Transportation path optimization method and system for cooperative emission reduction of carbon emission, wind, light and solid waste

Abstract

The invention discloses a transportation path optimization method and system for carbon emission and wind, light and solid waste collaborative emission reduction, and the method comprises the steps: obtaining the type of a CO2 emission source according to the type, latitude and longitude and yield of an emission enterprise; acquiring the trapping cost according to the type of the emission source; according to the geographic information software in combination with the spatial geographic data, determining a transportation site type of the available construction pipeline, and according to the transportation site type, obtaining a transportation cost; according to the type and the spatial position of the geological sequestration place, obtaining sequestration cost and sequestration income; according to the wind and light place, determining the wind and light power generation potential to evaluate the potential and cost of hydrogen production by electrolysis of water, and obtaining the first carbon utilization cost and first carbon utilization income of methanol production by carbon dioxide hydrogenation; according to the type, latitude and longitude and yield of the solid waste, obtaining second carbon utilization cost and second carbon utilization benefit of utilizing carbon dioxide by the solid waste; and constructing a transportation optimization model, optimizing a transportation path by taking the minimization of the total cost as an optimization target, and realizing CO2 transportation and emission reduction.

Description

Technical Field

[0001] This invention belongs to the field of energy economics technology, and in particular relates to a transportation route optimization method and system for synergistic emission reduction of carbon emissions and wind, solar and solid waste. Background Technology

[0002] In the field of energy economics, traditional research often treats energy storage technologies for renewable energy generation such as wind, solar, and hydropower, and carbon dioxide capture, transport, utilization, and storage (CCUS) as two independent and parallel emission reduction pathways. However, this separate research approach ignores the potential synergistic effect between renewable energy and carbon dioxide emission reduction; traditional direct air capture methods are expensive due to the low concentration of carbon dioxide in the air, which limits the economics and feasibility of green methanol production.

[0003] In the field of source-sink matching pipeline network design, previous research has focused primarily on carbon dioxide sequestration, such as deep saline aquifer sequestration, oil displacement sequestration, or gas displacement sequestration, while paying less attention to the feasibility of utilizing carbon dioxide as a "sink" and integrating it into transportation pipeline network design. Existing technologies have failed to adequately consider incorporating carbon dioxide utilization into transportation pipeline network design, thus missing the opportunity to reduce carbon emission reduction costs through resource utilization.

[0004] Furthermore, in research on green methanol, most studies use direct air capture (DAC) as the source of carbon dioxide. However, due to the low concentration of carbon dioxide in the air, capture costs remain high, and existing technologies have certain limitations in achieving a low-carbon economy and resource recycling. In addition, existing technologies are not economically efficient in selecting carbon dioxide sources for green methanol production, failing to fully utilize industrial carbon dioxide emissions, thus reducing overall efficiency and feasibility. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a transportation route optimization method and system for synergistic emission reduction of carbon emissions, wind and solar power, and solid waste. This method breaks through the limitations of traditional emission reduction routes by organically combining renewable energy power generation and storage, steel slag solidification, and carbon dioxide capture, transportation, utilization, and storage (CCUS). It constructs a comprehensive solution that integrates carbon emission reduction, efficient utilization of renewable energy, and solid waste utilization, thereby solving the problems existing in the prior art.

[0006] To achieve the above objectives, in a first aspect, the present invention provides a transportation route optimization method for synergistic emission reduction of carbon emissions and wind, solar, and solid waste, comprising:

[0007] Based on the type, latitude and longitude, and output of the emitting enterprise, the CO2 emission source type is obtained; based on the emission source type, the capture cost is obtained;

[0008] Based on geographic information software and spatial geographic data, the types of transportation locations where pipelines can be constructed are determined, and the transportation costs are obtained based on these transportation location types.

[0009] Based on the type and spatial location of the geological sequestration site, the sequestration cost and sequestration revenue are obtained;

[0010] Based on the location of wind and solar power, the potential of wind and solar power generation is determined to assess the potential and cost of hydrogen production by water electrolysis, and the first carbon utilization cost and first carbon utilization benefit of methanol production by carbon dioxide hydrogenation are obtained.

[0011] Based on the type, latitude, longitude, and output of solid waste, the second carbon utilization cost and second carbon utilization benefit of solid waste using carbon dioxide are obtained;

[0012] Based on the capture cost, transportation cost, storage cost, storage revenue, first carbon utilization cost, first carbon utilization revenue, second carbon utilization cost, and second carbon utilization revenue, a transportation optimization model is constructed. With the goal of minimizing total cost, the transportation route is optimized to achieve CO2 transportation and emission reduction.

[0013] Preferably, the types of CO2 emission sources include:

[0014] The sources of CO2 emissions are determined based on the specific locations of the emitting enterprises; these emitting enterprises include: coal-fired power plants, steel mills, cement plants, and chemical plants.

[0015] CO2 emissions are calculated based on the output of the emitting companies and their corresponding emission coefficients.

[0016] Preferably, the cost of obtaining CO2 capture includes:

[0017] Based on CO2 concentration and literature research, assess the capture cost for each emitting enterprise;

[0018] Considering the CO2 capture technologies of different emitting enterprises, a specific analysis of the capture cost for each emitting enterprise is conducted.

[0019] Preferably, the transportation costs include:

[0020] The impact of social, geographical, and geological factors on pipeline construction should be considered.

[0021] Quantify the impact of various influencing factors on pipeline construction costs;

[0022] Cost surface data for different influencing factors are constructed using geographic information system methods.

[0023] Preferably, the first carbon utilization cost and the first carbon utilization benefit of obtaining methanol from carbon dioxide hydrogenation include:

[0024] The potential for hydrogen production through water electrolysis is determined based on the latitude and longitude of each city and its wind and solar power generation potential.

[0025] Based on the potential for hydrogen production through water electrolysis in each city, the potential for producing green methanol through CO2 hydrogenation is obtained.

[0026] Based on the potential of CO2 hydrogenation to methanol, the cost and benefit of first carbon utilization are obtained.

[0027] Preferably, the costs and benefits of obtaining carbon dioxide from solid waste include:

[0028] Based on the output of the steel plant, the output of steel slag is obtained, and based on the output of steel slag, the potential for CO2 solidification by steel slag is obtained.

[0029] Based on the CO2 solidification potential of the steel slag, the second carbon utilization cost and the second carbon utilization benefit of solid waste carbon dioxide utilization are obtained.

[0030] Preferably, optimizing the transportation route includes:

[0031] Based on the capture cost, transportation cost, storage cost, storage revenue, first carbon utilization cost, first carbon utilization revenue, second carbon utilization cost, and second carbon utilization revenue, the total carbon transportation cost is obtained.

[0032] Based on the principle of minimizing total cost, optimize the CO2 capture, transportation, and storage processes while satisfying capture constraints, storage constraints, transportation constraints, and CCUS objective constraints.

[0033] Secondly, the present invention provides a transportation route optimization system for synergistic emission reduction of carbon emissions and wind, solar, and solid waste, comprising:

[0034] The first acquisition module is used to acquire the CO2 emission source type based on the type, latitude and longitude, and output of the emitting enterprise; and to obtain the capture cost based on the emission source type.

[0035] The second acquisition module is used to determine the transportation location type of available pipelines based on geographic information software and spatial geographic data, and to obtain the transportation cost based on the transportation location type.

[0036] The third acquisition module is used to obtain the storage cost and storage revenue based on the type and spatial location of the geological storage site;

[0037] The fourth acquisition module is used to determine the wind and solar power generation potential based on the wind and solar location in order to assess the potential and cost of hydrogen production by water electrolysis, and to obtain the first carbon utilization cost and first carbon utilization benefit of methanol production by carbon dioxide hydrogenation.

[0038] The fifth acquisition module is used to obtain the second carbon utilization cost and second carbon utilization benefit of solid waste using carbon dioxide based on the type, latitude and longitude, and output of solid waste.

[0039] The route optimization module is used to construct a transportation optimization model based on the capture cost, transportation cost, storage cost, storage revenue, first carbon utilization cost, first carbon utilization revenue, second carbon utilization cost, and second carbon utilization revenue. The optimization objective is to minimize the total cost and optimize the transportation route to achieve CO2 transportation and emission reduction.

[0040] Thirdly, the present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0041] Fourthly, the present invention also discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.

[0042] Compared with the prior art, the present invention has the following advantages and technical effects:

[0043] This invention provides a transportation route optimization method for synergistic emission reduction of carbon emissions through wind, solar, and solid waste. First, the CO2 emission source type is obtained based on the type, latitude, longitude, and output of the emitting enterprise. Based on the emission source type, the capture cost is calculated. Second, using geographic information software combined with spatial geographic data, the type of transportation location suitable for pipeline construction is determined, and the transportation cost is calculated based on the transportation location type. Third, based on the type and spatial location of the geological storage site, the storage cost and storage revenue are calculated. Next, based on the wind and solar power location, the wind and solar power generation potential is determined to assess the potential and cost of hydrogen production through water electrolysis, resulting in the first carbon utilization cost and first carbon utilization revenue for methanol production via carbon dioxide hydrogenation. Further, based on the type, latitude, longitude, and output of solid waste, the second carbon utilization cost and second carbon utilization revenue for carbon dioxide utilization in solid waste are calculated. Finally, based on the capture cost, transportation cost, storage cost, storage revenue, first carbon utilization cost, first carbon utilization revenue, second carbon utilization cost, and second carbon utilization revenue, a transportation optimization model is constructed. With the goal of minimizing total cost, the transportation route is optimized to achieve CO2 transportation and emission reduction.

[0044] This invention utilizes clean energy generated by wind and solar power to convert carbon dioxide captured in industrial waste gas into green methanol and combines solid waste steel slag with carbon dioxide to produce building materials, thereby effectively reducing carbon dioxide emissions, improving the utilization rate of wind and solar power, and achieving efficient energy conversion and sustainable environmental protection.

[0045] Furthermore, by introducing carbon dioxide utilization functionality into the design of carbon transport pipelines, this invention breaks through the limitations of traditional storage-based methods, further reduces carbon emission reduction costs, and achieves economic benefits through the production of green products, thereby optimizing the overall effectiveness and rationality of the carbon emission reduction system.

[0046] Meanwhile, by using industrial waste gas rather than direct air capture as the main source of carbon dioxide, this invention significantly reduces capture costs, improves the economics and feasibility of green methanol production, and provides an innovative solution for the efficient utilization of carbon resources and the deep integration of clean energy. Attached Figure Description

[0047] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0048] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the research framework for the suitability evaluation of the storage site in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of the transportation framework according to an embodiment of the present invention. Detailed Implementation

[0051] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0052] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0053] Example 1

[0054] like Figure 1 This embodiment provides a transportation route optimization method for synergistic emission reduction of carbon emissions and wind, solar, and solid waste, including:

[0055] S1. Based on the type, latitude and longitude, and output of the emitting enterprise, obtain the CO2 emission source type; based on the emission source type, obtain the capture cost;

[0056] Furthermore, the types of CO2 emission sources to be identified include:

[0057] The sources of CO2 emissions are determined based on the specific locations of the emitting enterprises; these emitting enterprises include: coal-fired power plants, steel mills, cement plants, and chemical plants.

[0058] CO2 emissions are calculated based on the output of the emitting companies and their corresponding emission coefficients.

[0059] Example: Datang Keqi Coal-to-Gas Phase I, longitude 117.552393, latitude 43.27093. Emissions calculation is based on production and emission coefficient, i.e., emissions = emission coefficient (R) × production capacity (tons). The emission coefficient varies depending on the technology and needs to be specific to each type of enterprise. For example, coal chemical industry includes direct coal-to-oil and indirect coal-to-oil. The coefficient for direct coal-to-oil is 3.33 tons of CO2 / ton of oil, while that for indirect coal-to-oil is 5.1. China Shenhua Coal Indirect Oil Chemical Co., Ltd. has a production of 180,000 tons / year, and its carbon emissions are 180,000 × 5.1 = 918,000 tons. In addition to calculating the annual emissions, emission sources also need to be screened. For example, sources with very little remaining lifespan are not considered. For a project with a 30-year lifespan, for example, if the remaining lifespan is 15 years, capture will cease after 15 years.

[0060] Furthermore, the cost of CO2 capture includes:

[0061] Based on CO2 concentration and literature research, assess the capture cost for each emitting enterprise;

[0062] Considering the CO2 capture technologies of different emitting enterprises, a specific analysis of the capture cost for each emitting enterprise is conducted.

[0063] Specifically, the cost of capture is mainly affected by CO2 concentration, which varies across industries due to differences in CO2 concentration. This is primarily based on literature review and existing projects. The emission source types considered in this patent can be summarized in the following table:

[0064] Table 1

[0065]

[0066] S2. Based on geographic information software and spatial geographic data, determine the transportation location types for which pipelines can be constructed, and obtain the transportation cost based on the transportation location types.

[0067] Furthermore, the transportation costs include:

[0068] The impact of social, geographical, and geological factors on pipeline construction should be considered.

[0069] Quantify the impact of various influencing factors on pipeline construction costs;

[0070] Cost surface data for different influencing factors are constructed using geographic information system methods.

[0071] Specifically, the influencing factors of pipeline construction include social, geographical, and geological factors. Based on this principle, the impact of these factors on pipeline construction is quantitatively analyzed to lay the foundation for subsequent pipeline route selection. The research framework of this embodiment is as follows: Figure 2As shown, the main research approach is as follows: ① First, obtain detailed geographical, geological, and social factors affecting pipeline construction, mainly including geospatial data of rivers, lakes, reservoirs, railways, highways, nature reserves, ecological functional zones, earthquake zones, topographic relief, and urban and rural settlements; ② Perform standard data processing and conversion, unifying the data into raster layer data with a precision of 1km×1km cell size; ③ Reclassify the raster layers, based on the proportion of different influencing factors' impact on pipeline construction costs in existing studies, to obtain cost raster layers for different influencing factors; ④ Perform map algebra operations, mathematically summing and superimposing the cost raster layers for different influencing factors to obtain a comprehensive cost raster layer for all influencing factors. Figure 3 The diagram shows a schematic of the transportation framework.

[0072] S3. Based on the type and spatial location of the geological storage site, obtain the storage cost and storage revenue;

[0073] S301, Assessment of Storage Potential;

[0074] A schematic diagram of the research framework for the suitability assessment of the storage site, as shown below. Figure 2 As shown.

[0075] Seven indicators affecting the suitability of CO2 storage sites were selected, divided into two categories: four prohibitive indicators and three restrictive indicators. Prohibitory indicators indicate that the construction of CO2 storage infrastructure in this area is prohibited or should be avoided. Considering the potential safety issues of CO2 geological storage, CCUS infrastructure construction should be strictly limited, including being located away from towns, surface rivers and lakes, reservoirs, and ponds. Restrictive indicators indicate that the construction of CO2 storage infrastructure in this area requires special application and approval, as well as relevant safety and economic assessments. These mainly include nature reserves, ecological functional zones, and earthquake zones.

[0076] The assessed CO2 storage basin area was divided into planar subdivisions based on prefecture-level city administrative regions, and these subdivisions were used as basic assessment unit grids. The CO2 geological storage suitability evaluation indicators are shown in Table 2.

[0077] Table 2

[0078]

[0079] S302, the potential for deep saline aquifer sequestration;

[0080] CO2 injected into deep saline aquifers will gradually dissolve in the water and eventually reach saturation. The theoretical CO2 sequestration potential of deep saline aquifers is assessed using a methodology proposed by the U.S. Department of Energy. A total of 24 major candidate sedimentary basins were identified, including 16 onshore basins and 8 offshore basins. The total CO2 sequestration potential of onshore deep saline aquifers is 2,288.2 billion tons, and the total CO2 sequestration potential of offshore deep saline aquifers is 776.8 billion tons.

[0081]

[0082] In the formula: This indicates the sequestration potential of CO2 in deep saline aquifers, expressed in kg; A represents the area of ​​the deep saline aquifer, expressed in m². 2 h represents the thickness of the deep saline aquifer, in meters (m). The porosity of deep saline aquifers is expressed as a percentage (%). This represents the density of CO2 under standard pressure, taken as 1.977 kg / m³. 3 E saline This represents the effective CO2 sequestration factor in deep saline aquifers, which is 2.4%.

[0083] S303, oil displacement and storage;

[0084] China has a total of 19 oil-bearing basins, including 16 onshore basins and 3 offshore basins. The following methods are used to assess the theoretical CO2 storage potential of these oil-bearing areas.

[0085] First, calculate the original crude oil geological reserves. OOIP represents the original crude oil geological reserves, UUR represents the final recoverable reserves, and API represents the crude oil density.

[0086]

[0087] Next, calculate the crude oil reserves that can come into contact with CO2, OOIP. c This represents the crude oil reserves that can come into contact with CO2, where C is the contact coefficient between CO2 and crude oil, which is 75% in this example.

[0088] OOIP c =OOIP×C (3)

[0089] The amount of oil enhanced by CO2 is recalculated, which is mainly related to the API of crude oil. EOR represents the total increase in oil production caused by CO2-driven enhanced oil recovery. EXTRA is the ratio of the enhanced oil production to OOIP, i.e., the displacement coefficient, in units of %.

[0090] EOR = EXTRA × OOIP c (4)

[0091]

[0092] Finally, the amount of CO2 that can be sequestered is calculated, where MCO2 represents the amount of CO2 that can be sequestered, and R... low-CO2 and R high-CO2 The ratios of net CO2 injection to enhanced crude oil production are 0.336 and 0.560 tons of CO2 per barrel of crude oil (2.113 and 3.552 t / m³, respectively). 3 ); P low-CO2 and P high-CO2 This indicates the percentage of low CO2 / crude oil replacement rates and high CO2 / crude oil replacement rates within an oil-bearing block.

[0093] MCO2=EOR×(P low-CO2 ×R low-CO2 +P high-CO2 ×R high-CO2 (6)

[0094] S304, Gas-expelled sealing;

[0095] The theoretical CO2 storage potential assessment of gas-bearing areas adopts the method of Liu et al., which assumes that the entire volume evacuated due to natural gas production can be refilled with CO2.

[0096]

[0097] In the formula: MCO2 represents the amount of CO2 that can be stored; 0.75 is an effective storage capacity coefficient used to represent the inefficiency of field-based replacement-based storage; R OGIP This indicates the recoverable natural gas resource quantity, representing the technically recoverable quantity without considering economic factors; This indicates the molar replacement ratio of CO2 to CH4 under in-situ pressure and temperature conditions; The density of CO2 under standard conditions is 1.98 kg / m³. 3 ).

[0098] R OGIP =OGIP×R s (8)

[0099] In the formula: OGIP represents the original geological resources of natural gas; R s This represents the recyclability coefficient used to estimate the recyclability of OGIP.

[0100]

[0101] In the formula: z represents the depth of the gas field.

[0102] S305, Unmineable coal seam sealing;

[0103] The calculation of CO2 storage potential is derived from the method proposed by Hendriks et al., as shown in formula (10):

[0104]

[0105] In the formula: denoted as the amount of CO2 that can be stored in the coal seam, in kg; a represents the proportion of recoverable coalbed methane basins to the total basin, which is taken as 10% in this embodiment, based on the experience of countries such as the United States. This represents the density of CO2 under standard pressure, taken as 1.977 kg / m³. 3 G i C represents the amount of coal resources at coal seam location i, expressed in t; ij The amount of coal resources of type j in coal seam sealing site i, t; C i The total coal resources of coal seam location i, t; RF ij ER represents the assumed coalbed methane recoverability coefficient for type j coal within reservoir i; ij This represents the replacement ratio of CO2 to methane (CH4) in coal of type j within reservoir i. The CH4 recovery rate and CO2 / CH4 replacement rate differ for different coal types.

[0106] S4. Based on the location of wind and solar power, determine the potential of wind and solar power generation to assess the potential and cost of hydrogen production by water electrolysis, and obtain the first carbon utilization cost and first carbon utilization benefit of methanol production by carbon dioxide hydrogenation.

[0107] Furthermore, the first carbon utilization cost and the first carbon utilization benefit of carbon dioxide hydrogenation to methanol include:

[0108] The potential for hydrogen production through water electrolysis is determined based on the latitude and longitude of each city and its wind and solar power generation potential.

[0109] Based on the potential for hydrogen production through water electrolysis in each city, the potential for producing green methanol through CO2 hydrogenation is obtained.

[0110] Based on the potential of CO2 hydrogenation to methanol, the cost and benefit of first carbon utilization are obtained.

[0111] Specifically, wind power and solar power potential assessment: Select suitable deployment sites for onshore wind power, offshore wind power, and solar power; assess the potential of capacity factors, installed capacity, and power generation; and analyze the spatiotemporal differences of renewable energy resource potential with high spatiotemporal resolution. The modeling framework follows these steps:

[0112] (1) Based on the hourly (time scale) wind speed, solar radiation intensity and temperature data of the MERRA-2 dataset released by NASA during the 42 years from 1980 to 2021, estimate the hourly wind energy and solar photovoltaic power generation and capacity factor of each grid cell (spatial scale) in China at 0.5 latitude × 0.625 longitude.

[0113] (2) A Geographic Information System (GIS) analysis was conducted, taking into account land use and topography, as well as wind and solar energy resource information obtained in step (1), to assess whether each site is suitable for development.

[0114] (3) By aggregating the hourly power generation of each grid across provinces and grid regions, this study describes the total development potential of onshore wind power, offshore wind power, and solar photovoltaic power, as well as their geographical distribution and disparities. Furthermore, by integrating hourly power generation at suitable locations, the temporal (monthly or daily) variations and fluctuations in renewable energy power generation were assessed. The specific methods are as follows:

[0115] S401, Real-time wind power generation potential assessment;

[0116] For the selection of onshore wind turbine specifications, it is assumed that the rated power of the turbine is 2.5MW, the blade diameter is 109m, and the hub height is 90m; for the selection of offshore wind turbine specifications, it is assumed that the rated power is 6.45MW, the blade diameter is 171m, and the hub height is 108m. Actual wind force equals theoretical wind force multiplied by the system efficiency coefficient, which is typically between 20% and 30%. In this chapter, the system efficiency coefficient for onshore wind turbines is 25.3%, and for offshore wind turbines it is 26.5%. Real-time wind speed data for onshore areas at 0.625° longitude and 0.5° latitude grids in China from 1980 to 2021 were obtained using NASA's MERRA-2, and the following relationships exist:

[0117]

[0118] The formula is the wind shear formula, V t This represents the real-time wind speed (m / s) at an altitude of 90m or 108m above the ground, where H represents 90m or 108m. 10 This represents a height of 10m above the ground, where α is the wind shear index (0.143), and V... t10 This represents the real-time wind speed (m / s) at 10m above the ground. P represents the real-time output (MW) of a single wind turbine. WR Indicates the rated power of the fan, V ci V represents the cut-in wind speed (3 m / s) and the cut-out wind speed (25 m / s). CO The rated wind speed is 12 m / s, C represents the system efficiency coefficient of the wind turbine, and A represents the swept area of ​​the wind turbine (m²). 3 ), ρ is the air density (1.29 kg / m³). 3 ).

[0119] Assuming that the optimal spacing for minimizing interference onshore wind turbines is equivalent to 9 rotor diameters (per 0.64 km), the rotor diameter for offshore wind turbines is approximately 7 x 7 rotor diameters (1.04 km). 2The area of ​​each latitude-longitude grid cell is divided by this value to calculate the maximum number of turbines that can fit into a given cell. It should be noted that this spacing does not account for downstream wake effects; the scale is too small to be accurately modeled using MERRA-2 data. Given that the average downstream power loss is approximately 5%, wake effects should not have a significant impact on the current results. Potential installed capacity is calculated by multiplying the number of turbines in the cell by the turbine power. Wind power installed capacity potential is determined by the maximum real-time output during the year, while wind power generation potential is determined by summing the real-time output per hour during the year, as shown in the following formula:

[0120]

[0121] In the formula, P W Gen w This represents the potential for wind power generation.

[0122] S402, Photovoltaic power generation potential assessment;

[0123] The photovoltaic (PV) resource potential calculation references the PVWatts model from the U.S. Renewable Energy Laboratory and considers the impact of various factors on resource potential. In this section, we select PV panels with a single-sided fixed tilt system. In this system, each province has an optimal tilt, azimuth angle, and spacing, which can enable the PV array to receive more solar radiation and increase the overall power generation of the solar PV system. Although bifacial systems and single-axis tracking systems increase power generation, single-sided fixed tilt installation systems are simpler, cheaper, and require less maintenance than tracking systems. Some studies assessing PV potential also assume the use of a fixed tilt system, considering factors such as optimal tilt angle, azimuth angle, and spacing to maximize power generation.

[0124] For a single photovoltaic module, assuming its rated power is 300W and its corresponding dimensions are 1.96m * 0.99m, the rated power of the photovoltaic module per unit area is:

[0125] P = P PV / S PV (15)

[0126] In the formula, P is the rated power of the photovoltaic module per unit area, P PV S represents the rated power of a single photovoltaic module. PV This refers to the area of ​​a single photovoltaic module.

[0127] The packing factor represents the effective photovoltaic panel area per square meter of land area, ranging from 0 to 1. The specific calculation formula is as follows:

[0128]

[0129] In the formula, the fill factor is determined by the solar altitude angle and azimuth angle at 3 PM on the winter solstice, as well as the optimal tilt angle of the photovoltaic module. The tilt angle of the photovoltaic module is β. n The solar altitude angle is the solar azimuth angle.

[0130] The losses in the photovoltaic system include the effects of soiling, shading, mismatch, wiring, connection, light-induced degradation, nameplate rating, and operational availability. As shown in Table 3, the loss values are listed.

[0131] Table 3

[0132]

[0133]

[0134] The total system loss is not the sum of individual losses. The total loss is calculated by multiplying the reduction rate of each loss factor, as shown in the formula:

[0135] [[ID=?]] [[ID=?]]

[0136] In the formula, β represents the total loss, L i and i represents the i-th loss mechanism.

[0137] The real-time wind speed data of 0.625° longitude and 0.5° latitude grid on land in China from 1980 to 2021 is obtained through NASA's MERRA-2, and there is the following relationship:

[0138]

[0139] In the formula, represents the real-time power (W) of a single-piece area photovoltaic module in t hours, P is the rated power (300W) of the photovoltaic module per unit area, R is the actual intensity of solar radiation in t hours (W / m [[ID=?]] 3 ),R [[ID=?]] STC is the light intensity under standard test conditions (1000W / m [[ID=?]] 2 ),γ is the temperature coefficient of the photovoltaic module (-0.47% / °C), T [[ID=?]] t represents the actual temperature (°C) of the photovoltaic module in t hours, T [[ID=?]] STC is the temperature under standard test conditions (25°C), β is the system efficiency of the photovoltaic module, and PF is the fill factor of the photovoltaic module.

[0140] The photovoltaic installation potential is determined by the maximum real-time output in a year, and the photovoltaic power generation potential is determined by summing the real-time outputs per hour in a year. The specific calculation formula is as follows:

[0141] It should be noted that there are some tags with "?" in the translation, which may be due to unclear or incorrect original content. You can check and correct the original text for a more accurate translation.

[0142] In the formula, P v Gen v Potential for photovoltaic power generation.

[0143] S403, Land use for wind and solar power;

[0144] The land use data comprises remote sensing data on land use types in 31 provinces (including municipalities) in 2018. The data comes from the Resource and Environment Data Cloud Platform of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, with a spatial resolution of 1km x 1km geographic grid. Land use types include seven primary types: forest, cultivated land, water area, grassland, unused land, and residential land, as well as 26 secondary types. Land use types are jointly determined by surface natural conditions and human development; different land use types are suitable for different variable renewable energy sources. Table 4 shows the land use types and suitable types for onshore wind power and solar photovoltaic deployments.

[0145] Table 4

[0146]

[0147] S404, potential assessment of methanol production from wind power and photovoltaic power;

[0148] The electricity generated by wind and solar power is divided into two parts: one part is used to produce hydrogen through water electrolysis, and the other part is used as power for the production of methanol from CO2 hydrogenation. The amount of CO2 consumed is determined based on the amount of wind or solar power generated. For example, if the wind power generation in a certain area is x kWh, the electricity consumption for producing one ton of methanol is y kWh, the hydrogen required for producing one ton of methanol is m tons, the electricity consumption for producing one ton of hydrogen is z kWh, and the CO2 consumption for producing one ton of methanol is q tons, then the total CO2 consumed Q can be calculated from the following relationship for producing w tons of methanol in this area:

[0149] Y×w+z×m×w=x (21)

[0150] Q = q × w (22)

[0151] S5. Based on the type, latitude and longitude, and output of solid waste, obtain the second carbon utilization cost and second carbon utilization benefit of solid waste using carbon dioxide.

[0152] Furthermore, the costs and benefits of obtaining carbon dioxide from solid waste through secondary carbon utilization include:

[0153] Based on the output of the steel plant, the output of steel slag is obtained, and based on the output of steel slag, the potential for CO2 solidification by steel slag is obtained.

[0154] Based on the CO2 solidification potential of the steel slag, the second carbon utilization cost and the second carbon utilization benefit of solid waste carbon dioxide utilization are obtained.

[0155] Specifically, the solidification of steel slag is calculated based on the steel production plus the amount of CO2 that can be absorbed. If producing one ton of steel generates N tons of steel slag, and one ton of steel slag can absorb K tons of CO2, then the amount of CO2 that the steel plant can absorb is T = K * N.

[0156] S6. Based on the capture cost, transportation cost, storage cost, storage revenue, first carbon utilization cost, first carbon utilization revenue, second carbon utilization cost, and second carbon utilization revenue, construct a transportation optimization model, optimize the transportation route with the goal of minimizing total cost, and achieve CO2 transportation and emission reduction.

[0157] Furthermore, optimizing transportation routes includes:

[0158] Based on the capture cost, transportation cost, storage cost, storage revenue, first carbon utilization cost, first carbon utilization revenue, second carbon utilization cost, and second carbon utilization revenue, the total carbon transportation cost is obtained.

[0159] Based on the principle of minimizing total cost, optimize the CO2 capture, transportation, and storage processes while satisfying capture constraints, storage constraints, transportation constraints, and CCUS objective constraints.

[0160] Specifically, S601, transportation model;

[0161] The total cost is the minimum difference (net expense) between the total cost of capture, transportation, and storage and the carbon sequestration revenue from oil and gas fields, unexploitable coal seams, and green methanol production. Objective function:

[0162]

[0163] S602, Constraints;

[0164] Capture and storage constraints stipulate that the amount of CO2 captured from any emission source shall not exceed its annual CO2 emissions, the amount of CO2 stored in any storage site during the planning period shall not exceed the total CO2 storage potential of the basin, and the amount of CO2 injected into any injection well in any storage site shall not be less than the annual storage volume of the basin.

[0165]

[0166] Transportation constraints: the transportation capacity of any pipeline is not less than the transportation volume to be carried, and the input and output of any emission source or storage node are kept in zero balance.

[0167]

[0168] The capture and storage of CCUS targets are constrained by the total capture amount in the coal-fired power industry being equal to the CCUS emission reduction target, and the total storage amount being equal to the total capture amount in the coal-fired power industry.

[0169] ∑i∈S a i -Amount_CCUS=0 (30)

[0170] ∑ j∈R b j -Amount_CCUS=0 (31)

[0171] Due to supply and demand constraints, coal-fired power production is equal to the electricity supply targets that must be met.

[0172] ∑ i∈S t i ·power i -Power_generation=0 (32)

[0173] Non-negativity constraints include: coal-fired power generation hours constraint; total capture volume of coal-fired power, steel and cement industries non-negative; injection volume at any storage site non-negative; and pipeline transportation volume non-negative.

[0174]

[0175] S603, Model Parameters and Decision Variables;

[0176] The parameters in the pipeline network optimization model are divided into variables and parameters. Table 5 lists the model parameters and decision variables.

[0177] Table 5

[0178]

[0179]

[0180]

[0181] Beneficial effects of this embodiment:

[0182] This embodiment innovatively combines renewable energy power generation with carbon dioxide capture, transport, utilization, and storage (CCUS) technology, using wind and solar power to drive the synthesis of green methanol from carbon dioxide and hydrogen. This solution not only solves the problem of power curtailment caused by the volatility of traditional wind and solar power generation, but also significantly improves the overall efficiency of energy utilization. Combining wind and solar power generation with CCUS technology not only provides a new path for efficient carbon dioxide emission reduction, but also further promotes the clean energy transition and lays a technological foundation for achieving coordinated development of energy and the environment.

[0183] In terms of carbon transport pipeline design, this embodiment breaks through the limitations of traditional methods that solely aim at carbon sequestration, incorporating carbon dioxide resource utilization as a crucial component of the "sink" for the first time. By adding branch pipelines leading to the green methanol production base and the steel slag solidification plant, flexible carbon dioxide allocation is achieved. Part of the carbon dioxide is used to react with steel slag to produce building materials, while another part is used for green methanol synthesis. This approach not only reduces the high costs of traditional sequestration but also enhances the economics and feasibility of emission reduction technologies through resource utilization, forming an innovative model that emphasizes both sequestration and utilization.

[0184] In the green methanol production process, this embodiment improves the carbon dioxide source by shifting from traditional direct air capture to industrial waste gas within a reasonable transport radius. This method not only significantly reduces capture costs but also improves the economics of methanol production, making large-scale industrial applications more feasible. Furthermore, by combining industrial waste gas capture with wind and solar power generation, it achieves efficient synergistic utilization of energy and carbon resources, providing an economical and efficient technical solution for the large-scale promotion of green methanol.

[0185] Overall, this embodiment, by deeply integrating CCUS with renewable energy, constructs a new model that emphasizes both carbon dioxide sequestration and resource utilization, while optimizing the production path of green methanol. This solution achieves efficient carbon dioxide emission reduction and maximizes resource value, providing an innovative approach to a green, low-carbon, and circular economy, and possesses significant technological advantages and promotional value.

[0186] Example 2

[0187] Based on the same inventive concept, this embodiment also provides a transportation route optimization system for synergistic emission reduction of carbon emissions and wind, solar, and solid waste, including:

[0188] The first acquisition module is used to acquire the CO2 emission source type based on the type, latitude and longitude, and output of the emitting enterprise; and to obtain the capture cost based on the emission source type.

[0189] The second acquisition module is used to determine the transportation location type of available pipelines based on geographic information software and spatial geographic data, and to obtain the transportation cost based on the transportation location type.

[0190] The third acquisition module is used to obtain the storage cost and storage revenue based on the type and spatial location of the geological storage site;

[0191] The fourth acquisition module is used to determine the wind and solar power generation potential based on the wind and solar location in order to assess the potential and cost of hydrogen production by water electrolysis, and to obtain the first carbon utilization cost and first carbon utilization benefit of methanol production by carbon dioxide hydrogenation.

[0192] The fifth acquisition module is used to obtain the second carbon utilization cost and second carbon utilization benefit of solid waste using carbon dioxide based on the type, latitude and longitude, and output of solid waste.

[0193] The route optimization module is used to construct a transportation optimization model based on the capture cost, transportation cost, storage cost, storage revenue, first carbon utilization cost, first carbon utilization revenue, second carbon utilization cost, and second carbon utilization revenue. The optimization objective is to minimize the total cost and optimize the transportation route to achieve CO2 transportation and emission reduction.

[0194] The transportation route optimization system for synergistic emission reduction of carbon emissions, wind and solar power, and solid waste provided in this embodiment has all the advantages of the transportation route optimization method for synergistic emission reduction of carbon emissions, wind and solar power, and solid waste provided in Embodiment 1.

[0195] Example 3

[0196] This embodiment also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0197] Example 4

[0198] This embodiment also discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0199] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A transportation route optimization method for synergistic emission reduction of carbon emissions and wind, solar, and solid waste, characterized in that, Includes the following steps: Based on the type, latitude and longitude, and output of the emitting enterprise, the CO2 emission source type is obtained; based on the emission source type, the capture cost is obtained; Based on geographic information software and spatial geographic data, the types of transportation locations where pipelines can be constructed are determined, and the transportation costs are obtained based on these transportation location types. Based on the type and spatial location of the geological sequestration site, the sequestration cost and sequestration revenue are obtained; Based on the location of wind and solar power, the potential of wind and solar power generation is determined to assess the potential and cost of hydrogen production by water electrolysis, and the first carbon utilization cost and first carbon utilization benefit of methanol production by carbon dioxide hydrogenation are obtained. Based on the type, latitude, longitude, and output of solid waste, the second carbon utilization cost and second carbon utilization benefit of solid waste using carbon dioxide are obtained; Based on the capture cost, transportation cost, storage cost, storage revenue, first carbon utilization cost, first carbon utilization revenue, second carbon utilization cost, and second carbon utilization revenue, a transportation optimization model is constructed. With the goal of minimizing total cost, the transportation route is optimized to achieve CO2 transportation and emission reduction.

2. The method according to claim 1, characterized in that, The types of CO2 emission sources include: The sources of CO2 emissions are determined based on the specific locations of the emitting enterprises; these emitting enterprises include: coal-fired power plants, steel mills, cement plants, and chemical plants. CO2 emissions are calculated based on the output of the emitting companies and their corresponding emission coefficients.

3. The method according to claim 1, characterized in that, The cost of CO2 capture includes: Based on CO2 concentration and literature research, assess the capture cost for each emitting enterprise; Considering the CO2 capture technologies of different emitting enterprises, a specific analysis of the capture cost for each emitting enterprise is conducted.

4. The method according to claim 1, characterized in that, The transportation costs include: The impact of social, geographical, and geological factors on pipeline construction should be considered. Quantify the impact of various influencing factors on pipeline construction costs; Cost surface data for different influencing factors are constructed using geographic information system methods.

5. The method according to claim 1, characterized in that, The first carbon utilization costs and benefits of obtaining methanol from carbon dioxide hydrogenation include: The potential for hydrogen production through water electrolysis is determined based on the latitude and longitude of each city and its wind and solar power generation potential. Based on the potential for hydrogen production through water electrolysis in each city, the potential for producing green methanol through CO2 hydrogenation is obtained. Based on the potential of CO2 hydrogenation to methanol, the cost and benefit of first carbon utilization are obtained.

6. The method according to claim 1, characterized in that, The costs and benefits of secondary carbon utilization, which involves using carbon dioxide from solid waste, include: Based on the output of the steel plant, the output of steel slag is obtained, and based on the output of steel slag, the potential for CO2 solidification by steel slag is obtained. Based on the CO2 solidification potential of the steel slag, the second carbon utilization cost and the second carbon utilization benefit of solid waste carbon dioxide utilization are obtained.

7. The method according to claim 1, characterized in that, Optimizing transportation routes includes: Based on the capture cost, transportation cost, storage cost, storage revenue, first carbon utilization cost, first carbon utilization revenue, second carbon utilization cost, and second carbon utilization revenue, the total carbon transportation cost is obtained. Based on the principle of minimizing total cost, optimize the CO2 capture, transportation, and storage processes while satisfying capture constraints, storage constraints, transportation constraints, and CCUS objective constraints.

8. A transportation route optimization system for synergistic emission reduction of carbon emissions and wind, solar, and solid waste, characterized in that, include: The first acquisition module is used to acquire the type of CO2 emission source based on the type, latitude and longitude, and output of the emitting enterprise; The capture cost is obtained based on the type of emission source. The second acquisition module is used to determine the transportation location type of available pipelines based on geographic information software and spatial geographic data, and to obtain the transportation cost based on the transportation location type. The third acquisition module is used to obtain the storage cost and storage revenue based on the type and spatial location of the geological storage site; The fourth acquisition module is used to determine the wind and solar power generation potential based on the wind and solar location in order to assess the potential and cost of hydrogen production by water electrolysis, and to obtain the first carbon utilization cost and first carbon utilization benefit of methanol production by carbon dioxide hydrogenation. The fifth acquisition module is used to obtain the second carbon utilization cost and second carbon utilization benefit of solid waste using carbon dioxide based on the type, latitude and longitude, and output of solid waste. The route optimization module is used to construct a transportation optimization model based on the capture cost, transportation cost, storage cost, storage revenue, first carbon utilization cost, first carbon utilization revenue, second carbon utilization cost, and second carbon utilization revenue. The optimization objective is to minimize the total cost and optimize the transportation route to achieve CO2 transportation and emission reduction.

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

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-7.

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