A low-carbon planning method and terminal based on carbon emission flow

Abstract

The present invention discloses a low-carbon planning method and terminal based on carbon emission flow. The method constructs an initial cost model based on a hydrogen production plant cost model and a distribution network cost model; solves the initial cost model with the goal of minimizing the hydrogen production plant cost and the distribution network cost to obtain flow distribution data and an initial minimum cost; calculates carbon emission data corresponding to each hydrogen production plant based on the flow distribution data; adds the carbon cost model to the initial cost model to obtain a target cost model; solves the target cost model with the hydrogen production plant cost, the distribution network cost, and the carbon cost minimized to obtain updated flow distribution data and an updated minimum cost; calculates updated carbon emission data corresponding to each hydrogen production plant based on the updated flow distribution data; determines whether the difference between the updated minimum cost and the initial minimum cost is less than a preset value, and if so, outputs the updated flow distribution data, the updated minimum cost, and the updated carbon emission data. The present invention improves the accuracy of the calculated carbon emission values.

Description

Technical Field

[0001] The present invention relates to the field of carbon emissions, and in particular to a low-carbon planning method and terminal based on carbon emission flows. Background Art

[0002] Current approaches to reducing greenhouse gas emissions typically involve establishing stricter carbon emission regulations and encouraging businesses to adopt cleaner and more sustainable production methods. Hydrogen, with its dual properties as both an energy carrier and an industrial raw material, can promote the integration of renewable energy generation and the chemical industry, ultimately creating a green hydrogen chemical system based on renewable energy.

[0003] Currently, clean energy such as hydrogen is an important path to deep decarbonization, but the large-scale application of hydrogen is still restricted by the immature technology in the storage and transportation links. Thanks to its special physical and chemical properties, ammonia can be used as a transport carrier for hydrogen energy, solving the problems of low-cost and long-distance transportation of hydrogen energy and the "long tail" of single hydrogen energy. It can also solve the problem of how to use large-scale green hydrogen. However, the current Haber-Bosch ammonia synthesis process has achieved large-scale industrial production of ammonia, but this process is carried out under extremely harsh conditions (high temperature and high pressure), accompanied by high energy consumption and large amounts of CO2 emissions, which does not meet the development requirements of achieving global "carbon neutrality". From the perspective of the synthesis of ammonia, according to public data, nearly 78% of ammonia in China comes from coal, which has problems such as high energy consumption and high emissions. At a time when calls for energy transformation are growing, it faces enormous pressure for green transformation.

[0004] The future of green ammonia lies in the use of electricity-to-ammonia processes powered by renewable energy. However, the intermittent and dispersed nature of renewable energy supply presents the greatest challenge for this process. "Blue ammonia" shares the same product characteristics as conventionally produced ammonia and, when combined with carbon capture and storage technologies, can also avoid carbon emissions. Blue ammonia will also play a key role in the transition to less carbon-intensive alternatives. To this end, technologies such as water electrolysis to produce hydrogen and ammonia will become a key path to decarbonizing synthetic ammonia. The synthetic ammonia industry has enormous potential to shift its energy supply structure from fossil fuels to renewable energy. Ammonia will form the foundation of the ammonia energy system, with "green electricity-green hydrogen-green ammonia" becoming a key development trend.

[0005] Existing technologies use accounting methods to calculate carbon emissions. Because accounting methods are indirect, compared to online monitoring data, they are susceptible to human interference, leading to inaccurate results. This makes it impossible to accurately monitor a company's performance based on carbon emissions. Furthermore, given the often delayed release of energy statistics, obtaining comprehensive and reliable energy consumption data for analysis is difficult to achieve efficiently. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a low-carbon planning method and terminal based on carbon emission flow, thereby improving the accuracy of carbon emission calculation and thus enhancing the feasibility of the low-carbon planning method.

[0007] In order to solve the above technical problems, a technical solution adopted by the present invention is:

[0008] A low-carbon planning method and terminal based on carbon emission flow, comprising the steps of:

[0009] Construct an initial cost model based on the hydrogen production plant cost model and the distribution network cost model;

[0010] Solving the initial cost model with the goal of minimizing the cost of the hydrogen production plant and the distribution network, and obtaining power flow distribution data and the initial minimum cost;

[0011] Calculating carbon emission data corresponding to each hydrogen production plant based on the power flow distribution data;

[0012] Adding a carbon cost model to the initial cost model to obtain a target cost model;

[0013] Solving the target cost model with the minimum cost of the hydrogen production plant, the minimum cost of the distribution network, and the minimum carbon cost to obtain updated power flow distribution data and updated minimum cost;

[0014] Calculate the updated carbon emission data corresponding to each hydrogen production plant based on the updated power flow distribution data;

[0015] It is determined whether the difference between the updated minimum cost and the initial minimum cost is less than a preset value. If so, the updated power flow distribution data, the updated minimum cost and the updated carbon emission data are output.

[0016] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0017] A low-carbon planning terminal based on carbon emission flow includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0018] Construct an initial cost model based on the hydrogen production plant cost model and the distribution network cost model;

[0019] Solving the initial cost model with the goal of minimizing the cost of the hydrogen production plant and the distribution network, and obtaining power flow distribution data and the initial minimum cost;

[0020] Calculating carbon emission data corresponding to each hydrogen production plant based on the power flow distribution data;

[0021] Adding a carbon cost model to the initial cost model to obtain a target cost model;

[0022] Solving the target cost model with the minimum cost of the hydrogen production plant, the minimum cost of the distribution network, and the minimum carbon cost to obtain updated power flow distribution data and updated minimum cost;

[0023] Calculate the updated carbon emission data corresponding to each hydrogen production plant based on the updated power flow distribution data;

[0024] It is determined whether the difference between the updated minimum cost and the initial minimum cost is less than a preset value. If so, the updated power flow distribution data, the updated minimum cost and the updated carbon emission data are output.

[0025] The beneficial effects of the present invention are: after constructing the initial cost model, the initial cost model is solved with minimum cost to obtain the flow distribution and the initial minimum cost, and then the carbon emission data of each hydrogen production plant, that is, the carbon data flow, is obtained based on the known flow distribution, and then the initial cost model is updated, and the carbon cost model is added to obtain the target cost model, and the target cost model is re-solved with the minimum cost in the target cost model to obtain the updated flow distribution data, carbon emission data and minimum cost. If the change in the minimum cost is less than the preset range, the updated value is output, indicating that the optimization has achieved the expected effect. In this way, by determining the carbon emission flow, that is, first determining the flow distribution and then calculating the carbon emissions based on the flow distribution, the accuracy of the calculated carbon emission value is improved, and the carbon emission cost is introduced in the optimization process. The final calculated result can comprehensively consider the cost of carbon emissions, further demonstrate the cost of carbon emissions and guide the investment and construction of new energy equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flowchart of the steps of a low-carbon planning method based on carbon emission flow according to an embodiment of the present invention;

[0027] Figure 2 This is a flowchart of another step of a low-carbon planning method based on carbon emission flow according to an embodiment of the present invention;

[0028] Figure 3 This is a structural diagram of a low-carbon planning terminal based on carbon emission flow according to an embodiment of the present invention;

[0029] Description of labels:

[0030] 1. A low-carbon planning terminal based on carbon emission flow; 2. A processor; 3. A memory. DETAILED DESCRIPTION

[0031] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0032] Please refer to Figure 1 , a low-carbon planning method based on carbon emission flow, comprising the steps of:

[0033] Construct an initial cost model based on the hydrogen production plant cost model and the distribution network cost model;

[0034] Solving the initial cost model with the goal of minimizing the cost of the hydrogen production plant and the distribution network, and obtaining power flow distribution data and the initial minimum cost;

[0035] Calculating carbon emission data corresponding to each hydrogen production plant based on the power flow distribution data;

[0036] Adding a carbon cost model to the initial cost model to obtain a target cost model;

[0037] Solving the target cost model with the minimum cost of the hydrogen production plant, the minimum cost of the distribution network, and the minimum carbon cost to obtain updated power flow distribution data and updated minimum cost;

[0038] Calculate the updated carbon emission data corresponding to each hydrogen production plant based on the updated power flow distribution data;

[0039] It is determined whether the difference between the updated minimum cost and the initial minimum cost is less than a preset value. If so, the updated power flow distribution data, the updated minimum cost and the updated carbon emission data are output.

[0040] From the above description, it can be seen that the beneficial effect of the present invention is that after constructing the initial cost model, the initial cost model is solved with minimum cost to obtain the flow distribution and the initial minimum cost, and then the carbon emission data of each hydrogen production plant, that is, the carbon data flow, is obtained based on the known flow distribution. The initial cost model is then updated, and the carbon cost model is added to obtain the target cost model. The target cost model is re-solved with the minimum cost in the target cost model to obtain the updated flow distribution data, carbon emission data and minimum cost. If the change in the minimum cost is less than the preset range, the updated value is output, indicating that the optimization has achieved the expected effect. In this way, by determining the carbon emission flow, that is, first determining the flow distribution and then calculating the carbon emissions based on the flow distribution, the accuracy of the calculated carbon emission value is improved, and the carbon emission cost is introduced in the optimization process. The final calculated result can comprehensively consider the cost of carbon emissions, further demonstrate the cost of carbon emissions and guide the investment and construction of new energy equipment.

[0041] Furthermore, constructing the initial cost model based on the hydrogen production plant cost model and the distribution network cost model includes:

[0042] minC'=C APS +C PDN ;

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] Wherein, C' represents the initial cost model; C APS represents the cost of hydrogen production plant; C PDN represents the cost of the distribution network;

[0049] C APS,INV Indicates the investment amount of equipment required for the hydrogen production plant; C S APS,OM Indicates the cost of operating and maintaining the equipment required for the hydrogen production plant; D S Indicates the number of operating days of the equipment in the hydrogen production plant in a calculation cycle;

[0050] r k represents the capital recovery factor of equipment k in the hydrogen production plant; n represents the service life of the equipment k; X i Indicates whether the device k is selected; C i INV,k Indicates the investment amount corresponding to the k-th device in the i-th node;

[0051] represents the operation and maintenance cost of the equipment at time t in scenario s of node i; Indicates the electricity purchase price; Indicates the amount of electricity purchased by the hydrogen production plant from the distribution grid; Indicates the gas purchase price; represents the gas purchase volume; Δt represents the time interval between two moments t;

[0052] represents the operation and maintenance cost coefficient of the electrolyzer; represents the power consumption of the electrolyzer at time t in scenario s of node i; represents the operation and maintenance cost coefficient of the carbon capture device; represents the power consumption of the carbon capture device at time t in scenario s at node i; Indicates the battery operation and maintenance cost coefficient; represents the charging power of the battery of node i at time t in scenario s; represents the discharge power of the battery of node i at time t in scenario s;

[0053] Indicates the investment cost of the fan equipment; represents the amount of electricity purchased by the distribution network from the main grid at time t in scenario s at node i; Indicates the operation and maintenance cost of the gas turbine; represents the electrical power generated by the combustion engine; represents the operation and maintenance cost of the wind turbine; Indicates the electrical power generated by the fan.

[0054] From the above description, it can be seen that in a hydrogen production plant, by considering the specific factors affecting the cost, such as the electrolyzer, equipment status, gas purchase volume, and gas turbine, to construct a cost model and refine the cost considerations, the accuracy of the solution can be guaranteed when solving the problem with the lowest cost.

[0055] Furthermore, constructing the initial cost model based on the hydrogen production plant cost model and the distribution network cost model includes:

[0056] Construct distribution network constraints and hydrogen production constraints.

[0057] From the above description, it can be seen that in the process of solving the model, the results should also conform to the settings and objective laws in hydrogen production and distribution networks. The initial cost model also includes corresponding constraints to ensure that the final calculated results can be put into production and have practical significance.

[0058] Furthermore, the construction of distribution network constraints includes:

[0059]

[0060] Among them, P ij,t k represents the per-unit value of the active power flow between nodes i and j at time t; ij2 and k ij1 is the preset coefficient, and the corresponding calculation formula has been given; x ij represents the per-unit reactance of the line between node i and node j at time t; θ i,t represents the per-unit value of the bus phase at node i at time t; V i,t represents the per-unit value of the voltage amplitude at node i at time t; Q ij,t represents the per-unit value of the reactive power flow of the line between node i and node j at time t;

[0061] P i,t represents the per-unit value of the injected active power of node i at time t; N B Indicates the total number of distribution network nodes; Q i,t represents the per-unit value of the injected reactive power of node i at time t; r ij represents the per-unit value of the resistance of the line between node i and node j at time t;

[0062] S N Indicates the benchmark capacity of the power grid system; N S Indicates the total number of configured scenarios; represents the injected reactive power of the wind turbine at time t in the scenario of node i; represents the electric power generated by the wind turbine at time t in the scenario of node i s; represents the reactive power injected by the motor at time t in the scenario of node i s; represents the active power generated by the motor at time t in the scenario of node i s; represents the injected reactive power at time t in the scenario of node i s; Represents the power purchased from the distribution network at time t in the scenario of node i s.

[0063] From the above description, it can be seen that the power flow distribution data is constrained according to the historical operation status of the distribution network and the objectively limited parameters to ensure that the final calculated power flow distribution can be reproduced in reality and avoid data distortion.

[0064] Furthermore, the constructing of hydrogen production constraints includes:

[0065] Construct blue ammonia production constraints and green ammonia production constraints respectively.

[0066] From the above description, it can be seen that for the hydrogen production process, since hydrogen itself has a great safety hazard during transportation, the process of converting hydrogen into ammonia compounds for transportation is equivalent to adding a process flow. In addition, this process flow has different implementation methods, and the corresponding carbon emissions are different. Therefore, blue ammonia production constraints and green ammonia production factors are constructed here respectively, which can adapt to the processes of hydrogen production plants at different nodes without the need for special model modifications.

[0067] Furthermore, the construction of blue ammonia production constraints includes:

[0068]

[0069] in, represents the hydrogen production during the natural gas hydrogen production process at time t in scenario s at node i; η SMR Indicates the conversion efficiency of natural gas to hydrogen; represents the amount of natural gas consumed in the natural gas hydrogen production process at time t in scenario s at node i; represents the power consumption of hydrogen production from natural gas at time t in scenario s at node i; Indicates that node i is producing hydrogen from natural gas; represents the electrical efficiency of the SMR device; Indicates the upper limit of gas consumption for hydrogen production from natural gas;

[0070] represents the power consumption of the carbon capture device at node i at time t in scenario s; e c Indicates the energy consumption of the carbon capture device to process unit mass of carbon dioxide; represents the amount of carbon dioxide absorbed by the carbon capture device at node i at time t in scenario s; represents the fixed consumed electric power of the carbon capture device at node i at time t in scenario s;

[0071] represents the upper limit of power consumption of the carbon capture device at node i; represents the amount of hydrogen consumed during the blue ammonia production process at time t in scenario s of node i.

[0072] As can be seen from the above description, blue ammonia directly uses energy to produce hydrogen. The values ​​in the blue ammonia production process are updated here to ensure that the final calculated results can be reproduced in the actual blue ammonia production process.

[0073] Furthermore, the green ammonia production constraints are constructed including:

[0074] Constructing Power-to-Hydrogen Constraints:

[0075]

[0076] in, represents the amount of hydrogen produced by the electrolyzer at time t in scenario s at node i; a j and b j is the related linear coefficient; T represents the total set of moments; represents the amount of electricity consumed by the electrolyzer at time t in scenario s at node i;

[0077] represents the power consumed by the hydrogen compressor at time t in scenario s at node i; R h represents the specific heat capacity constant of hydrogen; T in represents the temperature of hydrogen gas input to the compressor; κ represents the isentropic index of hydrogen gas; η co Indicates the operating efficiency of the compressor; V out Indicates the pressure at the compressor outlet; V in Indicates the pressure at the compressor inlet;

[0078] represents the upper limit of power consumed by the hydrogen compressor at node i; represents the amount of hydrogen stored at time t in scenario s of node i;

[0079] Constructing battery storage constraints:

[0080]

[0081] in, represents the battery storage capacity of node i at time t in scenario s; Indicates the battery charging efficiency; Indicates the discharge efficiency of the battery; represents the lower limit of the battery storage capacity of node i; represents the upper limit of the battery storage capacity of node i; represents the upper limit of charging power of node i; is a parameter indicating whether node i is charging at time t in scenario s; A parameter indicating whether node i discharges at time t in scenario s; represents the upper limit of the discharge power of node i;

[0082] Constructing new energy constraints:

[0083]

[0084] in, represents the actual output of new energy at time t in node i scenario s; represents the predicted output of new energy at time t in node i scenario s; α i,s,t represents the prediction error of photovoltaic output; P() represents the calculated probability; Indicates the lower limit of the backup capacity provided by the battery; represents the upper limit of the backup capacity provided by the battery; ε represents the preset confidence level.

[0085] As can be seen from the above description, for the green ammonia scenario, the battery condition and the difference between the actual output power and the predicted output power are taken into consideration, and the upper and lower limits of multiple parameters are pre-constrained according to the actual usage conditions to avoid ignoring the actual feasibility in the process of solving the target model.

[0086] Furthermore, it also includes:

[0087] Convert the new energy constraints into deterministic linear constraints based on chance constraints:

[0088]

[0089] Among them, (a ε ,b ε ,c ε ,d ε ) represents the parameter vector; represents the quantile with a nominal proportion of ε; represents the predicted output power of the renewable energy at time t in scenario s at node i;

[0090] Assume that the explanatory variable is xs , represents the predicted output power of the new energy, and the deterministic linear constraint is converted into:

[0091] z ε =(a ε ,b ε ,c ε ,d ε ) T ;

[0092]

[0093] Construct a cost function and solve the cost function to obtain the value of the parameter vector:

[0094] min∑ s∈S ρ ε (y s -f ε (x s ,z ε ));

[0095]

[0096] Where S represents the sample size; f ε Represents the mapping relationship between the explanatory variable xs and the parameter vector; y s represents the actual output power of the observed new energy; ρ ε (x) represents the absolute function corresponding to the nominal proportion ε.

[0097] From the above description, we can see that a method for solving constraints related to new energy is provided. A cost function is constructed using a chance constraint model to deal with the uncertainty of new energy. Batteries are introduced to provide backup capacity to ensure stable hydrogen production, further improving the reproducibility of the calculation results.

[0098] Furthermore, the determining whether the difference between the updated minimum cost and the initial minimum cost is less than a preset range further includes:

[0099] If not, return to the step of solving the target cost model with the minimum hydrogen plant cost, the minimum distribution network cost and the minimum carbon cost to obtain updated power flow distribution data and update the minimum cost.

[0100] Please refer to Figure 3 A low-carbon planning terminal based on carbon emission flow includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned low-carbon planning method based on carbon emission flow are implemented.

[0101] In the context of global carbon emission reduction, the use of renewable energy-driven electric ammonia production processes (a Power-to-X route) is the future of green ammonia. However, green hydrogen chemical industry based on the synergy of electricity and hydrogen still faces production process challenges. The intermittent and dispersed nature of renewable energy will lead to a mismatch between renewable energy power generation and the continuous and stable operation time of green ammonia production, which is one of the biggest challenges of the green ammonia process. At present, alkaline water electrolysis hydrogen production equipment has certain load fluctuation constraints, making it difficult to ensure large-scale, continuous and stable hydrogen supply. At the hydrogen user end, from the perspective of operational safety, equipment life, and economy, it is necessary to ensure the continuous and stable supply of hydrogen energy in the chemical industry. For example, the design operating time of ammonia synthesis projects is generally more than 7,000 hours / year, which is difficult to adapt to the fluctuating characteristics of renewable energy hydrogen production;

[0102] Therefore, it is particularly important to optimize and regulate the green hydrogen production and ammonia synthesis processes.

[0103] The above-mentioned low-carbon planning method and terminal based on carbon emission flow of the present invention can be applied to, as described below through specific implementation methods:

[0104] Please refer to Figure 1-2 , embodiment 1 of the present invention is:

[0105] A low-carbon planning method based on carbon emission flow, comprising the steps of:

[0106] S0. Currently, alkaline water electrolysis hydrogen production equipment has certain load fluctuation constraints, making it difficult to ensure large-scale, continuous and stable hydrogen supply. On the hydrogen user side, from the perspectives of operational safety, equipment life and economic efficiency, it is necessary to ensure a continuous and stable supply of hydrogen energy in the chemical industry. A stable hydrogen energy supply and ammonia synthesis model is proposed:

[0107] S01. A detailed alkaline electrolyzer model is proposed, which is modeled based on the empirical current-voltage (IU) relationship as shown below.

[0108] P el =N el I el U el ;

[0109]

[0110] Among them, P el Indicates the amount of electricity consumed by the electrolytic cell; N el Indicates the number of electrolytic cell units; I el Indicates the voltage of the electrolytic cell; U elrepresents the current of the electrolytic cell; U0 represents the reversible voltage of the electrolytic reaction; n represents the number of electrons transferred in the electrochemical reaction; F represents the Faraday constant (96485As); at room temperature, the upper heating value △H is 286kJ / mol, so the corresponding value of U0 is 1.48V; r1 and r2 are the ohmic resistivity of the electrolyte; t1, t2 and t3 are the electrode overvoltage coefficients; s is the voltage coefficient, and T is the temperature.

[0111] Faraday efficiency is an important tool for determining the energy consumption of an electrolyzer. According to Faraday's law, the amount of chemical species produced (or consumed) during an electrochemical reaction is proportional to the amount of electricity passing through the system. Based on this, the formula for expressing electrolyzer efficiency can be found below.

[0112]

[0113] Among them, η I represents the current efficiency; I is the electrolysis current, A is the electrolytic cell area, f1 and f2 are the Faraday efficiency correlation coefficients; η el represents the electrolyzer efficiency;

[0114] As can be seen from the above formula, the electrolyzer efficiency is a variable parameter. In order to keep the final optimization problem linear and describe the hydrogen output in more detail, the present invention uses piecewise linearization processing to convert the hydrogen production into a linear relationship with the hydrogen consumption power.

[0115]

[0116] in, represents the power consumption of the electrolyzer at time t in scenario s at node i; represents the amount of hydrogen produced by the electrolyzer at time t in node i scenario s, a j with b j is the related linear coefficient; the random optimization method used here means that the above formula must be satisfied in each scenario s, where scenario s includes different scenery conditions, etc.

[0117] S02. Propose a synthetic ammonia model as follows:

[0118] The amount of electricity required to produce 1 kg of ammonia is calculated as follows:

[0119]

[0120] e[x(t)]·10 3 =327.62-240.10[x(t)] 3 +576.64[x(t)] 2 -427.69x(t)

[0121] in, is the volume of hydrogen consumed by the ammonia synthesis process at node i under scenario s at time t. is the theoretical hydrogen consumption in the ammonia synthesis process.

[0122] Production The amount of electricity required to produce ammonia is calculated as follows:

[0123]

[0124] Where x(t) is the load level, η H2A is the hydrogen-to-amination coefficient, is the amount of hydrogen consumed in the ammonia synthesis process; is the amount of electricity consumed by the ammonia synthesis process at time t in scenario s at node i.

[0125] S1. Construct an initial cost model based on the hydrogen production plant cost model and the distribution network cost model, including:

[0126] S11. Construct the objective function:

[0127] minC'=C APS +C PDN ;

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] Wherein, C' represents the initial cost model; C APS represents the cost of hydrogen production plant; C PDN represents the cost of the distribution network;

[0134] C APS,INV Indicates the investment amount of equipment required for the hydrogen production plant; C S APS,OM Indicates the cost of operating and maintaining the equipment required for the hydrogen production plant; D S Indicates the number of operating days of the equipment in the hydrogen production plant in a calculation cycle;

[0135] r k represents the capital recovery factor of equipment k in the hydrogen production plant; n represents the service life of the equipment k; X i Indicates whether the device k is selected; C i INV,kIndicates the investment amount corresponding to the k-th device in the i-th node;

[0136] represents the operation and maintenance cost of the equipment at time t in scenario s of node i; Indicates the electricity purchase price; Indicates the amount of electricity purchased by the hydrogen production plant from the distribution grid; Indicates the gas purchase price; represents the gas purchase volume; Δt represents the time interval between two moments t; PV represents new energy equipment (such as photovoltaic equipment); EL represents electrolyzer equipment; ESS represents battery; SMR represents hydrogen production equipment; CCS represents carbon capture device; ASR represents synthetic ammonia equipment;

[0137] represents the operation and maintenance cost coefficient of the electrolyzer; represents the power consumption of the electrolyzer at time t in scenario s of node i; represents the operation and maintenance cost coefficient of the carbon capture device; represents the power consumption of the carbon capture device at time t in scenario s at node i; Indicates the battery operation and maintenance cost coefficient; represents the charging power of the battery of node i at time t in scenario s; represents the discharge power of the battery of node i at time t in scenario s;

[0138] Indicates the investment cost of the fan equipment; represents the amount of electricity purchased by the distribution network from the main grid at time t in scenario s at node i; Indicates the operation and maintenance cost of the gas turbine; represents the electrical power generated by the combustion engine; represents the operation and maintenance cost of the wind turbine; represents the electric power generated by the wind turbine; r represents the capital recovery coefficient of the wind turbine equipment.

[0139] S12. Construct distribution network constraints:

[0140]

[0141] Among them, P ij,t k represents the per-unit value of the active power flow between nodes i and j at time t; ij2 and k ij1 is the preset coefficient, and the corresponding calculation formula has been given; x ij represents the per-unit reactance of the line between node i and node j at time t; θ i,t represents the per-unit value of the bus phase at node i at time t; V i,trepresents the per-unit value of the voltage amplitude at node i at time t; Q ij,t represents the per-unit value of the reactive power flow of the line between node i and node j at time t;

[0142] P i,t represents the per-unit value of the injected active power of node i at time t; N B Indicates the total number of distribution network nodes; Q i,t represents the per-unit value of the injected reactive power of node i at time t; r ij represents the per-unit value of the resistance of the line between node i and node j at time t;

[0143] S N Indicates the benchmark capacity of the power grid system; N S Indicates the total number of configured scenarios; represents the injected reactive power of the wind turbine at time t in the scenario of node i; represents the electric power generated by the wind turbine at time t in the scenario of node i s; represents the reactive power injected by the motor at time t in the scenario of node i s; represents the active power generated by the motor at time t in the scenario of node i s; represents the injected reactive power at time t in the scenario of node i s; represents the power purchased from the distribution network at time t in the scenario of node i s;

[0144] S13. Establish hydrogen production constraints, including:

[0145] S131. Construct blue ammonia production constraints:

[0146]

[0147] in, represents the hydrogen production during the natural gas hydrogen production process at time t in scenario s at node i; η SMR Indicates the conversion efficiency of natural gas to hydrogen; represents the amount of natural gas consumed in the natural gas hydrogen production process at time t in scenario s at node i; represents the power consumption of hydrogen production from natural gas at time t in scenario s at node i; Indicates that node i is producing hydrogen from natural gas; represents the electrical efficiency of the SMR device; Indicates the upper limit of gas consumption for hydrogen production from natural gas;

[0148] represents the power consumption of the carbon capture device at node i at time t in scenario s; e c Indicates the energy consumption of the carbon capture device to process unit mass of carbon dioxide; represents the amount of carbon dioxide absorbed by the carbon capture device at node i at time t in scenario s; represents the fixed consumed electric power of the carbon capture device at node i at time t in scenario s;

[0149] represents the upper limit of power consumption of the carbon capture device at node i; represents the amount of hydrogen consumed during the blue ammonia production process at time t in scenario s of node i.

[0150] S132. Establish green ammonia production constraints:

[0151] Constructing Power-to-Hydrogen Constraints:

[0152]

[0153] in, represents the amount of hydrogen produced by the electrolyzer at time t in scenario s at node i; a j and b j is the related linear coefficient; T represents the total set of moments; represents the amount of electricity consumed by the electrolyzer at time t in scenario s at node i;

[0154] represents the power consumed by the hydrogen compressor at time t in scenario s at node i; R h represents the specific heat capacity constant of hydrogen; T in represents the temperature of hydrogen gas input to the compressor; κ represents the isentropic index of hydrogen gas; η co Indicates the operating efficiency of the compressor; V out Indicates the pressure at the compressor outlet; V in Indicates the pressure at the compressor inlet;

[0155] represents the upper limit of power consumed by the hydrogen compressor at node i; represents the amount of hydrogen stored at time t in scenario s of node i;

[0156] Constructing battery storage constraints:

[0157]

[0158] in, represents the battery storage capacity of node i at time t in scenario s; Indicates the battery charging efficiency; Indicates the discharge efficiency of the battery; represents the lower limit of the battery storage capacity of node i; represents the upper limit of the battery storage capacity of node i; represents the upper limit of charging power of node i; is a parameter indicating whether node i is charging at time t in scenario s; A parameter indicating whether node i discharges at time t in scenario s; represents the upper limit of the discharge power of node i;

[0159] Constructing new energy constraints:

[0160]

[0161] in, represents the actual output of new energy at time t in node i scenario s; represents the predicted output of new energy at time t in node i scenario s; α i,s,t represents the prediction error of photovoltaic output; P() represents the calculated probability; Indicates the lower limit of the backup capacity provided by the battery; represents the upper limit of the backup capacity provided by the battery; ε represents the preset confidence level;

[0162] Convert the new energy constraints into deterministic linear constraints based on chance constraints:

[0163]

[0164] Among them, (a ε ,b ε ,c ε ,d ε ) represents the parameter vector; represents the quantile with a nominal proportion of ε; It represents the predicted output power of the new energy at time t in node i scenario s, that is, where the nominal proportion refers to the quantile of the electricity output distribution. Quantile regression aims to model the conditional quantile rather than the conditional mean (as done by ordinary least squares). This is very useful for understanding the behavior of renewable energy output under various conditions, especially for extreme cases in planning and reliability assessments;

[0165] Assume that the explanatory variable is x s , represents the predicted output power of the new energy, and the deterministic linear constraint is converted into:

[0166] z ε =(a ε ,b ε ,c ε ,d ε ) T ;

[0167]

[0168] Construct a cost function and solve the cost function to obtain the value of the parameter vector:

[0169]

[0170] Where S represents the sample size; f ε Represents the mapping relationship between the explanatory variable xs and the parameter vector; y s represents the actual output power of the observed new energy, that is, ρ ε (x) represents the absolute function corresponding to the nominal ratio ε; this absolute function is the quantile loss function. In the context of renewable energy power output, using quantile regression can help us better understand and predict extreme situations (such as very low or very high output), which is crucial for system stability and risk management.

[0171] In an optional embodiment,

[0172] S2. Solve the initial cost model with the goal of minimizing the cost of the hydrogen production plant and the cost of the distribution network to obtain power flow distribution data and the initial minimum cost;

[0173] S3. Calculating carbon emission data corresponding to each hydrogen production plant based on the flow distribution data, including:

[0174] Before step S3, the following steps are included:

[0175] S301. Constructing a carbon emission model during the green ammonia production process:

[0176] The carbon emission flow distribution depends on the power injection of various resources into node i. For the green ammonia plant node with battery energy storage, its node carbon density ρ i,t The calculation is as follows:

[0177]

[0178] in, is the power entering the node. is the node carbon density of the battery. and They are the battery's carbon emissions and stored energy. They are the charging and discharging power of the battery respectively. is the initial storage capacity of the battery; Ωi represents the set of nodes i;

[0179] Combining the hydrogen production system with the power grid and energy storage will effectively improve the stability of hydrogen production. Its main source of carbon emissions comes from the electricity purchased from the power grid. The production process of green ammonia emits carbon dioxide. Green The calculation is as follows:

[0180]

[0181] Among them, CO EL Represents carbon emissions during hydrogen production; CO ASR represents the carbon emissions from the ammonia synthesis process; and are the electricity consumed by the electrolyzer and synthetic ammonia respectively; ρ i,t represents the carbon density of node i at time t.

[0182] S302. Constructing a carbon emission model during the blue ammonia production process:

[0183] Carbon density of natural gas nodes and carbon potential R g The calculation formula is as follows,

[0184]

[0185] in, is the carbon density of the gas pipeline branch l. f l is the natural gas flow of branch l. ρ w is the carbon density of gas w, f w is the injected gas flow rate. L is the lower heating value of the gas.

[0186] Gray ammonia CO Grey The carbon emissions from the production process include the carbon emissions from the methane hydrogen production process, CO SMR and synthetic ammonia CO ASR The carbon emissions from the process. The methane hydrogen production process includes carbon emissions from the consumption of natural gas and electricity. blue The carbon emissions absorbed by the carbon capture device are subtracted from the grey hydrogen. ccs .

[0187]

[0188] CO blue =CO grey -CO ccs ;

[0189] Then, step S3 includes: calculating the carbon emission data corresponding to each hydrogen production plant according to the flow distribution data, the carbon emission model during the green ammonia production process, and the carbon emission model during the blue ammonia production process, wherein the carbon emission data includes the carbon emission amount of each device and the carbon emission amount generated by different ammonia production plants;

[0190] S4. Adding the carbon cost model to the initial cost model to obtain a target cost model;

[0191] The target cost model is: minC=C APS +CPDS +C C ;

[0192] Among them, C C Represent the carbon cost model:

[0193]

[0194] Among them, λ represents the carbon price on the import side; represents the carbon price of the exporter; CO QUOTA It represents the free emission quotas obtained by importing companies producing similar products (such as hydrogen production companies in this plan); that is, if some high-emitting companies obtain carbon quotas exceeding this quota, they will need to pay carbon tax;

[0195] That is, CBAM tax = CBAM tax rate x carbon emissions = (EU ETS carbon price - exporting country carbon price) x (product carbon emissions - free emission quotas obtained by EU companies with similar products);

[0196] The carbon transaction cost is obtained by multiplying the carbon emission data in step S3 by the carbon price difference between the exporter and the importer;

[0197] Specifically, the target cost model is solved to obtain the carbon emissions of each ammonia plant, thereby calculating the carbon emission cost; the installed capacity of each device is obtained to obtain the equipment investment cost; the power consumption of each device is obtained to obtain the electricity cost and operation and maintenance cost;

[0198] S5. Solve the target cost model with the minimum cost of the hydrogen production plant, the minimum cost of the distribution network, and the minimum carbon cost to obtain updated power flow distribution data and updated minimum cost;

[0199] S6. Calculate updated carbon emission data corresponding to each hydrogen production plant based on the updated power flow distribution data;

[0200] S7, determining whether the difference between the updated minimum cost and the initial minimum cost is less than a preset value, if so, executing S8, otherwise executing S9;

[0201] S8. Outputting the updated power flow distribution data, updated minimum cost, and updated carbon emission data;

[0202] S9. Return to execute S5.

[0203] Please refer to Figure 3 , the second embodiment of the present invention is:

[0204] A low-carbon planning terminal 1 based on carbon emission flow includes a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, each step in the first embodiment is implemented.

[0205] In summary, the present invention provides a low-carbon planning method and terminal based on carbon emission flows. First, the present invention designs a detailed alkaline electrolyzer to optimize operation. This model allows real-time monitoring and adjustment. This achieves optimal operating parameters to maximize efficiency and stability. Second, the present invention incorporates battery energy storage to balance fluctuations. Battery energy storage acts as a buffer, storing excess energy during peak power generation and releasing it during lulls. This approach ensures a stable power supply for the electrolysis system. Third, the present invention uses chance constraints to address the uncertainty of renewable energy. By modeling this uncertainty and predicting its impact, operations can be adjusted in real time to ensure stable hydrogen production. This facilitates worst-case scenario planning and ensures that the system can cope with unforeseen fluctuations in renewable energy generation. The present invention incorporates carbon emission flow technology. By materializing carbon emissions in the power system as carbon emission flows and combining and linking them with power flows, carbon emissions from two major industrial production processes, electricity-green hydrogen-green ammonia and natural gas-blue hydrogen-blue ammonia, can be tracked in real time. Carbon taxation can then be used to force high-emission industries to transition to green production, thereby achieving carbon reduction goals.

[0206] The distribution network is integrated with hydrogen and ammonia production planning. The first step is to minimize equipment investment and operating costs to determine the power flow distribution. This is then incorporated into a carbon emission flow model to determine the carbon emissions at each node. The second step is to minimize equipment investment, operating costs, and carbon tariff costs, calculating the new power flow and carbon emission flow. Iterative calculations are then performed to achieve the optimal result. The carbon emission flow model calculates detailed carbon emissions, and the imposition of carbon tariffs is used to encourage investment in renewable energy equipment and green ammonia production.

[0207] To ensure a scalable, continuous, and stable hydrogen supply, a process model for stabilizing hydrogen and ammonia synthesis was proposed. A detailed alkaline electrolyzer design was first developed to optimize operation. Energy storage was then incorporated to balance fluctuations, and chance constraints were used to address the uncertainty of renewable energy. A carbon emissions model was then added to track and refine carbon emissions from the production of gray, blue, and green ammonia. Finally, a carbon tax was implemented to achieve a green transition in the industrial ammonia synthesis process.

[0208] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A low-carbon planning method based on carbon emission flow, characterized in that: Including steps: Construct an initial cost model based on the hydrogen production plant cost model and the distribution network cost model; Solving the initial cost model with the goal of minimizing the cost of the hydrogen production plant and the distribution network, and obtaining power flow distribution data and the initial minimum cost; Calculating carbon emission data corresponding to each hydrogen production plant based on the power flow distribution data; Adding a carbon cost model to the initial cost model to obtain a target cost model; Solving the target cost model with the minimum cost of the hydrogen production plant, the minimum cost of the distribution network, and the minimum carbon cost to obtain updated power flow distribution data and updated minimum cost; Calculate the updated carbon emission data corresponding to each hydrogen production plant based on the updated power flow distribution data; Determine whether the difference between the updated minimum cost and the initial minimum cost is less than a preset value, and if so, output the updated power flow distribution data, the updated minimum cost, and the updated carbon emission data; The construction of the initial cost model based on the hydrogen production plant cost model and the distribution network cost model includes: Constructing distribution network constraints and hydrogen production constraints; The construction of hydrogen production constraints includes: Construct blue ammonia production constraints and green ammonia production constraints respectively; The determining whether the difference between the updated minimum cost and the initial minimum cost is less than a preset value further includes: If not, return to the step of solving the target cost model with the minimum hydrogen plant cost, the minimum distribution network cost, and the minimum carbon cost to obtain updated power flow distribution data and update the minimum cost; Before calculating the carbon emission data corresponding to each hydrogen production plant according to the flow distribution data, the method includes: S301. Constructing a carbon emission model during the green ammonia production process; The carbon emission flow distribution depends on the power injection of various resources into node i. For the green ammonia plant node with battery energy storage, its node carbon density ρ i，t The calculation is as follows: Among them, P j B is the power entering the node, is the node carbon density of the battery, and The carbon emissions and storage capacity of the battery are respectively are the charging and discharging power of the battery, is the initial storage capacity of the battery; Ωi represents the set of nodes i; Green ammonia production process carbon emissions CO Green The calculation is as follows: Among them, CO EL Represents carbon emissions during hydrogen production; CO ASR represents the carbon emissions from the ammonia synthesis process; and are the electricity consumed by the electrolyzer and synthetic ammonia respectively; ρ i，t represents the carbon density of node i at time t; S302, constructing a carbon emission model during the blue ammonia generation process; Carbon density of natural gas nodes and carbon potential R g The calculation formula is as follows, in, is the carbon density of the gas pipeline branch l, f l is the natural gas flow of branch l, ρ w is the carbon density of gas w, f w is the injected gas flow rate, L is the lower heating value of the gas; Gray ammonia CO Grey The carbon emissions from the production process include the carbon emissions from the methane hydrogen production process, CO SMR and synthetic ammonia CO ASR Carbon emissions from the process; The methane hydrogen production process includes carbon emissions from natural gas and electricity consumption, and blue hydrogen carbon emissions CO blue The carbon emissions absorbed by the carbon capture device are subtracted from the grey hydrogen. ccs : WHAT blue =WHAT grey -WHAT ccs ; Calculating the carbon emission data corresponding to each hydrogen production plant according to the flow distribution data includes: The carbon emission data corresponding to each hydrogen production plant is calculated based on the flow distribution data, the carbon emission model during the green ammonia production process, and the carbon emission model during the blue ammonia production process. The carbon emission data includes the carbon emissions of each device and the carbon emissions generated by different ammonia production plants.

2. A low-carbon planning method based on carbon emission flow according to claim 1, characterized in that: The construction of the initial cost model based on the hydrogen production plant cost model and the distribution network cost model includes: mimC′=C APS +C PDN ; Wherein, C' represents the initial cost model; C APS represents the cost of hydrogen production plant; C PDN represents the cost of the distribution network; C APS,INV Indicates the investment amount of equipment required for the hydrogen production plant; C S APS,OM Indicates the cost of operating and maintaining the equipment required for the hydrogen production plant; D S Indicates the number of operating days of the equipment in the hydrogen production plant in a calculation cycle; r k represents the capital recovery factor of equipment k in the hydrogen production plant; n represents the service life of the equipment k; X i Indicates whether the device k is selected; C i INV,k Indicates the investment amount corresponding to the k-th device in the i-th node; represents the operation and maintenance cost of device w at time t in scenario s of node i; Indicates the electricity purchase price; Indicates the amount of electricity purchased by the hydrogen production plant from the distribution grid; Indicates the gas purchase price; represents the gas purchase volume; Δt represents the time interval between two moments t; represents the operation and maintenance cost coefficient of the electrolyzer; represents the power consumption of the electrolyzer at time t in scenario s of node i; represents the operation and maintenance cost coefficient of the carbon capture device; represents the power consumption of the carbon capture device at time t in scenario s at node i; Indicates the battery operation and maintenance cost coefficient; represents the charging power of the battery of node i at time t in scenario s; represents the discharge power of the battery of node i at time t in scenario s; Indicates the investment cost of the fan equipment; represents the amount of electricity purchased by the distribution network from the main grid at time t in scenario s at node i; Indicates the operation and maintenance cost of the gas turbine; represents the electrical power generated by the combustion engine; represents the operation and maintenance cost of the wind turbine; Indicates the electrical power generated by the fan.

3. A low-carbon planning method based on carbon emission flow according to claim 1, characterized in that: The distribution network constraints are constructed as follows: Among them, P ij,t k represents the per-unit value of the active power flow between nodes i and j at time t; ij2 and k ij1 is the preset coefficient; x ij represents the per-unit reactance of the line between node i and node j at time t; θ i，t represents the per-unit value of the bus phase at node i at time t; V i，t represents the per-unit value of the voltage amplitude at node i at time t; Q ij，t represents the per-unit value of the reactive power flow of the line between node i and node j at time t; P i，t represents the per-unit value of the injected active power of node i at time t; N B Indicates the total number of distribution network nodes; Q i，t represents the per-unit value of the injected reactive power of node i at time t; r ij represents the per-unit value of the resistance of the line between node i and node j at time t; S N Indicates the benchmark capacity of the power grid system; N S Indicates the total number of configured scenarios; represents the injected reactive power of the wind turbine at time t in the scenario of node i; represents the electric power generated by the wind turbine at time t in the scenario of node i s; represents the reactive power injected by the motor at time t in the scenario of node i s; represents the active power generated by the motor at time t in the scenario of node i s; represents the injected reactive power at time t in the scenario of node i s; Represents the power purchased from the distribution network at time t in the scenario of node i s.

4. A low-carbon planning method based on carbon emission flow according to claim 1, characterized in that: The blue ammonia production constraints are constructed as follows: in, represents the hydrogen production during the natural gas hydrogen production process at time t in scenario s at node i; η SMR Indicates the conversion efficiency of natural gas to hydrogen; represents the amount of natural gas consumed in the natural gas hydrogen production process at time t in scenario s at node i; represents the power consumption of hydrogen production from natural gas at time t in scenario s at node i; Indicates that node i is producing hydrogen from natural gas; represents the electrical efficiency of the SMR device; Indicates the upper limit of gas consumption for hydrogen production from natural gas; represents the power consumption of the carbon capture device at node i at time t in scenario s; e c Indicates the energy consumption of the carbon capture device to process unit mass of carbon dioxide; represents the amount of carbon dioxide absorbed by the carbon capture device at node i at time t in scenario s; represents the fixed consumed electric power of the carbon capture device at node i at time t in scenario s; represents the upper limit of power consumption of the carbon capture device at node i; represents the amount of hydrogen consumed during the blue ammonia production process at time t in scenario s of node i.

5. The low-carbon planning method based on carbon emission flow according to claim 1 is characterized in that: The green ammonia production constraints are constructed as follows: Constructing Power-to-Hydrogen Constraints: in, represents the amount of hydrogen produced by the electrolyzer at time t in scenario s at node i; a j and b j is the related linear coefficient; T represents the total set of moments; represents the amount of electricity consumed by the electrolyzer at time t in scenario s at node i; represents the power consumed by the hydrogen compressor at time t in scenario s at node i; R h represents the specific heat capacity constant of hydrogen; T in represents the temperature of hydrogen gas input to the compressor; κ represents the isentropic index of hydrogen gas; η co Indicates the operating efficiency of the compressor; V out Indicates the pressure at the compressor outlet; V in Indicates the pressure at the compressor inlet; represents the upper limit of power consumed by the hydrogen compressor at node i; represents the amount of hydrogen stored at time t in scenario s of node i; Constructing battery storage constraints: in, represents the battery storage capacity of node i at time t in scenario s; Indicates the battery charging efficiency; Indicates the discharge efficiency of the battery; represents the lower limit of the battery storage capacity of node i; represents the upper limit of the battery storage capacity of node i; represents the upper limit of charging power of node i; is a parameter indicating whether node i is charging at time t in scenario s; A parameter indicating whether node i discharges at time t in scenario s; represents the upper limit of the discharge power of node i; Constructing new energy constraints: in, represents the actual output of new energy at time t in node i scenario s; represents the predicted output of new energy at time t in node i scenario s; α i，s，t represents the prediction error of photovoltaic output; P() represents the calculated probability; Indicates the lower limit of the backup capacity provided by the battery; represents the upper limit of the backup capacity provided by the battery; ε represents the preset confidence level.

6. A low-carbon planning method based on carbon emission flow according to claim 5, characterized in that: Also includes: Convert the new energy constraints into deterministic linear constraints based on chance constraints: Among them, (a ε ,b ε ,c ε ,d ε ) represents the parameter vector; represents the quantile with a nominal proportion of ε; represents the predicted output power of the renewable energy at time t in scenario s at node i; Assume that the explanatory variable is x s , represents the predicted output power of the new energy, and the deterministic linear constraint is converted into: z ε =(a ε ,b ε ,c ε ,d ε ) T ; Construct a cost function and solve the cost function to obtain the value of the parameter vector: min∑ s∈S ρ ε (y s -f ε ( x s ,z ε )): Where S represents the sample size; f ε Represents the mapping relationship between the explanatory variable xs and the parameter vector; y s represents the actual output power of the observed new energy; ρ ε (x) represents the absolute function corresponding to the nominal proportion ε.

7. A low-carbon planning terminal based on carbon emission flow, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the low-carbon planning method based on carbon emission flow according to any one of claims 1 to 6 are implemented.

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