A method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation

Abstract

This invention relates to the field of carbon emission reduction technology, specifically to a method for calculating carbon emission reduction in inter-regional power transmission based on clean energy configuration. The method includes steps such as data acquisition, data preprocessing, carbon emission reduction factor calculation, carbon emission reduction benefit simulation, carbon emission reduction effect optimization, carbon emission reduction effect evaluation, and decision support and feedback. It involves real-time collection of multi-source heterogeneous data within the inter-regional area, preprocessing the data, constructing a carbon emission reduction factor model based on the processed data, evaluating the carbon emission reduction benefits of clean energy in inter-regional power transmission, and using a multi-objective simulation algorithm to simulate the benefits of different clean energy configuration schemes and optimize the configuration scheme. Finally, a decision support system generates decision suggestions for energy dispatch and inter-regional power transmission, which are fed back to the power grid operation and dispatch platform to implement the optimal energy dispatch scheme. This invention can effectively improve the utilization efficiency of clean energy and reduce carbon emissions during power transmission, possessing significant ecological and economic value.

Description

Technical Field

[0001] This invention relates to the field of carbon emission reduction technology, and in particular to a method for calculating carbon emission reduction in cross-regional power transmission based on clean energy configuration. Background Technology

[0002] In recent years, clean energy technologies such as wind power and photovoltaics have made continuous progress and their costs have gradually decreased, making them an important part of the power system. Promoting the development of clean energy and enhancing its application in inter-regional power transmission has become a key path to achieving the goal of carbon neutrality.

[0003] While existing technologies have explored issues such as clean energy power generation dispatch and load forecasting, certain shortcomings remain in the comprehensive optimization of inter-regional power transmission and carbon emission reduction benefits. First, current technologies face difficulties in integrating and processing multi-source heterogeneous data. Inter-regional power transmission systems involve multiple data sources, including clean energy production data, transmission line operating status, electricity demand data, and environmental parameters. However, the collection and fusion of this data present technical obstacles, hindering effective global optimization. Second, existing carbon emission reduction assessment methods are often limited to a single region or a single type of energy, failing to deeply consider the complex relationship between clean energy substitution effects and power transmission losses, making it difficult to accurately assess the carbon emission reduction benefits of inter-regional power transmission under different clean energy configuration schemes. Finally, in optimizing clean energy inter-regional power transmission, most existing methods focus on economic efficiency or power supply reliability optimization, with less consideration given to the comprehensive optimization of carbon emission reduction benefits, transmission losses, and environmental fluctuations. Summary of the Invention

[0004] To achieve the above objectives, this invention provides a method for calculating carbon emission reduction in inter-regional power transmission based on clean energy configuration.

[0005] The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation includes the following steps:

[0006] S1, Data Acquisition: Real-time collection of multi-source heterogeneous data in each cross-regional area through monitoring equipment and sensors. The multi-source heterogeneous data includes clean energy production data, cross-regional transmission line operation status data, electricity demand data, and environmental parameter data.

[0007] Clean energy production data includes the power generation capacity and real-time output power of wind farms and photovoltaic power plants;

[0008] The operational status data of inter-regional transmission lines include line loss, load capacity, and power flow direction;

[0009] Electricity demand data includes regional electricity load characteristics and fluctuations;

[0010] Environmental parameters include temperature, humidity, and air pressure;

[0011] S2, Data Preprocessing: Preprocessing the collected multi-source heterogeneous data, including data cleaning, outlier removal and normalization.

[0012] S3, Carbon Emission Reduction Factor Calculation: Based on preprocessed multi-source heterogeneous data, a carbon emission reduction factor model is constructed, and the carbon emission reduction benefits of clean energy in inter-regional power transmission are calculated through the carbon emission reduction factor model.

[0013] S4, Carbon Emission Reduction Benefit Simulation: Based on preprocessed multi-source heterogeneous data and carbon emission reduction factor model, multi-objective simulation algorithm is used to simulate the carbon emission reduction benefits under different clean energy configuration schemes, and output the carbon emission reduction simulation results of each scheme;

[0014] S5, Carbon Emission Reduction Effect Optimization: Based on the simulation results of carbon emission reduction benefits, the genetic algorithm is used to optimize the cross-regional power transmission configuration scheme of clean energy. The optimization objectives include maximizing carbon emission reduction benefits and minimizing transmission losses. At the same time, the optimization strategy is dynamically adjusted according to line capacity and environmental constraints, and the optimal configuration scheme is output.

[0015] S6, Carbon Emission Reduction Effect Assessment: Evaluate the carbon emission reduction effect of the optimized clean energy configuration scheme and generate a corresponding carbon emission reduction report;

[0016] S7, Decision Support and Feedback: Based on the assessment results, provide decision recommendations for inter-regional power transmission and energy allocation and dispatch, and feed them back to the power grid operation and dispatch platform through the dispatch system so as to implement optimal energy dispatch.

[0017] Optionally, S1 includes:

[0018] S11, Collection of clean energy production data: By deploying monitoring equipment in wind farms and photovoltaic power stations, the power generation capacity and real-time output power data of each clean energy production unit are collected in real time. The monitoring equipment includes power sensors, voltage sensors, current sensors and power meters, which are used to measure the actual power generation capacity and real-time output power of wind farms and photovoltaic power stations. The collected data includes the power generation and output power of wind farms and photovoltaic power stations per unit time.

[0019] S12, Data acquisition of the operating status of inter-regional transmission lines: By installing monitoring sensors at various key nodes of the inter-regional transmission lines, the operating status data of the lines are collected in real time, including line loss, load capacity and power flow data. The monitoring sensors include current sensors, voltage sensors, load monitoring devices and power flow analysis equipment.

[0020] S13, Electricity demand data collection: Real-time collection of electricity load characteristics and fluctuations in various cross-regional areas through regional electricity demand monitoring equipment, which includes load monitors, power meters and data recording devices.

[0021] S14, Environmental Parameter Data Acquisition: By deploying environmental monitoring equipment, the temperature, humidity and air pressure of each cross-regional area are collected in real time. The environmental monitoring equipment includes temperature and humidity sensors, air pressure sensors and environmental data recording devices, which record and upload changes in environmental parameter data in real time.

[0022] Optionally, S2 includes:

[0023] S21, Data Cleaning: Perform preliminary cleaning on the collected multi-source heterogeneous data to remove invalid and duplicate data;

[0024] S22, Outlier Removal: The Z-Score method is used to detect outliers in the pre-cleaned multi-source heterogeneous data;

[0025] S23, Normalization: The minimum-maximum normalization method is used to standardize data from different sources and with different dimensions, unifying the data into a dimensionless range.

[0026] Optionally, S3 includes:

[0027] S31, Construction of carbon emission reduction factor model: Based on preprocessed multi-source heterogeneous data, construct a carbon emission reduction factor model;

[0028] S32, Calculation of carbon emission reduction benefits: Calculate the carbon emission reduction benefits of clean energy in inter-regional power transmission by constructing a carbon emission reduction factor model;

[0029] S33, Comprehensive Carbon Emission Reduction Benefit Output: The carbon emission reduction benefit calculation results output the carbon emission reduction benefits of clean energy inter-regional power transmission.

[0030] Optionally, S4 includes:

[0031] S41, Integration and preparation of input data: Based on the preprocessed multi-source heterogeneous data and carbon emission reduction factor model, integrate clean energy production data, transmission line status data, electricity demand data, environmental parameter data and the calculation results of carbon emission reduction factor model to provide input data for multi-objective simulation algorithm;

[0032] S42, Simulation Algorithm Selection and Application: The simulated annealing algorithm is selected as the multi-objective simulation algorithm, and the carbon emission reduction benefits under different clean energy configuration schemes are simulated and calculated.

[0033] S43, Simulation Results Output and Analysis: Based on the simulation results, output the simulation results of carbon emission reduction benefits under different clean energy configuration schemes.

[0034] Optionally, S4 further includes:

[0035] S44, Constraint handling during simulation: During the simulation calculation, constraints are applied to each clean energy configuration scheme to ensure that the simulation results conform to the actual operating environment. Constraints include capacity constraints, dynamic adjustment of environmental parameters, and load fluctuation constraints.

[0036] S45, Calculation and evaluation of carbon emission reduction benefits: During the simulation, the carbon emission reduction benefits of each configuration scheme are calculated by considering the constraints, and the comprehensive carbon emission reduction benefits of each configuration scheme are generated by combining environmental parameters, energy supply and demand and transmission capacity factors.

[0037] S46, Simulation Results Output and Scheme Evaluation: After the simulation is completed, the carbon emission reduction benefit simulation results of each clean energy configuration scheme are output. The different schemes are compared and analyzed through multi-dimensional evaluation indicators (such as cost-benefit ratio, carbon emission reduction, resource utilization efficiency, etc.). The optimal configuration scheme is identified through the evaluation results.

[0038] Optionally, S5 includes:

[0039] S51, Optimization Target Setting: Based on the carbon emission reduction benefit simulation results, set the optimization objective function, which includes maximizing carbon emission reduction benefits and minimizing transmission losses;

[0040] S52, Selection and Implementation of Genetic Algorithm: Genetic algorithm is selected as the optimization algorithm, and the configuration optimization of clean energy inter-regional power transmission is implemented based on genetic algorithm;

[0041] S53, Dynamic adjustment of constraints: During the optimization process, the clean energy configuration scheme is dynamically adjusted to cope with the capacity constraints of transmission lines and changes in environmental parameters;

[0042] S54, Optimization Result Output: After multiple iterations, the optimal clean energy inter-regional power transmission configuration scheme is output.

[0043] Optionally, S6 includes:

[0044] S61, Preparation of Input Data for Evaluation: When evaluating the carbon emission reduction effect of the optimized clean energy configuration scheme, prepare the input data required for the evaluation. The input data includes clean energy configuration data of the optimized scheme, environmental parameter data, and carbon emission reduction factor model data.

[0045] S62, Carbon Emission Reduction Benefit Calculation: Using the prepared input data, calculate the carbon emission reduction benefits of the optimized clean energy configuration scheme based on the carbon emission reduction factor model;

[0046] S63, Multidimensional Analysis of Evaluation Indicators: After calculating the carbon emission reduction benefits, multidimensional evaluation indicators are generated.

[0047] S64, Carbon Emission Reduction Report Generation: Based on the results of carbon emission reduction benefit calculation and evaluation indicator analysis, a detailed carbon emission reduction report is generated.

[0048] Optionally, S7 includes:

[0049] S71, Decision Support Input Preparation: Based on the carbon emission reduction effect assessment report, collect and prepare the input data required for decision support. The input data includes carbon emission reduction benefit data, energy allocation scheme data, environmental parameter data, and electricity demand data.

[0050] S72, Decision Recommendation Generation: Based on the prepared input data, decision support algorithms are used to generate decision recommendations for inter-regional power transmission and energy allocation and scheduling;

[0051] S73, Decision Support Output: Based on the generated decision recommendations, an energy dispatching scheme is generated;

[0052] S74, Feedback to the power grid operation and dispatch platform: The generated energy dispatch plan is fed back to the power grid operation and dispatch platform to execute the optimal energy dispatch plan.

[0053] The beneficial effects of this invention are:

[0054] This invention, through a method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation, employs precise acquisition and preprocessing of multi-source heterogeneous data, combined with a carbon emission reduction factor model and optimization algorithm, to accurately assess the contribution of clean energy allocation to carbon emission reduction. By optimizing energy allocation, maximizing carbon emission reduction benefits, and reducing the use of traditional fossil fuels, this invention helps promote the development of green and low-carbon power grids, providing strong support for achieving carbon neutrality goals.

[0055] This invention employs multi-objective simulation algorithms and genetic algorithms to optimize the inter-regional power transmission configuration scheme for clean energy. This approach ensures power supply while reducing transmission losses and dynamically adjusts optimization strategies to address environmental changes and fluctuations in electricity demand. This not only improves energy efficiency and reduces system operating costs but also enhances the adaptability and flexibility of the inter-regional power transmission system under different environmental and load conditions, ensuring the economic efficiency and stability of the power system. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the S4 process in an embodiment of the present invention. Detailed Implementation

[0059] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0060] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0061] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0062] like Figures 1-2 As shown, the method for calculating carbon emission reduction in inter-regional power transmission based on clean energy configuration includes the following steps:

[0063] S1, Data Acquisition: Real-time collection of multi-source heterogeneous data in each cross-regional area through monitoring equipment and sensors. The multi-source heterogeneous data includes clean energy production data, cross-regional transmission line operation status data, electricity demand data, and environmental parameter data.

[0064] Clean energy production data includes the power generation capacity and real-time output power of wind farms and photovoltaic power plants;

[0065] The operational status data of inter-regional transmission lines include line loss, load capacity, and power flow direction;

[0066] Electricity demand data includes regional electricity load characteristics and fluctuations;

[0067] Environmental parameters include temperature, humidity, and air pressure;

[0068] S2, Data Preprocessing: Preprocessing the collected multi-source heterogeneous data, including data cleaning, outlier removal and normalization.

[0069] S3, Carbon Emission Reduction Factor Calculation: Based on preprocessed multi-source heterogeneous data, a carbon emission reduction factor model is constructed. The carbon emission reduction factor model is used to calculate the carbon emission reduction benefits of clean energy in inter-regional power transmission. The carbon emission reduction factor model considers the carbon emission intensity of different energy sources, as well as the relationship between the substitution effect of clean energy and power transmission loss.

[0070] S4, Carbon Emission Reduction Benefit Simulation: Based on preprocessed multi-source heterogeneous data and carbon emission reduction factor model, multi-objective simulation algorithm is used to simulate the carbon emission reduction benefits under different clean energy configuration schemes, and output the carbon emission reduction simulation results of each scheme;

[0071] S5, Carbon Emission Reduction Effect Optimization: Based on the simulation results of carbon emission reduction benefits, the genetic algorithm is used to optimize the cross-regional power transmission configuration scheme of clean energy. The optimization objectives include maximizing carbon emission reduction benefits and minimizing transmission losses. At the same time, the optimization strategy is dynamically adjusted according to line capacity and environmental constraints, and the optimal configuration scheme is output.

[0072] S6, Carbon Emission Reduction Effect Assessment: Evaluate the carbon emission reduction effect of the optimized clean energy configuration scheme and generate a corresponding carbon emission reduction report;

[0073] S7, Decision Support and Feedback: Based on the assessment results, provide decision recommendations for inter-regional power transmission and energy allocation and dispatch, and feed them back to the power grid operation and dispatch platform through the dispatch system so as to implement optimal energy dispatch.

[0074] S1 includes:

[0075] S11, Collection of Clean Energy Production Data: By deploying monitoring equipment in wind farms and photovoltaic power stations, the power generation capacity and real-time output power data of each clean energy production unit are collected in real time. The monitoring equipment includes power sensors, voltage sensors, current sensors and power meters, which are used to measure the actual power generation capacity and real-time output power of wind farms and photovoltaic power stations. The collected data includes the power generation and output power of wind farms and photovoltaic power stations per unit time, ensuring that the clean energy production status is reflected in real time.

[0076] S12, Data acquisition of the operation status of inter-regional transmission lines: By installing monitoring sensors at various key nodes of inter-regional transmission lines, the operation status data of the lines are collected in real time, including line loss, load capacity and power flow data. The monitoring sensors include current sensors, voltage sensors, load monitoring devices and power flow analysis equipment, which are used to collect the power transmission status, loss and load capacity of the lines, and monitor the power flow and energy distribution of each grid node in real time.

[0077] S13, Electricity demand data collection: Real-time collection of electricity load characteristics and fluctuations in various cross-regional areas through regional electricity demand monitoring equipment, which includes load monitors, power meters and data recording devices.

[0078] S14, Environmental Parameter Data Acquisition: By deploying environmental monitoring equipment, the temperature, humidity and air pressure of each cross-regional area are collected in real time. The environmental monitoring equipment includes temperature and humidity sensors, air pressure sensors and environmental data recording devices. The changes in environmental parameter data are recorded and uploaded in real time to help analyze the impact of environmental factors on clean energy production and electricity demand fluctuations.

[0079] S2 includes:

[0080] S21, Data Cleaning: Preliminary cleaning of the collected multi-source heterogeneous data, removing invalid and duplicate data to ensure accuracy and integrity. This includes:

[0081] Check clean energy production data, transmission line operation status data, electricity demand data, and environmental parameter data, and delete data entries with missing values ​​or incorrect formats.

[0082] Check the data recorded by the sensors or monitoring equipment for obvious acquisition errors, such as abnormalities like excessively high or low power values, and mark or remove them.

[0083] Synchronize and adjust illogical timestamp data to ensure the consistency and accuracy of the data time series;

[0084] S22, Outlier Removal: The Z-Score method is used to detect outliers in the initially cleaned multi-source heterogeneous data. Specific methods include:

[0085] Calculate the Z-Score for each data item. The formula for calculating the Z-Score is:

[0086]

[0087] Where X is the data value, μ is the mean of the dataset, and σ is the standard deviation of the dataset;

[0088] Set a reasonable threshold (such as 3) as the rejection criterion. When the Z-Score of a data item exceeds the set threshold, the data is considered an outlier and is rejected.

[0089] The dataset after outlier removal is smoothed to ensure data stability and consistency.

[0090] S23, Normalization: The minimum-maximum normalization method is used to standardize data from different sources and dimensions, unifying the data to a dimensionless range. Specific methods include:

[0091] The clean energy production data (such as power data from wind and solar power plants), transmission line data (such as current and voltage), and environmental data (such as temperature and humidity) are subjected to minimum-maximum normalization. The normalization formula is as follows:

[0092]

[0093] Where X is the original data, X min and X max X represents the minimum and maximum values ​​in the dataset, respectively. ′ These are the normalized data values, ranging from [0,1].

[0094] The normalized data is checked to ensure that the normalization process does not result in information loss and to ensure the validity of the data.

[0095] S3 includes:

[0096] S31, Carbon Emission Reduction Factor Model Construction: Based on preprocessed multi-source heterogeneous data, a carbon emission reduction factor model is constructed. This model considers the carbon emission intensity of different energy types, the substitution effect of clean energy, and the impact of power transmission losses on carbon emission reduction benefits. Specific steps include:

[0097] (1) Carbon emission intensity calculation: For each traditional energy source (such as thermal power, natural gas power plants, etc.), its carbon emission intensity is determined based on the relationship between its fuel consumption and CO2 emissions. The formula for calculating carbon emission intensity is as follows:

[0098]

[0099] Among them, C energy E represents the carbon intensity of energy. CO2 E represents carbon dioxide emissions from energy consumption. energy Energy consumption;

[0100] (2) Substitution Effect Modeling: By analyzing the substitution relationship between the power generation capacity of clean energy (such as wind power and photovoltaic power plants) and the power generation of traditional energy, a clean energy substitution effect model is constructed. The clean energy substitution effect model considers the substitution effect of different clean energy sources on traditional energy under different time periods and load conditions. The coefficient of clean energy substitution for traditional energy is defined as follows:

[0101]

[0102] Among them, P clean energy For clean energy power generation, P total energy Total energy demand power;

[0103] (3) Power transmission loss modeling: During power transmission, electricity experiences losses, which in turn affect carbon emission reduction benefits. The power transmission loss model calculates the proportion of power loss during transmission and corrects for the clean energy substitution effect. The formula for calculating power loss is as follows:

[0104]

[0105] Among them, L loss P represents the transmission loss ratio. input For input power, P output This refers to the output power.

[0106] S32, Carbon Emission Reduction Benefit Calculation: Using a constructed carbon emission reduction factor model, the carbon emission reduction benefits of clean energy in inter-regional power transmission are calculated, including:

[0107] (1) Carbon emission reduction benefits of clean energy replacing traditional energy: Based on the substitution effect of clean energy and the carbon emission intensity model, the contribution of clean energy to reducing carbon emissions from traditional energy sources is calculated. The formula for calculating carbon emission reduction benefits is as follows:

[0108] C reduction =P clean energy ×C energy ×Substitution Effect;

[0109] Among them, C reduction For carbon emission reduction benefits, P clean energy For clean energy power generation, C energy The carbon emission intensity of traditional energy sources is represented by the Substitution Effect coefficient.

[0110] (2) Carbon emission reduction benefits after adjusting for transmission loss: Based on the power transmission loss model, the carbon emission reduction benefits of clean energy are adjusted to obtain the final carbon emission reduction, expressed as:

[0111] C adjusted =Creduction ×(1-L loss );

[0112] Among them, C adjusted For the adjusted carbon emission reduction benefits, L loss This represents the proportion of power transmission loss.

[0113] S33, Comprehensive Carbon Emission Reduction Benefit Output: The carbon emission reduction benefit calculation results output the carbon emission reduction benefits of clean energy inter-regional power transmission.

[0114] S4 includes:

[0115] S41, Input Data Integration and Preparation: Based on the preprocessed multi-source heterogeneous data and carbon emission reduction factor model, clean energy production data, transmission line status data, electricity demand data, environmental parameter data, and the calculation results of the carbon emission reduction factor model are integrated to provide input data for the multi-objective simulation algorithm. Specifically, this includes:

[0116] The clean energy production data (such as the power output of wind power and photovoltaic power plants) is combined with the output of the carbon emission reduction factor model (such as carbon emission reduction benefits) to form a clean energy contribution dataset;

[0117] By combining data on transmission line capacity limitations, load capacity, and power flow with carbon reduction benefits, a transmission network characteristic dataset is formed.

[0118] Based on the load curve, fluctuation characteristics, and environmental parameter data of electricity demand, prepare the time series data required for the simulation.

[0119] S42, Simulation Algorithm Selection and Application: Simulated annealing algorithm is selected as the multi-objective simulation algorithm. Simulation calculations are performed based on the carbon emission reduction benefits under different clean energy configuration schemes. The simulated annealing algorithm can simultaneously optimize multiple objective functions. Consider the following constraints:

[0120] Capacity constraints: Consider the capacity limitations of the transmission lines to ensure that the maximum load capacity of the transmission lines is not exceeded during the simulation.

[0121] Environmental change: The impact of dynamically changing environmental parameters (such as temperature and humidity) on clean energy power generation capacity and demand fluctuations;

[0122] Demand fluctuations: Fluctuations in regional electricity demand, especially changes in peak and off-peak loads.

[0123] The specific implementation steps of the simulated annealing algorithm include:

[0124] (1) Generation of initial solution: Generate initial solution based on input data of clean energy configuration scheme and initialize energy configuration parameters.

[0125] (2) Definition of Energy Function: An energy function is defined, with carbon emission reduction benefits as the primary optimization objective, taking into account the impact of factors such as capacity constraints, environmental changes, and demand fluctuations. The energy function is:

[0126]

[0127] Among them, E total For the total energy, C reduction (i) represents the carbon emission reduction benefit of the i-th configuration scheme, L loss (i) represents power transmission loss, and α is the adjustment coefficient;

[0128] (3) Solution acceptance and update: By using the temperature update strategy of simulated annealing algorithm, new solutions are accepted at a certain "temperature" or poor solutions are accepted according to the set probability, and the optimal solution is gradually approached.

[0129] S43, Simulation Results Output and Analysis: Based on the simulation results, output the carbon emission reduction benefits simulation results under different clean energy configuration schemes. The simulation results include relevant indicators such as carbon emission reduction, transmission loss, and environmental adaptability for each configuration scheme, providing reference data for subsequent optimization and decision support.

[0130] S4 also includes:

[0131] S44, Constraint handling during simulation: During the simulation calculation, constraints are applied to each clean energy configuration scheme to ensure that the simulation results conform to the actual operating environment. The constraints include capacity constraints, dynamic adjustment of environmental parameters, and load fluctuation constraints.

[0132] Capacity constraints: Based on the actual capacity limitations of the transmission lines, the power output of each configuration scheme is limited during the simulation process to prevent exceeding the load capacity of the transmission lines and ensure that the simulation results are practically feasible.

[0133] Dynamic adjustment of environmental parameters: The clean energy power generation capacity is dynamically adjusted based on real-time meteorological data (such as temperature and humidity) to simulate the changes in clean energy power generation under different environmental conditions;

[0134] Load fluctuation constraints: Considering the fluctuation of regional electricity load, especially during peak load periods, limit the maximum power generation of clean energy to prevent grid instability or power shortages;

[0135] S45, Carbon Emission Reduction Benefit Calculation and Evaluation: During the simulation, the carbon emission reduction benefits of each configuration scheme are calculated by considering constraints. Combined with environmental parameters, energy supply and demand, and transmission capacity factors, the comprehensive carbon emission reduction benefits of each configuration scheme are generated. Specific calculations include:

[0136] Calculate the carbon emission reduction for each clean energy configuration scheme, and combine the carbon emission reduction factor with the clean energy power generation;

[0137] Under the influence of capacity constraints and load fluctuations, the carbon emission reduction effect of each scheme is adjusted to ensure the practical feasibility of the optimization results;

[0138] S46, Simulation Results Output and Scheme Evaluation: After the simulation is completed, the carbon emission reduction benefit simulation results of each clean energy configuration scheme are output. The different schemes are compared and analyzed through multi-dimensional evaluation indicators (such as cost-benefit ratio, carbon emission reduction, resource utilization efficiency, etc.). The optimal configuration scheme is identified through the evaluation results, providing a reference for subsequent optimization and decision-making.

[0139] S5 includes:

[0140] S51, Optimization Target Setting: Based on the carbon emission reduction benefit simulation results, an optimization objective function is set. The optimization objective function includes maximizing carbon emission reduction benefits and minimizing transmission losses, wherein;

[0141] Maximizing carbon emission reduction benefits: This means maximizing the carbon emission reduction benefits of each configuration scheme by rationally allocating clean energy.

[0142] Minimize transmission losses: Optimize clean energy inter-regional power transmission schemes to reduce power losses during transmission;

[0143] The objective function is expressed as:

[0144]

[0145] Where Obj is the objective function, and C reduction (i) represents the carbon emission reduction benefit of the i-th configuration scheme, L loss (i) represents the transmission loss of the i-th configuration scheme, and w1 and w2 are weighting coefficients, which respectively represent the importance of carbon emission reduction benefits and transmission loss in the objective function;

[0146] S52, Selection and Implementation of Genetic Algorithm: The genetic algorithm is selected as the optimization algorithm. Clean energy inter-regional power transmission configuration optimization based on the genetic algorithm is implemented. The main steps of the genetic algorithm include:

[0147] Initializing the population: Multiple initial configuration schemes are randomly generated to form the initial population of the optimization algorithm, with each individual representing a clean energy configuration scheme;

[0148] Fitness assessment: Fitness assessment is performed on each individual, with the fitness function being the aforementioned optimization objective function. The merits of each scheme are evaluated based on carbon emission reduction benefits and transmission losses.

[0149] Selection operation: Select parent individuals based on fitness values. Use methods such as roulette wheel selection and tournament selection to select individuals with higher fitness as parents for crossover and mutation operations.

[0150] Crossover operations: These operations generate new configuration schemes, simulating the gene recombination process and producing better solutions. Crossover operations can be performed using single-point crossover, multi-point crossover, or uniform crossover, among other methods.

[0151] Mutation operation: Introducing the mutation operation increases the diversity of the population by randomly changing some parameters of the configuration scheme, thus avoiding getting trapped in local optima;

[0152] Termination conditions: Set the termination conditions for the algorithm. Common conditions include the maximum number of iterations, the fitness reaching a set threshold, and the population fitness stabilizing.

[0153] S53, Dynamic Adjustment of Constraints: During the optimization process, the clean energy configuration scheme is dynamically adjusted to cope with changes in transmission line capacity constraints and environmental parameters, specifically including:

[0154] Capacity constraint handling: Based on the real-time load conditions and capacity limitations of the transmission lines, each optimization solution is adjusted to ensure that the transmission lines are not overloaded;

[0155] Environmental parameter adaptation: The clean energy power generation capacity is dynamically adjusted based on real-time meteorological data (such as temperature and humidity) to optimize the configuration scheme and adapt to environmental changes;

[0156] S54, Optimization Result Output: After multiple iterations, the optimal clean energy inter-regional power transmission configuration scheme is output. This scheme should maximize carbon emission reduction benefits while minimizing transmission losses, and meet transmission line capacity and environmental constraints. The output results include:

[0157] Carbon emission reduction benefits of the optimal configuration scheme

[0158] Transmission loss of the optimal configuration scheme

[0159] Various clean energy configuration parameters, such as wind power, photovoltaic power generation capacity, and power flow direction.

[0160] S6 includes:

[0161] S61, Preparation of Input Data for Evaluation: When evaluating the carbon emission reduction effect of the optimized clean energy configuration scheme, prepare the required input data for the evaluation. The input data includes clean energy configuration data of the optimized scheme, environmental parameter data, and carbon emission reduction factor model data, specifically including:

[0162] Clean energy configuration data for the optimization plan: such as the power generation capacity of wind and solar power plants, real-time output power, load capacity of inter-regional transmission lines, power flow, etc.

[0163] Environmental parameter data: such as real-time meteorological data (temperature, humidity, air pressure, etc.), and environmental factors that affect clean energy production;

[0164] Carbon emission reduction factor model data: including model parameters such as carbon emission intensity and substitution effect of different clean energy sources, as well as the impact of transmission loss on carbon emission reduction benefits;

[0165] S62, Carbon Emission Reduction Benefit Calculation: Using the prepared input data, the carbon emission reduction benefits of the optimized clean energy configuration scheme are calculated based on the carbon emission reduction factor model. This calculation process considers the substitution benefits of clean energy power generation, transmission losses, and the impact of environmental changes on carbon emission reduction. The calculation formula is:

[0166]

[0167] Among them, C reduction For carbon emission reduction benefits, P generation (i) represents the power generation capacity configured for the i-th clean energy source, C factor (i) is the carbon emission reduction factor for the i-th clean energy source, L loss (i) represents the transmission loss of the i-th configuration scheme, C loss Carbon emission factor for transmission loss per unit of electricity;

[0168] S63, Multidimensional Analysis of Evaluation Indicators: After calculating the carbon emission reduction benefits, multidimensional evaluation indicators are generated. Specific evaluation indicators include:

[0169] Total carbon emission reduction: Calculate the total carbon emission reduction of the entire optimized configuration scheme during inter-regional power transmission and evaluate the environmental benefits of the scheme;

[0170] Carbon emission reduction efficiency: This refers to the carbon emission reduction effect produced per unit of electricity, used to evaluate the efficiency of clean energy utilization.

[0171] Environmental adaptability: Evaluate the stability and adaptability of the optimization scheme under different environmental conditions (such as changes in temperature, humidity, etc.);

[0172] Economic analysis: Based on the relationship between carbon emission reduction and cost, evaluate the economic benefits and feasibility of the plan.

[0173] S64, Carbon Emission Reduction Report Generation: Based on the results of carbon emission reduction benefit calculations and assessment indicator analysis, a detailed carbon emission reduction report is generated. The report content includes:

[0174] Overview of carbon emission reduction benefits: This section summarizes the carbon emission reduction benefits of various clean energy configuration schemes and provides the total carbon emission reduction amount.

[0175] Environmental Impact Assessment: Based on environmental parameters and carbon emission reduction factors, assess the performance of each scheme under different environmental conditions and provide an analysis of environmental adaptability;

[0176] Economic benefit analysis: Assess the relationship between carbon emission reduction and costs, and provide the results of the economic benefit analysis;

[0177] Recommendations for optimization: Provide suggestions for further optimization to help decision-makers choose the most environmentally and economically beneficial option.

[0178] S7 includes:

[0179] S71, Decision Support Input Preparation: Based on the carbon emission reduction effect assessment report, collect and prepare the input data required for decision support. The input data includes carbon emission reduction benefit data, energy allocation scheme data, environmental parameter data, and electricity demand data, specifically including:

[0180] Carbon emission reduction benefit data: Indicators such as total carbon emission reduction, carbon emission reduction efficiency and environmental adaptability of each scheme, derived from the assessment results;

[0181] Energy configuration scheme data: Optimized clean energy configuration scheme, including wind power and photovoltaic power generation capacity, power flow direction and transmission line configuration, etc.;

[0182] Environmental parameter data: such as real-time environmental data like temperature and humidity, used to further adjust the adaptability of energy configuration;

[0183] Electricity demand data: Cross-regional electricity demand forecast data, including the electricity load characteristics and fluctuations of each region;

[0184] S72, Decision Recommendation Generation: Based on the prepared input data, decision support algorithms are used to generate decision recommendations for inter-regional power transmission and energy allocation and scheduling. The decision recommendations include:

[0185] Energy configuration optimization recommendations: Based on carbon emission reduction benefits and electricity demand fluctuations, we provide optimal clean energy inter-regional power transmission configuration recommendations to ensure maximum carbon emission reduction benefits and the highest energy utilization efficiency;

[0186] Power dispatch optimization recommendations: Based on optimized energy allocation schemes and power demand forecasts, dispatch recommendations are provided to ensure the balance of inter-regional power flows and the stability of power supply. These recommendations include optimal generation and transmission load allocation schemes to ensure that power supply between different regions can be allocated as needed.

[0187] Dynamic adjustment recommendations: Based on real-time changes in environmental parameters and fluctuations in electricity demand, dynamic adjustment recommendations are proposed to help the power grid operation system respond to changes in the external environment in a timely manner and ensure the flexibility and stability of the dispatching scheme;

[0188] S73, Decision Support Output: Based on the generated decision recommendations, an energy dispatching scheme is generated, which includes:

[0189] Optimal inter-regional power transmission scheme: A detailed list of the clean energy generation capacity, power flow direction, and load distribution of transmission lines in each region;

[0190] Dispatch optimization scheme: including power dispatch strategy for each time period and the matching of clean energy generation with demand;

[0191] Environmental Adaptability Adjustment: Suggestions for flexibly adjusting scheduling schemes based on environmental changes;

[0192] S74, Feedback to the power grid operation and dispatch platform: The generated energy dispatch plan is fed back to the power grid operation and dispatch platform to execute the optimal energy dispatch plan. This feedback process includes:

[0193] The dispatching platform receives the generated energy dispatching plan, including specific plans for energy allocation and power flow.

[0194] Implementing energy dispatch: According to the energy dispatch plan, the power grid operation platform implements cross-regional power dispatch operations to ensure that power supply and demand in each region are matched;

[0195] Real-time monitoring and adjustment: By monitoring the operational status data fed back by the real-time monitoring system, the implementation of scheduling is monitored and energy scheduling is dynamically adjusted to cope with sudden changes in demand or environment.

[0196] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0197] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation, characterized in that, Includes the following steps: S1, Data Acquisition: Real-time collection of multi-source heterogeneous data in each cross-regional area through monitoring equipment and sensors. The multi-source heterogeneous data includes clean energy production data, cross-regional transmission line operation status data, electricity demand data, and environmental parameter data. Clean energy production data includes the power generation capacity and real-time output power of wind farms and photovoltaic power plants; The operational status data of inter-regional transmission lines include line loss, load capacity, and power flow direction; Electricity demand data includes regional electricity load characteristics and fluctuations; Environmental parameters include temperature, humidity, and air pressure; S2, Data Preprocessing: Preprocessing the collected multi-source heterogeneous data, including data cleaning, outlier removal and normalization. S3, Carbon Emission Reduction Factor Calculation: Based on preprocessed multi-source heterogeneous data, a carbon emission reduction factor model is constructed, and the carbon emission reduction benefits of clean energy in inter-regional power transmission are calculated through the carbon emission reduction factor model. S4, Carbon Emission Reduction Benefit Simulation: Based on preprocessed multi-source heterogeneous data and carbon emission reduction factor model, multi-objective simulation algorithm is used to simulate the carbon emission reduction benefits under different clean energy configuration schemes, and output the carbon emission reduction simulation results of each scheme; S5, Carbon Emission Reduction Effect Optimization: Based on the simulation results of carbon emission reduction benefits, the genetic algorithm is used to optimize the cross-regional power transmission configuration scheme of clean energy. The optimization objectives include maximizing carbon emission reduction benefits and minimizing transmission losses. At the same time, the optimization strategy is dynamically adjusted according to line capacity and environmental constraints, and the optimal configuration scheme is output. S6, Carbon Emission Reduction Effect Assessment: Evaluate the carbon emission reduction effect of the optimized clean energy configuration scheme and generate a corresponding carbon emission reduction report; S7, Decision Support and Feedback: Based on the assessment results, provide decision recommendations for inter-regional power transmission and energy allocation scheduling, and feed them back to the power grid operation and scheduling platform through the scheduling system.

2. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation according to claim 1, characterized in that, S1 includes: S11, Collection of clean energy production data: By deploying monitoring equipment in wind farms and photovoltaic power stations, the power generation capacity and real-time output power data of each clean energy production unit are collected in real time. The monitoring equipment includes power sensors, voltage sensors, current sensors and power meters, which are used to measure the actual power generation capacity and real-time output power of wind farms and photovoltaic power stations. The collected data includes the power generation and output power of wind farms and photovoltaic power stations per unit time. S12, Data acquisition of the operating status of inter-regional transmission lines: By installing monitoring sensors at each node of the inter-regional transmission line, the operating status data of the line is collected in real time, including line loss, load capacity and power flow data. The monitoring sensors include current sensors, voltage sensors, load monitoring devices and power flow analysis equipment. S13, Electricity demand data collection: Real-time collection of electricity load characteristics and fluctuations in various cross-regional areas through regional electricity demand monitoring equipment, which includes load monitors, power meters and data recording devices. S14, Environmental Parameter Data Acquisition: By deploying environmental monitoring equipment, the temperature, humidity and air pressure of each cross-regional area are collected in real time. The environmental monitoring equipment includes temperature and humidity sensors, air pressure sensors and environmental data recording devices, which record and upload changes in environmental parameter data in real time.

3. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation according to claim 2, characterized in that, S2 includes: S21, Data Cleaning: Perform preliminary cleaning on the collected multi-source heterogeneous data to remove invalid and duplicate data; S22, Outlier Removal: The Z-Score method is used to detect outliers in the pre-cleaned multi-source heterogeneous data; S23, Normalization: The minimum-maximum normalization method is used to standardize data from different sources and with different dimensions, unifying the data into a dimensionless range.

4. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation according to claim 3, characterized in that, S3 includes: S31, Construction of carbon emission reduction factor model: Based on preprocessed multi-source heterogeneous data, construct a carbon emission reduction factor model; S32, Calculation of carbon emission reduction benefits: Calculate the carbon emission reduction benefits of clean energy in inter-regional power transmission by constructing a carbon emission reduction factor model; S33, Comprehensive Carbon Emission Reduction Benefit Output: The carbon emission reduction benefit calculation results output the carbon emission reduction benefits of clean energy inter-regional power transmission.

5. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation according to claim 4, characterized in that, S4 includes: S41, Integration and preparation of input data: Based on the preprocessed multi-source heterogeneous data and carbon emission reduction factor model, integrate clean energy production data, transmission line status data, electricity demand data, environmental parameter data and the calculation results of carbon emission reduction factor model to provide input data for multi-objective simulation algorithm; S42, Simulation Algorithm Selection and Application: Simulated annealing algorithm is selected as the multi-objective simulation algorithm, and simulation calculations are performed based on the carbon emission reduction benefits under different clean energy configuration schemes. S43, Simulation Results Output and Analysis: Based on the simulation results, output the simulation results of carbon emission reduction benefits under different clean energy configuration schemes.

6. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation according to claim 5, characterized in that, S4 further includes: S44, Constraint handling during simulation: During the simulation calculation, constraints are applied to each clean energy configuration scheme. These constraints include capacity constraints, dynamic adjustment of environmental parameters, and load fluctuation constraints. S45, Calculation and evaluation of carbon emission reduction benefits: During the simulation, the carbon emission reduction benefits of each configuration scheme are calculated by considering the constraints, and the comprehensive carbon emission reduction benefits of each configuration scheme are generated by combining environmental parameters, energy supply and demand and transmission capacity factors. S46, Simulation Results Output and Scheme Evaluation: After the simulation is completed, the carbon emission reduction efficiency simulation results of each clean energy configuration scheme are output, and the different schemes are compared and analyzed through multi-dimensional evaluation indicators. Based on the evaluation results, the optimal configuration scheme is identified.

7. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation according to claim 1, characterized in that, S5 includes: S51, Optimization Target Setting: Based on the carbon emission reduction benefit simulation results, set the optimization objective function, which includes maximizing carbon emission reduction benefits and minimizing transmission losses; S52, Selection and Implementation of Genetic Algorithm: Genetic algorithm is selected as the optimization algorithm, and the configuration optimization of clean energy inter-regional power transmission is implemented based on genetic algorithm; S53, Dynamic adjustment of constraints: During the optimization process, the clean energy configuration scheme is dynamically adjusted to cope with the capacity constraints of transmission lines and changes in environmental parameters; S54, Optimization Result Output: After multiple iterations, the optimal clean energy inter-regional power transmission configuration scheme is output.

8. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation according to claim 7, characterized in that, S6 includes: S61, Preparation of Input Data for Assessment: Prepare the input data required for the assessment, including clean energy configuration data of the optimization scheme, environmental parameter data, and carbon emission reduction factor model data; S62, Carbon Emission Reduction Benefit Calculation: Using the prepared input data, calculate the carbon emission reduction benefits of the optimized clean energy configuration scheme based on the carbon emission reduction factor model; S63, Multidimensional analysis of evaluation indicators: After calculating the carbon emission reduction benefits, multidimensional evaluation indicators are generated; S64, Carbon Emission Reduction Report Generation: Generate a carbon emission reduction report based on the results of carbon emission reduction benefit calculation and evaluation indicator analysis.

9. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy allocation according to claim 8, characterized in that, S7 includes: S71, Decision Support Input Preparation: Based on the carbon emission reduction effect assessment report, collect and prepare the input data required for decision support. The input data includes carbon emission reduction benefit data, energy allocation scheme data, environmental parameter data, and electricity demand data. S72, Decision Recommendation Generation: Based on the prepared input data, decision support algorithms are used to generate decision recommendations for inter-regional power transmission and energy allocation and scheduling; S73, Decision Support Output: Based on the generated decision recommendations, an energy dispatching scheme is generated; S74, Feedback to the power grid operation and dispatch platform: The generated energy dispatch plan is fed back to the power grid operation and dispatch platform to execute the optimal energy dispatch plan.

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