbon emission according to the multi-source operation parameters and the market data; constructing a representative scene set according to set historical data; integrating the carbon footprint tracking model and the representative scene set to construct a multi-period optimization scheduling model for collaborative optimization of economic cost and carbon emission cost; and solving the multi-period optimization scheduling model by adopting a decomposition coordination algorithm, and outputting an optimal scheduling scheme. Through the mode, the technical problem that multi-target collaboration is difficult in the low-carbon transformation process of the hydrogen-electricity collaborative power distribution network is solved, and the carbon emission control precision, the operation robustness and the comprehensive decision-making capability of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of low-carbon dispatching technology for power systems, and in particular to a carbon footprint optimization dispatching method, system, electronic equipment, and medium for hydrogen-electricity coordinated distribution networks. Background Technology

[0002] The power system is facing the challenge of a profound low-carbon transformation. The large-scale integration of renewable energy has significantly improved the greenness of system operation, but the volatility of photovoltaic and wind power output, as well as the randomness of power load, has introduced significant uncertainties, making traditional deterministic dispatch models difficult to adapt and necessitating innovative methods to address multiple disturbances. Against the backdrop of a diversified energy structure, hydrogen energy, as a zero-carbon secondary energy source, is gradually being integrated into the framework of integrated energy systems. Hydrogen production through water electrolysis can effectively absorb fluctuating renewable energy, while hydrogen storage and fuel cells can provide flexible adjustment means for the power system, helping to peak shaving, valley filling, and emergency support. The synergistic coupling of hydrogen energy and the power grid enhances the vitality of the distribution network, but the hydrogen production process is affected by electricity prices and supply conditions, and the dynamic changes in hydrogen storage and consumption further exacerbate the uncertainty and complexity of system operation.

[0003] On the other hand, carbon emission constraints have become a key and rigid requirement for power system dispatch. Traditional carbon emission statistics methods mostly rely on monthly or annual energy consumption data, which makes it difficult to depict the distribution and attribution of carbon emissions during energy flow, and cannot meet the needs of refined carbon management in the context of distributed energy. To achieve transparent and traceable carbon management, carbon emissions need to be combined with energy flow to track the carbon footprint of the entire process from power generation, transmission, storage to consumption, avoiding double counting or omissions, and providing support for carbon trading and quota allocation.

[0004] Currently, optimization research on hydrogen-electricity co-generation distribution networks mainly focuses on economic dispatch and uncertainty handling. For example, existing technology 1 (CN118825990 A) discloses a robust dispatch method for integrated energy systems considering a tiered carbon trading mechanism. This method addresses the output power uncertainty of wind and solar power generation through robust optimization and introduces tiered carbon trading to reduce carbon emissions. However, this method emphasizes system-level carbon trading cost optimization and does not fully address the dynamic transmission and allocation mechanisms of carbon footprint in energy production, energy conversion and storage, and load processes. Furthermore, while its uncertainty modeling can handle wind and solar fluctuations, it does not cover the correlation of multiple random variables such as electricity and hydrogen prices, making it difficult to achieve carbon footprint tracking and co-optimization under multiple uncertainties. Specifically, existing methods mostly focus on economic optimization, lacking the integration of refined carbon footprint tracking into the dispatch model. This leads to ambiguity in carbon emission responsibility attribution, making it impossible to simultaneously guarantee the low-carbon and economical operation of the system under multiple disturbances such as wind and solar power, load, and prices. Therefore, existing hydrogen-electricity co-generation distribution networks face the technical challenge of multi-objective coordination difficulties in the process of low-carbon transformation. Summary of the Invention

[0005] To address the aforementioned shortcomings or drawbacks, this invention provides a method, system, electronic equipment, and medium for optimizing the carbon footprint of hydrogen-electricity coordinated distribution networks, which can solve the technical problem of multi-objective coordination difficulties faced by hydrogen-electricity coordinated distribution networks in the process of low-carbon transformation.

[0006] This invention provides a carbon footprint optimization scheduling method for hydrogen-electricity coordinated distribution networks, comprising: Acquire multi-source operating parameters and market data from energy production, energy conversion and storage, and load processes.

[0007] Based on multi-source operating parameters and market data, a carbon footprint tracking model is constructed to achieve dynamic allocation of node carbon emissions.

[0008] A representative scenario set is constructed based on the established historical data, which includes multiple uncertain parameters such as the output power, load, electricity price, and hydrogen price of wind and photovoltaic power generation.

[0009] By integrating a carbon footprint tracking model with a set of representative scenarios, a multi-period optimization scheduling model is constructed to coordinate the optimization of economic costs and carbon emission costs.

[0010] The decomposition and coordination algorithm is used to solve the multi-time period optimization scheduling model and output the optimal scheduling scheme.

[0011] According to a second aspect, this invention provides a carbon footprint optimization scheduling system for a hydrogen-electricity coordinated distribution network, comprising: The integrated operation data acquisition module is used to acquire multi-source operation parameters and market data from the energy production, energy conversion and storage, and load processes.

[0012] The carbon footprint tracking model building module is used to build a carbon footprint tracking model that dynamically allocates node carbon emissions based on multi-source operating parameters and market data.

[0013] The representative scenario set construction module is used to construct a representative scenario set based on the set historical data, which includes multiple uncertain parameters such as the output power, load, electricity price, and hydrogen price of wind power and photovoltaic power generation.

[0014] The optimization scheduling model construction module is used to integrate the carbon footprint tracking model with a representative scenario set to build a multi-period optimization scheduling model for synergistically optimizing economic costs and carbon emission costs.

[0015] The optimal scheduling scheme generation module is used to solve the multi-time period optimization scheduling model using the decomposition and coordination algorithm and output the optimal scheduling scheme.

[0016] According to a third aspect, the present invention provides an electronic device comprising: At least one processor; and The memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to execute any of the hydrogen-electricity co-distribution network carbon footprint optimization scheduling methods in the embodiments of the present invention.

[0017] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute any of the hydrogen-electricity coordinated distribution network carbon footprint optimization scheduling methods in the embodiments of the present invention.

[0018] This invention provides a carbon footprint optimization scheduling method for a hydrogen-electricity coordinated distribution network. This method acquires multi-source operating parameters and market data from energy production, energy conversion and storage, and load processes, providing a complete data foundation for subsequent modeling. Based on these multi-source operating parameters and market data, a carbon footprint tracking model is constructed to dynamically allocate carbon emissions at each node. This model accurately depicts the distribution path of carbon emissions along the distribution nodes. A representative scenario set is constructed based on historical data, incorporating multiple uncertain parameters such as the output power of wind and solar power generation, load, electricity price, and hydrogen price, to characterize the stochastic factors in system operation. Integrating the carbon footprint tracking model and the representative scenario set, a multi-period optimization scheduling model is constructed to collaboratively optimize economic costs and carbon emission costs, thereby achieving dual-objective optimization within a unified framework. A decomposition and coordination algorithm is used to solve the multi-period optimization scheduling model, ultimately outputting the optimal scheduling scheme that achieves optimal system operation.

[0019] Throughout the process, this invention addresses the lack of refined carbon footprint management in existing methods described in the background section. It achieves dynamic allocation of node-level carbon emissions by constructing a carbon footprint tracking model, resolving the ambiguity in carbon emission responsibility attribution in traditional scheduling. Addressing the challenge of multiple uncertainties arising from wind and solar power, load, and pricing, it incorporates uncertainty into the optimization framework by constructing a representative scenario set, overcoming the poor adaptability of deterministic optimization models in volatile environments. Furthermore, addressing the difficulty of coordinating economic efficiency and low-carbon goals, it constructs a multi-period optimization scheduling model to treat both as unified objectives for collaborative optimization, avoiding performance imbalances caused by single-objective optimization. Therefore, the technical solution of this invention solves the technical problem of multi-objective coordination difficulties faced by hydrogen-electricity co-located power distribution networks during the low-carbon transformation process, achieving traceable carbon footprints, quantifiable uncertainties, and optimized scheduling that coordinates economic and low-carbon goals, thereby improving the system's carbon emission control accuracy, operational robustness, and comprehensive decision-making capabilities. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a system architecture for distributed resource aggregation participating in the peak-shaving market according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a carbon footprint optimization scheduling system for a hydrogen-electricity coordinated distribution network according to an embodiment of the present invention; Figure 3 This is a block diagram of an electronic device used to implement embodiments of the present invention. Detailed Implementation

[0021] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] This invention provides a carbon footprint optimization scheduling method for a hydrogen-electricity coordinated distribution network, which can be applied to a regional energy management system (hereinafter referred to as the "system"). The system operates at a distribution dispatch center or distributed edge computing node through local deployment or cloud collaboration to complete multi-source data fusion, dynamic carbon footprint tracking, and multi-objective optimization decision-making.

[0023] Specifically, the physical equipment of the system includes, but is not limited to, monitoring terminals, actuators and communication modules deployed in power plants, substations, energy storage power stations, electrolytic hydrogen production plants and key load nodes. Therefore, the system can collect real-time data on generator output power, hydrogen production power, energy storage charging and discharging status, and load data through power monitoring terminals at power plants, flow sensors at electrolytic hydrogen production plants, battery management systems at energy storage power stations, and smart meters at key load nodes. This multi-source data is then aggregated to a cloud-based dispatch center via 5G or fiber optic networks. The system obtains real-time carbon price and quota data based on a carbon market interface, dynamically calculates carbon emissions at each node using a carbon footprint tracking model, and combines wind and solar power output predictions with electricity price fluctuation scenarios. It then employs a multi-objective optimization algorithm to generate a dispatch scheme aimed at minimizing economic costs and controlling carbon emissions. For example, during peak solar power output at midday, the system automatically adjusts the electrolyzer power to 5 MW to absorb surplus green electricity and controls energy storage devices to charge and store low-cost electricity. Simultaneously, it issues a power reduction command to the gas turbine, enabling the entire distribution network to achieve hydrogen-electricity coordinated optimization dispatch while meeting power balance and carbon quota constraints. Finally, the command is sent to each actuator via a communication module, completing a closed-loop control from data perception to decision execution.

[0024] like Figure 1 As shown, the method may include: Step S110: Obtain multi-source operating parameters and market data for energy production, energy conversion and storage, and load processes.

[0025] Among them, multi-source operating parameters refer to the set of monitoring data reflecting the real-time operating status of energy production, energy conversion and storage, and load processes; generally speaking, these can specifically involve electricity (generation), hydrogen energy (generation), and energy storage systems. Market data refers to price and policy parameters obtained from energy trading platforms.

[0026] Specifically, the system can collect generator output power data through power transmitters deployed in power plants, with a sampling frequency of once every 15 minutes; and collect electrolysis hydrogen production power data through flow meters at hydrogen energy stations. The communication protocol can adopt... (Modbus Transmission Control Protocol); Next, the system obtains energy storage charging and discharging power data through the communication interface of the battery management system, and at the same time obtains carbon emission factor and carbon quota data from the carbon trading market and real-time electricity price and hydrogen price data from the power trading center through its own configured API (Application Programming Interface).

[0027] For example, the system collects the operating data of the distribution network in area A: the output of thermal power units is 50 MW, the hydrogen production power of electrolyzers is 3 MW, the energy storage discharge power is 2 MW, and the node load is 45 MW. These operating data constitute the above-mentioned multi-source operating parameters; market data include a carbon emission factor of 0.8 tons of carbon dioxide / MWh, a carbon quota of 100 tons of carbon dioxide, a real-time electricity price of 0.6 yuan / kWh, and a hydrogen price of 3 yuan / cubic meter.

[0028] Step S120: Based on multi-source operating parameters and market data, construct a carbon footprint tracking model that realizes dynamic allocation of node carbon emissions.

[0029] The carbon footprint tracking model is a mathematical model that achieves refined allocation of carbon emissions by quantifying energy flow paths. Specifically, the system first calculates the carbon emission intensity on the power supply side based on generator output power data and carbon emission factors; then, it establishes power flow distribution and carbon transfer paths based on the distribution network topology; finally, it distributes carbon emissions to each node using a linear weighted algorithm, updating the data hourly. For example, if the system calculates the carbon emission intensity of node A to be 0.5 tons of CO2 per megawatt-hour, and power flow analysis determines that node A transmits 60% of its power to node B, then node B's share of carbon emissions is 60% of node A's total carbon emissions.

[0030] For example, the system can establish a carbon footprint tracking mechanism at the distribution network level using the following formula (1) to construct a carbon footprint tracking model: (1) in, This represents the dynamic carbon emissions of node n in the distribution network during time period t. (The generator's output power) is collected every 15 minutes via the Supervisory Control and Data Acquisition (SCADA) system, in megawatts. (Hydrogen production power) is obtained in real time through the electrolyzer controller communication interface (Modbus TCP protocol), in megawatts; (Energy storage charging and discharging power) is collected by the Battery Management System (BMS), with charging being positive and discharging being negative, and the unit is megawatts; (Carbon emission factor) is dynamically updated from the carbon market data interface, with units of tons of carbon dioxide per megawatt-hour, and i, j, s representing the indices of generators, hydrogen production equipment, and energy storage equipment, respectively.

[0031] Next, in this embodiment, the system can also determine the values ​​of each input parameter in formula (1) through the following sub-calculation process: Generator carbon emission contribution The output power data of the generator is collected in real time and multiplied by the corresponding carbon emission factor. For example, the output power of a thermal power unit (i=1) in time period t is calculated. Megawatts, carbon emission factor The carbon emissions contributed by this unit are calculated in tons of carbon dioxide per megawatt-hour. Tons of carbon dioxide; carbon emissions from hydrogen production The power output is calculated by multiplying the electrolyzer power data by the average carbon emission factor of the power grid. For example, the hydrogen production power of the electrolyzer (j=1) is... Megawatts, average carbon emission factor of the power grid This contribution to carbon emissions is calculated in tons of CO2 per megawatt-hour. Tons of carbon dioxide; Energy storage carbon footprint adjustment items Calculated using factors related to the energy storage charge / discharge power and the state of charge / discharge. For example, during energy storage discharge... Megawatts (negative values ​​indicate discharge). This contribution is in tons of carbon dioxide per megawatt-hour. Tons of carbon dioxide (discharge increases system carbon emissions). Therefore, by combining formula (1) and its parameter calculation system, the system can achieve minute-level dynamic tracking and accurate allocation of carbon emissions at the distribution network node level, providing a quantitative basis for carbon quota management and low-carbon scheduling.

[0032] Step S130: Construct a representative scenario set based on the set historical data, which includes multiple uncertain parameters such as the output power, load, electricity price, and hydrogen price of wind power and photovoltaic power generation.

[0033] The representative scenario set refers to a collection of typical operating scenarios generated using probabilistic statistical methods that can cover system uncertainties. Specifically, the system can use kernel density estimation to fit the probability distribution of the output power of wind power and photovoltaic power generation, use the Gaussian Copula function to characterize the correlation between electricity price and hydrogen price, generate 1000 initial scenarios through Monte Carlo sampling, and finally compress them into 10 representative scenarios using the K-means clustering algorithm. For example, based on photovoltaic output data from the past year, the system generates a typical summer day scenario: maximum photovoltaic output of 40 MW, peak load of 55 MW, electricity price of 0.65 yuan / kWh, and hydrogen price of 2.8 yuan / cubic meter.

[0034] The Gaussian Copula function is a statistical coupling function based on the multivariate normal distribution, used to connect the marginal distributions of multiple random variables into a joint distribution to characterize their correlation structure. The specific parameter configuration of the Copula function is the correlation coefficient matrix (obtained by calculating the Pearson correlation coefficient between variables using historical data; for example, the correlation coefficient between the output power and electricity price of wind power and photovoltaic power is 0.3). The calculation method can be as follows: first, transform the marginal distribution values ​​of each variable into standard normal distribution quantiles through probability integral transformation, then use the correlation coefficient matrix to calculate the cumulative probability of the joint normal distribution, and finally output the joint distribution function. The K-means clustering algorithm is an unsupervised machine learning algorithm used to compress a large number of initial scenarios into a representative scenario set. Its specific parameter configuration is the number of clusters k (set according to the number of scenarios and experience; for example, compressing 1000 initial scenarios into k=10 representative scenarios). The calculation method can be as follows: randomly select k initial cluster centers, calculate the Euclidean distance from each scenario to each center and assign it to the nearest cluster, iteratively update the cluster center to the mean of the samples within the cluster until the change in the center point is less than a threshold (such as 0.001), and finally output the k cluster centers as representative scenarios and their probabilities (the proportion of the number of samples within the cluster to the total number of samples; for example, the probability of a representative scenario is 0.12).

[0035] In some embodiments, the system can calculate the probability density estimate of the random variable using the following formula (2): (2) Formula (2) is a kernel density estimation method used to nonparametrically estimate the probability density function based on historical data, without requiring pre-defined distribution assumptions. The specific meanings of the parameters are as follows: for , refers to the probability density estimate at point x, dimensionless, representing the relative probability of the random variable taking the value x; n refers to the number of historical samples, in units of points, determined by the length of the historical data sequence, for example, n=1000 means using 1000 historical data points; h refers to the bandwidth parameter, dimensionless, controlling the smoothness of the estimate, automatically calculated by the Silverman rule or cross-validation, for example, h=0.1; A kernel function is a weighting function with a non-negative integral value of 1, such as the Gaussian kernel function (a commonly used bell curve function), used to assign weights to each sample point; for This refers to the i-th historical sample value, with units consistent with the random variable (e.g., megawatts, yuan / kilowatt-hour), read directly from the database; for , refers to the probability density point to be evaluated, with units of and . Consistent.

[0036] Next, in this embodiment, the system can also calculate the joint distribution function of the multiple random variables using formula (3): (3) Formula (3) is the Copula coupling method, used to connect marginal distributions into a joint distribution to characterize the correlation structure between variables. The specific meanings of the parameters are as follows: for , refers to the joint distribution function of n-dimensional random variables, which is dimensionless and indicates that the variables are simultaneously less than or equal to The probability of; for This refers to the Copula function, a multidimensional distribution function used to describe the dependencies between variables, such as the Gaussian Copula (a coupling function based on the multivariate normal distribution); for , refers to the marginal distribution function of the i-th random variable, which is dimensionless and is obtained by integrating the probability density estimate of formula (2).

[0037] Therefore, by combining the above formulas (2) and (3), the system can sequentially complete the marginal distribution estimation and correlation modeling, and generate a joint probability model that accurately reflects the uncertainty and correlation of the output power, load, electricity price and hydrogen price of wind power generation and photovoltaic power generation, providing a theoretical basis for subsequent Monte Carlo sampling, thereby ensuring the robustness and adaptability of the multi-time period optimization scheduling model in complex stochastic environments.

[0038] Step S140: Integrate the carbon footprint tracking model with a representative scenario set to construct a multi-period optimization scheduling model for synergistically optimizing economic costs and carbon emission costs.

[0039] The multi-period optimization scheduling model is a decision-making model that considers time coupling, with an optimization period of 24 hours and a time resolution of 1 hour. Specifically, the system uses a weighted summation method to combine economic cost and carbon cost into a single objective function, where the economic cost weight is set to 0.7 and the carbon cost weight is set to 0.3; the constraints include power balance constraints, energy storage charge / discharge state constraints, and carbon quota constraints. For example, the constraints of the system in time period t using the multi-period optimization scheduling model are expressed as follows: Furthermore, the total carbon emissions must be lower than the carbon quota limit.

[0040] In some embodiments, the system can calculate the value of the objective function of the optimized scheduling model using the following formula (4): (4) Formula (4) is a multi-objective optimization function used to minimize the weighted sum of economic cost and carbon emission cost, thereby achieving a balance between low-carbon and economic efficiency in hydrogen-electricity coordinated power grid dispatch. The specific meanings of the parameters are as follows: For F, it refers to the objective function value, representing the total cost in yuan (RMB), which is obtained by optimizing the solution to the minimum value; for p, it refers to the weighting factor, which is dimensionless and ranges from 0 to 1, used to adjust the relative importance of economic cost and carbon emission cost, for example, setting it to 0.7 indicates a greater emphasis on economics; t refers to the time period index, from 1 to n, where n is the total number of time periods within the scheduling cycle (e.g., n=24 for 24-hour scheduling), in units of times; for This refers to the system's operating cost over time period t, including electricity purchase costs, equipment maintenance costs, etc., expressed in yuan, and calculated using real-time market data. This refers to carbon trading costs, specifically the transaction expenses or revenues incurred from participating in the carbon market, expressed in yuan, and calculated based on carbon prices and carbon emissions. Hydrogen sales revenue refers to the income obtained from the sale of hydrogen, expressed in yuan, and calculated based on the hydrogen price and the volume of hydrogen sold; for Carbon emission costs, including carbon taxes or penalty costs, are expressed in yuan and are determined by carbon emissions and carbon prices. Specifically, the system initializes the weighting factor p based on historical data, loads cost parameters from the market interface every 15 minutes, and calculates the F-value as the basis for optimization decisions.

[0041] Next, in this embodiment, the system can also calculate the power balance state using formula (5): (5) Formula (5) represents the power balance constraint, used to ensure the instantaneous power conservation of power generation, consumption, and energy storage in the distribution network. The specific meanings of the parameters are as follows: for This refers to purchased power, specifically electricity bought from the upstream power grid or market, measured in megawatts (MW), and collected in real-time via SCADA. This refers to renewable energy output, including the power generation capacity of renewable energy sources such as wind power and solar power, measured in megawatts (MW), and calculated from meteorological data and inverter output; for This refers to the energy storage discharge power, which is the electricity released by a battery or hydrogen storage system, measured in megawatts (MW). Discharging is positive and charging is negative. This refers to the charging power of energy storage, or the electricity absorbed by the energy storage system, measured in megawatts (MW). Charging is positive and discharging is negative. This refers to the electrical power used in hydrogen electrolysis, or the electricity consumed by the electrolyzer for hydrogen production, measured in megawatts (MW). , refers to load power, or the total electricity demand of the distribution network, in megawatts. Specifically, the system can check whether the equation of formula (5) holds true every 5 minutes. If the deviation exceeds the threshold (e.g., 1%), the output power of the generator or the energy storage plan will be adjusted.

[0042] Furthermore, in this embodiment, the system can also calculate the hydrogen balance state using formula (6): (6) Formula (6) represents the hydrogen balance constraint, used to ensure the flow balance between production, consumption, and storage in the hydrogen energy system. The specific meanings of the parameters are as follows: for , which is the hydrogen production capacity, refers to the hydrogen production flow rate of the electrolyzer during time period t, in kilograms per hour (kg / h), and is measured by a flow meter; This refers to the amount of hydrogen released from the hydrogen storage tank, which is the flow rate of hydrogen released by the hydrogen storage facility, expressed in kilograms per hour. Releasing hydrogen is positive, and filling hydrogen is negative. Hydrogen load refers to the amount of hydrogen consumed by fuel cells or industrial users, expressed in kilograms per hour. This refers to the amount of hydrogen sold externally, specifically the flow rate of hydrogen sold externally, expressed in kilograms per hour. This refers to the hydrogen filling capacity of the hydrogen storage tank, which is the flow rate of hydrogen absorbed by the hydrogen storage facility, expressed in kilograms per hour. Filling hydrogen is positive, and releasing hydrogen is negative. Specifically, the system monitors the hydrogen flow rate in real time based on sensor data and verifies formula (6) every 15 minutes. If there is an imbalance, the power of the electrolyzer is adjusted.

[0043] Furthermore, in this embodiment, the system can also calculate the carbon emission constraint satisfaction status using formula (7): (7) Formula (7) represents carbon emission constraints, including time-based constraints and total emission constraints, used to ensure that system carbon emissions do not exceed quota limits. The specific meanings of the parameters are as follows: The meaning is the same as in formula (1) above, and is calculated by the carbon footprint tracking model; Carbon emission allowance for time period t, expressed in tons of carbon dioxide, obtained from carbon market policies; This refers to the carbon emission allowance within the total scheduling cycle, expressed in tons of carbon dioxide; 'n' refers to the distribution network node index, representing electrical connection points, expressed in units of [number]. Specifically, the system verifies [data] for each time period. Is it less than And at the end of the cycle, the total constraint is checked.

[0044] Therefore, by combining the above formulas (4) to (7), the system can construct a complete hydrogen-electricity coordinated distribution network optimization scheduling model to achieve multi-objective coordination of economy, low carbon emissions and safety. Formula (4) provides the cost minimization objective, formulas (5) and (6) ensure the real-time balance of energy and hydrogen energy, and formula (7) controls carbon emission compliance.

[0045] Step S150: Use the decomposition and coordination algorithm to solve the multi-time period optimization scheduling model and output the optimal scheduling scheme.

[0046] Among them, the decomposition and coordination algorithm refers to a mathematical method that decomposes a complex optimization problem into a main problem and sub-problems for iterative solution. Specifically, the main problem decides which variables (such as unit start-up and shutdown, long-term contracts), and the sub-problems generate what kind of "cutting plane" to feed back to the main problem when verifying which scenario constraints (such as real-time power balance, carbon emission constraints). The system can use the Benders decomposition algorithm, with the main problem solving for unit combination and carbon quota allocation, and the sub-problems verifying the feasibility of constraints under each scenario. The convergence tolerance is set to 0.01%, and the maximum number of iterations is 100.

[0047] Among them, the Benders decomposition algorithm is a decomposition optimization algorithm for solving large-scale mixed integer programming problems. Its core idea is to decompose the original problem into a main problem that handles global decision variables (usually containing integer variables and complex constraints, such as equipment start-up / shutdown and carbon quota allocation) and sub-problems that handle uncertain scenarios (usually linear programming problems used to verify the feasibility of constraints under each scenario). Iterative solutions are then obtained using the cutting plane information between the main problem and sub-problems (i.e., linear inequality constraints generated when sub-problem constraints are violated). Specific parameter configurations include convergence tolerance (e.g., set to 0.1%), minimum... The calculation method for the large number of iterations (e.g., 200 iterations) and the penalty coefficient (e.g., a penalty of 100 yuan / MW for unit power deviation) is as follows: First, initialize the main problem and solve it to obtain the global decision variables (e.g., power purchase of 50MW). Then, solve all scenario sub-problems in parallel (e.g., 10 scenarios of wind power and photovoltaic power output). If a scenario has a power imbalance or carbon emission exceedance, generate a Benders cut (e.g., requiring the main problem to increase the power purchase by at least 2MW). Add the cut plane to the main problem and solve it again. Repeat the iteration until the rate of change of the objective function is less than the convergence tolerance (e.g., a change of 5 consecutive iterations). The final output is the optimal scheduling scheme that satisfies all scenario constraints.

[0048] For example, after 15 iterations, the algorithm converges and outputs the optimal scheduling scheme for the next 24 hours: the thermal power unit outputs 45 MW from 09:00 to 11:00, the electrolyzer produces 5 MW of hydrogen from 14:00 to 16:00, and the energy storage system charges 4 MW during off-peak electricity prices.

[0049] Therefore, according to the above implementation method, firstly, by acquiring multi-source operating parameters and market data from the energy production, energy conversion and storage, and load stages, a complete data foundation is provided for subsequent modeling. Based on the multi-source operating parameters and market data, a carbon footprint tracking model is constructed to achieve dynamic allocation of node carbon emissions. This model is used to accurately depict the distribution path of carbon emissions along the distribution nodes. A representative scenario set is constructed based on historical data, including multiple uncertain parameters such as the output power, load, electricity price, and hydrogen price of wind and photovoltaic power generation, to characterize the stochastic factors in system operation. The carbon footprint tracking model and the representative scenario set are integrated to construct a multi-period optimization scheduling model for collaboratively optimizing economic costs and carbon emission costs, thereby achieving dual-objective optimization within a unified framework. A decomposition and coordination algorithm is used to solve the multi-period optimization scheduling model, ultimately outputting the optimal scheduling scheme that achieves optimal system operation.

[0050] Throughout the process, this embodiment addresses the lack of refined carbon footprint management in existing methods described in the background section. By constructing a carbon footprint tracking model, it achieves dynamic allocation of node-level carbon emissions, resolving the ambiguity in carbon emission responsibility attribution in traditional scheduling. Addressing the challenge of multiple uncertainties arising from wind and solar power, load, and prices, it incorporates uncertainty into the optimization framework by constructing a representative scenario set, overcoming the poor adaptability of deterministic optimization models in volatile environments. Furthermore, addressing the difficulty of coordinating economic efficiency and low-carbon goals, it constructs a multi-period optimization scheduling model to treat both as unified objectives for collaborative optimization, avoiding performance imbalances caused by single-objective optimization. Therefore, the technical solution of this embodiment solves the technical problem of multi-objective coordination difficulties faced by hydrogen-electricity co-located distribution networks during the low-carbon transformation process, achieving traceable carbon footprints, quantifiable uncertainties, and optimized scheduling that coordinates economic and low-carbon goals, thereby improving the system's carbon emission control accuracy, operational robustness, and comprehensive decision-making capabilities.

[0051] In some embodiments, multi-source operating parameters include generator output power data, electrolysis hydrogen production power data, energy storage charging and discharging power data, and node load data; market data includes carbon emission factors, carbon quota data, and energy price data. Acquiring multi-source operating parameters and market data for energy production, energy conversion and storage, and load processes includes: Multi-source operating parameters are collected by monitoring equipment deployed on the power generation side, load side, and energy storage side.

[0052] The monitoring equipment includes power transmitters from power plants (measurement accuracy...). The system includes a gas flow meter (range 0-1000 cubic meters / hour) for the hydrogen energy station and a battery management system for the energy storage system (sampling period 1 second). Specifically, the power transmitter outputs the generator's active power via a 4-20 mA analog signal; the gas flow meter uses ultrasonic principles to measure the hydrogen production flow rate of the electrolyzer and transmits data via an RS-485 interface; the battery management system collects the energy storage charging and discharging voltage and current in real time via a CAN bus (Controller Area Network bus). For example, a gas-fired power plant collects output data of 100 MW, the electrolyzer's hydrogen production power corresponds to a hydrogen production flow rate of 50 cubic meters / hour, and the energy storage system's charging power is 2 MW.

[0053] Market data is obtained through external carbon trading market data interfaces and energy market data interfaces.

[0054] The carbon trading market data interface is used to obtain regional carbon quota allocation schemes (unit: tons of carbon dioxide) and real-time carbon prices (unit: yuan / ton); the energy market data interface is used to obtain time-of-use electricity prices (unit: yuan / kWh) and hydrogen trading prices (unit: yuan / kg). Specifically, the system accesses the provincial carbon trading platform API (Application Programming Interface) every 30 minutes via HTTPS (Hypertext Transfer Protocol Secure) to obtain the latest carbon quota data; simultaneously, it connects to the power trading center database via OPC UA protocol (Open Platform Communications Unified Architecture) to read the electricity price curve for the next 24 hours. For example, the data obtained from the interface shows: the current remaining carbon quota is 5,000 tons, and the carbon price is 60 yuan / ton; the peak electricity price for the next day is 1.2 yuan / kWh, and the wholesale price of hydrogen is 40 yuan / kg.

[0055] Therefore, according to the above implementation method, the system can establish a complete data acquisition channel to realize real-time operation status monitoring and market environment perception of the power, hydrogen energy and energy storage links, and provide a data foundation for subsequent carbon footprint tracking and optimized scheduling.

[0056] In some embodiments, a node is an electrical connection point in the distribution network topology that connects power sources, loads, energy storage, and hydrogen production equipment; based on multi-source operating parameters and market data, a carbon footprint tracking model is constructed to achieve dynamic allocation of node carbon emissions, including: The carbon emission intensity of each node is determined based on multi-source operating parameters and market data.

[0057] Among them, node carbon emission intensity (tons of CO2 / MWh) refers to the carbon emissions corresponding to a unit of transmission power, and the calculation formula is: .

[0058] Specifically, the system can calculate the carbon emissions of a power node by multiplying the generator's output power data by the corresponding unit's carbon emission factor, and then calculate the node's injected power based on power flow. For example, if a thermal power node has an output of 100 MW and a carbon emission factor of 0.8 tons of CO2 / MWh, then the node's carbon emission intensity is 0.8 tons of CO2 / MWh.

[0059] Establish carbon flow tracing paths based on the distribution network topology.

[0060] The carbon flow tracing path refers to the transmission relationship of carbon emissions along power flow between nodes, described by a topological correlation matrix. Specifically, the system can establish a carbon flow tracing path by using the upstream tracing method based on node power injection and branch power distribution, distributing the generator's carbon emissions along the power flow direction to each load node; alternatively, the system can construct node power injection-distribution relationships based on an adjacency matrix, determine the carbon flow distribution ratio using a proportional sharing principle, and then establish a carbon flow tracing path. For example, if node A transmits 60% of its power to node B, then 60% of node A's carbon emissions are distributed to node B.

[0061] A carbon footprint tracking model is constructed based on carbon emission intensity and carbon flow tracing paths.

[0062] The carbon footprint tracking model is a dynamically updated model, outputting the carbon emissions of each node at different times. Specifically, the system combines carbon emission intensity with carbon flow paths through matrix operations, updating node carbon emission data every 15 minutes. For example, the model calculates the carbon emissions allocated to node B at 10:00 as follows: .

[0063] Therefore, according to the above implementation method, the system can achieve minute-level dynamic tracking of carbon emissions at the distribution network node level, providing accurate carbon footprint data support for subsequent optimized scheduling.

[0064] In some embodiments, the representative scenario set includes multi-dimensional related scenarios reflecting the uncertainty of output power, load, electricity price, and hydrogen price of wind power and photovoltaic power generation; the representative scenario set is constructed based on pre-stored historical data, including: The marginal probability distribution of multiple uncertainty parameters is constructed using the kernel density estimation method.

[0065] Kernel density estimation is a nonparametric statistical method used to estimate the probability density function of random variables based on historical data, without requiring a predefined distribution. Multiple uncertainty parameters refer to random input variables such as the output power, load, electricity price, and hydrogen price of wind and solar power generation. Specifically, the system uses a Gaussian kernel function (a commonly used type of kernel function), and the bandwidth parameter is automatically calculated using the Silverman rule, generating a smooth probability density curve for each parameter based on historical data sequences. The Silverman rule is an empirical rule used in statistics to automatically calculate the optimal bandwidth when performing nonparametric density estimation (especially kernel density estimation). Its core idea is to calculate a bandwidth value that achieves the best balance between smoothness and detail preservation in the estimated probability density function, based on the standard deviation and sample size of the sample data. For example, for the output power parameter of wind and solar power generation, the system loads historical data from the past 365 days, sets the bandwidth to 0.1, and generates a probability density function. The probability density is highest when the output power of wind and solar power generation is in the range of 0 to 50 megawatts.

[0066] A joint probability distribution is constructed by coupling marginal probability distributions using a Copula function.

[0067] The Copula function is a mathematical function used to connect multiple marginal distributions into a joint distribution to characterize the correlation structure between variables. Coupling refers to integrating marginal distributions into a multidimensional distribution through a correlation coefficient matrix (e.g., calculating the Pearson correlation coefficient between the output power, load, electricity price, and hydrogen price of wind and solar power based on historical data). Specifically, the system uses the Gaussian Copula function, and the correlation coefficient matrix is ​​calculated from historical data, for example, using the Pearson correlation coefficient (a statistic that measures linear correlation). For example, the correlation coefficient between the output power and electricity price of wind and solar power is 0.3, and the correlation coefficient between load and hydrogen price is 0.2. The joint distribution constructed by the Copula function can reflect these correlations and generate a four-dimensional probabilistic model.

[0068] Monte Carlo sampling is performed based on the joint probability distribution to generate an initial scene set.

[0069] Monte Carlo sampling is a random sampling technique that generates a large number of random samples from a probability distribution to simulate uncertainty. The initial scenario set refers to the original set of scenarios obtained from the sampling, with each scenario containing a set of parameter values. Specifically, the system performs 10,000 samplings, generating a scenario vector each time, including the output power of wind and solar power generation (in megawatts), the load value (in megawatts), the electricity price (in yuan per kilowatt-hour), and the hydrogen price (in yuan per kilogram). The sampling results are stored in a matrix format, with rows representing scenarios and columns representing parameters. For example, the initial scenario set generated by sampling contains 1,000 scenarios, where the first scenario value is: the output power of wind and solar power generation is 25 megawatts, the load is 30 megawatts, the electricity price is 0.65 yuan per kilowatt-hour, and the hydrogen price is 3.5 yuan per kilogram.

[0070] The initial scene set is reduced to obtain a representative scene set.

[0071] Scene reduction is a data compression technique that reduces the number of scenes by clustering or filtering while retaining key statistical features. The representative scene set refers to the set of scenes after reduction, used to optimize computation and improve efficiency. Specifically, the system uses the K-means clustering algorithm (an unsupervised machine learning method) to cluster the initial scenes into 10 clusters. The center point of each cluster is used as a representative scene, and the probability of each representative scene (i.e., the proportion of scenes within a cluster to the total number of scenes) is calculated.

[0072] Specifically, the K-means clustering algorithm is an unsupervised machine learning algorithm whose core objective is to automatically divide a set of data points into K non-overlapping clusters to achieve a classification effect where data points within the same cluster have high similarity and data points between different clusters have low similarity. The algorithm's parameters include a pre-specified number of clusters K (e.g., setting K=10 in scene reduction to compress a large number of initial scenes into 10 representative scenes), a maximum number of iterations (e.g., 100 to prevent infinite loops), and an initialization method (commonly randomly selecting K data points as initial cluster centers). The calculation method is implemented through an iterative process: first, K cluster centers are randomly initialized, and then the Euclidean distance from each data point to all cluster centers is calculated (the formula is...). ,in For data point features, (Assuming the cluster center feature) and assigning it to the nearest cluster, then updating each cluster center to the average of all features of all data points within that cluster, repeating this process until the change in cluster center position is less than a threshold (e.g., ...). (or reaching the maximum number of iterations); for example, in the reduction of hydrogen-electricity coordinated distribution network scenarios, the system is based on 1000 initial scenarios (each scenario contains data such as the output power, load, and electricity price of wind power and photovoltaic power generation), sets K=10 and the maximum number of iterations to 100, and through iterative allocation and updates, finally outputs 10 representative scenarios and their probabilities (the proportion of the number of scenarios in the cluster to the total number of scenarios), thereby efficiently compressing massive uncertain scenarios and reducing the complexity of the optimization scheduling model.

[0073] Therefore, according to the above implementation method, the system can efficiently generate a high-precision representative scene set, accurately capture the uncertainty and correlation of the output power, load, electricity price and hydrogen price of wind power generation and photovoltaic power generation, and provide reliable input for subsequent optimized scheduling.

[0074] In some embodiments, a carbon footprint tracking model and a representative set of scenarios are integrated to construct a multi-period optimization scheduling model for synergistically optimizing economic costs and carbon emission costs, including: An objective function is constructed with the goal of minimizing the weighted sum of economic costs and carbon emission costs.

[0075] Here, economic cost refers to the financial expenditures in system operation, including fuel costs, equipment maintenance costs, and purchased electricity costs, etc., in yuan; carbon emission cost refers to the expenses incurred due to carbon emissions, such as carbon tax or carbon trading expenditures, in yuan; weighted sum is a mathematical optimization method that balances the importance of multiple objectives through weight coefficients, with the sum of weights being 1. Specifically, the system uses a linear weighted method, setting the economic cost weight to 0.7 and the carbon emission cost weight to 0.3, with the objective function expression as follows: The weighting coefficients can be dynamically adjusted according to policy requirements, such as by loading them through a configuration file. For example, if the economic cost is 10,000 yuan and the carbon emission cost is 5,000 yuan, then the objective function value is... .

[0076] Set power balance constraints, hydrogen balance constraints, energy storage operation constraints, and carbon emission quota constraints to construct a set of constraints.

[0077] Among them, power balance constraints refer to the real-time balance between power generation, load power, and purchased power; hydrogen balance constraints refer to the dynamic balance between hydrogen production, hydrogen consumption, and hydrogen storage; energy storage operation constraints limit the charging and discharging power and state of charge of energy storage equipment; and carbon emission quota constraints mean that the total carbon emissions of the system must not exceed the allocated carbon quota limit. Specifically, the system establishes a set of equality or inequality constraints: power balance constraints are expressed as: The hydrogen balance constraint is expressed as: Energy storage operation constraints include charging and discharging power limits and energy conservation equations; carbon emission quota constraints are: .

[0078] For example, in time period t, the power balance constraint (in megawatts) is: ; Carbon emission quota constraints are: .

[0079] The node carbon emissions output by the carbon footprint tracking model are used as input to the carbon emission quota constraint.

[0080] In this system, node carbon emissions refer to the carbon emission values ​​of each node during a specific period, calculated by the carbon footprint tracking model, expressed in tons of carbon dioxide. Input refers to the parameters that use the data as constraints. Specifically, the system reads the node carbon emission sequence in real time from the carbon footprint tracking model interface and aggregates it into the system's total carbon emissions, used to verify carbon emission quota constraints. The data update frequency is once every 15 minutes. For example, if the carbon footprint tracking model outputs that node A emits 10 tons of carbon and node B emits 15 tons, then the total carbon emissions are 25 tons, which serve as the constraint input to ensure... .

[0081] By embedding a set of representative scenarios into the objective function and the set of constraints, a multi-period optimization scheduling model is constructed.

[0082] Embedding refers to integrating uncertain scenarios into the optimization model, allowing the objective function and constraints to consider the expected values ​​of multiple scenarios. The multi-period optimization scheduling model is a decision model that considers time coupling, with an optimization cycle of 24 hours and a period resolution of 1 hour. Specifically, the system expands the objective function into a multi-scenario expected value form, for example... ,in Let be the probability of scenario s; constraints must be satisfied in all scenarios, and robust optimization or stochastic programming methods are used for handling. For example, a representative scenario set contains 10 scenarios, each with a probability of 0.1, and the objective function calculates the weighted average of the 10 scenarios; constraints such as power balance must be satisfied independently in each scenario.

[0083] Therefore, based on the above implementation method, the system can construct an efficient and robust multi-period optimization scheduling model, realize the synergistic optimization of economy and low carbon, and provide scientific scheduling decision support for hydrogen-electricity coordinated distribution networks.

[0084] In some embodiments, the decomposition and coordination algorithm is the Benders decomposition algorithm; the decomposition and coordination algorithm is used to solve the multi-stage optimization scheduling model and output the optimal scheduling scheme, including: The multi-time period optimization scheduling model is decomposed into a main problem and multiple scenario sub-problems.

[0085] The main problem refers to the upper-level optimization model that determines the global optimization variables, including power purchase capacity, hydrogen production capacity, energy storage scheduling plan, and carbon quota allocation scheme. The scenario sub-problems refer to the lower-level optimization models that verify the feasibility of constraints for each representative scenario, used to test the adaptability of the main problem solution under that scenario. Specifically, the system decomposes the original problem according to the number of scenarios: the main problem contains continuous decision variables, and the scenario sub-problems contain random parameters under the corresponding scenario. For example, an optimization model containing 10 scenarios is decomposed into 1 main problem and 10 scenario sub-problems. The main problem determines the power purchase capacity (range 0~100 MW), and the sub-problems verify the power balance constraints under each scenario.

[0086] Solve the main problem to obtain the global scheduling decision variables.

[0087] The global scheduling decision variables refer to the optimization results affecting the operation of the entire system, including unit output and energy storage charging and discharging status over a 24-hour time series. Specifically, the system uses a linear programming solver (such as the Gurobi optimizer, a high-performance commercial solver software for solving mathematical programming problems) to solve the main problem and output an initial scheduling scheme; the calculation process must satisfy coupling constraints (such as total carbon quota limits). For example, the main problem solution yields a thermal power output of 80 MW, an energy storage charging power of 5 MW, and a carbon quota allocation of 50 tons of carbon dioxide for the period at 10:00 the following day.

[0088] Solve each sub-problem of the scenario, verify the constraints, and generate feedback information.

[0089] In this context, verifying constraints refers to checking whether constraints such as power balance and hydrogen balance in sub-problems are satisfied; feedback information refers to the cut plane (a type of mathematical constraint) generated when constraints are violated, used to transmit correction signals to the main problem. Specifically, the system solves all scenario sub-problems in parallel, uses duality theory to calculate constraint violations, and generates a Benders cut (a linear inequality) as feedback. For example, in scenario 3, a power deficit of 2 MW is found, and the generated cut plane requires the main problem to increase the purchased power by at least 2 MW.

[0090] The main problem and scenario sub-problems are iteratively updated based on feedback information until the convergence condition is met, and the optimal scheduling scheme is output.

[0091] The convergence condition refers to the iteration stopping criterion, including a threshold for the rate of change of the objective function (e.g., less than 0.1%) or a maximum number of iterations (e.g., 200). Specifically, the system progressively adds Benders cuts to the main problem, re-solves and updates the subproblems until the rate of change of the objective function is less than 0.1% for five consecutive iterations. For example, after 15 iterations, the objective function value stabilizes at 8500 yuan, and the final scheduling scheme is output: 85 MW of thermal power output, 3 MW of energy storage discharge, and full utilization of carbon quotas.

[0092] In some embodiments, the system can calculate the overall optimization objective function value using the following formula (8): (8) Formula (8) is the core objective function of the decomposition coordination algorithm, used to minimize the total cost of hydrogen-electricity coordinated distribution network scheduling under uncertain scenarios. The specific meanings of the parameters are as follows: The overall objective function value represents the total system cost, expressed in yuan (RMB). The minimum value is obtained through iterative optimization. The decision variable vector includes global variables such as power purchase, hydrogen production, energy storage scheduling plan, and carbon emission quota allocation, with units of megawatts (MW), megawatts (MW), megawatts (MW), and tons of carbon dioxide, respectively, and is output through the optimization model; The objective function value of the main problem represents the cost of global decision-making, including electricity purchase cost, hydrogen production cost, energy storage operation cost, and carbon trading cost, etc., in yuan, and is calculated by the main problem model; The value of the subproblem correction term for scenario ω represents the penalty cost for constraint violation (such as power imbalance or carbon emission exceeding limits) in this scenario, expressed in units of yuan, and obtained by solving the subproblem. The scenario index identifies an uncertain scenario (such as fluctuations in the output power of wind and solar power generation, or changes in electricity prices), with the unit being a single scenario. This refers to a set of scenes, the size of which is determined by a representative set of scenes, for example... This represents 10 representative scenarios. Specifically, the system initializes based on this set of representative scenarios. Real-time data is loaded every 15 minutes, and formula (8) is solved by iterative algorithm.

[0093] Therefore, according to the above implementation method, the system can significantly reduce computational complexity through a hierarchical solution strategy, and efficiently handle large-scale scenario uncertainties while ensuring the feasibility of the solution.

[0094] In some embodiments, the above method is applied to a hydrogen-electricity coordinated distribution network; after outputting the optimal scheduling scheme, the method further includes: Adjust the output power of generators, hydrogen electrolysis power, energy storage charging and discharging power, and load distribution in the hydrogen-electricity coordinated distribution network according to the optimal scheduling scheme.

[0095] The adjustment refers to issuing operational commands to each execution unit through the control system to ensure that its operating status is consistent with the optimization results. The hydrogen-electricity coordinated distribution network refers to an integrated energy system encompassing traditional power generation, renewable energy, electrolytic hydrogen production, hydrogen / electricity storage, and diverse loads. Specifically, the system converts the optimization results into control commands through a dispatch command issuance interface (a communication protocol based on the IEC 61850 standard), sending them to each execution unit every 15 minutes. For example, the system issues a command to the gas turbine to adjust its output from 80 MW to 85 MW; and sends a command to the electrolyzer to increase its hydrogen production power from 3 MW to 4 MW.

[0096] The generator's output power is used to balance the power distribution network and regulate carbon flow.

[0097] Specifically, the system uses Automatic Generation Control (AGC) to adjust the output of traditional generator sets such as thermal power and gas turbines in real time to ensure a balance between total power generation and total load (including hydrogen production load). Simultaneously, by altering the output ratio of high-carbon and low-carbon units, it prioritizes the generation of clean energy sources with lower carbon emission factors, thereby regulating the distribution of carbon flow at nodes. For example, at 10:00 AM, reducing the output of coal-fired units by 5 MW while increasing the output of gas turbine units by 5 MW results in a decrease in the system's average carbon emission intensity of 0.1 tons of CO2 per megawatt-hour during that period.

[0098] The power generated by electrolysis for hydrogen production is used to absorb surplus power from renewable energy sources and to adjust the distribution of node carbon footprints.

[0099] Specifically, when the output power of wind and solar power exceeds the load demand, the system increases the power setpoint of the electrolyzer to convert surplus electricity into hydrogen. Because of its zero-carbon nature, the electricity consumed in the hydrogen production process reduces the equivalent carbon emissions on the power supply side, thereby optimizing the carbon footprint of the region where the hydrogen production node is located. For example, during the peak solar power output period at noon (12:00), the electrolysis hydrogen production power is increased from 2 MW to 6 MW to absorb 4 MW of surplus solar power, reducing the carbon footprint of the hydrogen production node by 15%.

[0100] Energy storage charging and discharging power is used to smooth out power fluctuations and participate in the spatiotemporal transfer of carbon footprint.

[0101] Specifically, the system controls battery energy storage to discharge during peak load periods and charge during off-peak periods, smoothing the net load curve. Simultaneously, by charging during periods of low carbon emission intensity (such as peak nighttime wind power) and discharging during periods of high carbon emission intensity (such as peak daytime load), the system shifts the carbon footprint from low-intensity periods to high-intensity periods, reducing overall carbon emissions. For example, during the period of high wind power output and low carbon intensity from 22:00 to 06:00, energy is stored at a power of 3 MW; during the period of higher carbon intensity from 08:00 to 10:00, energy is discharged at a power of 2 MW, using the low-carbon electricity stored overnight for daytime power supply.

[0102] Load allocation is used to optimize carbon flow paths through demand-side response.

[0103] Specifically, the system uses a demand-side management (DSM) platform to send price or incentive signals to responsive industrial or commercial loads, guiding them to consume electricity during periods of lower system carbon intensity. This alters the flow direction, directing more electricity (and its associated carbon flow) to areas with lower carbon intensity. For example, a signal is sent to an aluminum smelter to adjust some of its interruptible production load (approximately 10 MW) from 14:00 (when system carbon intensity is high) to 16:00 (when system carbon intensity is low), thus optimizing the regional carbon flow path.

[0104] Therefore, according to the above implementation method, the system can accurately implement the optimized scheduling scheme into the coordinated operation instructions of physical equipment, and realize multi-objective coordination of power balance, new energy consumption, fine control of carbon footprint and optimization of demand-side resources.

[0105] Figure 2 This is a structural block diagram of a carbon footprint optimization scheduling system for a hydrogen-electricity coordinated distribution network according to an embodiment of the present invention.

[0106] like Figure 2 As shown, the carbon footprint optimization scheduling system for the hydrogen-electricity coordinated distribution network includes: The integrated operation data acquisition module 210 is used to acquire multi-source operation parameters and market data of energy production, energy conversion and storage and load processes.

[0107] The carbon footprint tracking model building module 220 is used to build a carbon footprint tracking model that dynamically allocates node carbon emissions based on multi-source operating parameters and market data.

[0108] The representative scenario set construction module 230 is used to construct a representative scenario set based on the set historical data, which includes multiple uncertain parameters such as the output power, load, electricity price and hydrogen price of wind power and photovoltaic power generation.

[0109] The optimization scheduling model construction module 240 is used to integrate the carbon footprint tracking model and representative scenario set to build a multi-period optimization scheduling model for collaboratively optimizing economic costs and carbon emission costs.

[0110] The optimal scheduling scheme generation module 250 is used to solve the multi-time period optimization scheduling model using the decomposition and coordination algorithm and output the optimal scheduling scheme.

[0111] The specific functions and examples of each module and submodule of the device in this embodiment of the invention can be found in the relevant descriptions of the corresponding steps in the above method embodiments, and will not be repeated here.

[0112] According to embodiments of the present invention, the above-described method of the present invention can be applied to an electronic device and a readable storage medium.

[0113] Figure 3 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0114] like Figure 3 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the electronic device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0115] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of displays, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0116] The computing unit 601 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a carbon footprint optimization scheduling method for a hydrogen-electricity co-distribution network. For example, in some embodiments, a carbon footprint optimization scheduling method for a hydrogen-electricity co-distribution network can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the carbon footprint optimization scheduling method for a hydrogen-electricity co-distribution network described above can be performed. Alternatively, in other embodiments, computing unit 601 may be configured, by any other suitable means (e.g., by means of firmware), to perform a carbon footprint optimization scheduling method for a hydrogen-electricity co-distribution network.

[0117] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0118] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0119] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0120] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT or LCD monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual, auditory, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0121] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0122] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0123] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0124] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this invention should be included within the scope of protection of this invention.

Claims

1. A carbon footprint optimization scheduling method for a hydrogen-electricity coordinated distribution network, characterized in that, include: Acquire multi-source operating parameters and market data from energy production, energy conversion and storage, and load processes; Based on the aforementioned multi-source operating parameters and market data, a carbon footprint tracking model is constructed to achieve dynamic allocation of node carbon emissions. A representative scenario set is constructed based on the set historical data, which includes multiple uncertain parameters such as the output power, load, electricity price, and hydrogen price of wind power and photovoltaic power generation. By integrating the carbon footprint tracking model with the representative scenario set, a multi-period optimization scheduling model for synergistically optimizing economic costs and carbon emission costs is constructed. The decomposition and coordination algorithm is used to solve the multi-period optimization scheduling model and output the optimal scheduling scheme.

2. The method according to claim 1, characterized in that, The multi-source operating parameters include generator output power data, electrolysis hydrogen production power data, energy storage charging and discharging power data, and node load data; the market data includes carbon emission factors, carbon quota data, and energy price data; the acquisition of multi-source operating parameters and market data for energy production, energy conversion and storage, and load processes includes: The multi-source operating parameters are collected by monitoring equipment deployed on the power generation side, load side, and energy storage side; The market data is obtained through external carbon trading market data interfaces and energy market data interfaces.

3. The method according to claim 2, characterized in that, The node is an electrical connection point in the distribution network topology that connects power sources, loads, energy storage, and hydrogen production equipment; the carbon footprint tracking model, which dynamically allocates node carbon emissions based on the multi-source operating parameters and market data, includes: Based on the multi-source operating parameters and market data, the carbon emission intensity of each node is determined; Establish a carbon flow tracing path based on the aforementioned power distribution network topology; Based on the carbon emission intensity and carbon flow tracing path, the carbon footprint tracking model is constructed.

4. The method according to claim 3, characterized in that, The representative scenario set includes multi-dimensional related scenarios that reflect the uncertainty of output power, load, electricity price, and hydrogen price of wind power and photovoltaic power generation. The construction of a representative scene set based on the set historical data includes: The marginal probability distribution of the multiple uncertainty parameters is constructed using the kernel density estimation method; A joint probability distribution is constructed by coupling the marginal probability distributions using a Copula function; Monte Carlo sampling is performed based on the joint probability distribution to generate an initial scene set; The initial scene set is subjected to scene reduction processing to obtain the representative scene set.

5. The method according to claim 4, characterized in that, The integration of the carbon footprint tracking model and the representative scenario set to construct a multi-period optimization scheduling model for synergistically optimizing economic costs and carbon emission costs includes: An objective function is constructed with the goal of minimizing the weighted sum of the economic cost and the carbon emission cost. Set power balance constraints, hydrogen balance constraints, energy storage operation constraints, and carbon emission quota constraints to construct a set of constraints. The node carbon emissions output by the carbon footprint tracking model are used as the input to the carbon emission quota constraint. The representative scenario set is embedded into the objective function and the constraint set to construct the multi-time period optimization scheduling model.

6. The method according to claim 5, characterized in that, The decomposition and coordination algorithm is the Benders decomposition algorithm; The step of using a decomposition and coordination algorithm to solve the multi-time-period optimization scheduling model and outputting the optimal scheduling scheme includes: The multi-period optimization scheduling model is decomposed into a main problem and multiple scenario sub-problems; Solve the main problem to obtain the global scheduling decision variables; Solve each sub-problem of the scenario, verify the constraints, and generate feedback information; Based on the feedback information, iteratively update the main problem and the sub-problems of the scenario until the convergence condition is met, and output the optimal scheduling scheme.

7. The method according to claim 6, characterized in that, The method is applied to hydrogen-electricity co-distribution networks; After outputting the optimal scheduling scheme, the method further includes: The output power of generators, the electrolysis hydrogen production power, the energy storage charging and discharging power, and the load distribution in the hydrogen-electricity coordinated distribution network are adjusted according to the optimal scheduling scheme. The generator's output power is used to balance the power distribution network and regulate carbon flow. The hydrogen production capacity from electrolysis is used to absorb surplus renewable energy power and adjust the distribution of node carbon footprint. The energy storage charging and discharging power is used to smooth out power fluctuations and participate in the spatiotemporal transfer of carbon footprint. The load allocation is used to optimize carbon flow paths through demand-side response.

8. A carbon footprint optimization scheduling system for a hydrogen-electricity coordinated distribution network, characterized in that, include: The integrated operation data acquisition module is used to acquire multi-source operation parameters and market data from the energy production, energy conversion and storage, and load processes. The carbon footprint tracking model building module is used to build a carbon footprint tracking model that dynamically allocates node carbon emissions based on the multi-source operating parameters and market data. The representative scenario set construction module is used to construct a representative scenario set based on set historical data, which includes multiple uncertain parameters such as the output power, load, electricity price, and hydrogen price of wind power and photovoltaic power generation. An optimized scheduling model construction module is used to integrate the carbon footprint tracking model with the representative scenario set to construct a multi-period optimized scheduling model for collaboratively optimizing economic costs and carbon emission costs. The optimal scheduling scheme generation module is used to solve the multi-time period optimization scheduling model using a decomposition and coordination algorithm and output the optimal scheduling scheme.

9. An electronic device, characterized in that, include: At least one processor; as well as The memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, Computer instructions are used to cause a computer to perform the method according to any one of claims 1-7.

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