Low-carbon power system planning method, system and device based on carbon network simulation and medium

Abstract

The invention discloses a low-carbon power system planning method, system and device based on carbon network simulation and a medium. The method comprises the steps of obtaining power system data, carbon ecological data, traffic energy consumption data and natural carbon sink data; performing weighted summation on the power system data and the traffic energy consumption data to obtain the total energy consumption, and comparing the total energy consumption with the natural carbon sink data to determine whether the total carbon is unbalanced; if the total carbon amount is unbalanced, obtaining total carbon amount imbalance information through time period analysis; generating a low-carbon emission reduction scheme based on the total carbon amount imbalance information; and performing multi-factor verification on the low-carbon emission reduction scheme, and adjusting and optimizing the low-carbon emission reduction scheme according to a verification result. According to the method, a scientific closed loop is formed from data acquisition, dynamic analysis, emission reduction and total factor verification, the problems of incomplete data and extensive analysis in the prior art are effectively solved, and scientificity, accuracy and engineering feasibility of low-carbon planning can be improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, system, equipment and medium for planning low-carbon power systems based on carbon grid simulation. Background Technology

[0002] The field of power grid security technology encompasses core aspects such as power system planning, operation, and security protection. It revolves around ensuring the stable and reliable operation of the power grid, involving various technologies in the processes of power supply, distribution, and transmission. It covers multiple aspects, including power generation equipment management, transmission line maintenance, and power dispatch optimization, aiming to enhance the power grid's ability to cope with various internal and external disturbances and ensure the continuity and security of power supply. Among these, the low-carbon power system planning method and system based on carbon grid simulation refers to applying key technologies such as carbon ecosystems, multi-grid integration, and low-carbon planning to the field of power grid planning in the context of tropical island scenarios. Specifically for the Hainan island-type power system, it conducts in-depth low-carbon planning technology research based on carbon ecosystems, performing top-level design and definition of the island's carbon ecosystem, forming a carbon grid architecture, and exploring its operating mechanisms and simulation methods. Simultaneously, it combines power systems, transportation systems, natural carbon sinks, and other high-carbon emission behaviors to conduct in-depth planning for low-carbon emission reduction on the island.

[0003] Existing technologies have significant shortcomings in the planning of low-carbon power systems for tropical islands. Specifically, data collection is not comprehensive enough, often omitting key data such as carbon ecosystems and transportation systems, resulting in incomplete basic data sets. When calculating total energy consumption, reasonable weighting based on energy strategies is not applied, making it difficult to accurately reflect the current energy consumption situation. The calculation of natural carbon sink absorption capacity ignores regional differences, leading to poor accuracy. Due to the lack of multi-dimensional comprehensive analysis, it is impossible to accurately assess the current energy situation and carbon balance, hindering the scientific and effective planning of low-carbon power systems. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a low-carbon power system planning method based on carbon grid simulation to solve the problems of incomplete data collection, inaccurate energy calculation, lack of zoning for carbon sink consideration, and insufficient comprehensive analysis in existing planning methods.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a low-carbon power system planning method based on carbon grid simulation, comprising:

[0008] Acquire data on power systems, carbon ecology, transportation energy consumption, and natural carbon sinks;

[0009] The power system data and traffic energy consumption data are weighted and summed to obtain the total energy consumption. The total energy consumption is then compared with natural carbon sink data to determine whether the total carbon balance is unbalanced.

[0010] If the total carbon balance is imbalanced, information on the total carbon balance imbalance is obtained through time period analysis.

[0011] Based on the aforementioned information on total carbon imbalance, a low-carbon emission reduction plan is generated.

[0012] The low-carbon emission reduction scheme was verified by multiple factors, and the scheme was adjusted and optimized based on the verification results.

[0013] As a preferred embodiment of the low-carbon power system planning method based on carbon grid simulation described in this invention, the weighted summation of the power system data and traffic energy consumption data includes:

[0014] Different weights were assigned to different power sources and modes of transportation.

[0015] Based on the aforementioned weights, and combined with the installed capacity of the power supply and the energy consumption of the vehicles, the total energy consumption is calculated using a weighted average method.

[0016] The natural carbon sequestration capacity was obtained by dividing the data into zones based on different seasons and geographical areas, and then using a weighted average method.

[0017] As a preferred embodiment of the low-carbon power system planning method based on carbon grid simulation described in this invention, the method includes: obtaining information on total carbon imbalance through time-period analysis, including:

[0018] Calculate the change in power generation of different power sources within a set time interval to obtain power output fluctuation data of different power sources;

[0019] Based on the time range of peak and trough traffic flow in historical traffic flow data, the peak and trough periods of traffic travel can be obtained.

[0020] Based on the power output fluctuation data of different power sources and the peak and off-peak periods of traffic, and combined with carbon ecological data analysis, the differences in carbon grid operation time are obtained to obtain information on the total carbon imbalance.

[0021] The beneficial effects of this preferred technical solution are as follows: by analyzing the dynamic correlation between power output fluctuations and traffic periods, the carbon emission characteristics of the power system and the transportation system in the time dimension are quantified, the time periods and scenarios of total carbon imbalance are accurately located, the traditional static planning ignores the "time coupling effect", making the low-carbon emission reduction solution more timely and targeted, and improving the carbon grid operation efficiency and carbon balance control accuracy.

[0022] As a preferred embodiment of the low-carbon power system planning method based on carbon grid simulation described in this invention, the method generates a low-carbon emission reduction plan based on the total carbon imbalance information, including:

[0023] Based on the aforementioned total carbon imbalance information, the carbon absorption of natural carbon sinks and the carbon emissions from high-carbon emission behaviors are obtained.

[0024] Calculate the difference between the carbon absorption of natural carbon sinks and the carbon emissions of high-carbon emission behaviors. Based on the difference, adjust the proportion of clean energy power generation in the power system and the proportion of clean energy transportation in the transportation system to generate a low-carbon emission reduction plan.

[0025] The beneficial effects of this preferred technical solution are as follows: It quantifies the carbon deficit based on the carbon balance difference, dynamically adjusting the proportion of clean energy based on the difference between natural carbon sink absorption and carbon emissions from high-carbon emission behaviors, achieving precise emission reduction by matching the amount needed to the deficit. This avoids resource waste caused by a one-size-fits-all approach and improves the scientific nature of emission reduction efficiency and energy structure optimization.

[0026] As a preferred embodiment of the low-carbon power system planning method based on carbon grid simulation described in this invention, the method includes: performing multi-factor verification on the low-carbon emission reduction scheme, and adjusting and optimizing the low-carbon emission reduction scheme based on the verification results, including:

[0027] Data on power transmission loss, traffic congestion duration, and seasonal fluctuations in natural carbon sinks are selected, weighted, and summed. The sum is then compared with pre-set reliability standard parameters for low-carbon planning to obtain verification results of the low-carbon emission reduction scheme.

[0028] The beneficial effects of this preferred technical solution are as follows: By integrating multi-dimensional constraints such as power transmission loss, traffic congestion duration, and seasonal fluctuations in carbon sequestration, a comprehensive reliability verification model is constructed. This ensures that the low-carbon emission reduction plan balances environmental goals and engineering feasibility in actual operation, avoids a disconnect between theoretical solutions and practical implementation, and improves the reliability and implementability of the plan.

[0029] As a preferred embodiment of the low-carbon power system planning method based on carbon grid simulation described in this invention, the data on power transmission losses, traffic congestion duration, and seasonal fluctuations in natural carbon sinks include:

[0030] Power transmission loss data is obtained by calculating the power line resistance, current, and line length.

[0031] The duration of traffic congestion periods is obtained by statistically analyzing the start and end times of congestion recorded by the traffic monitoring system.

[0032] Data on the seasonal fluctuations of natural carbon sinks were obtained by long-term monitoring of carbon absorption changes in carbon sink areas during different seasons.

[0033] As a preferred embodiment of the low-carbon power system planning method based on carbon grid simulation described in this invention, it further includes:

[0034] Different weights are assigned to the data on power transmission loss, duration of traffic system congestion, and seasonal fluctuations in natural carbon sinks to calculate the energy consumption power S.

[0035] The energy consumption power S is compared with the pre-set low-carbon planning reliability standard parameter S. standard The comparison results obtained;

[0036] Based on the comparison results, the degree of compliance of the low-carbon emission reduction scheme with the reliability standard is determined when considering multiple factors.

[0037] Secondly, the present invention provides a low-carbon power system planning system based on carbon grid simulation, including: a data acquisition module for acquiring power system data, carbon ecology data, transportation energy consumption data and natural carbon sink data;

[0038] The calculation module is used to perform a weighted summation of the power system data and traffic energy consumption data to obtain the total energy consumption.

[0039] The judgment module is used to compare the total energy consumption with natural carbon sink data to determine whether the total carbon balance is imbalanced; if the total carbon balance is imbalanced, information on the total carbon balance imbalance is obtained through time period analysis.

[0040] The scheme acquisition module is used to generate low-carbon emission reduction schemes based on the total carbon imbalance information.

[0041] The optimization module is used to perform multi-factor verification of the low-carbon emission reduction scheme and adjust and optimize the low-carbon emission reduction scheme based on the verification results.

[0042] Thirdly, the present invention provides an electronic device, comprising:

[0043] Memory and processor;

[0044] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of a low-carbon power system planning method based on carbon grid simulation.

[0045] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the low-carbon power system planning method based on carbon grid simulation.

[0046] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention constructs a basic dataset by comprehensively collecting data on the carbon ecology, electricity, transportation, and natural carbon sinks of tropical islands. Based on energy strategies, it assigns weights to power sources and transportation vehicles to accurately calculate total energy consumption. It also takes into account regional differences and uses a weighted average of natural carbon sink absorption capacity. Through multi-dimensional operations such as analyzing the relationship between power output fluctuations and traffic periods, and calculating carbon emissions from high-carbon emission behaviors, it accurately assesses the current energy status and carbon balance. This provides data support for formulating scientific and reasonable low-carbon power system plans, overcoming the shortcomings of existing technologies such as incomplete data collection, inaccurate energy calculations, lack of regional considerations for carbon sinks, and insufficient comprehensive analysis. This improves the effectiveness, reliability, and energy utilization efficiency of planning, and effectively promotes the achievement of carbon emission reduction targets. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the overall process of a low-carbon power system planning method based on carbon grid simulation according to an embodiment of the present invention. Detailed Implementation

[0049] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0050] Example 1, referring to Figure 1 As an embodiment of the present invention, a low-carbon power system planning method based on carbon grid simulation is provided, comprising:

[0051] S100: Acquire data on power systems, carbon ecology, transportation energy consumption, and natural carbon sinks;

[0052] S102: Weighted summation of power system data and traffic energy consumption data to obtain total energy consumption, and comparison of total energy consumption with natural carbon sink data to determine whether total carbon is unbalanced;

[0053] S104: If the total carbon balance is imbalanced, obtain information on the total carbon balance imbalance through time period analysis;

[0054] S106: Generate low-carbon emission reduction plans based on information on total carbon imbalance;

[0055] S108: Conduct multi-factor verification of low-carbon emission reduction schemes, and adjust and optimize the low-carbon emission reduction schemes based on the verification results.

[0056] It should be noted that by comprehensively acquiring data on electricity, carbon ecology, transportation energy consumption, and natural carbon sinks, a multi-dimensional data foundation is laid for planning. Weighted summation of electricity and transportation data, combined with natural carbon sink data, determines whether there is an imbalance in total carbon emissions. Differentiated weighting accurately reflects actual energy consumption and carbon sink capacity, avoiding biases caused by uniform standards. If an imbalance is found, peak carbon emission scenarios are identified through time-period analysis, such as the coupling period between power output fluctuations and peak traffic hours, enabling dynamic problem diagnosis. Based on the imbalance information, the proportion of clean energy is adjusted to generate emission reduction plans, specifically addressing the carbon gap. Finally, multi-factor verification ensures the reliability of the plan's implementation. This invention forms a scientific closed loop from data collection to dynamic analysis, precise emission reduction, and full-element verification, effectively solving problems such as incomplete data and extensive analysis in existing technologies. It can improve the scientific rigor, accuracy, and engineering feasibility of low-carbon planning, helping tropical islands achieve energy structure optimization and carbon emission reduction targets.

[0057] Example 2, refer to Figure 1 This is one embodiment of the present invention. Based on the above embodiment, a low-carbon power system planning method based on carbon grid simulation is provided.

[0058] In this embodiment of the invention, step S100, which involves acquiring power system data, carbon ecology data, transportation energy consumption data, and natural carbon sink data, specifically includes:

[0059] The collected data includes the carbon content of various substances in the tropical island carbon ecosystem, the installed power capacity of the power system, the energy consumption of different modes of transportation, and the natural carbon sink absorption capacity.

[0060] In this embodiment of the invention, the carbon content of each substance in the tropical island carbon ecosystem is specifically obtained through field sampling and analysis of carbon elements in island soil, vegetation, and marine organisms.

[0061] For example, taking a tropical island as an example, researchers selected 50 representative soil sampling points, 30 vegetation quadrats, and 20 marine biological sampling stations in different areas of the island.

[0062] Soil samples were obtained using professional soil sampling tools, and the soil organic carbon content was determined by chemical analysis. The average content was assumed to be 30 g / kg.

[0063] For vegetation, the biomass of various plants is measured, and the carbon content is determined by an elemental analyzer. For example, if the biomass of a dominant tree species is 1,000 tons and the carbon content is 45%, then its carbon content is 450 tons.

[0064] In this embodiment of the invention, the natural carbon sink absorption capacity data is obtained by measuring the area of ​​island forests and wetlands and the corresponding carbon absorption efficiency. Let the forest area be A. f The carbon absorption efficiency per unit area of ​​forest is R. f The wetland area is A w The carbon absorption efficiency per unit area of ​​wetland is R. w The natural carbon sequestration capacity is:

[0065] N = A f ×R f +A w ×R w

[0066] Assume the area of ​​the island's forest is A. f For a forest area of ​​500 square kilometers, the carbon absorption efficiency R per unit area is... f 10 tons / square kilometer per year; wetland area A w For a wetland area of ​​100 square kilometers, the carbon absorption efficiency R per unit area is... w 20 tons per square kilometer per year;

[0067] Substituting the data into the formula, we can obtain the island's natural carbon sink absorption capacity N = 500 × 10 + 100 × 20 = 5000 + 2000 = 7000 tons.

[0068] In this embodiment of the invention, regarding marine organisms, multiple marine organism sampling stations are set up in the waters surrounding the islands. Common fish, shellfish and other organisms are captured using sampling tools such as trawls and gillnets. After laboratory processing, their carbon content is measured. For example, if the average carbon content of a certain type of fish is 2g / individual, and the number of individuals captured in a unit area is 10,000, then the carbon content of that type of fish is 0.02 tons.

[0069] In this embodiment of the invention, the installed capacity of the power system is directly retrieved from the power system operation and management database. Log in to the power system operation and management database of the island to query the installed capacity data. Assume that the island currently has a thermal power installed capacity of 500MW, a wind power installed capacity of 200MW, and a photovoltaic installed capacity of 100MW.

[0070] In this embodiment of the invention, the energy consumption of different modes of transportation in the transportation system is calculated based on statistical data of vehicle type and mileage. Let vehicle type be i, and corresponding mileage be L. i Energy consumption per unit distance is E i Then the energy consumption C of this type of transportation vehiclei =L i ×E i ;

[0071] For example, we can count the main types of transportation on the island. Assume that the island's resident transportation includes 1,000 gasoline-powered cars, 500 electric cars, and 20 ships.

[0072] For gasoline-powered vehicles, the survey shows an average annual mileage of 10,000 kilometers and an average fuel consumption of 8 liters per 100 kilometers. One liter of gasoline weighs approximately 0.74 kg and contains about 0.85% carbon. Burning one ton of carbon produces about 3.67 tons of carbon dioxide. Therefore, the annual fuel consumption of each gasoline-powered vehicle is 10,000 ÷ 100 × 8 = 800 liters. The mass of 800 liters of gasoline is 800 × 0.74 = 592 kg, and the carbon content is 592 × 0.85 = 503.2 kg. The corresponding carbon dioxide emissions are approximately 503.2 ÷ 1000 × 3.67 ≈ 1.847 tons (this is a simplified calculation; the actual emission coefficient will vary due to various factors). The annual carbon emissions corresponding to the energy consumption of 1,000 gasoline-powered vehicles are approximately 1.847 × 1000 = 1,847 tons.

[0073] Based on the average driving distance and electricity consumption per unit distance, assuming the carbon emission per kilowatt-hour is 0.8 kg, and each electric vehicle travels 10,000 kilometers per year, consuming 0.2 kWh per kilometer, then each electric vehicle consumes 10,000 × 0.2 = 2,000 kWh of electricity per year, and the carbon emission is 2,000 × 0.8 = 1,600 kg = 1.6 tons. The carbon emission corresponding to the annual energy consumption of 500 electric vehicles is approximately 1.6 × 500 = 800 tons.

[0074] Based on a ship's power, voyage distance, and fuel consumption, assuming a ship has a power of 10,000 kW, sails for 1,000 hours per year, and a fuel consumption rate of 0.3 kg / kWh, then a ship's annual fuel consumption is 10,000 × 1,000 × 0.3 = 3,000,000 kg = 3,000 tons. Assuming the carbon content of the fuel is 0.8%, then a ship's annual carbon emissions are 3,000 × 0.8 = 2,400 tons. The annual carbon emissions corresponding to the energy consumption of 20 ships are approximately 2,400 × 20 = 48,000 tons.

[0075] In this embodiment of the invention, step S102 involves weighted summation of power system data and traffic energy consumption data to obtain total energy consumption, and comparison of total energy consumption with natural carbon sink data to determine whether the total carbon balance is unbalanced. This also includes sub-steps A1-A2:

[0076] A1: Assign different weights to different power sources and modes of transportation; based on the weights, and combining the installed capacity of the power sources and the energy consumption of the transportation vehicles, calculate the total energy consumption using a weighted average method.

[0077] A2: The natural carbon sink absorption capacity is obtained by dividing the data into zones based on different seasons and geographical regions, and then using a weighted average method.

[0078] In this embodiment of the invention, various carbon content data are organized into a carbon content dataset, power generation capacity is organized into an installed capacity dataset, transportation energy consumption is organized into an energy consumption dataset, and natural carbon sink absorption capacity is organized into a carbon sink dataset. These datasets together form a basic data set, providing raw data support for subsequent steps.

[0079] Specifically, when calculating total energy consumption, different types of power sources and vehicles are assigned corresponding weights. When adding the installed capacity of the power system to the energy consumption of the transportation system, different types of power sources and vehicles are assigned corresponding weights before summing. Let the power source type be j and the corresponding installed capacity be P. j The weight is w pj The vehicle type is i, and the corresponding energy consumption is C. i The weight is w ci The total energy consumption after weighted summation is:

[0080]

[0081] For example, assuming that according to the island's energy development strategy, the weight of thermal power is w p1 Set to 0.6, wind power weight w p2 Set to 0.3, photovoltaic weight w p3 Set to 0.1; weight w for gasoline vehicles c1 Set to 0.7, weight w for electric vehicles c2 Set to 0.2, ship weight w c3 Set it to 0.1.

[0082] For example, for the power system, assuming that 1MW of thermal power capacity is equivalent to 1000 tons of carbon equivalent emissions per year, and wind power and photovoltaic power are converted according to their replacement ratios of thermal power and the emission coefficients of thermal power, with the wind power replacing thermal power ratio assumed to be 0.4 and the photovoltaic power replacing thermal power ratio assumed to be 0.3 (this ratio is set according to the actual energy structure and technological conditions of the island), then 1MW of wind power is equivalent to a reduction of 1000 × 0.4 = 400 tons of carbon equivalent emissions per year, and 1MW of photovoltaic power is equivalent to a reduction of 1000 × 0.3 = 300 tons of carbon equivalent emissions per year. Substituting the assumed data, we can obtain:

[0083] The installed capacity of thermal power is 500MW, and its carbon emission equivalent is 500×1000=500,000 tons of carbon equivalent;

[0084] The installed capacity of wind power is 200MW, and its emission reduction equivalent is 200×400=80,000 tons of carbon equivalent;

[0085] The photovoltaic installed capacity is 100MW, and its emission reduction equivalent is 100×300=30,000 tons of carbon equivalent;

[0086] The energy consumption equivalent of the power system is 500,000 - 80,000 - 30,000 = 390,000 tons of carbon equivalent.

[0087] For example, for a transportation system, the annual carbon emissions corresponding to the energy consumption of 1,000 gasoline-powered vehicles are approximately 1,847 tons, the annual carbon emissions corresponding to the energy consumption of 500 electric vehicles are approximately 800 tons, and the annual carbon emissions corresponding to the energy consumption of 20 ships are approximately 48,000 tons.

[0088] The total energy consumption of the transportation system is:

[0089] 1847×0.7+800×0.2+48000×0.1=1292.9+160+4800=6252.9

[0090] The total energy consumption T is the sum of the power system and the transportation system, T = 390000 + 6252.9 = 396252.9 tons of carbon equivalent.

[0091] In this embodiment of the invention, the natural carbon sequestration capacity is measured in different regions based on different seasons and island geographical areas, and a weighted average method is used to obtain a value for comparison. Let the number of regions be k, and the natural carbon sequestration capacity of each region be N. k The corresponding weight is w nk The weighted average natural carbon sequestration capacity is:

[0092]

[0093] To calculate the natural carbon sequestration capacity, it is assumed that the island is divided into 5 zones based on geographical region. Each zone has different forest and wetland areas and carbon absorption efficiencies. The natural carbon sequestration capacity N of each zone is determined. k The weights are as follows: N1 = 800 tons, N2 = 600 tons, N3 = 700 tons, N4 = 500 tons, and N5 = 400 tons. Weights are assigned to each zone based on its importance in the island's carbon cycle. n1 =0.3, w n2 =0.2, w n3 =0.2, w n4 =0.15, w n5 =0.15. Therefore, the weighted average natural carbon sequestration capacity N avg for:

[0094] N avg =800×0.3+600×0.2+700×0.2+500×0.15+400×0.15+=635;

[0096] The calculation results clearly show that the weighted average natural carbon sink absorption capacity of the islands is 635 tons of carbon equivalent. The weighted average calculation here is to more accurately reflect the overall natural carbon sink absorption capacity of the islands. Considering the differences in the contribution of different regions to the carbon cycle, a more representative value is obtained by weighting.

[0097] In this embodiment of the invention, the total energy consumption T = 396252.9 tons of carbon equivalent is compared with the natural carbon sink absorption capacity N. avg =Compared to 635 tons of carbon equivalent;

[0098] If T>N avg The results indicate that there is a need to further optimize the carbon balance under the current energy use and natural carbon sink conditions. Based on this, a preliminary carbon network architecture correlation was established. This correlation is based on the numerical comparison results of the two and reflects the preliminary link between energy consumption and carbon sink, providing a key basis for constructing the carbon network architecture.

[0099] If T <N avg This indicates that the current natural carbon sink absorption capacity is relatively strong, and there is a certain carbon sink surplus space. It can serve as a reference for assessing the low-carbon development potential of islands and for rationally allocating energy use and further improving carbon sink efficiency in subsequent planning. When constructing the carbon grid architecture, we can focus on how to better utilize this surplus space and plan a more forward-looking low-carbon development path.

[0100] In one optional implementation, a threshold method can be used to determine whether the total carbon balance is unbalanced. A carbon balance threshold is preset, with 1.2 times the natural carbon sink absorption capacity as the critical value. The calculated total energy consumption is compared with the natural carbon sink absorption capacity. If the total energy consumption exceeds 1.2 times the natural carbon sink absorption capacity, it is judged as severely unbalanced; if it is between the natural carbon sink absorption capacity and 1.2 times, it is judged as slightly unbalanced; if it is less than or equal to the natural carbon sink absorption capacity, it is judged as balanced.

[0101] In another optional implementation, trend analysis can be used to determine whether there is an imbalance in total carbon emissions. Historical data is collected to construct a time-series curve of total energy consumption versus natural carbon sequestration capacity, and the degree of deviation of current data from historical trends is analyzed. The ratio of current total energy consumption to natural carbon sequestration capacity is calculated and compared with the historical average. If the deviation exceeds a preset standard of 20%, it is determined that there is a carbon imbalance. Simultaneously, by combining historical data trends, including continuous growth and cyclical fluctuations, the nature and development trend of the imbalance are determined, providing a reference for dynamically adjusting the carbon grid architecture.

[0102] In this embodiment of the invention, if the total carbon balance is imbalanced in step S104, information on the total carbon balance imbalance is obtained through time period analysis, and the method further includes sub-steps B1-B3:

[0103] B1: Calculate the change in power generation of different power sources within a set time interval to obtain power output fluctuation data of different power sources;

[0104] B2: Based on the time range of peak and trough traffic flow in historical traffic flow data, obtain the peak and trough periods of traffic travel;

[0105] B2: Based on the power output fluctuation data of different power sources and the peak and off-peak periods of traffic, combined with carbon ecological data analysis, the differences in carbon grid operation time are obtained to obtain information on the total carbon imbalance.

[0106] It should be noted that, since the power generation is a variable value, it is necessary to calculate and obtain power output fluctuation data, install power monitoring devices on various power generation equipment, and record the power generation in real time.

[0107] Let the power source type be j, and the initial power generation within the time interval [t1, t2] be P. j1 The final power generation capacity is P. j2 Then the power output fluctuation ΔP j =P j2 -P j1 ;

[0108] For example, with a set time interval of 15 minutes, in the monitoring of a certain day, for thermal power plants set as power source type j=1, from 8:00 AM t1 to 8:15 AM t2, the initial generating power P 11 The total capacity is 420MW, and the final power generation capacity P is... 12 The value is 415MW, according to the formula ΔP j =P j2 -P j1 Therefore, the power output fluctuation of thermal power during this period is ΔP1 = 415 - 420 = -5MW;

[0109] For example, for wind power j=2, the initial power generation P in the same time period 21 The total capacity is 120MW, and the final power generation capacity P is... 22 The output is 140MW, and its power output fluctuation ΔP2 = 140 - 120 = 20MW;

[0110] For example, for photovoltaic (PV) j=3, the sunlight gradually increases at 8:00 AM, and the initial power generation P... 31 The power output was 30MW, and at 8:15, it increased to 40MW. The power output fluctuation ΔP3 = 40 - 30 = 10MW.

[0111] In this embodiment of the invention, the peak and off-peak travel periods of the transportation system are determined based on the time range of peak and off-peak traffic in historical traffic flow data.

[0112] For example, peak travel times on weekdays are 7-9 am and 5-7 pm, during which traffic flow increases significantly; while peak travel times are 0-5 am, during which traffic flow decreases sharply. Peak periods are quantified as 1, and peak periods as 0.2.

[0113] In this embodiment of the invention, carbon ecological data refers to the carbon cycle cycle of the carbon ecosystem, which is determined by referring to the average duration of carbon element circulation in various ecological links in multi-year ecological monitoring data of the island. The carbon cycle cycle is denoted as T. c The power output fluctuation ΔP of different power sources in the power system j The peak and off-peak travel times of the transportation system can be quantified as the duration proportion r, which can be multiplied to obtain ΔP. j ×r, and then the carbon cycle period T of the carbon ecosystem c The difference values ​​obtained from the comparison are |ΔP j ×rT c This demonstrates the degree to which the dynamic changes of the power and transportation systems over time match the carbon cycle.

[0114] For example, let's take the time period from 8:00 AM to 8:15 AM as an example:

[0115] For thermal power plants, the output fluctuation ΔP1 = -5MW occurs during the morning rush hour on weekdays (r = 1). According to weighted equation 4, ΔP... j Using the method of calculating ×r, the correlation value between thermal power and traffic during this period is -5×1=-5;

[0116] For wind power, its output fluctuation ΔP2 = 20MW, and the associated value is 20 × 1 = 20;

[0117] For photovoltaics, the power output fluctuation ΔP3 = 10MW, and the associated value is 10 × 1 = 10.

[0118] In this embodiment of the invention, the carbon cycle T of the island's carbon ecosystem is determined by referring to multi-year ecological monitoring data of the island. c One year is approximately 8760 hours.

[0119] Taking wind power as an example, let's correlate the value of 20 between 8:00 AM and 8:15 AM with traffic hours. Converting this to an annual correlation value (assuming there are 4 similar time periods in a day and 365 days in a year), the annual correlation value would be 20 × 4 × 365 = 29200, which corresponds to the carbon cycle period T. c =8760, the difference is |29200-8760|=20440;

[0120] It should be noted that by multiplying the output fluctuations of different power sources during different traffic periods with the quantitative values ​​of traffic periods and comparing them with the carbon cycle, a series of difference values ​​are obtained. These data reflect the dynamic relationship between power output and traffic demand in the time dimension and the degree of matching with the carbon cycle. They are incorporated into the analysis of the correlation between the preliminary carbon grid architecture. For example, if it is found that the power output of a certain area is insufficient during peak traffic periods, it is possible to consider adding energy storage devices or adjusting the power layout near that area to ensure the coordinated operation of power supply and other systems such as transportation, and further optimize the carbon grid architecture.

[0121] In one optional implementation, carbon imbalance information is obtained through load correlation analysis. The load correlation between fluctuations in power output of different power sources and traffic travel periods is analyzed. For example, the overlap frequency between periods of declining thermal power output and peak traffic periods is statistically analyzed. If the overlap rate of days with declining thermal power output and peak traffic flow exceeds 60% in a certain period, then that period is determined to be a key period of carbon imbalance. The specific degree and scope of the imbalance are determined by combining the differences in carbon emissions and carbon sink capacity in that period.

[0122] In another alternative implementation, information on total carbon imbalance is obtained through thermal imaging monitoring. Infrared thermal imaging technology is used to monitor the temperature distribution of power facilities such as overhead ground wires at different times. Combined with traffic flow data, if it is found that the temperature of power facilities in a certain area rises abnormally during peak traffic hours, exceeding the normal operating temperature by 20%, and the carbon sequestration capacity of the area is low and the vegetation cover is sparse, then it is determined that there is a risk of total carbon imbalance in the area during that time period.

[0123] In this embodiment of the invention, step S106, which generates a low-carbon emission reduction scheme based on total carbon imbalance information, further includes sub-steps C1-C2:

[0124] C1: Based on information on total carbon imbalance, obtain the carbon absorption of natural carbon sinks and the carbon emissions of high-carbon emission behaviors;

[0125] C2: Calculate the difference between the carbon absorption by natural carbon sinks and the carbon emissions from high-carbon emission behaviors. Based on the difference, adjust the proportion of clean energy power generation in the power system and the proportion of clean energy transportation in the transportation system to generate a low-carbon emission reduction plan.

[0126] In this embodiment of the invention, the carbon absorption of natural carbon sinks is calculated through dynamic monitoring of changes in the area of ​​carbon sink areas such as island forests and wetlands, and the carbon absorption per unit area. Let ΔA be the change in forest area during the monitoring period. f The carbon absorption per unit area of ​​forest is Q. f The change in wetland area is ΔA w The carbon absorption capacity per unit area of ​​wetland is Q.w The amount of carbon absorbed by the natural carbon sink is:

[0127] A=ΔA f ×Q f +ΔA w ×Q w

[0128] In this embodiment of the invention, the carbon emissions of high-carbon emission activities are estimated based on the scale and carbon emission coefficients of various high-carbon emission activities such as industrial production and residential life. Let the type of high-carbon emission activity be m, and the scale of the activity be S. m The carbon emission factor is k m The carbon emissions from high-carbon emission behaviors are:

[0129]

[0130] Quantitative analysis of high-carbon emission behaviors can clearly identify the main sources of carbon emissions on islands, providing data for the subsequent formulation of targeted low-carbon planning measures. The difference D = AE obtained by subtracting the carbon absorption of natural carbon sinks (A) from the carbon emissions of high-carbon emission behaviors (E) is used to reflect the current carbon balance of islands in terms of numerical surplus or deficit.

[0131] It should be noted that the difference indicates that current high-carbon emission activities far exceed the carbon absorption capacity of natural carbon sinks, highlighting the severe challenges facing the carbon balance of islands. Based on this difference, when planning a low-carbon power system, it is necessary to focus on controlling and optimizing high-carbon emission activities such as thermal power and shipping. This includes improving the efficiency of thermal power generation to reduce carbon emissions per unit of electricity generated, and exploring clean energy alternatives for shipping. These measures will gradually narrow the difference and improve the carbon balance of islands.

[0132] In this embodiment of the invention, step S108, which involves multi-factor verification of the low-carbon emission reduction scheme and adjusting and optimizing the scheme based on the verification results, further includes:

[0133] Data on power transmission loss, traffic congestion duration, and seasonal fluctuations in natural carbon sinks are selected, weighted, and summed. The sum is then compared with pre-set reliability standard parameters for low-carbon planning to obtain verification results of the low-carbon emission reduction scheme.

[0134] In this embodiment of the invention, the data on power transmission losses, the duration of traffic system congestion, and seasonal fluctuations in natural carbon sinks include:

[0135] Power transmission loss data is obtained by calculating the power line resistance, current, and line length.

[0136] The duration of traffic congestion periods is obtained by statistically analyzing the start and end times of congestion recorded by the traffic monitoring system.

[0137] Data on the seasonal fluctuations of natural carbon sinks were obtained by long-term monitoring of carbon absorption changes in carbon sink areas during different seasons.

[0138] In this embodiment of the invention, different weights are assigned to data on power transmission loss, duration of traffic system congestion, and seasonal fluctuations in natural carbon sinks, and energy consumption power S is calculated and obtained.

[0139] Compare the energy consumption power S with the pre-set low-carbon planning reliability standard parameter S. standard The comparison results are obtained.

[0140] Based on the comparison results, determine the degree to which the low-carbon emission reduction scheme conforms to the reliability standards under the comprehensive consideration of multiple factors.

[0141] Specifically, let the power transmission loss weight be w. pl The duration weight of traffic congestion periods is w. ct The data weight for natural carbon sequestration is w, which is affected by seasonal fluctuations. qt Power transmission loss P loss The combined value of traffic system congestion duration (quantified as duration t) and seasonal fluctuations in natural carbon sequestration data, Q. season By performing a weighted summation, the energy consumption power after weighted summation can be calculated as follows:

[0142] S = P loss ×w pl +t×w ct +Q season ×w qt

[0143] It should be noted that the unit of power transmission loss is watt (W), the unit of data on seasonal fluctuations in natural carbon sinks is ton, and the unit of traffic congestion duration is hour. When calculating the duration of traffic congestion, the hours are first converted to seconds to obtain the dimension of time. However, in order to maintain consistency with the other two indicators in terms of dimension and to be able to perform comprehensive calculations in the same model, it is necessary to multiply by 1000 according to the specific requirements of the model to convert it into a unit related to energy or carbon. In this way, the three indicators can be weighted and summed under the same dimension to obtain a comprehensive indicator S with practical significance.

[0144] Compare S with the pre-set low-carbon planning reliability standard parameter S standard Comparison results | SS standardThe results indicate the degree to which the current low-carbon planning scheme conforms to reliability standards when considering multiple factors. This verification result of the low-carbon planning scheme is based on data comparison and is used to judge the reliability of the planning scheme, providing a key reference for evaluating and improving the low-carbon planning scheme.

[0145] In one alternative implementation, the low-carbon emission reduction scheme can be optimized using sensitivity analysis. Sensitivity tests are conducted on each parameter in the weighted summation model, such as the weight of power transmission loss and the weight of traffic congestion duration. The impact of changes in each parameter on the comprehensive index S is calculated, and the fluctuation range of S when the weight is adjusted by ±10% is calculated. Parameters that have a significant impact on S are adjusted first. If the traffic congestion duration weight has the highest sensitivity, the traffic system diversion scheme is optimized to shorten the congestion duration.

[0146] In another alternative implementation, a multi-objective genetic algorithm can be used to optimize the low-carbon emission reduction scheme. Constraints such as power transmission loss, traffic congestion duration, and seasonal fluctuations in carbon sinks are set as multi-objective functions. The global search capability of the genetic algorithm is used to automatically search for the optimal combination of parameters, the weight of each factor, and the proportion of clean energy in the feasible solution space. Through iterative evolution, an optimized scheme that takes into account both low-carbon goals and reliability standards is generated.

[0147] Example 3 illustrates a schematic scheme for a low-carbon power system planning method based on carbon network simulation. It should be noted that the technical solution of this low-carbon power system planning system based on carbon network simulation belongs to the same concept as the technical solution of the aforementioned low-carbon power system planning method based on carbon network simulation. Details not described in detail in this embodiment can be found in the description of the technical solution of the aforementioned low-carbon power system planning method based on carbon network simulation.

[0148] This embodiment also provides a low-carbon power system planning system based on carbon grid simulation, including:

[0149] The data acquisition module is used to acquire power system data, carbon ecology data, transportation energy consumption data, and natural carbon sink data.

[0150] The calculation module is used to perform weighted summation of power system data and traffic energy consumption data to obtain the total energy consumption.

[0151] The judgment module is used to compare the total energy consumption with natural carbon sink data to determine whether the total carbon balance is imbalanced; if the total carbon balance is imbalanced, information on the total carbon balance imbalance is obtained through time period analysis.

[0152] The scheme acquisition module is used to generate low-carbon emission reduction schemes based on information on total carbon imbalance.

[0153] The optimization module is used to perform multi-factor verification of low-carbon emission reduction schemes and adjust and optimize the schemes based on the verification results.

[0154] This embodiment also provides an electronic device applicable to low-carbon power system planning based on carbon grid simulation, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the low-carbon power system planning method based on carbon grid simulation as proposed in the above embodiment.

[0155] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the low-carbon power system planning method based on carbon grid simulation as proposed in the above embodiments.

[0156] The storage medium proposed in this embodiment and the method for planning a low-carbon power system based on carbon grid simulation proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0157] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0158] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A low-carbon power system planning method based on carbon grid simulation, characterized in that, include: Acquire data on power systems, carbon ecology, transportation energy consumption, and natural carbon sinks; The power system data and traffic energy consumption data are weighted and summed to obtain the total energy consumption. The total energy consumption is then compared with natural carbon sink data to determine whether the total carbon balance is unbalanced. If the total carbon balance is imbalanced, information on the total carbon balance imbalance is obtained through time period analysis. Based on the aforementioned information on total carbon imbalance, a low-carbon emission reduction plan is generated. The low-carbon emission reduction scheme was verified by multiple factors, and the scheme was adjusted and optimized based on the verification results.

2. The low-carbon power system planning method based on carbon grid simulation as described in claim 1, characterized in that, The power system data and traffic energy consumption data are weighted and summed, including: Different weights were assigned to different power sources and modes of transportation. Based on the aforementioned weights, and combined with the installed capacity of the power supply and the energy consumption of the vehicles, the total energy consumption is calculated using a weighted average method. The natural carbon sequestration capacity was obtained by dividing the data into zones based on different seasons and geographical areas, and then using a weighted average method.

3. The low-carbon power system planning method based on carbon grid simulation as described in claim 2, characterized in that, Information on total carbon imbalance is obtained through time-period analysis, including: Calculate the change in power generation of different power sources within a set time interval to obtain power output fluctuation data of different power sources; Based on the time range of peak and trough traffic flow in historical traffic flow data, the peak and trough periods of traffic travel can be obtained. Based on the power output fluctuation data of different power sources and the peak and off-peak periods of traffic, and combined with carbon ecological data analysis, the differences in carbon grid operation time are obtained to obtain information on the total carbon imbalance.

4. The low-carbon power system planning method based on carbon grid simulation as described in claim 3, characterized in that, Based on the aforementioned carbon imbalance information, a low-carbon emission reduction plan is generated, including: Based on the aforementioned total carbon imbalance information, the carbon absorption of natural carbon sinks and the carbon emissions from high-carbon emission behaviors are obtained. Calculate the difference between the carbon absorption of natural carbon sinks and the carbon emissions of high-carbon emission behaviors. Based on the difference, adjust the proportion of clean energy power generation in the power system and the proportion of clean energy transportation in the transportation system to generate a low-carbon emission reduction plan.

5. The low-carbon power system planning method based on carbon grid simulation as described in claim 4, characterized in that, The low-carbon emission reduction scheme was validated by multiple factors, and the scheme was adjusted and optimized based on the validation results, including: Data on power transmission loss, traffic congestion duration, and seasonal fluctuations in natural carbon sinks are selected, weighted, and summed. The sum is then compared with pre-set reliability standard parameters for low-carbon planning to obtain verification results of the low-carbon emission reduction scheme.

6. The low-carbon power system planning method based on carbon grid simulation as described in claim 2, characterized in that, Data on power transmission losses, duration of traffic congestion, and seasonal fluctuations in natural carbon sinks include: Power transmission loss data is obtained by calculating the power line resistance, current, and line length. The duration of traffic congestion periods is obtained by statistically analyzing the start and end times of congestion recorded by the traffic monitoring system. Data on the seasonal fluctuations of natural carbon sinks were obtained by long-term monitoring of carbon absorption changes in carbon sink areas during different seasons.

7. The low-carbon power system planning method based on carbon grid simulation as described in claim 5, characterized in that, Also includes: Different weights are assigned to the data on power transmission loss, duration of traffic system congestion, and seasonal fluctuations in natural carbon sinks to calculate the energy consumption power S. The energy consumption power S is compared with the pre-set low-carbon planning reliability standard parameter S. standard The comparison results obtained; Based on the comparison results, the degree of compliance of the low-carbon emission reduction scheme with the reliability standard is determined when considering multiple factors.

8. A low-carbon power system planning system based on carbon grid simulation, using the method described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to acquire power system data, carbon ecology data, transportation energy consumption data, and natural carbon sink data. The calculation module is used to perform a weighted summation of the power system data and traffic energy consumption data to obtain the total energy consumption. The judgment module is used to compare the total energy consumption with natural carbon sink data to determine whether the total carbon balance is imbalanced; if the total carbon balance is imbalanced, information on the total carbon balance imbalance is obtained through time period analysis. The scheme acquisition module is used to generate low-carbon emission reduction schemes based on the total carbon imbalance information. The optimization module is used to perform multi-factor verification of the low-carbon emission reduction scheme and adjust and optimize the low-carbon emission reduction scheme based on the verification results.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the low-carbon power system planning method based on carbon grid simulation as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the low-carbon power system planning method based on carbon grid simulation as described in any one of claims 1 to 7.

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