Low-carbon planning method for electricity-natural gas-hydrogen coupling network considering uncertainty

Abstract

The present invention provides a low-carbon planning method for an electricity-natural gas-hydrogen coupling network considering uncertainty, comprising the following steps: step S1, establishing a Gaussian mixture model of probability distribution of wind speed and light intensity; step S2, establishing an uncertainty classic scenario generation model based on Wasserstein distance for wind power and photovoltaic output modeling; step S3, establishing an electricity-natural gas-hydrogen integrated model with a hydrogen production station as the coupling center, the power network provides electricity for the hydrogen production station for electrolysis of water to produce hydrogen, the natural gas network provides natural gas for hydrogen production by steam methane reforming to produce hydrogen, and the hydrogen produced by the hydrogen production station is transported to the hydrogen refueling station through the hydrogen energy network; step S4, establishing a carbon emission flow model in the power network, natural gas network and hydrogen energy network for the calculation of carbon emissions in the coupling network. The beneficial effects of the present invention are: the method of the present invention can effectively reduce carbon emissions in the electrolysis of water and steam methane reforming to produce hydrogen.

Description

Technical Field

[0001] The present invention relates to the field of low-carbon optimization of integrated energy systems, and particularly to a low-carbon planning method for a power-natural gas-hydrogen coupled network considering uncertainty. Background Art

[0002] The development of hydrogen energy vehicles provides a path for energy transformation and low-carbon development. Hydrogen energy vehicles use hydrogen as fuel, and mainly produce water after combustion, with characteristics such as high energy conversion efficiency and zero emissions, which can effectively reduce carbon emissions. However, a large amount of carbon emissions are still generated in the hydrogen production process. Hydrogen energy vehicles do not achieve true "zero carbon emissions" throughout the process, but transfer carbon emissions to the fossil energy power generation link.

[0003] Therefore, to achieve true carbon emission reduction, not only the hydrogen production method needs to be reasonably planned, but also the system coupled with electricity, natural gas, and hydrogen needs to be optimized. New energy sources such as wind power and photovoltaic power should be reasonably utilized to replace thermal power to provide green energy supply for loads such as hydrogen production stations, reducing carbon dioxide emissions.

[0004] In view of this, it is necessary to provide a low-carbon optimization method for integrated energy systems that helps the low-carbon operation of integrated energy systems, reduces carbon emissions, and promotes the development of hydrogen energy vehicles. Summary of the Invention

[0005] To solve the problems in the prior art, the present invention provides a low-carbon planning method for a power-natural gas-hydrogen coupled network considering uncertainty, aiming to carry out carbon emission reduction planning for an integrated energy network with uncertain energy output to meet the increasingly strict carbon emission reduction requirements, which is of great significance for the low-carbon transformation of the energy system and the promotion of the development of hydrogen energy vehicles.

[0006] The present invention provides a low-carbon planning method for a power-natural gas-hydrogen coupled network considering uncertainty, including the following steps:

[0007] Step S1, establish a Gaussian mixture model of the probability distribution of wind speed and light intensity.

[0008] Step S2, establish an uncertainty classical scenario generation model based on the Wasserstein distance for modeling the output of wind power and photovoltaic power.

[0009] Step S3, establish a power-natural gas-hydrogen integration model with a hydrogen production station as the coupling center. The power network provides electrical energy for the hydrogen production station for water electrolysis hydrogen production, the natural gas network provides natural gas for the hydrogen production station for steam methane reforming hydrogen production, and the hydrogen produced by the hydrogen production station is transported to the hydrogen refueling station through the hydrogen energy network.

[0010] Step S4, establish a carbon emission flow model for the power network, natural gas network, and hydrogen energy network, which is used for calculating the carbon emissions of the coupled network.

[0011] Step S5, conduct a three-level collaborative carbon emission reduction plan for the power network, natural gas network, and hydrogen network. Introduce wind turbines and off-grid photovoltaic hydrogen production technology, build methane steam reforming equipment and carbon dioxide capture and storage equipment, and plan necessary power lines, natural gas pipelines, and hydrogen pipelines.

[0012] Step S6, use the genetic algorithm to solve the three-level collaborative carbon emission reduction plan model, solve the decision variables of the candidate equipment, and obtain the optimal plan.

[0013] As a further improvement of the present invention, the specific steps of Step S1 include:

[0014] Step S11, based on historical observation data, establish a Gaussian mixture model for the probability distributions of wind speed and light intensity:

[0015]

[0016] where f X (x j ∣θ) represents the probability density of the random variable X when taking the value x j , θ is taken from the set C is the number of mixture components, ω i is the weight coefficient of the i-th mixture component, and it needs to satisfy ω i > 0 and

[0017] Step S12, each mixture component follows a Gaussian distribution, and the specific probability density function expression is as follows:

[0018]

[0019] where the parameters μ i and σ i 2 correspond to the mean and variance of the i-th mixture component respectively.

[0020] Step S13, use the Bayesian information criterion to determine the number of mixture components C, and use the expectation maximization algorithm to estimate the other parameters of the Gaussian mixture model, and establish a Gaussian mixture model for the probability density of wind speed and light intensity.

[0021] As a further improvement of the present invention, the specific steps of Step S2 include:

[0022] Step S21: Discretize the continuous probability density function of a random variable using the Wasserstein distance to obtain the optimal approximation discrete distribution of the continuous probability density function. The Wasserstein distance between two probability distributions can be expressed as:

[0023] W(p 1 ,p 2 )=inf∫d(x 1 ,x 2 ) r π(dx 1 ,dx 2 )

[0024] where p 1 and p 2 represent the probability density functions of two random variables, π(dx 1 ,dx 2 ) represents the joint distribution of p 1 and p 2 , and d(x 1 ,x 2 ) r represents the r-order distance measure of the random variable.

[0025] Step S22: When the Wasserstein distance between the probability distributions p 1 and p 2 is minimized, the approximation effect of the discrete distribution on the original continuous probability density function is optimal. When the number of discrete points is taken as Q, the value z q corresponding to the q-th discrete quantile can be obtained by the following formula:

[0026]

[0027] where p c (x) is the continuous probability density function of the random variable;

[0028] Step S23: The probability corresponding to the q-th discrete quantile can be obtained by the following formula:

[0029]

[0030] The integral of the probability density from (z q-1 +z q ) / 2 to (z q +z q+1 ) / 2 represents the probability that the random variable takes the value z q .

[0031] Step S24: The optimal quantile of the discrete distribution obtained based on the Wasserstein distance is the classical scenario of the random variable, and the probability obtained is the probability of each classical scenario occurring; the classical scenario generation technology based on the Wasserstein distance simplifies the uncertainty problem containing random variables into a series of deterministic problems, which is then used for the modeling of wind power and photovoltaic power output.

[0032] As a further improvement of the present invention, step S3 specifically includes:

[0033] Step S31: Establish a hydrogen production station model; the hydrogen production station uses two methods, namely electrolysis of water and steam reforming of methane, to produce hydrogen.

[0034] Step S32: Establish an electric power network model; before the carbon emission reduction plan, the initial electric power network is powered by coal-fired generators, and the electric energy is sent to each hydrogen production station through power transmission lines for hydrogen production by electrolysis of water.

[0035] Step S33: Establish a natural gas network model; the natural gas source supplies natural gas to the network, and the natural gas is transported to each hydrogen production station through natural gas pipelines as the raw material for hydrogen production by steam reforming of methane.

[0036] Step S34: Establish a hydrogen energy network model; the hydrogen produced by the hydrogen production station is used as the input of the hydrogen energy network and is transported to each hydrogen refueling station through hydrogen energy pipelines for use by hydrogen energy vehicles.

[0037] Step S35: Establish an integrated power-natural gas-hydrogen network with the hydrogen production station as the coupling center.

[0038] As a further improvement of the present invention, step S31 specifically includes:

[0039] Hydrogen production by electrolysis of water step; the basic principle of electrolysis of water is to use electric energy as the energy source to make water molecules undergo an electrochemical reaction to produce hydrogen and oxygen. The chemical reaction equation for hydrogen production by electrolysis of water is as follows:

[0040]

[0041] Under the action of a catalyst, an oxygen evolution reaction occurs on the anode electrode to generate oxygen, and a hydrogen evolution reaction occurs on the cathode electrode to generate hydrogen.

[0042] Hydrogen production by steam reforming of methane step; under high temperature and in the presence of a catalyst, methane and water vapor in natural gas react to produce hydrogen. The chemical reaction equation is as follows:

[0043]

[0044] As a further improvement of the present invention, step S4 specifically includes:

[0045] Steps for establishing the carbon emission flow model in the power system are as follows:

[0046] Step A1: Carbon emissions in the power system are mainly related to the active power flow. Define the carbon emission intensity ρ of the power system nodes n as the ratio of the total carbon emissions to the injected active power. The calculation formula is as follows:

[0047]

[0048] where G p is the generator connected to power node n, P s is the active power injected into the node by the generator, ρ s is the carbon emission intensity of the generator, l+ is the inflow branch connected to node n, and the active power injected into the node is P i , and the carbon emission intensity of the branch is ρ i .

[0049] Step A2: The carbon emission intensity of the outflow branch in the power system is equal to the carbon emission intensity of the outflow node. The calculation formula is as follows:

[0050]

[0051] where l- is the outflow branch connected to node n, and ρ l- is the carbon emission intensity of the outflow branch.

[0052] Step A3: According to the carbon emission intensity of the node, the total carbon emissions F of the load node n within the time period T n The calculation formula is as follows:

[0053]

[0054] P n,t is the active power of the load carried by node n at time t, and ρ n,t is the corresponding carbon emission intensity. Steps for establishing the carbon emission flow model of the natural gas network specifically include:

[0055] Step B1: Extend the carbon emission calculation method in the power grid to the natural gas network to obtain the corresponding carbon emission intensities ρ p- and ρ n of the output pipeline and node. The calculation is as follows:

[0056]

[0057] where ρ p- is the carbon emission intensity of the branch of the outflow pipeline p - , ρ n is the carbon emission intensity of the natural gas network node n, and f sWith ρ s They are respectively the natural gas flow rate and carbon emission intensity of the natural gas source G g f i With ρ i They are respectively the natural gas flow rate flowing into the pipeline p+ and the branch carbon emission intensity.

[0058] Step B2, calculate the carbon emissions F of the natural gas network load nodes within the time period T n The formula is as follows:

[0059]

[0060] Among them, f n,t is the natural gas flow rate of node n at time t, and ρ n,t is the corresponding carbon emission intensity. The establishment of the carbon emission flow model of the hydrogen network is specifically as follows:

[0061] Step C1, the calculation of the carbon emission flow of the hydrogen network is similar to that of the natural gas network, and the corresponding carbon emission intensities ρ p- and ρ n of the output pipeline and nodes are calculated as follows:

[0062]

[0063] Among them, ρ p- is the branch carbon emission intensity of the pipeline p - flowing out, ρ n is the carbon emission intensity of node n in the hydrogen network, f s and ρ s They are respectively the hydrogen flow rate and carbon emission intensity of the hydrogen source G h f i and ρ i They are respectively the hydrogen flow rate flowing into the pipeline p+ and the branch carbon emission intensity.

[0064] Step C2, the carbon emissions F of the hydrogen load nodes within the time period T n are calculated as follows:

[0065]

[0066] Among them, f n,t is the hydrogen flow rate of node n at time t, and ρ n,t is the corresponding carbon emission intensity.

[0067] As a further improvement of the present invention, the step S5 specifically includes:

[0068] Step S51, conduct a carbon emission reduction plan for the hydrogen production station with two hydrogen production methods of electrolysis of water and steam reforming of methane to produce hydrogen.

[0069] Step S52: Carry out carbon emission reduction planning for the power-gas network. The original power network uses coal-fired units for power generation. Build a wind farm to improve the power cleanliness of the entire network. At the same time, plan the necessary power lines and natural gas pipelines to ensure the stable operation of the integrated energy system.

[0070] Step S53: Carry out carbon emission reduction planning for the hydrogen energy network.

[0071] As a further improvement of the present invention, the step S51 specifically includes:

[0072] Step S5101: Use the on-site self-built photovoltaic hydrogen production system to provide green power for the electrolytic water hydrogen production process, and use the carbon dioxide capture and storage device to effectively absorb carbon dioxide in the methane steam reforming hydrogen production process;

[0073] Step S5102: The hydrogen production station planning makes decisions on whether to build an on-site self-built photovoltaic hydrogen production system, whether to build a methane steam reforming hydrogen production device and the supporting carbon dioxide capture and storage device used therewith. By introducing the corresponding carbon emission reduction equipment, the goal of reducing carbon emissions in the electrolytic water and methane steam reforming hydrogen production processes is achieved;

[0074] Step S5103: The hydrogen production station planning model is a single-objective planning, and the objective function is to minimize the total investment construction and operation cost of the hydrogen production station:

[0075]

[0076] Among them, χ i is the decision variable for whether to build a methane steam reforming hydrogen production device, a carbon dioxide capture and storage device, and an on-site self-built photovoltaic array, is the unit cost of building the corresponding equipment, and are the unit operating costs of electrolytic water hydrogen production and methane steam reforming hydrogen production, c grid 、c gas are the market retail prices of electricity and natural gas respectively, are the power supply from the power grid to the hydrogen production station and the photovoltaic power supply at time t respectively, is the natural gas consumption of methane steam reforming hydrogen production without a carbon dioxide capture and storage device, is the natural gas consumption of methane steam reforming hydrogen production with a carbon dioxide capture and storage device, c CCS is the cost of the carbon dioxide capture and storage device for processing each kilogram of carbon emissions, is the carbon emission intensity of natural gas, H g is the calorific value of natural gas;

[0077] Step S5104: The total carbon emissions of each hydrogen production station need to be less than the allocated carbon emission constraint upper limit, and the carbon emission constraint must be satisfied:

[0078]

[0079] Among them, are the carbon emission intensities of the power grid node and natural gas node of the hydrogen production station ζ at time t, respectively. is the power supply from the power grid to the hydrogen production station ζ at time t. is the natural gas consumption for hydrogen production by steam reforming of methane without a carbon dioxide capture and storage device. is the natural gas consumption for hydrogen production by steam reforming of methane with a carbon dioxide capture and storage device, H g is the calorific value of natural gas, γ is the carbon emission reduction efficiency of the carbon dioxide capture and storage device, S ζ is the carbon emission upper limit of the hydrogen production station h.

[0080] Step S5105, according to the carbon emission flow theory, derive the calculation formula for the power carbon emission intensity of a hydrogen production station with a self-built photovoltaic power station:

[0081]

[0082] Among them, is the carbon emission intensity of photovoltaic power generation. is the carbon emission intensity from the power grid node. are the active power of the power grid and the active power of the self-built photovoltaic power plant in the factory, respectively.

[0083] Step S5106, use the classical scenario generation method based on the Wasserstein distance described in Steps S1 and S2 to fit the photovoltaic power output.

[0084] Step S5107, to ensure the stable operation of the supply-demand balance of the hydrogen production station, the hydrogen production volume and the load volume need to meet the constraint:

[0085]

[0086] Among them, η SE is the hydrogen production volume by electrolyzing water when consuming 1 kW of electricity, η SMR is the hydrogen production volume when consuming 1 m 3 of natural gas. G ζ,t are the electric power, photovoltaic power, and natural gas flow required by the hydrogen production station ζ at time t, respectively. is the hydrogen production demand of the hydrogen production station ζ at time t.

[0087] Step S5108, the natural gas supplied to the hydrogen production station is all used for hydrogen production by steam reforming of methane, and the natural gas supply volume and consumption volume are equal, and need to meet the constraint:

[0088]

[0089] Among them, G ζ,t is the total natural gas consumption of hydrogen production station ζ at time t;

[0090] Step S5109, the natural gas consumption of methane steam reforming hydrogen production without carbon dioxide capture and storage device at time t in hydrogen production station ζ The natural gas consumption of methane steam reforming hydrogen production with carbon dioxide capture and storage device and the photovoltaic power generation can be calculated by the following formula:

[0091]

[0092] Step S5110, the capacity constraints of methane steam reforming hydrogen production equipment, the capacity constraints of methane steam reforming hydrogen production equipment with carbon dioxide capture and storage device, and the active power constraints of photovoltaic power generation device are as follows:

[0093]

[0094] Among them, are the upper limits of natural gas flow rate of methane steam reforming hydrogen production equipment, the upper limit of natural gas flow rate of methane steam reforming hydrogen production equipment with carbon dioxide capture and storage device, and the upper limit of active power of photovoltaic power generation device respectively.

[0095] Step S5111, according to the optimization objectives and constraint conditions at the hydrogen production station level, use the genetic algorithm to solve and obtain the planning results of the hydrogen production station, including the construction of photovoltaic, methane steam reforming hydrogen production equipment and carbon dioxide capture and storage device.

[0096] As a further improvement of the present invention, the step S52 specifically includes:

[0097] Step S521, the planning model of the power-gas network is a single-objective planning model, with the goal of minimizing the construction and operation costs, and the objective function is as follows:

[0098]

[0099] The objective function consists of two parts: the investment cost of new equipment and the system operation cost. Among them, χ i is the decision variable for building new power pipelines, natural gas pipelines and wind farms, are the unit construction costs of power pipelines, natural gas pipeline construction costs and wind farm construction unit costs respectively, are the unit operation costs of the power network and the natural gas network respectively, is the flow rate of the natural gas source, is the output active power of the generator set, in the case where no new wind farm is established, is the active power output of the thermal power unit; if a new wind farm is established, then represent the output of the thermal power unit and the output of the wind farm.

[0100] Step S522: Use the classical scenario generation method based on the Wasserstein distance described in Steps S1 and S2 to process the uncertain variables.

[0101] Step S523: The load balance constraints of the power network and the natural gas network are as follows:

[0102]

[0103] where is the active power output of the generator set, is the output of the natural gas source, T n P and T n G are the generator set - bus incidence matrix and the natural gas source - node incidence matrix respectively, are the bus - power line incidence matrix and the node - natural gas pipeline incidence matrix respectively, are the power flow of the transmission line l at time t and the natural gas flow of the natural gas pipeline l respectively, is the load of node n in the power network and the natural gas network, Ω PB and Ω GB are the sets of power lines and natural gas pipelines.

[0104] Step S524: The power network needs to satisfy the DC power flow equation constraint:

[0105]

[0106] where θ m,t and θ n,t are the voltage phase angles of nodes m and n at time t, is the active power from m to n, r mn is the reactance.

[0107] Step S525: The output of the generator set and the output of the natural gas source need to satisfy the constraints:

[0108]

[0109] where are the upper and lower limits of the generator set output, are the upper and lower limits of the natural gas source output.

[0110] Step S526: The power flow and the gas pipeline flow need to satisfy the constraints:

[0111]

[0112]

[0113] Among them, are the upper and lower limits of the power flow, are the upper and lower limits of the gas pipeline flow.

[0114] As a further improvement of the present invention, the step S53 specifically includes:

[0115] Step S531, plan the hydrogen pipeline in the hydrogen energy network to optimize the hydrogen supply structure. The planning problem is set as a bi-objective optimization problem. The first objective is to minimize the total cost of the construction and operation of the hydrogen energy network, and the second objective is to minimize the total carbon emissions of the hydrogen refueling stations. The objective function is as follows:

[0116]

[0117] Among them, f 3 is the total cost expression of the hydrogen network, including the construction investment cost of the new hydrogen transmission pipeline and the conventional operation cost of the hydrogen network. χ i is the decision variable for constructing the hydrogen transmission pipeline, which is obtained by solving with the genetic algorithm, is the unit construction cost of each candidate hydrogen pipeline, is the unit operation cost of the hydrogen network, is the hydrogen output flow of the hydrogen production station i at time t; according to the carbon emission flow model of the hydrogen network, f 4 is the total carbon emission expression of the hydrogen network, is the carbon emission intensity of the load node i in the hydrogen network at time t, is the hydrogen load of node i at time t, that is, the flow of each hydrogen refueling station, H h is the calorific value of hydrogen.

[0118] Step S532, the nodes of the hydrogen energy network need to satisfy the flow balance constraint equation:

[0119]

[0120] Among them, is the output of the hydrogen production station, T n H is the hydrogen source-node incidence matrix, are the node-hydrogen pipeline incidence matrices respectively, is the hydrogen power flow of the hydrogen pipeline l at time t, is the load of the node n in the electric-hydrogen network.

[0121] Step S533, the hydrogen output of the hydrogen source, i.e., the hydrogen production station, of the hydrogen energy network needs to satisfy the constraint:

[0122]

[0123] Among them, are the upper and lower limits of the hydrogen source output.

[0124] Step S534, the upper and lower limits of the hydrogen pipeline flow rate are constrained.

[0125]

[0126] Among them, are the upper and lower limits of the hydrogen pipeline flow rate.

[0127] As a further improvement of the present invention, the step S6 specifically includes:

[0128] Step S61, data preparation; input the system parameters of the integrated power-natural gas-hydrogen network, including the load of each hydrogen refueling station, the wind power output scenario, the photovoltaic power output scenario, the hydrogen production demand of the hydrogen production station, and the node carbon emission intensity in the initial integrated energy network.

[0129] Step S62, obtaining the upper limit of carbon emission constraints; calculating the initial carbon emission intensity and carbon emissions of the hydrogen production station according to the carbon emission model, and distributing the total carbon emission constraints to each hydrogen production station according to the proportional distribution principle to obtain the upper limit of carbon emission constraints for the three hydrogen production stations.

[0130] Step S63, carrying out a carbon emission reduction plan for the hydrogen production station; if the initial carbon emissions of the hydrogen production station exceed its allocated carbon emission constraint value, use the genetic algorithm to carry out an optimal carbon emission reduction plan for it, including whether to establish a self-built photovoltaic hydrogen production system for the plant, new methane steam reforming equipment and carbon dioxide capture and storage devices, and distributing the proportion of hydrogen production by different hydrogen production methods, that is, the proportion of electricity and gas used.

[0131] Step S64, updating the carbon emission intensity and carbon emission constraint value; if carbon emission reduction equipment is established in the hydrogen production station, it means that the carbon emission intensity of the hydrogen production station will change, so it is necessary to recalculate the carbon emission intensity and carbon emissions.

[0132] Step S65, judging whether the constraints are satisfied; if the carbon emission constraints can be satisfied through the planning of the hydrogen production station, bring the planned hydrogen production station model data into step S66 to carry out the carbon emission reduction plan for the power-natural gas network; if the carbon emission constraints cannot be satisfied, discard the planning result and bring the initial hydrogen production station model data into step S66 to carry out the carbon emission reduction plan for the power-natural gas network.

[0133] Step S66: Plan for carbon emission reduction in the power - natural gas network; use the genetic algorithm to reasonably plan the location of wind farms in the power network, the construction of lines, and the construction of pipelines in the natural gas network, so that the total system cost is minimized under the condition of meeting the carbon emission constraint.

[0134] Step S67: Update the carbon emission intensity and carbon emission constraint value; due to the change in the system structure through the planning of the power - natural gas network, it is necessary to recalculate the carbon emission intensity and carbon emission amount, and re - allocate the carbon emission constraint amount for the hydrogen production stations.

[0135] Step S68: Judge whether the constraints are met; if the carbon emissions can meet the constraint conditions through the planning of the power - natural gas network, directly proceed to Step S69 for the planning of the hydrogen network to reduce unnecessary expenditures; if the carbon emissions exceed the constraint value, conduct secondary planning for the hydrogen production stations, transfer the node carbon emission intensity and excessive carbon emissions of each hydrogen production station to the first - level model, and return to Step S63 to re - plan the hydrogen production stations. It is necessary to further install carbon emission reduction equipment in the hydrogen production stations to meet the constraints.

[0136] Step S69: Plan for carbon emission reduction in the hydrogen energy network; use the genetic algorithm to reasonably plan the hydrogen pipelines in the hydrogen network, with the dual goals of the lowest cost and the minimum total carbon emissions of hydrogen refueling stations, and complete the three - level collaborative planning of the power - natural gas - hydrogen integrated system.

[0137] The beneficial effects of the present invention are as follows: 1. The present invention fully considers the impact of intermittent renewable energy on the accuracy of system planning, and uses the classical scenario generation method based on the Wasserstein distance to generate wind power and photovoltaic power output scenarios, providing a basis for uncertain scenarios for the subsequent planning of the integrated energy system; 2. The present invention proposes a three - level collaborative planning method to plan for carbon emission reduction in the power - natural gas - hydrogen integrated network. The present invention reasonably uses new energy sources such as wind power and photovoltaic power to replace traditional coal - fired units for power generation, effectively reducing the carbon emissions of electrolytic water hydrogen production and making the electric energy for hydrogen production cleaner; at the same time, introducing photovoltaic power generation hydrogen production technology, methane steam reforming hydrogen production equipment and carbon dioxide capture and storage devices in the hydrogen production stations effectively reduces the carbon emissions in the methane steam reforming hydrogen production link; 3. The present invention takes the minimum total cost of the construction and operation of the integrated energy system as the objective function. Under the carbon emission constraint conditions, it plans clean energy equipment such as photovoltaic and wind farms in the system, and optimizes the structures of hydrogen production stations, power - natural gas networks, and hydrogen networks. Using the three - level collaborative planning method, the energy load and carbon emission intensity information of each level of nodes can be transferred to the next level, thus planning the optimal construction plan. Description of the Drawings

[0138] Figure 1It is the flowchart of the low-carbon planning method for the power-gas-hydrogen coupled network considering uncertainty in the present invention;

[0139] Figure 2 It is the flowchart of the classical scenario generation method based on the Wasserstein distance in the present invention;

[0140] Figure 3 It is the flowchart of using the genetic algorithm to solve the three-level collaborative carbon emission reduction planning model in the present invention. Specific embodiments

[0141] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0142] Through uncertainty modeling, the present invention introduces wind power and photovoltaic power to provide clean electric energy for hydrogen production by electrolyzing water, and introduces carbon dioxide capture and storage equipment to absorb carbon emissions in the methane steam reforming process, realizing the low-carbon transformation of the power-gas-hydrogen system with uncertain energy output. The present invention uses the Gaussian mixture model and the Wasserstein distance to generate the classical output scenarios of wind power and photovoltaic power. The carbon emission flow model is applied to describe the carbon emissions in the integrated energy network, and the carbon emissions of each hydrogen production station are constrained. With the goal of minimizing the total cost of construction and operation of the integrated energy network and maximizing the reduction of carbon emissions, a low-carbon planning model for the power-gas-hydrogen coupled network is established. A three-level collaborative planning algorithm based on the genetic algorithm is used to plan the low-carbon equipment and hydrogen production methods in the hydrogen production station, the structure and equipment of the power system and the natural gas system, and the hydrogen energy network structure. The present invention fully considers the output of uncertain energy, can effectively reduce the carbon emissions of the power-gas-hydrogen coupled network, and is of great significance to the transformation of the energy structure and the development of hydrogen energy vehicles.

[0143] As Figure 1 shown, the present invention discloses a low-carbon planning method for a power-gas-hydrogen coupled network considering uncertainty, including the following steps:

[0144] Step S1, establish a Gaussian mixture model of the probability distributions of wind speed and light intensity.

[0145] Step S2, establish an uncertainty classical scenario generation model based on the Wasserstein distance for wind power and photovoltaic power output modeling.

[0146] Step S3, establish a power-gas-hydrogen integration model with the hydrogen production station as the coupling center. The power network provides electric energy for the hydrogen production station to produce hydrogen by electrolyzing water, the natural gas network provides natural gas for the hydrogen production station to produce hydrogen by methane steam reforming, and the hydrogen produced by the hydrogen production station is transported to the hydrogen refueling station through the hydrogen energy network.

[0147] Step S4, establish a carbon emission flow model for the power grid, natural gas grid, and hydrogen energy grid, which is used for calculating the carbon emissions of the coupled grid.

[0148] Step S5, conduct a three-level collaborative carbon emission reduction plan for the power grid, natural gas grid, and hydrogen grid, introduce wind turbines and off-grid photovoltaic hydrogen production technology, build methane steam reforming equipment and carbon dioxide capture and storage equipment, and plan necessary power lines, natural gas pipelines, and hydrogen pipelines.

[0149] Step S6, use the genetic algorithm to solve the three-level collaborative carbon emission reduction plan model, solve the decision variables of the candidate equipment, and obtain the optimal plan.

[0150] As Figure 2 shown, steps S1 and S2 are specifically as follows:

[0151] The specific steps of step S1 include:

[0152] Step S11, based on historical observation data, establish a Gaussian mixture model for the probability distributions of wind speed and light intensity:

[0153]

[0154] where f X (x j ∣θ) represents the probability density of the random variable X taking the value x j at this time, θ is taken from the set C is the number of mixture components, ω i is the weight coefficient of the i-th mixture component, and it needs to satisfy ω i > 0 and

[0155] Step S12, each mixture component follows a Gaussian distribution, and the specific probability density function expression is as follows:

[0156]

[0157] where the parameters μ i and correspond to the mean and variance of the i-th mixture component respectively.

[0158] Step S13, use the Bayesian information criterion to determine the number C of mixture components, use the expectation maximization algorithm to estimate the other parameters of the Gaussian mixture model, and establish a Gaussian mixture model for the probability density of wind speed and light intensity.

[0159] The specific steps of step S2 include:

[0160] Step S21: Discretize the continuous probability density function of a random variable using the Wasserstein distance to obtain the optimal approximation discrete distribution of the continuous probability density function. The Wasserstein distance between two probability distributions can be expressed as:

[0161] W(p 1 ,p 2 )=inf∫d(x 1 ,x 2 ) r π(dx 1 ,dx 2 )

[0162] where p 1 and p 2 represent the probability density functions of two random variables, π(dx 1 ,dx 2 ) represents the joint distribution of p 1 and p 2 , and d(x 1 ,x 2 ) r represents the r-th order distance measure of the random variable.

[0163] Step S22: When the Wasserstein distance between the probability distributions p 1 and p 2 is minimized, the approximation effect of the discrete distribution on the original continuous probability density function is optimal. When the number of discrete points is taken as Q, the value z q corresponding to the q-th discrete quantile can be obtained by the following formula:

[0164]

[0165] where p c (x) is the continuous probability density function of the random variable.

[0166] Step S23: The probability corresponding to the q-th discrete quantile can be obtained by the following formula:

[0167]

[0168] The integral of the probability density from (z q-1 +z q ) / 2 to (z q +z q+1 ) / 2 represents the probability that the random variable takes the value z q .

[0169] Step S24: The optimal quantile of the discrete distribution obtained based on the Wasserstein distance is the classical scenario of the random variable, and the obtained probability is the probability of each classical scenario occurring. Using the classical scenario generation technology based on the Wasserstein distance, the uncertainty problem containing random variables is simplified into a series of deterministic problems, which are then used for the modeling of wind power and photovoltaic power output.

[0170] The specific steps of step S3 are as follows:

[0171] Step S31: Establish a hydrogen production station model. Two mainstream hydrogen production methods are used in the hydrogen production station, namely water electrolysis hydrogen production and methane steam reforming hydrogen production.

[0172] Water electrolysis hydrogen production:

[0173] The basic principle of water electrolysis is to use electrical energy as the energy source to cause an electrochemical reaction of water molecules to produce hydrogen and oxygen. The chemical reaction equation for water electrolysis hydrogen production is as follows:

[0174]

[0175] Under the action of a catalyst, an oxygen evolution reaction occurs on the anode electrode to generate oxygen, and a hydrogen evolution reaction occurs on the cathode electrode to generate hydrogen.

[0176] Methane steam reforming hydrogen production:

[0177] Under high temperature and in the presence of a catalyst, methane and water vapor in natural gas react to produce hydrogen. The chemical reaction equation is as follows:

[0178]

[0179] Step S32: Establish a power network model. The initial power network before carbon emission reduction planning is powered by coal-fired generators, and electric energy is sent to each hydrogen production station through power transmission lines for water electrolysis hydrogen production.

[0180] Step S33: Establish a natural gas network model. The natural gas source supplies natural gas to the network, and natural gas is transported to each hydrogen production station through natural gas pipelines as the raw material for methane steam reforming hydrogen production.

[0181] Step S34: Establish a hydrogen energy network model. The hydrogen produced by the hydrogen production station is used as the input of the hydrogen energy network and is transported to each hydrogen refueling station through hydrogen energy pipelines for use by hydrogen energy vehicles.

[0182] Step S35: Establish a power-natural gas-hydrogen integrated network with the hydrogen production station as the coupling center.

[0183] 1) The hydrogen production station is the core of the mutual coupling of comprehensive energy, connecting the power-natural gas network upward and the hydrogen network downward.

[0184] 2) The hydrogen production station serves as a load for the power grid and the natural gas grid, realizing the conversion of electric energy and natural gas into hydrogen energy.

[0185] 3) The hydrogen production station serves as a hydrogen source for the hydrogen network. After hydrogen is transported to each hydrogen refueling station through transmission pipelines, it can be used by hydrogen-powered vehicles in the transportation network.

[0186] The specific steps of step S4 include:

[0187] First, the steps for establishing the carbon emission flow model in the power system;

[0188] In China's power industry structure, coal-fired power generation is the mainstay. The carbon emissions of the power grid are directly generated during the coal-fired power generation process, and there is no actual carbon emission transfer during the power transmission process. Therefore, the carbon emission flow does not actually exist in the power grid but is a virtual flow model.

[0189] The active power flow in the power system is transmitted from the power generation end to the load through power lines. Since the carbon emission flow is embedded in the power flow and exists depending on the active power, its flow trajectory is the same as that of the active power flow.

[0190] The steps for establishing the carbon emission flow model in the power system include:

[0191] Step A1: The carbon emissions in the power system are mainly related to the active power flow. Define the carbon emission intensity ρ of the power system nodes n equal to the ratio of the total carbon emissions to the injected active power. The calculation formula is as follows:

[0192]

[0193] where G p is the generator connected to power node n, P s is the active power injected into the node by the generator, ρ s is the carbon emission intensity of the generator, l+ is the incoming branch connected to node n, the active power injected into the node is P i , and the carbon emission intensity of the branch is ρ i .

[0194] Step A2: The carbon emission intensity of the outgoing branch in the power system is equal to the carbon emission intensity of the outgoing node. The calculation formula is as follows:

[0195]

[0196] where l- is the outgoing branch connected to node n, and ρ l- is the carbon emission intensity of the outgoing branch.

[0197] Step A3. According to the carbon emission intensity of nodes, the total carbon emissions \(F\) of the load node \(n\) within the time period \(T\) n The calculation formula is as follows:

[0198]

[0199] \(P\) n,t is the active power of the load carried by node \(n\) at time \(t\), and \(\rho\) n,t is the corresponding carbon emission intensity.

[0200] Second, the steps for establishing the carbon emission flow model of the natural gas network;

[0201] Since natural gas contains about 85% methane, and methane is a gas containing carbon elements, the carbon emission flow in the natural gas network is a real carbon flow model. The carbon emission flow is embedded in the natural gas flow and transferred from the gas source to the load side.

[0202] The steps for establishing the carbon emission flow model of the natural gas network include:

[0203] Step B1. Both the natural gas transmission network and the power transmission network are energy transmission systems and have many commonalities. By extending the carbon emission calculation method in the power grid to the natural gas network, the corresponding carbon emission intensities \(\rho\) p- and \(\rho\) n of the output pipeline and nodes can be obtained. The calculation is as follows:

[0204]

[0205] where \(\rho\) p- is the branch carbon emission intensity of the outflow pipeline \(p\) - , \(\rho\) n is the carbon emission intensity of node \(n\) in the natural gas network, \(f\) s and \(\rho\) s are the natural gas flow rate and carbon emission intensity of the natural gas source \(G\) g respectively, and \(f\) i and \(\rho\) i are the natural gas flow rate and branch carbon emission intensity of the inflow pipeline \(p +\) respectively.

[0206] Step B2. Calculate the carbon emissions \(F\) of the load nodes in the natural gas network within the time period \(T\) n , and the formula is as follows:

[0207]

[0208] where \(f\) n,t is the natural gas flow rate of node \(n\) at time \(t\), and \(\rho\) n,t is the corresponding carbon emission intensity.

[0209] Third, the steps for establishing the carbon emission flow model of the hydrogen network;

[0210] Similar to the power grid, carbon emissions in the hydrogen network are mainly generated during the hydrogen production process. Therefore, the carbon emission flow in the hydrogen network is a virtual flow model.

[0211] The steps for establishing the carbon emission flow model of the hydrogen network include:

[0212] Step C1, the calculation of the carbon emission flow in the hydrogen network is similar to that in the natural gas network, and the corresponding carbon emission intensities ρ p- 、ρ n are calculated as follows:

[0213]

[0214] where ρ p- is the branch carbon emission intensity of the outflow pipeline p - , ρ n is the carbon emission intensity of the hydrogen network node n, f s and ρ s are the hydrogen flow rate and carbon emission intensity of the hydrogen source G h respectively, and f i and ρ i are the hydrogen flow rate and branch carbon emission intensity of the inflow pipeline p+ respectively.

[0215] Step C2, the carbon emissions F n of the hydrogen load nodes within the time period T are calculated as follows:

[0216]

[0217] where f n,t is the hydrogen flow rate of the node n at time t, and ρ n,t is the corresponding carbon emission intensity.

[0218] The specific steps of step S5 include:

[0219] Step S51, carbon emission reduction planning is carried out for hydrogen production plants with two hydrogen production methods: electrolytic water hydrogen production and steam methane reforming hydrogen production.

[0220] Specifically, it includes:

[0221] Step S5101, in order to reduce carbon emissions during hydrogen production, the present invention incorporates the factory-built photovoltaic hydrogen production system, steam methane reforming hydrogen production equipment, and the carbon dioxide capture and storage device used in conjunction therewith into the plan; the factory-built photovoltaic hydrogen production system can provide green electricity for the electrolytic water hydrogen production process to reduce the dependence of the hydrogen production plant on the power grid; the carbon dioxide capture and storage device can effectively absorb carbon dioxide during the steam methane reforming hydrogen production process.

[0222] Step S5102, in the planning of the hydrogen production station, decisions need to be made on whether to build a self-built photovoltaic hydrogen production system for plant use, whether to build a methane steam reforming hydrogen production device and the carbon dioxide capture and storage device used in conjunction with it. By introducing corresponding carbon emission reduction equipment, the goal of reducing carbon emissions in the process of electrolytic water and methane steam reforming hydrogen production is achieved.

[0223] Step S5103, the planning model of the hydrogen production station is a single-objective planning, and the objective function is to minimize the total investment construction and operation cost of the hydrogen production station:

[0224]

[0225] where χ i is the decision variable for whether to build a methane steam reforming hydrogen production device, a carbon dioxide capture and storage device, and a self-built photovoltaic hydrogen production system for plant use. is the unit cost of building the corresponding equipment. and are the unit operating costs of electrolytic water hydrogen production and methane steam reforming hydrogen production. c grid and c gas are the market retail prices of electricity and natural gas respectively. are the power supply from the power grid to the hydrogen production station and the photovoltaic power supply at time t respectively. is the amount of natural gas consumed by methane steam reforming hydrogen production without a carbon dioxide capture and storage device. is the amount of natural gas consumed by methane steam reforming hydrogen production with a carbon dioxide capture and storage device. c CCS is the cost of processing each kilogram of carbon emissions by the carbon dioxide capture and storage device. is the carbon emission intensity of natural gas. H g is the calorific value of natural gas.

[0226] Step S5104, the total carbon emissions of each hydrogen production station need to be less than the allocated upper limit of carbon emission constraints, and the carbon emission constraints must be satisfied:

[0227]

[0228] where are the carbon emission intensities of the power grid node and the natural gas node of the hydrogen production station ζ at time t respectively. is the power supply from the power grid to the hydrogen production station ζ at time t. is the amount of natural gas consumed by methane steam reforming hydrogen production without a carbon dioxide capture and storage device. is the amount of natural gas consumed by methane steam reforming hydrogen production with a carbon dioxide capture and storage device. H g is the calorific value of natural gas. γ is the carbon emission reduction efficiency of the carbon dioxide capture and storage device. S ζ is the carbon emission upper limit of the hydrogen production station h.

[0229] Step S5105, based on the carbon emission flow theory, derive the calculation formula for the carbon emission intensity of the electricity of the hydrogen production station with a self-built photovoltaic power station:

[0230]

[0231] Among them, is the carbon emission intensity of photovoltaic power generation, is the carbon emission intensity from the grid nodes, They are respectively the active power of the power grid and the active power of the self-built photovoltaic power plant.

[0232] Step S5106, since the carbon emission intensity of photovoltaic power generation is much smaller than that of coal-fired power generation, the higher the proportion of photovoltaic power station output to total electricity consumption, the lower the carbon emission intensity of the hydrogen production station; since photovoltaic power generation output is greatly affected by sunlight intensity, and sunlight intensity is an uncertain variable with random fluctuations, photovoltaic power generation is not easy to be accurately modeled, so the classic scene generation method based on Wasserstein distance described in steps S1 and S2 is used to fit the photovoltaic output.

[0233] Step S5107, to ensure the stable operation of the supply and demand balance of the hydrogen production station, the hydrogen production volume and load volume must meet the constraints:

[0234]

[0235] where η SE is the amount of hydrogen produced by electrolysis of water when consuming 1kW of electricity, η SMR Consume 1m 3 Hydrogen production per natural gas, G ζ,t respectively the electric power, photovoltaic power and natural gas flow required by the hydrogen production station ζ at time t, is the hydrogen production demand of hydrogen production station ζ at time t.

[0236] Step S5108, the natural gas supplied to the hydrogen production station is all used for methane steam reforming to produce hydrogen, and the natural gas supply is equal to the consumption, which must meet the following constraints:

[0237]

[0238] Among them, G ζ,t is the total natural gas consumption of the hydrogen production station ζ at time t.

[0239] Step S5109, the amount of natural gas consumed by hydrogen production station ζ at time t for methane steam reforming without a carbon dioxide capture and storage device Natural gas consumption for hydrogen production from steam methane reforming with CO2 capture and storage and photovoltaic power generation It can be calculated by the following formula:

[0240]

[0241] Step S5110, the capacity constraints of the methane steam reforming hydrogen production equipment, the capacity constraints of the methane steam reforming hydrogen production equipment with a carbon dioxide capture and storage device, and the active power constraints of the photovoltaic power generation device are as follows:

[0242]

[0243] Among them, are respectively the upper limit of the natural gas flow of the methane steam reforming hydrogen production equipment, the upper limit of the natural gas flow of the methane steam reforming hydrogen production equipment with a carbon dioxide capture and storage device, and the upper limit of the active power of the photovoltaic power generation device.

[0244] Step S5111, according to the optimization objectives and constraint conditions at the hydrogen production station level, use the genetic algorithm to solve and obtain the planning results of the hydrogen production station, including the construction of photovoltaic, methane steam reforming hydrogen production equipment and carbon dioxide capture and storage devices.

[0245] Step S52, carry out carbon emission reduction planning for the power-gas network; the original power network uses coal-fired units for power generation, build a wind farm to improve the power cleanliness of the entire network, and at the same time, plan the necessary power lines and gas pipelines to ensure the stable operation of the integrated energy system.

[0246] Specifically include:

[0247] Step S521, the planning model of the power-gas network is a single-objective planning model, with the minimum construction and operation cost as the objective, and the objective function is as follows:

[0248]

[0249] The objective function consists of two parts: the investment cost of new equipment and the system operation cost. Among them, χ i is the decision variable for building new power pipelines, gas pipelines and wind farms, are respectively the unit construction cost of power pipelines, the construction cost of gas pipelines and the unit construction cost of wind farms, are respectively the unit operation costs of the power network and the gas network, is the flow rate of the gas source, is the output active power of the generator set. In the case of not building a new wind farm, is the output active power of the thermal power unit; if a new wind farm is built, then represents the output of the thermal power unit and the output of the wind farm.

[0250] Step S522. The output power of the wind farm is greatly affected by the wind speed. To perform more accurate modeling, the classical scenario generation method based on the Wasserstein distance described in Steps S1 and S2 is used to process uncertain variables, so as to improve the planning accuracy and feasibility of the integrated energy network.

[0251] Step S523. To ensure the stable operation of the power grid and the natural gas grid, the power input to the network nodes must be balanced with the load. The load balance constraints for the power network and the natural gas network are as follows:

[0252]

[0253] where, is the active power output of the generator set, is the output of the natural gas source, T n P and T n G are the generator set-bus incidence matrix and the natural gas source-node incidence matrix respectively, are the bus-power line incidence matrix and the node-natural gas pipeline incidence matrix respectively, are the power flow of the transmission line l and the natural gas flow of the natural gas pipeline l at time t respectively, is the load of node n in the power network and the natural gas network, Ω PB and Ω GB are the sets of electric wires and natural gas pipelines.

[0254] Step S524. The power network needs to satisfy the DC power flow equation constraint:

[0255]

[0256] where, θ m,t and θ n,t are the voltage phase angles of nodes m and n at time t, is the active power from m to n, r mn is the reactance.

[0257] Step S525. The output of the generator set and the output of the natural gas source need to satisfy the constraints:

[0258]

[0259]

[0260]

[0261] Step S526. To ensure the safe operation of the power transmission line, i.e., the gas pipeline, the power flow and the gas pipeline flow need to satisfy the constraints:

[0262]

[0263]

[0264] Step S53. Carry out a carbon emission reduction plan for the hydrogen energy network.

[0265] Specifically, it includes:

[0266] Step S531. Plan the hydrogen pipelines in the hydrogen energy network to optimize the hydrogen supply structure. The planning problem is set as a bi-objective optimization problem. The first objective is to minimize the total cost of the construction and operation of the hydrogen energy network, and the second objective is to minimize the total carbon emissions of the hydrogen refueling stations. The objective function is as follows:

[0267]

[0268] Among them, f 3 is the total cost expression of the hydrogen network, including the construction investment cost of the new hydrogen transmission pipeline and the conventional operation cost of the hydrogen network. χ i is the decision variable for constructing the hydrogen transmission pipeline, which is obtained by solving with the genetic algorithm. is the unit construction cost of each candidate hydrogen pipeline. is the unit operation cost of the hydrogen network. is the hydrogen output flow of the hydrogen production station i at time t; according to the carbon emission flow model of the hydrogen network, f 4 is the total carbon emission expression of the hydrogen network. is the carbon emission intensity of the load node i in the hydrogen network at time t. is the hydrogen load of node i at time t, that is, the flow of each hydrogen refueling station. H h is the calorific value of hydrogen.

[0269] Step S532. The nodes of the hydrogen energy network need to satisfy the flow balance constraint equation:

[0270]

[0271] Among them, is the output of the hydrogen production station. T n H is the hydrogen source-node incidence matrix. are the node-hydrogen pipeline incidence matrices respectively. is the hydrogen power flow of the hydrogen pipeline l at time t. is the load of the node n in the electric-hydrogen network.

[0272] Step S533, the hydrogen output of the hydrogen source in the hydrogen energy network, i.e., the hydrogen production station, needs to meet the constraint:

[0273]

[0274] where are the upper and lower limits of the hydrogen source output.

[0275] Step S534, the upper and lower limits of the hydrogen pipeline flow constraint:

[0276]

[0277] where are the upper and lower limits of the hydrogen pipeline flow.

[0278] As Figure 3 shown, the specific steps of step S6 include:

[0279] Step S61, data preparation; input the system parameters of the integrated power-natural gas-hydrogen network, including the load of each hydrogen refueling station, the wind power output scenario, the photovoltaic power output scenario, the hydrogen production demand of the hydrogen production station, and the node carbon emission intensity in the initial integrated energy network.

[0280] Step S62, obtain the upper limit of the carbon emission constraint; calculate the initial carbon emission intensity and carbon emissions of the hydrogen production station according to the carbon emission model, and allocate the total carbon emission constraint to each hydrogen production station according to the proportional distribution principle to obtain the upper limit of the carbon emission constraint of the three hydrogen production stations.

[0281] Step S63, carry out a carbon emission reduction plan for the hydrogen production station; if the initial carbon emissions of the hydrogen production station exceed its allocated carbon emission constraint value, use the genetic algorithm to carry out an optimal carbon emission reduction plan for it, including whether to establish a factory-owned photovoltaic hydrogen production system, new methane steam reforming equipment and carbon dioxide capture and storage devices, and allocate the proportion of hydrogen production by different hydrogen production methods, that is, the proportion of electricity and gas used.

[0282] Step S64, update the carbon emission intensity and carbon emission constraint value; if carbon emission reduction equipment is established in the hydrogen production station, it means that the carbon emission intensity of the hydrogen production station will change, so it is necessary to recalculate the carbon emission intensity and carbon emissions.

[0283] Step S65, judge whether the constraint is satisfied; if the carbon emission constraint can be met through the planning of the hydrogen production station, bring the planned hydrogen production station model data into S66 to carry out the carbon emission reduction plan for the power-natural gas network. If the carbon emission constraint cannot be met, discard the planning result and bring the initial hydrogen production station model data into S66 to carry out the carbon emission reduction plan for the power-natural gas network.

[0284] Step S66, conduct carbon emission reduction planning for the power-gas network; use the genetic algorithm to reasonably plan the siting of wind farms in the power network, the construction of lines, and the construction of pipelines in the gas network, so that the total system cost is minimized under the condition of meeting the carbon emission constraint.

[0285] Step S67, update the carbon emission intensity and carbon emission constraint value; due to the change in the system structure through the planning of the power-gas network, it is necessary to recalculate the carbon emission intensity and carbon emission amount, and reallocate the carbon emission constraint amount for the hydrogen production station.

[0286] Step S68, judge whether the constraints are met; if the carbon emissions can meet the constraint conditions through the planning of the power-gas network, directly proceed to Step S69 for the planning of the hydrogen network to reduce unnecessary expenses. If the carbon emissions exceed the constraint value, conduct secondary planning for the hydrogen production station, transfer the node carbon emission intensity and excessive carbon emissions of each hydrogen production station to the primary model, and return to Step S63 to re-plan the hydrogen production station. It is necessary to further build carbon emission reduction equipment in the hydrogen production station to meet the constraints.

[0287] Step S69, conduct carbon emission reduction planning for the hydrogen energy network; use the genetic algorithm to reasonably plan the hydrogen pipelines in the hydrogen network, with the dual objectives of the lowest cost and the minimum total carbon emissions of hydrogen refueling stations, and complete the three-level collaborative planning of the power-gas-hydrogen integrated system.

[0288] In summary, a low-carbon planning method for a power-gas-hydrogen coupled network considering uncertainty disclosed by the present invention takes into account the impact of uncertain energy output on the planning results of the integrated energy system. By using the mixture Gaussian model and the Wasserstein distance to generate classical scenarios of wind power and photovoltaic power output, it is an effective way to solve the problem of optimal planning and operation of a system containing renewable energy. Using the carbon emission flow model to numericalize the virtual carbon emission flow, clarify the carbon emission responsibility on the load side, and thus promote carbon emission reduction and energy structure transformation. The three-level collaborative planning algorithm plans the low-carbon equipment and hydrogen production methods in the hydrogen production station, the structures and equipment of the power system and the gas system, and the structure of the hydrogen energy network, which can effectively reduce the carbon emissions of the power-gas-hydrogen coupled network and is of great significance to the transformation of the energy structure and the development of hydrogen energy vehicles.

[0289] The above content is a further detailed description of the present invention in combination with specific preferred implementation manners. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, which should all be regarded as belonging to the protection scope of the present invention.

Claims

1. A low-carbon planning method for an electricity-natural gas-hydrogen coupling network considering uncertainty, characterized in that: The following steps are involved: Step S1, establishing a Gaussian mixture model of wind speed and light intensity probability distribution; Step S2, establishing an uncertainty classic scenario generation model based on Wasserstein distance for wind power and photovoltaic output modeling; Step S3, establishing an integrated electricity-natural gas-hydrogen model with the hydrogen production station as the coupling center, the power network provides electricity to the hydrogen production station for water electrolysis to produce hydrogen, the natural gas network provides natural gas to the hydrogen production station for methane steam reforming to produce hydrogen, and the hydrogen produced by the hydrogen production station is transported to the hydrogen refueling station through the hydrogen energy network; Step S4, establishing a carbon emission flow model in the power network, natural gas network and hydrogen energy network for calculating the carbon emissions of the coupled networks; Step S5, conducting three-level coordinated carbon emission reduction planning for the power network, natural gas network and hydrogen network, introducing wind turbines and off-grid photovoltaic power generation and hydrogen production technology, building methane steam reforming equipment and carbon dioxide capture and storage equipment, and planning power lines, natural gas pipelines and hydrogen pipelines; Step S6, using a genetic algorithm to solve the three-level collaborative carbon emission reduction planning model, solving the decision variables of the candidate equipment, and obtaining the optimal planning solution.

2. The low-carbon planning method for the electricity-natural gas-hydrogen coupling network according to claim 1 is characterized in that: The step S1 specifically includes: Step S11, based on historical observation data, establish a Gaussian mixture model of wind speed and light intensity probability distribution: Among them, f X (x j |θ) indicates that the random variable X takes the value x j The probability density when θ is taken from the set C is the number of mixture components, ω i is the weight coefficient of the i-th mixed component, which must satisfy ω i >0 and Step S12, each mixed component obeys Gaussian distribution, and the specific probability density function expression is as follows: Among them, the parameter μ i and σ i 2 They correspond to the mean and variance of the i-th mixture component respectively; Step S13, using the Bayesian information criterion to determine the number C of mixed components, using the expectation maximization algorithm to estimate other parameters of the Gaussian mixture model, and establishing a Gaussian mixture model of the probability density of wind speed and light intensity.

3. The low-carbon planning method for the electricity-natural gas-hydrogen coupling network according to claim 1 is characterized in that: The step S2 specifically includes: Step S21, using Wasserstein distance to discretize the continuous probability density function of the random variable, to obtain the optimal approximate discrete distribution of the continuous probability density function, the Wasserstein distance between the two probability distributions can be expressed as: Among them, p1 and p2 represent the probability density functions of two random variables, π(dx1,dx2) represents the joint distribution of p1 and p2, and d(x1,x2) r represents the r-order distance measure of random variables; Step S22: When the Wasserstein distance between probability distributions p1 and p2 is the smallest, the discrete distribution has the best approximation effect on the original continuous probability density function. When the number of discrete points is Q, the value z corresponding to the qth discrete quantile is q It can be obtained by the following formula: Among them, p c (x) is the continuous probability density function of the random variable; In step S23, the probability corresponding to the qth discrete quantile can be calculated by the following formula: Use (z q-1 +z q ) / 2 to (z q +z q+1 ) / 2The integral of the probability density represents the random variable taking the value z q The probability of Step S24, the optimal quantile of the discrete distribution obtained based on the Wasserstein distance is the classic scenario of the random variable, and the obtained probability is the probability of occurrence of each classic scenario; the classic scenario generation technology based on the Wasserstein distance is used to simplify the uncertainty problem containing random variables into a series of certain problems, which are then used for wind power and photovoltaic output modeling.

4. The low-carbon planning method for the electricity-natural gas-hydrogen coupling network according to claim 1 is characterized in that: The step S3 specifically includes: Step S31, establishing a hydrogen production station model; the hydrogen production station produces hydrogen by electrolysis of water and methane steam reforming; Step S32, establishing a power network model; the initial power network before the carbon emission reduction plan is powered by coal-fired generators, and the power is sent to each hydrogen production station through power transmission lines for electrolysis of water to produce hydrogen; Step S33, establishing a natural gas network model; the natural gas source provides natural gas to the network, and the natural gas is transported to each hydrogen production station through a natural gas pipeline as a raw material for methane steam reforming hydrogen production; Step S34, establishing a hydrogen energy network model; the hydrogen produced by the hydrogen production station is used as the input of the hydrogen energy network, and is transported to various hydrogen refueling stations through hydrogen energy pipelines for use by hydrogen energy vehicles; Step S35, establishing an integrated electricity-natural gas-hydrogen network with the hydrogen production station as the coupling center.

5. The low-carbon planning method for the electricity-natural gas-hydrogen coupling network according to claim 4 is characterized in that: The step S31 specifically includes: Steps for producing hydrogen by electrolysis of water; The basic principle of water electrolysis is to use electricity as an energy source to make water molecules undergo electrochemical reactions to produce hydrogen and oxygen. The chemical reaction equation for producing hydrogen by electrolysis of water is shown below: Under the action of the catalyst, an oxygen evolution reaction occurs on the anode electrode to generate oxygen, and a hydrogen evolution reaction occurs on the cathode electrode to generate hydrogen; Methane steam reforming hydrogen production step: Under high temperature and catalyst conditions, the methane in the natural gas reacts with water vapor to produce hydrogen. The chemical reaction equation is as follows:

6. The low-carbon planning method for the electricity-natural gas-hydrogen coupling network according to claim 1 is characterized in that: The step S4 specifically includes: The steps to establish the carbon emission flow model in the power system are as follows: Step A1: Carbon emissions in the power system are mainly related to active power flow. Define the carbon emission intensity of power system nodes ρ n It is equal to the ratio of total carbon emissions to injected active power, and the calculation formula is as follows: Among them, G p The generator connected to the power node n, P s is the active power injected into the node by the generator, ρ s is the carbon emission intensity of the generator, l+ is the inflow branch connected to node n, and the active power injected into the node is P i , the branch carbon emission intensity is ρ i ; Step A2: The carbon emission intensity of the outflow branch in the power system is equal to the carbon emission intensity of the outflow node. The calculation formula is as follows: Among them, l- is the outflow branch connected to node n, ρ l- is the carbon emission intensity of the outflow branch; Step A3: According to the node carbon emission intensity, the total carbon emission F of load node n in time period T n The calculation formula is as follows: P n,t is the active power of the load carried by node n at time t, ρ n,t The steps for establishing the carbon emission flow model of the natural gas network are as follows: Step B1: Extend the carbon emission calculation method in the power grid to the natural gas grid to obtain the corresponding output pipeline and node carbon emission intensity ρ p- , n Calculation: Among them, ρ p- For the outflow pipe p - The branch carbon emission intensity, ρ n is the carbon emission intensity of natural gas network node n, f s and ρ s The natural gas source G g Natural gas flow and carbon emission intensity, f i and ρ i are the natural gas flow into pipeline p+ and the carbon emission intensity of the branch respectively; Step B2: Calculate the carbon emissions F of the natural gas grid load nodes within the time period T n , the formula is as follows: Among them, f n,t is the natural gas flow rate at node n at time t, ρ n,t is the corresponding carbon emission intensity; the establishment of the carbon emission flow model of the hydrogen network is as follows: Step C1: The calculation of carbon emission flow of hydrogen network is the same as that of natural gas network. The corresponding output pipeline and node carbon emission intensity ρ p- , n Calculation: Among them, ρ p- For the outflow pipe p - The branch carbon emission intensity, ρ n is the carbon emission intensity of hydrogen network node n, f s and ρ s The hydrogen source G h Hydrogen flow and carbon emission intensity, f i and ρ i are the hydrogen flow rate flowing into pipeline p+ and the carbon emission intensity of the branch respectively; Step C2: Carbon emissions F of hydrogen load nodes in time period T n The calculation is as follows: Among them, f n,t is the hydrogen flow rate at node n at time t, ρ n,t is the corresponding carbon emission intensity.

7. The low-carbon planning method for the electricity-natural gas-hydrogen coupling network according to claim 1 is characterized in that: The step S5 specifically includes: Step S51, performing carbon emission reduction planning for a hydrogen production station having two hydrogen production methods: water electrolysis and methane steam reforming; Step S52, planning carbon emission reduction for the power-gas network; the original power network uses coal-fired units to generate electricity, and wind farms are built to improve the cleanliness of the power of the entire network. At the same time, power lines and natural gas pipelines are planned to ensure the stable operation of the integrated energy system; Step S53, conducting carbon emission reduction planning for the hydrogen energy network.

8. The low-carbon planning method for the electricity-natural gas-hydrogen coupling network according to claim 7 is characterized in that: The step S51 specifically includes: Step S5101, using the factory's self-built photovoltaic hydrogen production system to provide green electricity for the water electrolysis hydrogen production process, and using a carbon dioxide capture and storage device to effectively absorb carbon dioxide in the methane steam reforming hydrogen production process; Step S5102: The hydrogen production station planning makes decisions on whether to build a self-built photovoltaic hydrogen production system for plant use, whether to build methane steam reforming hydrogen production equipment and the carbon dioxide capture and storage equipment used in conjunction with it, and achieves the goal of reducing carbon emissions in the process of electrolysis of water and methane steam reforming hydrogen production by introducing corresponding carbon emission reduction equipment; Step S5103: The hydrogen production station planning model is a single-objective planning model, and the objective function is to minimize the total investment, construction and operation cost of the hydrogen production station: Among them, χ i is the decision variable for whether to build methane steam reforming hydrogen production equipment, carbon dioxide capture and storage devices, and self-built photovoltaic arrays for plant use. The unit cost of building the corresponding equipment is and is the unit operating cost of hydrogen production from water electrolysis and hydrogen production from methane steam reforming, c grid 、c gas are the market retail prices of electricity and natural gas, P t grid , P t PV are the power supply from the power grid to the hydrogen production station and the power supply from photovoltaic power generation at time t, The amount of natural gas consumed for hydrogen production from steam methane reforming without CO2 capture and storage, Natural gas consumption for hydrogen production from steam methane reforming with CO2 capture and storage, c CCS Cost per kg of carbon emissions processed by a CO2 capture and storage device, ρ t gas is the carbon emission intensity of natural gas, H g is the calorific value of natural gas; Step S5104: The total carbon emissions of each hydrogen production station must be less than the allocated carbon emission constraint upper limit and must meet the carbon emission constraint: in, are the carbon emission intensity of the grid node and natural gas node of the hydrogen production station ζ at time t, is the power supply from the power grid to the hydrogen production station ζ at time t, The amount of natural gas consumed for hydrogen production from steam methane reforming without CO2 capture and storage, is the amount of natural gas consumed by steam methane reforming to produce hydrogen with CO2 capture and storage, H g is the calorific value of natural gas, γ is the carbon emission reduction efficiency of the carbon dioxide capture and storage device, S ζ is the carbon emission cap of the hydrogen production station h; Step S5105: Based on the carbon emission flow theory, the calculation formula for the carbon emission intensity of the electricity of the hydrogen production station with a self-built photovoltaic power station is derived: in, is the carbon emission intensity of photovoltaic power generation, is the carbon emission intensity from the grid node, They are the active power of the grid and the active power of the self-built photovoltaic system used by the factory; Step S5106, fitting the photoelectric output using the classic scene generation method based on Wasserstein distance described in step S1 and step S2; Step S5107: To ensure the stable operation of the hydrogen production station with balanced supply and demand, the hydrogen production and load must meet the constraints: Among them, η SE is the amount of hydrogen produced by electrolysis of water when consuming 1kW of electricity, η SMR To consume 1m 3 The amount of hydrogen produced when natural gas is used, G ζ,t are the electric power, photovoltaic power and natural gas flow required by the hydrogen production station ζ at time t, is the hydrogen production demand of the hydrogen production station ζ at time t; Step S5108: All natural gas supplied to the hydrogen production station is used for hydrogen production by steam methane reforming. The natural gas supply is equal to the consumption, and the following constraints must be met: Among them, G ζ,t is the total natural gas consumption of the hydrogen production station ζ at time t; Step S5109, the amount of natural gas consumed by the hydrogen production station ζ at time t for hydrogen production by steam reforming of methane without a carbon dioxide capture and storage device Natural gas consumption for hydrogen production from steam methane reforming with CO2 capture and storage and photovoltaic power generation It can be calculated by the following formula: Step S5110, the capacity constraint of the methane steam reforming hydrogen production equipment, the capacity constraint of the methane steam reforming hydrogen production equipment with a carbon dioxide capture and storage device, and the active power constraint of the photovoltaic power generation device are as follows: in, They are the upper limit of natural gas flow rate of methane steam reforming hydrogen production equipment, the upper limit of natural gas flow rate of methane steam reforming hydrogen production equipment with carbon dioxide capture and storage device, and the upper limit of active power of photovoltaic power generation device; Step S5111, according to the optimization objectives and constraints at the hydrogen production station level, a genetic algorithm is used to solve and obtain the planning results of the hydrogen production station, including the construction status of photovoltaic, methane steam reforming hydrogen production equipment and carbon dioxide capture and storage device.

9. The low-carbon planning method for the electricity-natural gas-hydrogen coupling network according to claim 7 is characterized in that: The step S52 specifically includes: Step S521: The planning model of the electricity-gas network is a single-objective planning model, with the goal of minimizing construction and operation costs. The objective function is as follows: The objective function consists of two parts: the investment cost of new equipment and the system operation cost, where χ i Decision variables for building new electricity pipelines, natural gas pipelines, and wind farms. They are the unit cost of power pipeline construction, natural gas pipeline construction cost and wind farm construction cost, are the unit operating costs of the electricity network and the gas network, is the flow rate of natural gas source, is the output active power of the generator set. In the absence of building a new wind farm, is the output active power of the thermal power unit; if a new wind farm is built, then Represents the output of thermal power units and wind farms; Step S522, using the classic scene generation method based on Wasserstein distance described in step S1 and step S2 to process uncertain variables; Step S523, the load balancing constraints of the power network and the natural gas network are as follows: in, For the active power output of the generator set, is the output of the natural gas source, T n P , T n G They are the generator-bus association matrix and the natural gas source-node association matrix, They are the busbar-power line association matrix and the node-natural gas pipeline association matrix, are the power flow of transmission line l and the natural gas flow of natural gas pipeline l at time t, is the load of node n in the power network and gas network, Ω PB ,Ω GB for the collection of electrical wires and gas pipelines; Step S524: The power network must satisfy the DC power flow equation constraint: Among them, θ m,t ,θ n,t is the voltage phase angle between nodes m and n at time t, is the active power from m to n, r mn is the reactance; Step S525: The output of the generator set and the output of the natural gas source must meet the constraints: Among them, P i gen , The upper and lower limits of the generator output. The upper and lower limits of natural gas source output; Step S526: The power flow and gas pipeline flow must meet the following constraints: in, f l P , are the upper and lower limits of power flow, f l G , The upper and lower limits of natural gas pipeline flow.

10. The low-carbon planning method for the electricity-natural gas-hydrogen coupling network according to claim 7, characterized in that: The step S53 specifically includes: Step S531, planning of hydrogen pipelines in the hydrogen energy network to optimize the hydrogen supply structure, the planning problem is set as a dual-objective optimization problem, objective one is to minimize the total cost of hydrogen energy network construction and operation, objective two is to minimize the total carbon emissions of hydrogen refueling stations, the objective function is as follows: Where f3 is the total cost expression of the hydrogen network, including the construction investment cost of the new hydrogen transmission pipeline and the conventional operation cost of the hydrogen network, i The decision variables for building a hydrogen pipeline are obtained by solving the genetic algorithm. is the unit construction cost of each candidate hydrogen pipeline, is the unit operating cost of the hydrogen network, is the hydrogen output flow of hydrogen production station i at time t; according to the carbon emission flow model of the hydrogen network, f4 is the expression of the total carbon emissions of the hydrogen network, is the carbon emission intensity of load node i in the hydrogen network at time t, is the hydrogen load of node i at time t, that is, the flow rate of each hydrogen refueling station, H h is the calorific value of hydrogen; Step S532: The hydrogen energy network node needs to satisfy the flow balance constraint equation: in, is the output of the hydrogen production station, T n H is the hydrogen source-node association matrix, are the node-hydrogen pipeline association matrix, is the hydrogen flow in hydrogen pipeline l at time t, is the load of node n in the electricity-hydrogen network; Step S533: The hydrogen source of the hydrogen energy network, i.e., the hydrogen output of the hydrogen production station, must meet the following constraints: in, P i HG , The upper and lower limits of hydrogen source output; Step S534, upper and lower limits of hydrogen pipeline flow: in, f l H , The upper and lower limits of the hydrogen pipeline flow rate.

11. The low-carbon planning method for the electricity-natural gas-hydrogen coupling network according to claim 1, characterized in that: The step S6 specifically includes: Step S61, data preparation: input the system parameters of the integrated electricity-natural gas-hydrogen network, including the load of each hydrogen refueling station, wind power output scenario, photovoltaic output scenario, hydrogen production demand of the hydrogen production station, and the carbon emission intensity of the nodes in the initial integrated energy network; Step S62, obtaining the upper limit of carbon emission constraints; calculating the initial carbon emission intensity and carbon emission of the hydrogen production station according to the carbon emission model, distributing the total carbon emission constraints to each hydrogen production station according to the principle of proportional distribution, and obtaining the upper limits of carbon emission constraints of the three hydrogen production stations; Step S63, plan the carbon emission reduction for the hydrogen production station; if the initial carbon emission of the hydrogen production station exceeds its assigned carbon emission constraint value, use a genetic algorithm to plan the optimal carbon emission reduction, including whether to build a self-built photovoltaic hydrogen production system, a new methane steam reforming device and a carbon dioxide capture and storage device, and the proportion of hydrogen production by different hydrogen production methods, that is, the proportion of electricity and gas; Step S64, updating the carbon emission intensity and carbon emission constraint value; if carbon emission reduction equipment is established in the hydrogen production station, it means that the carbon emission intensity of the hydrogen production station will change, so the carbon emission intensity and carbon emissions need to be recalculated; Step S65, determine whether the constraint is met; if the carbon emission constraint can be met through the planning of the hydrogen production station, the planned hydrogen production station model data is brought into step S66 to carry out carbon emission reduction planning for the power-natural gas network; if the carbon emission constraint cannot be met, the planning result is discarded, and the initial hydrogen production station model data is brought into step S66 to carry out carbon emission reduction planning for the power-natural gas network; Step S66, planning carbon emission reduction for the power-gas network; using genetic algorithms to rationally plan the site selection of wind farms in the power network, the construction of lines, and the construction of pipelines in the gas network, so that the total cost of the system is minimized while meeting the carbon emission constraints; Step S67, updating the carbon emission intensity and carbon emission constraint value; through the planning of the electricity-natural gas network, the system structure changes, so it is necessary to recalculate the carbon emission intensity and carbon emissions, and reallocate the carbon emission constraint value for the hydrogen production station; Step S68, determine whether the constraints are met; if the carbon emissions can meet the constraints through the planning of the electricity-natural gas network, then directly proceed to step S69 to plan the hydrogen network to reduce unnecessary expenses; if the carbon emissions exceed the constraint value, then perform secondary planning on the hydrogen production station, transfer the node carbon emission intensity and excess carbon emissions of each hydrogen production station to the primary model, return to step S63 to re-plan the hydrogen production station, and further build new carbon emission reduction equipment at the hydrogen production station to meet the constraints; Step S69, plan the carbon emission reduction for the hydrogen energy network; use genetic algorithms to rationally plan the hydrogen pipelines in the hydrogen network, with the dual goals of minimizing cost and minimizing total carbon emissions at hydrogen refueling stations, and complete the three-level coordinated planning of the electricity-natural gas-hydrogen system.

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