Hydrogen-electricity collaborative power distribution network dispatching optimization method, system, equipment and medium

Abstract

The invention relates to the technical field of power grid dispatching optimization, and provides a dispatching optimization method, system, equipment and medium for a hydrogen-electricity cooperative power distribution network, and the method comprises the steps: building a power grid uncertainty model based on a built hydrogen-electricity cooperative power distribution network model; generating a plurality of typical operation scenes according to the power grid uncertainty model, and solving a scene optimal scheduling strategy of each typical operation scene according to the power distribution network scheduling optimization model; respectively obtaining an operation cost minimum point and a carbon emission minimum point on a power grid operation cost boundary and a carbon emission feasible boundary obtained by summarizing and analyzing the optimal scheduling strategies of all scenes; and generating a target power grid dispatching scheme according to the operation cost minimum point and the carbon emission minimum point. According to the method, the reliability, the safety and the economical efficiency of power grid operation can be effectively improved on the basis of a multi-source uncertainty modeling mechanism in combination with a power grid operation cost and carbon emission cost balance optimization mechanism integrating the energy transfer coupling relationship among the electrolytic cell, the fuel cell and the hydrogen storage equipment.

Description

Technical Field

[0001] This invention relates to the field of power distribution network operation optimization technology, and in particular to a hydrogen-electricity coordinated power distribution network scheduling optimization method, system, equipment and medium. Background Technology

[0002] With the increasing penetration of distributed energy, photovoltaic power generation, and hydrogen energy systems in new power distribution networks, a diversified and integrated energy structure centered on hydrogen-electricity synergy is gradually forming. Hydrogen energy, as a medium with both energy storage and supply functions, plays a crucial role in mitigating the volatility of renewable energy and improving the operational flexibility and low-carbon characteristics of microgrids. However, existing power distribution networks connected to hydrogen energy systems face challenges beyond just photovoltaic output being affected by natural conditions and load exhibiting strong time-varying characteristics. They also encounter the problem of complex dynamic coupling relationships between fuel cells, electrolyzers, and hydrogen storage equipment. This lack of effective means to characterize the economically feasible domain and carbon emission feasible domain under multi-source uncertainty conditions makes it impossible to formulate reasonable power distribution network operation schemes to ensure the operational safety and carbon economy of hydrogen-electricity synergistic power distribution networks in uncertain environments. Summary of the Invention

[0003] The purpose of this invention is to provide a method for optimizing the scheduling of hydrogen-electricity coordinated distribution networks. This method can construct various typical operating scenarios by performing uncertainty modeling on different resource elements within the distribution network. Then, based on the principle of balancing operating costs and carbon emission costs, and integrating the energy transfer coupling relationship between electrolyzers, fuel cells, and hydrogen storage devices, a distribution network scheduling optimization model is constructed. This model is used to analyze scenario scheduling strategies and operating boundaries to obtain a grid scheduling optimization mechanism for the target grid scheduling scheme. This improves the rationality and scientific nature of the grid operation scheme formulation, thereby effectively enhancing the reliability, safety, and economy of hydrogen-electricity coordinated grid operation.

[0004] To achieve the above objectives, it is necessary to provide a method, system, equipment, and medium for optimizing the scheduling of hydrogen-electricity coordinated power distribution networks.

[0005] In a first aspect, embodiments of the present invention provide a method for optimizing the scheduling of a hydrogen-electricity coordinated distribution network, the method comprising: A hydrogen-electricity integrated distribution network model is constructed, and a power grid uncertainty model is built based on the hydrogen-electricity integrated distribution network model; Based on the power grid uncertainty model, multiple typical operating scenarios are generated, and the optimal scheduling strategy for each typical operating scenario is solved according to the pre-constructed distribution network scheduling optimization model. The distribution network scheduling optimization model is constructed based on the principle of balancing operating costs and carbon emission costs. Based on the summarization and analysis of the optimal scheduling strategies for all the scenarios, the power grid operating cost boundary and the carbon emission feasible boundary are obtained, and the minimum operating cost point and the minimum carbon emission point on the power grid operating cost boundary and the carbon emission feasible boundary are obtained respectively. A target power grid dispatch scheme is generated based on the minimum operating cost point and the minimum carbon emission point.

[0006] Furthermore, the construction steps of the hydrogen-electricity coordinated distribution network model include: A collaborative distribution network topology is constructed based on a preset set of distribution network nodes; the preset set of distribution network nodes includes a subset of photovoltaic power generation nodes, a subset of fuel cell nodes, a subset of electrolyzer nodes, a subset of hydrogen storage nodes, a subset of load nodes, and a subset of grid access nodes; Based on a preset set of node operating parameters, the operating parameters of each node in the collaborative distribution network topology are configured to generate the hydrogen-electric collaborative distribution network model.

[0007] Furthermore, the power grid uncertainty model includes the photovoltaic output model of each photovoltaic node, the load demand model of each load node, the hydrogen production model of each electrolyzer node, and the energy storage state change model of each fuel cell node in the hydrogen-electricity coordinated distribution network model. The steps for constructing a power grid uncertainty model based on the hydrogen-electricity integrated distribution network model include: Based on the historical solar irradiance dataset of each photovoltaic node, a corresponding photovoltaic power output model is constructed based on the Beta distribution. Based on the historical load stability sequence of each load node, a corresponding load demand model is constructed using an autoregressive integral moving average model. Based on the typical daily efficiency curves and corresponding historical power sequences of each electrolyzer node, a corresponding hydrogen production model is constructed based on the energy conservation relationship. Operational uncertainty analysis is performed on the historical operating data of each fuel cell node to obtain the corresponding operational random disturbance terms. Based on the operational random disturbance terms, a corresponding energy storage state change model is constructed based on the energy conservation relationship. The operational random disturbance terms include efficiency disturbance terms, self-loss disturbance terms, control execution error terms, and capacity offset terms.

[0008] Furthermore, the construction steps of the distribution network dispatch optimization model include: Based on the cumulative values ​​of electricity purchase cost and carbon emission cost of the hydrogen-electricity co-operated distribution network at each scheduling time, the total operating cost of the distribution network is obtained, and a scheduling optimization objective function is constructed with minimizing the total operating cost of the distribution network as the optimization objective. Based on the aforementioned scheduling optimization objective function and preset scheduling optimization constraints, the distribution network scheduling optimization model is obtained; the preset scheduling optimization constraints include energy balance constraints, photovoltaic output binding constraints, fuel cell output constraints, electrolyzer energy supply constraints, hydrogen storage dynamic balance constraints, and grid power purchase constraints.

[0009] Furthermore, the optimal scheduling strategy for the scenario includes the target electricity purchase, total grid operating cost, total carbon emissions, and the corresponding scenario scheduling scheme; The step of summarizing and analyzing the optimal scheduling strategies for all the scenarios to obtain the power grid operating cost boundary and the carbon emission feasible boundary includes: The target electricity purchase in the optimal scheduling strategy for each scenario is combined with the corresponding total grid operating cost and total carbon emissions to generate the corresponding grid operating domain point and carbon emission feasible domain point. All the power grid operation domain points and the carbon emission feasible domain points are classified and summarized to generate a power grid operation domain point set and a carbon emission feasible domain point set; Based on the power grid operation domain point set and the carbon emission feasible domain point set, feasible solution boundaries are extracted using the convex hull algorithm to obtain the corresponding power grid operation cost boundary and carbon emission feasible boundary.

[0010] Furthermore, the step of generating the target power grid dispatch scheme based on the minimum operating cost point and the minimum carbon emission point includes: Obtain the minimum cost scheduling scheme and the minimum carbon emission scheduling scheme corresponding to the minimum operating cost point and the minimum carbon emission point, respectively; The complete scheduling scheme is obtained by weighted summing the minimum cost scheduling scheme and the minimum carbon emission scheduling scheme. Based on the uncertainty disturbances in power grid operation, the complete scheduling scheme is corrected for errors to obtain the target power grid scheduling scheme; the uncertainty disturbances in power grid operation are obtained based on the power grid uncertainty model.

[0011] Furthermore, the step of correcting the error of the complete dispatching scheme based on the uncertainty disturbance of power grid operation to obtain the target power grid dispatching scheme includes: Based on the scenario scheduling schemes in all the optimal scheduling strategies for the scenarios, a multi-scenario average scheduling scheme is obtained; Based on the uncertainties in grid operation, an error adjustment factor is calculated, and based on the error adjustment factor, a weighted sum is performed on the multi-scenario average dispatch scheme and the complete dispatch scheme to obtain a robust dispatch scheme; the uncertainties in grid operation include photovoltaic output prediction deviation, load demand prediction deviation, and hydrogen production prediction deviation; The robust scheduling scheme is projected onto a preset grid operation feasible region to obtain the target grid scheduling scheme.

[0012] Secondly, embodiments of the present invention provide a hydrogen-electricity coordinated distribution network scheduling optimization system, the system comprising: An uncertainty modeling module is used to construct a hydrogen-electricity integrated distribution network model and, based on the hydrogen-electricity integrated distribution network model, to construct a power grid uncertainty model; The scenario scheduling analysis module is used to generate multiple typical operating scenarios based on the power grid uncertainty model, and to solve the optimal scheduling strategy for each typical operating scenario based on the pre-built distribution network scheduling optimization model; the distribution network scheduling optimization model is constructed based on the principle of balancing operating costs and carbon emission costs. The boundary analysis module is used to summarize and analyze the optimal scheduling strategies for all the scenarios to obtain the power grid operating cost boundary and the carbon emission feasible boundary, and to obtain the minimum operating cost point and the minimum carbon emission point on the power grid operating cost boundary and the carbon emission feasible boundary, respectively. The scheduling scheme generation module is used to generate a target power grid scheduling scheme based on the minimum operating cost point and the minimum carbon emission point.

[0013] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0015] This invention provides a method, system, equipment, and medium for optimizing the scheduling of a hydrogen-electricity coordinated distribution network. The method constructs a hydrogen-electricity coordinated distribution network model and, based on this model, builds a grid uncertainty model. According to the grid uncertainty model, multiple typical operating scenarios are generated, and the optimal scheduling strategy for each typical operating scenario is solved using the pre-constructed distribution network scheduling optimization model. The distribution network scheduling optimization model is constructed based on the principle of balancing operating costs and carbon emission costs. The optimal scheduling strategies for all scenarios are summarized and analyzed to obtain the grid operating cost boundary and the carbon emission feasible boundary, and the minimum operating cost point and the minimum carbon emission point on the grid operating cost boundary and the carbon emission feasible boundary are obtained respectively. Based on the minimum operating cost point and the minimum carbon emission point, a target grid scheduling scheme is generated. Compared with existing technologies, this hydrogen-electricity coordinated distribution network scheduling optimization method, based on a multi-source uncertainty modeling mechanism and combined with a grid operating cost and carbon emission cost balancing optimization mechanism that integrates the energy transfer coupling relationship between electrolyzers, fuel cells, and hydrogen storage devices, effectively improves the reliability, safety, and economy of grid operation. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the hydrogen-electricity coordinated distribution network scheduling optimization method in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the hydrogen-electricity coordinated distribution network scheduling optimization system in an embodiment of the present invention; Figure 3 This is an internal structural diagram of the computer device in an embodiment of the present invention; The attached figures are labeled as follows: 1. Uncertainty modeling module; 2. Scene scheduling analysis module; 3. Boundary analysis module; 4. Scheduling scheme generation module. Detailed Implementation

[0017] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are only part of the embodiments of this invention and are used to illustrate the invention, but are not intended to limit the scope of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0018] In one embodiment, such as Figure 1 As shown, a hydrogen-electricity coordinated distribution network scheduling optimization method is provided, including: S11. Construct a hydrogen-electricity integrated distribution network model, and based on the hydrogen-electricity integrated distribution network model, construct a power grid uncertainty model; wherein, the hydrogen-electricity integrated distribution network model can be understood as a distribution network operation system composed of photovoltaic power sources, fuel cells, electrolyzers, hydrogen storage devices, and electrical loads. Specifically, the construction steps of the hydrogen-electricity integrated distribution network model include: Based on the preset set of distribution network nodes, a collaborative distribution network topology is constructed. The preset set of distribution network nodes can be understood as the set of all network nodes required for the actual hydrogen-electric collaborative distribution network, including a subset of photovoltaic power generation nodes, a subset of fuel cell nodes, a subset of electrolyzer nodes, a subset of hydrogen storage nodes, a subset of load nodes, and a subset of grid access nodes.

[0019] Based on a preset set of node operating parameters, the operating parameters of each node in the collaborative distribution network topology are configured to generate the hydrogen-electric collaborative distribution network model. The preset set of node operating parameters can be understood as a dataset of operating parameters of each node in the preset distribution network node set. Specifically, it includes the photovoltaic capacity of each photovoltaic power generation node, the output range and efficiency of each fuel cell node, the maximum power and electrolysis power of each electrolyzer node, the hydrogen storage capacity and initial hydrogen quantity of each hydrogen storage node, the grid purchase limit and unit electricity price of each grid access node, and the carbon emission factor and unit carbon price of each fuel cell node and each grid access node.

[0020] After obtaining the hydrogen-electricity integrated distribution network model through the above methods and steps, it is necessary to configure relevant time parameters according to the actual operation cycle of the distribution network. For example, if the scheduling cycle is set to T and the time granularity is on the hourly level, the corresponding discrete time set for scheduling is {1,2,3,.....,T}. The photovoltaic output, fuel cell output, electrolyzer hydrogen production power, grid electricity purchase, and load demand in the sequentially constructed hydrogen-electricity integrated distribution network model are the optimization variables for the actual distribution network scheduling optimization problem, and the data in the above-mentioned preset node operating parameter set serve as the constraint basis for subsequent scheduling optimization.

[0021] After constructing the hydrogen-electricity coordinated distribution network model that requires scheduling optimization using the above method, uncertainty modeling can be performed on key elements such as photovoltaic output, load demand, hydrogen production, and hydrogen storage status in the hydrogen-electricity coordinated distribution network model. Based on the obtained grid uncertainty model, a variety of typical operating scenarios can be generated for subsequent scheduling optimization analysis. Specifically, the power grid uncertainty model includes the photovoltaic output model of each photovoltaic node, the load demand model of each load node, the hydrogen production model of each electrolyzer node, and the energy storage state change model of each fuel cell node in the hydrogen-electricity coordinated distribution network model. The photovoltaic output model is a power output prediction model constructed based on the Beta distribution, considering the significant fluctuations in photovoltaic power output over time and weather conditions. The load demand model is a load demand prediction model based on the ARIMA model with added Gaussian perturbations. The hydrogen production model is a hydrogen production prediction model constructed based on the operating condition-dependent efficiency curve (typical daily efficiency curve) of the electrolyzer and the energy conservation principle of the hydrogen production process. The energy storage state change model is a storage state prediction model constructed based on a stochastic process. Specifically, the steps for constructing the power grid uncertainty model based on the hydrogen-electricity coordinated distribution network model include: Based on the historical solar irradiance datasets of each photovoltaic (PV) node, a corresponding PV power output model is constructed using the Beta distribution. The historical solar irradiance dataset includes solar irradiance data for typical days at different nodes in the distribution network under different scenarios. In practical applications, due to differences in irradiance data between different PV nodes under the same scenario and between the same PV node and different scenarios, the expression form of the PV power output model obtained for each PV node will differ. Furthermore, the PV power output model corresponding to each PV node is further subdivided into scenario-based PV power output models for different scenarios. However, the construction steps for each scenario-based PV power output model are consistent. Specifically, the construction process for the PV power output model corresponding to each PV node may include: The solar irradiance data in the historical solar irradiance dataset are normalized by using the local clear-sky irradiance or the historical maximum quantile to determine the theoretical maximum irradiance, resulting in dimensionless normalized irradiance data, represented as follows: In the formula, and Distributed power generation nodes n At any moment h Solar irradiance data and maximum irradiance; For distributed power generation nodes n At any moment h The normalized irradiance data are dimensionless values ​​in the range [0,1], which facilitates subsequent fitting and sampling. The daily lighting schedule is divided into several time periods according to the scene (each time period corresponds to one scene), and then... Inside, it is assumed that the normalized irradiance follows a Beta distribution. , and These are shape parameters, describing the rate of increase and the rate of decrease of the distribution, respectively. Based on the normalized irradiance data for each scene, the corresponding sample mean is calculated. With variance And the estimated values ​​of the shape parameters are obtained through the method of moments: , In the formula, and These are estimated values ​​for the shape parameters; After obtaining the Beta distribution parameters for each scenario through the above steps, the corresponding irradiance distribution model for that scenario can be obtained. It should be noted that, to better reflect seasonality and meteorological changes, the Beta distribution parameters can be extended by introducing months or weather types. For example, the Beta distribution parameters can be extended to... ,in, Indexes for months or weather types can be obtained by dynamically correcting the aforementioned Beta distribution parameters using external variables such as solar altitude angle, cloud cover, and temperature. First, based on historical data, corresponding distribution parameter sets are fitted for different weather types (such as sunny, cloudy, and rainy). When generating operational scenarios, Monte Carlo sampling is performed on the distribution parameters corresponding to the weather type that matches the specific weather forecast for the scheduling day. If the forecast is "cloudy," the parameters fitted based on historical cloudy data are directly used to construct the solar irradiance distribution model for that scenario. This makes the generated photovoltaic power output scenario more consistent with the expected weather conditions, significantly improving the accuracy of the model and the robustness of the scheduling scheme.

[0022] After obtaining the irradiance distribution model for each scenario, the corresponding photovoltaic output model for that scenario can be obtained based on the photovoltaic power conversion principle, which can be expressed as: in, For photovoltaic power generation nodes n The effective area of ​​the photovoltaic array; For photovoltaic power generation nodes n exist h The temperature correction efficiency at any given time can be set based on experience; and In the scene respectively sPhotovoltaic power generation nodes under certain conditions n exist h The corresponding solar irradiance and photovoltaic output at that moment; The required photovoltaic output model can be obtained by summarizing the photovoltaic output models of all scenarios for the same photovoltaic power generation node.

[0023] Based on the historical load stationary sequences of each load node, a corresponding load demand model is constructed using an autoregressive integral moving average model. The historical load stationary sequence can be understood as a load differential sequence obtained after performing stationarity checks and differential processing on the historical active power load measurement data corresponding to the load node. This sequence is used to construct the historical load time series data for the load demand model. The acquisition process includes: exporting the historical active power sampling sequences of the load nodes over the past few years (including active power sequences for typical days under different scenarios) from the distribution network energy management system; cleaning the sequence to handle missing values ​​and outliers; and then using the extended Dickey-Fuller test to determine the stationarity of the sequence. If the test result indicates that the sequence is non-stationary, a d-order differential operation is performed on the sequence. This operation involves recursively subtracting the load value from the previous time step from the load value of the next time step until the differential sequence passes the stationarity check again. The final stationary sequence obtained is the required historical load stationary sequence. Specifically, in the historical load stationary sequence... t The load differential value corresponding to a given time can be expressed as: in, For the shift operator; for Order difference operation; This represents the load differential value at time t in the historical load stationary sequence. t represents the actual load value of load node m at time t; T represents the total number of scheduling times.

[0024] In practical applications, the process of constructing a corresponding load demand model based on the historical load stationary sequences of each load node may include: The autocorrelation function and partial autocorrelation function are calculated for historical stationary load series, and the autoregression order is preliminarily determined based on its truncation characteristics. With moving average order Establish an autoregressive integral moving average model : In the formula, in, For the autoregressive part; This is the moving average portion; This is the white noise term.

[0025] The parameters in the above autoregressive integral moving average model are obtained through maximum likelihood estimation. and After obtaining the estimated values, the corresponding model residuals are then analyzed. Independence and white noise tests are performed. If the residuals exhibit autocorrelation, the order is readjusted. If the residuals approximate white noise and the variance is stable, the model is considered to have passed the fit test, and the corresponding load demand model is obtained.

[0026] Based on the typical daily efficiency curves and corresponding historical power sequences of each electrolyzer node, a hydrogen production model is constructed based on the energy conservation principle. The typical daily efficiency curves for different scenarios are obtained from performance curves provided by electrolyzer equipment manufacturers, or by fitting and regressing long-term historical operating data (such as input power, hydrogen production, and temperature) of the node. These curves reflect the system efficiency changes of the electrolyzer under different operating loads. The historical power sequences are power sequences for different typical daily scenarios. The corresponding hydrogen production model can be understood as a model of the relationship between power and output in the electrolyzer hydrogen production process. The specific construction process includes: Assume a certain electrolytic cell node is in the scenario s The typical daily efficiency curve is as follows The power sequence is The minimum stable output is ;in, For the scene s Lower electrolytic cell node l exist t Relative load rate at any given time Electrolytic cell node l exist t The temperature of the electrolytic cell at any given time. Electrolytic cell node l exist t Constant operational pressure.

[0027] For each node Time step t and scene s First, determine whether the output of the electrolytic cell is within the effective range based on the following expression, and then perform power preprocessing: In the formula, and Scenes s Lower electrolytic cell node l exist t The actual power value at any given moment and the corresponding preprocessed value; Based on relative load rate The Faraday efficiency under this operating condition can be obtained by retrieving the equipment performance curve. Furthermore, combining this with a typical daily efficiency curve... The desired system efficiency can then be obtained. .

[0028] Based on the law of conservation of energy, the expression for the instantaneous hydrogen production rate at each node of the electrolyzer is obtained: In the formula, This is the lower heating value of hydrogen. For the scene s Lower electrolytic cell node l exist t Instantaneous hydrogen production rate at any given moment.

[0029] It should be noted that in the above model estimation, if the standby power consumption of the electrolyzer node is considered... It can be included in the energy consumption calculation of the electrolyzer node, but it is not included in the hydrogen production calculation, and will not be discussed in detail here.

[0030] Based on historical operating data of the fuel cell node, operational uncertainty analysis is performed to obtain the corresponding operational stochastic disturbance terms. Then, based on these stochastic disturbance terms and the energy conservation principle, a corresponding energy storage state change model is constructed. Under operating conditions, the fuel cell node... e The rated efficiency is When the scene When the actual power is affected by temperature changes, aging degree, and random disturbances, its actual efficiency is: in, For fuel cell nodes e In the scene Next moment t The actual efficiency; For fuel cell nodes e In the scene s Next moment t Temperature; For reference temperature; This refers to the service life or operational degradation indicators of fuel cells. and These are calibration coefficients; For fuel cell nodes e In the scene Next moment t The zero-mean random perturbation is the efficiency perturbation term.

[0031] fuel cell node e The nominal self-loss under rated conditions is denoted as In the scene ,time Under these conditions, the self-loss load variation and random noise can be expressed as: in, For fuel cell nodes e In the scene Next moment t The actual self-loss; For fuel cell nodes e In the scene Next moment t The actual output power; For fuel cell nodes e The nominal rated power; The load correlation coefficient; This is a random disturbance, specifically a self-depleting disturbance term.

[0032] After determining the above-mentioned stochastic disturbance term, the corresponding stochastic difference equation can be constructed based on the energy conservation relationship to obtain the required energy storage state change model, which is expressed as: in, For fuel cell nodes self-discharge rate, and Fuel cell nodes The charging efficiency and discharging efficiency; and Scenes Next fuel cell node At any moment The charging power and discharging power; and Scenes Next fuel cell node At any moment The efficiency disturbance term and the self-loss disturbance term; For time step; and Scenes Next fuel cell node At any moment and in time The corresponding energy storage status. The scheduling scheme generated based on this model can proactively adapt to equipment performance fluctuations, resist control errors, and mitigate capacity decay, thereby improving the reliability, economy, and security of the distribution network operation.

[0033] This embodiment employs a power grid uncertainty modeling mechanism based on core elements such as photovoltaic output, load demand, hydrogen production, and hydrogen storage dynamics to analyze operational uncertainties. This mechanism can construct a high-dimensional uncertainty input space that closely reflects actual operating conditions, and can be used to generate diverse and multi-granular typical scenario samples, providing a reliable analytical foundation for subsequent scheduling optimization.

[0034] S12. Based on the power grid uncertainty model, generate a variety of typical operating scenarios, and solve the optimal scheduling strategy for each typical operating scenario according to the pre-built distribution network scheduling optimization model; the distribution network scheduling optimization model is constructed based on the principle of balancing operating costs and carbon emission costs.

[0035] The typical operating scenario in this embodiment is generated based on the aforementioned power grid uncertainty model, and the specific generation process may include: Based on the solar irradiance distribution model corresponding to the solar power output model of each solar power generation node in each scenario and at each time period, Monte Carlo sampling is performed to generate several solar irradiance sampling samples. After the obtained solar irradiance sampling samples are inversely normalized based on the corresponding maximum irradiance to obtain the true irradiance, they are substituted into the corresponding solar power output model to obtain the solar power output samples in each scenario and at each time period. By collecting all the solar power output samples, multi-scenario solar power output sample data can be obtained, which can be used as solar power output input data for subsequent multi-scenario scheduling optimization.

[0036] Based on the load demand models of each load node, the load demand in future scheduling cycles is predicted to generate a basic load forecast sequence. Then, using the basic load forecast sequence as a benchmark, normal disturbance terms under different scenarios are introduced, and the basic load forecast sequence is corrected using the following formula to obtain multi-scenario load demand sample data: In the formula, For load nodes m At any moment t Basic load forecast below; For the scene i Download node m At any moment t Load sample data below; For the scene i Download node m At any moment t The normal disturbance term has a mean of 0 and a variance determined based on the statistical analysis of historical load demand forecasting errors.

[0037] The process of obtaining multi-scenario hydrogen production capacity sample data based on the aforementioned hydrogen production model, and the process of obtaining multi-scenario energy storage state change sample data based on the energy storage state change model, can be found in the model construction process description above, and will not be detailed here. Based on the multi-scenario sample set obtained above, which includes sample data of photovoltaic output, load demand, hydrogen production capacity, and energy storage state change across various scenarios with multiple random disturbances, the impact of various fluctuation factors can be precisely characterized, providing reliable model input data for subsequent scenario scheduling optimization.

[0038] The power grid dispatch optimization model in this embodiment can be understood as a power grid collaborative dispatch model constructed considering the energy transfer coupling relationship between electrolyzers, fuel cells, and hydrogen storage equipment, based on energy balance constraints, photovoltaic output binding constraints, upper and lower limits of fuel cell output, energy supply constraints of electrolyzers, dynamic balance of hydrogen storage equipment, and node output constraints, while also taking into account the need to reduce electricity purchase costs and carbon emission costs. Specifically, the construction steps of the power grid dispatch optimization model include: Based on the cumulative values ​​of electricity purchase cost and carbon emission cost of the hydrogen-electricity co-operated distribution network at each scheduling time, the total operating cost of the distribution network is obtained. A scheduling optimization objective function is then constructed with minimizing this total operating cost as the optimization objective. The scheduling optimization objective function is expressed as follows: in, For scheduling time t The grid purchase price of electricity is expressed in yuan / kWh; For scheduling time t Total purchased power, in kWh; This is the unit carbon price, expressed in yuan / ton; For scheduling time t The total carbon emissions, which is the sum of the carbon emissions from photovoltaic power generation nodes, fuel cell nodes, electrolyzer nodes and hydrogen storage nodes, are expressed in tons. For power supply nodes The output power is expressed in kWh. For power supply nodes The carbon emission factor is measured in tons per kWh.

[0039] Based on the aforementioned scheduling optimization objective function and preset scheduling optimization constraints, the distribution network scheduling optimization model is obtained; wherein, the preset scheduling optimization constraints include energy balance constraints, photovoltaic output binding constraints, fuel cell output constraints, electrolyzer energy supply constraints, hydrogen storage dynamic balance constraints, and grid power purchase constraints, specifically expressed as follows: 1) Energy balance constraint, understood as each scheduling moment The energy balance that a distribution network needs to satisfy between its total system output and total load can be expressed as: in, It is the scheduling time Total photovoltaic output of all photovoltaic power generation nodes; It is the scheduling time Total output of all fuel cell nodes; It is the scheduling time Total power of all electrolytic cell nodes; It is the scheduling time The electricity purchased by all grid connection nodes; For scheduling time The total load demand of all load nodes.

[0040] 2) Photovoltaic output binding constraints can be understood as the effective range of the total photovoltaic output of all photovoltaic power generation nodes. The photovoltaic output of each photovoltaic power generation node is directly taken from the sample data already generated in the scenario, and is expressed as: In the formula, For the scene s Next photovoltaic power generation node n During scheduling t Photovoltaic power output; N This represents the total number of photovoltaic power generation nodes. It is a scene Next scheduling time Total photovoltaic output of all photovoltaic power generation nodes.

[0041] 3) Fuel cell output constraint, which can be understood as the effective range of the total output of all fuel cell nodes, is expressed as: In the formula, For all fuel cell nodes at the scheduling time t Total output value, and This represents the lower and upper limits of the total output of all fuel cell nodes.

[0042] 4) The energy supply constraint of the electrolytic cell can be understood as the effective range of the total output of all electrolytic cell nodes, expressed as: In the formula, For all electrolyzer nodes at the scheduling time t Total output; This represents the upper limit of the total output of all electrolytic cell nodes.

[0043] 5) The dynamic equilibrium constraint of hydrogen storage can be understood as the dynamic change relationship of the total hydrogen storage capacity of all hydrogen storage nodes, expressed as: In the formula, and These represent the lower and upper limits of the total hydrogen storage capacity of all hydrogen storage nodes, respectively. and These are hydrogen production efficiency and hydrogen supply efficiency, respectively.

[0044] 6) The power purchase constraint of the power grid can be understood as the effective range of the total power purchase of all distribution network nodes, expressed as: In the formula, The total power purchased by all distribution network nodes at dispatch time t; This is the upper limit of the total power purchase capacity for all distribution network nodes.

[0045] In this embodiment, carbon emission costs are explicitly introduced into the scheduling objective function through carbon emission intensity and carbon price, and together with electricity purchase costs, a dual-objective optimization mechanism is constructed. This effectively characterizes the trade-off between low-carbon performance and operational economy in scheduling strategies under dual carbon constraints. Simultaneously, based on the dynamic coupling characteristics of fuel cells, electrolyzers, and hydrogen storage devices, a complete dynamic collaborative control and multi-cycle scheduling modeling mechanism among multiple devices within a hydrogen-powered coordinated distribution network can be established, encompassing energy balance and equipment operation constraints. This effectively enhances the modeling accuracy of hydrogen energy device operation behavior, enabling effective simulation of the real operating mechanism of the hydrogen-electricity coordinated distribution network. Consequently, it provides a reliable guarantee for the scientific and rational nature of subsequent distribution network optimization scheduling.

[0046] The distribution network scheduling optimization model constructed through the above methods and steps is actually a mixed-integer linear optimization model. When performing scheduling analysis on various typical operating scenarios based on this model, the photovoltaic output data, load demand data, hydrogen production data, and energy storage status change data under each typical operating scenario can be used as data inputs to solve the model and obtain the scenario-optimal scheduling strategy for each typical operating scenario. The scenario-optimal scheduling strategy includes a complete set of time-series scheduling schemes and their corresponding comprehensive evaluation indicators. The time-series scheduling scheme consists of the planned curves of all controllable equipment within the scheduling period, including the time series of total grid power purchase, photovoltaic power generation output, fuel cell output, electrolyzer output, and hydrogen storage status. The comprehensive evaluation indicators include the target power purchase, total grid operating cost (the sum of power purchase cost and carbon emission cost), and total carbon emissions under the scenario. These strategies together form the basis for subsequent boundary analysis and scheme generation.

[0047] S13. Summarize and analyze the optimal scheduling strategies for all the scenarios to obtain the grid operation cost boundary and the carbon emission feasible boundary, and obtain the minimum operation cost point and the minimum carbon emission point on the grid operation cost boundary and the carbon emission feasible boundary, respectively; wherein, the grid operation cost boundary can be understood as the boundary capability obtained by analyzing the optimal electricity purchase and total grid operation cost under different typical operation scenarios, and the carbon emission feasible boundary can be understood as the boundary capability obtained by analyzing the optimal electricity purchase and total carbon emissions under different typical operation scenarios.

[0048] Specifically, the step of summarizing and analyzing the optimal scheduling strategies for all the scenarios to obtain the power grid operating cost boundary and the carbon emission feasible boundary includes: The target electricity purchase in the optimal scheduling strategy for each scenario is combined with the corresponding total grid operating cost and total carbon emissions to generate corresponding grid operation domain points and carbon emission feasible domain points; that is, the grid operation domain point corresponding to typical operation scenario i is... The feasible carbon emission zone is , , , and These are the target electricity purchase volume, total grid operating cost, and total carbon emissions for scenario i, respectively. I This is a collection of typical operating scenarios.

[0049] All the power grid operation domain points and the carbon emission feasible domain points are classified and summarized to generate a power grid operation domain point set and a carbon emission feasible domain point set; that is, the power grid operation domain point set is the set of power grid operation domain points for all typical operation scenarios, and the carbon emission feasible domain point set is the set of carbon emission feasible domain points for all typical operation scenarios.

[0050] Based on the power grid operating domain point set and the carbon emission feasible domain point set, feasible solution boundaries are extracted using the convex hull algorithm to obtain the corresponding power grid operating cost boundary and carbon emission feasible boundary. The convex hull algorithm is an algorithm for generating the minimum convex polygon from the base input point set data; based on this algorithm, the convex hull boundary corresponding to the point set can be extracted. In this embodiment, the Qhull algorithm is preferably used, and the corresponding power grid operating cost boundary and carbon emission feasible boundary can be expressed as follows: in, This represents the smallest convex polygon generated using the Qhull algorithm; and These are the grid operating cost boundary and the carbon emission feasible boundary, respectively.

[0051] The obtained grid operating cost boundary and carbon emission feasible boundary can also be visualized in a two-dimensional graphical manner to reflect the system's dispatch strategy boundary capability under multi-source uncertainty (the lowest achievable operating cost and lowest carbon emission level under different power purchase conditions), effectively assisting in dispatch strategy selection and boundary capability assessment. The grid operating domain point and carbon emission feasible domain point construction method based on dispatch optimization strategies under multiple typical operating scenarios provided in this embodiment combines the total power purchase, total grid operating cost, and total carbon emissions in the optimal solutions of multiple scenarios, and uses a convex hull algorithm to identify the optimal solution boundary. This provides a reliable screening basis for obtaining interpretable and robust dispatch schemes, overcoming the limitations of traditional single optimal solution methods under uncertainty conditions.

[0052] S14. Based on the minimum operating cost point and the minimum carbon emission point, generate a target power grid dispatching scheme; wherein, the minimum operating cost point can be understood as a state that characterizes the lowest (economically optimal) operating cost of the distribution network, expressed as: In the formula, The operating cost in scenario y; The scenario corresponding to the point of minimum operating cost; The minimum carbon emission point can be understood as the low-carbon limit operating state of the power distribution network, represented as: In the formula, For the scene z Carbon emissions; This represents the scenario corresponding to the point of minimum carbon emissions.

[0053] To ensure the scientific validity and rationality of the target power grid dispatching scheme, this embodiment preferably performs a comprehensive analysis of the dispatching schemes corresponding to the minimum operating cost point and the minimum carbon emission point, and then obtains the final dispatching scheme by introducing an error adjustment factor that considers uncertainty disturbances; specifically, the step of generating the target power grid dispatching scheme based on the minimum operating cost point and the minimum carbon emission point includes: Obtain the minimum cost scheduling scheme and the minimum carbon emission scheduling scheme corresponding to the minimum operating cost point and the minimum carbon emission point, respectively; wherein, the minimum cost scheduling scheme and the minimum carbon emission scheduling scheme can be expressed as follows: and ,and , , and These represent typical operating scenarios corresponding to the point of minimum operating cost. Scheduling time t Next photovoltaic power generation node n Photovoltaic output and load nodesm Load demand, electrolyzer nodes l hydrogen production and fuel cell nodes e The energy storage state, and , , and These represent typical operating scenarios corresponding to the minimum carbon emissions. Scheduling time t Next photovoltaic power generation node n Photovoltaic output and load nodes m Load demand, electrolyzer nodes l hydrogen production and fuel cell nodes e The energy storage status.

[0054] The weighted sum of the minimum cost scheduling scheme and the minimum carbon emission scheduling scheme yields the complete scheduling scheme. The complete scheduling scheme can be understood as the weighted sum of data of the same category from the minimum cost scheduling scheme and the minimum carbon emission scheduling scheme. In principle, the complete scheduling scheme can be used as the final scheduling scheme. However, to ensure the feasibility and stability of the final scheduling scheme in the face of uncertain disturbances during actual operation, this embodiment preferably introduces an error adjustment factor and uses the following method to modify the complete scheduling scheme to form a robust scheduling trajectory.

[0055] Based on grid operation uncertainty disturbances, the complete dispatching scheme is error-corrected to obtain the target grid dispatching scheme. Here, grid operation uncertainty disturbances can be understood as disturbances determined by the error values ​​of prediction results such as photovoltaic output, load demand, and hydrogen production obtained based on grid uncertainty models during actual distribution network operation. Specifically, the grid operation uncertainty disturbances include photovoltaic output prediction deviations, load demand prediction deviations, and hydrogen production prediction deviations. The steps for error correction of the complete dispatching scheme based on grid operation uncertainty disturbances to obtain the target grid dispatching scheme include: Based on the scenario scheduling schemes in all the optimal scheduling strategies for the scenarios, a multi-scenario average scheduling scheme is obtained; wherein, the multi-scenario average scheduling scheme can be understood as a scheduling scheme obtained by averaging the data of the same category of all scenario scheduling schemes.

[0056] Based on the power grid operation uncertainty disturbance, an error adjustment factor is calculated, and based on the error adjustment factor, a weighted sum is performed on the multi-scenario average scheduling scheme and the complete scheduling scheme to obtain a robust scheduling scheme; wherein, the robust scheduling scheme can be expressed as: In the formula, and These are the minimum cost scheduling schemes. and minimum carbon emission scheduling scheme The corresponding weight parameters; This is the error adjustment factor, with a value range of (0,1]. and They are time points t A complete scheduling scheme and a multi-scenario average scheduling scheme; A robust scheduling scheme; This is the learning rate parameter; The uncertainty disturbance in power grid operation at time t is obtained by calculating the weighted L2 norm of the actual dispatch scheme and the complete dispatch scheme; and They are time points t +1 and time t Error adjustment factor.

[0057] The present invention provides a model for constructing a hydrogen-electricity integrated distribution network. Based on this model, a power grid uncertainty model is built. Multiple typical operating scenarios are generated according to the uncertainty model. The optimal scheduling strategy for each typical operating scenario is solved using a pre-built distribution network scheduling optimization model. Then, the optimal scheduling strategies for all scenarios are summarized and analyzed to obtain the power grid operating cost boundary and the carbon emission feasible boundary. The minimum operating cost point and the minimum carbon emission point on the power grid operating cost boundary and the carbon emission feasible boundary are obtained respectively. Based on the minimum operating cost point and the minimum carbon emission point, a target power grid scheduling scheme is generated. This method, based on a multi-source uncertainty modeling mechanism and combined with a power grid operating cost and carbon emission cost balance optimization mechanism that integrates the energy transfer coupling relationship between electrolyzers, fuel cells, and hydrogen storage devices, effectively improves the reliability, safety, and economy of power grid operation.

[0058] It should be noted that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0059] In one embodiment, such as Figure 2 As shown, a hydrogen-electricity coordinated distribution network scheduling optimization system is provided, the system comprising: Uncertainty modeling module 1 is used to construct a hydrogen-electricity integrated distribution network model and, based on the hydrogen-electricity integrated distribution network model, construct a power grid uncertainty model; The scenario scheduling analysis module 2 is used to generate a variety of typical operating scenarios based on the power grid uncertainty model, and to solve the optimal scheduling strategy for each typical operating scenario based on the pre-built distribution network scheduling optimization model; the distribution network scheduling optimization model is constructed based on the principle of balancing operating costs and carbon emission costs. Boundary analysis module 3 is used to summarize and analyze the optimal scheduling strategies for all the scenarios to obtain the power grid operating cost boundary and the carbon emission feasible boundary, and to obtain the minimum operating cost point and the minimum carbon emission point on the power grid operating cost boundary and the carbon emission feasible boundary, respectively. The scheduling scheme generation module 4 is used to generate a target power grid scheduling scheme based on the minimum operating cost point and the minimum carbon emission point.

[0060] Specific limitations regarding the hydrogen-electricity coordinated distribution network dispatch optimization system can be found in the limitations of the hydrogen-electricity coordinated distribution network dispatch optimization method described above; the corresponding technical effects are equivalent and will not be repeated here. Each module in the aforementioned hydrogen-electricity coordinated distribution network dispatch optimization system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0061] Figure 3 An internal structural diagram of a computer device is shown in one embodiment. This computer device may specifically be a terminal or a server. Figure 3 As shown, the computer device includes a processor, memory, network interface, display, camera, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program can implement a hydrogen-electricity coordinated distribution network scheduling optimization method. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0062] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. Specific computing devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0063] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0064] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0065] In summary, the hydrogen-electricity coordinated power distribution network scheduling optimization method, system, equipment, and medium provided by the embodiments of the present invention can effectively improve the reliability, safety, and economy of power grid operation based on a multi-source uncertainty modeling mechanism and a power grid operation cost and carbon emission cost balance optimization mechanism that integrates the energy transfer coupling relationship between the electrolyzer, fuel cell, and hydrogen storage equipment.

[0066] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0067] The above-described embodiments are merely preferred embodiments of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the scheduling of a hydrogen-electricity coordinated distribution network, characterized in that, The method includes: A hydrogen-electricity integrated distribution network model is constructed, and a power grid uncertainty model is built based on the hydrogen-electricity integrated distribution network model; Based on the power grid uncertainty model, multiple typical operating scenarios are generated, and the optimal scheduling strategy for each typical operating scenario is solved according to the pre-constructed distribution network scheduling optimization model. The distribution network scheduling optimization model is constructed based on the principle of balancing operating costs and carbon emission costs. Based on the summarization and analysis of the optimal scheduling strategies for all the scenarios, the power grid operating cost boundary and the carbon emission feasible boundary are obtained, and the minimum operating cost point and the minimum carbon emission point on the power grid operating cost boundary and the carbon emission feasible boundary are obtained respectively. A target power grid dispatch scheme is generated based on the minimum operating cost point and the minimum carbon emission point.

2. The hydrogen-electricity coordinated distribution network scheduling optimization method as described in claim 1, characterized in that, The construction steps of the hydrogen-electricity coordinated distribution network model include: A collaborative distribution network topology is constructed based on a preset set of distribution network nodes; the preset set of distribution network nodes includes a subset of photovoltaic power generation nodes, a subset of fuel cell nodes, a subset of electrolyzer nodes, a subset of hydrogen storage nodes, a subset of load nodes, and a subset of grid access nodes; Based on a preset set of node operating parameters, the operating parameters of each node in the collaborative distribution network topology are configured to generate the hydrogen-electric collaborative distribution network model.

3. The hydrogen-electricity coordinated distribution network scheduling optimization method as described in claim 1, characterized in that, The power grid uncertainty model includes the photovoltaic output model of each photovoltaic node, the load demand model of each load node, the hydrogen production model of each electrolyzer node, and the energy storage state change model of each fuel cell node in the hydrogen-electricity coordinated distribution network model. The steps for constructing a power grid uncertainty model based on the hydrogen-electricity integrated distribution network model include: Based on the historical solar irradiance dataset of each photovoltaic node, a corresponding photovoltaic power output model is constructed based on the Beta distribution. Based on the historical load stability sequence of each load node, a corresponding load demand model is constructed using an autoregressive integral moving average model. Based on the typical daily efficiency curves and corresponding historical power sequences of each electrolyzer node, a corresponding hydrogen production model is constructed based on the energy conservation relationship. Operational uncertainty analysis is performed on the historical operating data of each fuel cell node to obtain the corresponding operational random disturbance terms. Based on the operational random disturbance terms, a corresponding energy storage state change model is constructed based on the energy conservation relationship. The operational random disturbance terms include efficiency disturbance terms, self-loss disturbance terms, control execution error terms, and capacity offset terms.

4. The hydrogen-electricity coordinated distribution network scheduling optimization method as described in claim 1, characterized in that, The steps for constructing the distribution network dispatch optimization model include: Based on the cumulative values ​​of electricity purchase cost and carbon emission cost of the hydrogen-electricity co-operated distribution network at each scheduling time, the total operating cost of the distribution network is obtained, and a scheduling optimization objective function is constructed with minimizing the total operating cost of the distribution network as the optimization objective. Based on the aforementioned scheduling optimization objective function and preset scheduling optimization constraints, the distribution network scheduling optimization model is obtained; the preset scheduling optimization constraints include energy balance constraints, photovoltaic output binding constraints, fuel cell output constraints, electrolyzer energy supply constraints, hydrogen storage dynamic balance constraints, and grid power purchase constraints.

5. The hydrogen-electricity coordinated distribution network scheduling optimization method as described in claim 1, characterized in that, The optimal scheduling strategy for the scenario includes the target electricity purchase, total grid operating cost, total carbon emissions, and the corresponding scenario scheduling scheme; The step of summarizing and analyzing the optimal scheduling strategies for all the scenarios to obtain the power grid operating cost boundary and the carbon emission feasible boundary includes: The target electricity purchase in the optimal scheduling strategy for each scenario is combined with the corresponding total grid operating cost and total carbon emissions to generate the corresponding grid operating domain point and carbon emission feasible domain point. All the power grid operation domain points and the carbon emission feasible domain points are classified and summarized to generate a power grid operation domain point set and a carbon emission feasible domain point set; Based on the power grid operation domain point set and the carbon emission feasible domain point set, feasible solution boundaries are extracted using the convex hull algorithm to obtain the corresponding power grid operation cost boundary and carbon emission feasible boundary.

6. The hydrogen-electricity coordinated distribution network scheduling optimization method as described in claim 1, characterized in that, The step of generating the target power grid dispatch scheme based on the minimum operating cost point and the minimum carbon emission point includes: Obtain the minimum cost scheduling scheme and the minimum carbon emission scheduling scheme corresponding to the minimum operating cost point and the minimum carbon emission point, respectively; The complete scheduling scheme is obtained by weighted summing the minimum cost scheduling scheme and the minimum carbon emission scheduling scheme. Based on the uncertainty disturbances in power grid operation, the complete scheduling scheme is corrected for errors to obtain the target power grid scheduling scheme; the uncertainty disturbances in power grid operation are obtained based on the power grid uncertainty model.

7. The hydrogen-electricity coordinated distribution network scheduling optimization method as described in claim 6, characterized in that, The step of correcting the error of the complete dispatching scheme based on the uncertainty disturbance of power grid operation to obtain the target power grid dispatching scheme includes: Based on the scenario scheduling schemes in all the optimal scheduling strategies for the scenarios, a multi-scenario average scheduling scheme is obtained; Based on the uncertainties in grid operation, an error adjustment factor is calculated, and based on the error adjustment factor, a weighted sum is performed on the multi-scenario average dispatch scheme and the complete dispatch scheme to obtain a robust dispatch scheme; the uncertainties in grid operation include photovoltaic output prediction deviation, load demand prediction deviation, and hydrogen production prediction deviation; The robust scheduling scheme is projected onto a preset grid operation feasible region to obtain the target grid scheduling scheme.

8. A hydrogen-electricity coordinated distribution network dispatch optimization system, characterized in that, The system includes: An uncertainty modeling module is used to construct a hydrogen-electricity integrated distribution network model and, based on the hydrogen-electricity integrated distribution network model, to construct a power grid uncertainty model; The scenario scheduling analysis module is used to generate a variety of typical operating scenarios based on the power grid uncertainty model, and to solve the optimal scheduling strategy for each typical operating scenario based on the pre-built distribution network scheduling optimization model; the distribution network scheduling optimization model is constructed based on the principle of balancing operating costs and carbon emission costs. The boundary analysis module is used to summarize and analyze the optimal scheduling strategies for all the scenarios to obtain the power grid operating cost boundary and the carbon emission feasible boundary, and to obtain the minimum operating cost point and the minimum carbon emission point on the power grid operating cost boundary and the carbon emission feasible boundary, respectively. The scheduling scheme generation module is used to generate a target power grid scheduling scheme based on the minimum operating cost point and the minimum carbon emission point.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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