Cross-regional power transmission carbon emission reduction measuring and calculating method based on clean energy configuration

Abstract

The invention relates to the technical field of carbon emission reduction, in particular to a cross-regional power transmission carbon emission reduction measuring and calculating method based on clean energy configuration, which comprises the steps of 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. The method comprises the following steps: collecting multi-source heterogeneous data in a cross-regional region in real time, preprocessing the data, constructing a carbon emission reduction factor model based on the processed data, evaluating the carbon emission reduction benefit of clean energy in cross-regional power transmission, simulating the benefits of different clean energy configuration schemes by using a multi-target simulation algorithm, and optimizing the configuration schemes; and finally, generating decision suggestions of energy scheduling and cross-regional power transmission through a decision support system, feeding back the decision suggestions to the power grid operation scheduling platform, and implementing an optimal energy scheduling scheme. According to the method, the utilization efficiency of clean energy can be effectively improved, carbon emission in the power transmission process is reduced, and the method has important ecological and economic values.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission reduction, and in particular to a method for calculating carbon emission reduction in inter-regional power transmission based on clean energy configuration. Background Art

[0002] In recent years, the technologies of clean energy such as wind power and photovoltaics have continued to advance, their costs have gradually decreased, and they have gradually become an important part of the power system. Promoting the development of clean energy and enhancing its application in cross-regional power transmission have become key paths to achieving carbon neutrality goals.

[0003] In the existing technology, although many studies have been conducted on issues such as clean energy power generation scheduling and load forecasting, there are still certain deficiencies in the comprehensive optimization of inter-regional power transmission and carbon emission reduction benefits. First, the existing technology has difficulties in the integration and processing of multi-source heterogeneous data. The inter-regional power transmission system involves multiple data sources such as clean energy production data, the operating status of transmission lines, electricity demand data, and environmental parameters, but there are technical barriers to the collection and integration of these data, which makes it difficult to achieve effective global optimization. Secondly, the existing carbon emission reduction assessment methods are often limited to a single region or a single type of energy, and do not deeply consider the complex relationship between the clean energy substitution effect and power transmission loss, making it difficult to accurately evaluate the carbon emission reduction benefits of inter-regional power transmission under different clean energy configuration schemes. Finally, in terms of the optimization of clean energy inter-regional power transmission, most existing methods focus on economic or power supply reliability optimization, while less consideration is given to the comprehensive optimization of carbon emission reduction benefits, transmission losses, and environmental fluctuations. Summary of the invention

[0004] Based on the above objectives, the present invention provides a method for calculating carbon emission reduction in cross-regional power transmission based on clean energy configuration.

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

[0006] S1, data collection: collect multi-source heterogeneous data in each cross-region area in real time through monitoring equipment and sensors. The multi-source heterogeneous data includes clean energy production data, operation status data of cross-regional transmission lines, electricity demand data and environmental parameter data, among which;

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

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

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

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

[0011] S2, data preprocessing: preprocess the collected multi-source heterogeneous data, including data cleaning, outlier removal and normalization;

[0012] S3, carbon emission reduction factor calculation: Based on the pre-processed 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 the pre-processed multi-source heterogeneous data and carbon emission reduction factor model, a multi-objective simulation algorithm is used to simulate the carbon emission reduction benefits under different clean energy configuration schemes, and the carbon emission reduction simulation results of each scheme are output;

[0014] S5, Optimization of carbon emission reduction effect: Based on the simulation results of carbon emission reduction benefits, the genetic algorithm is used to optimize the cross-regional transmission configuration plan 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 to output the optimal configuration plan;

[0015] S6, Carbon emission reduction effect evaluation: Evaluate the carbon emission reduction effect of the optimized clean energy configuration plan and generate a corresponding carbon emission reduction report;

[0016] S7, Decision support and feedback: Based on the evaluation results, provide decision suggestions for cross-regional power transmission and energy configuration scheduling, and feedback to the power grid operation scheduling platform through the scheduling system to implement optimal energy scheduling.

[0017] Optionally, the 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 per unit time of wind farms and photovoltaic power stations;

[0019] S12, operation status data collection of inter-regional transmission lines: by installing monitoring sensors at various key nodes of the 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, wherein the monitoring sensors include current sensors, voltage sensors, load monitoring devices and power flow analysis equipment;

[0020] S13, electricity demand data collection: collect the electricity load characteristics and fluctuations of each cross-regional area in real time through regional power demand monitoring equipment, which includes load monitors, power meters and data recording devices;

[0021] S14, environmental parameter data collection: by deploying environmental monitoring equipment, the temperature, humidity and air pressure of each cross-region 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: preliminary cleaning of the collected multi-source heterogeneous data to remove invalid data and duplicate data;

[0024] S22, outlier removal: The Z-Score method is used to detect outliers in the multi-source heterogeneous data after preliminary cleaning;

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

[0026] Optionally, the S3 includes:

[0027] S31, Carbon emission reduction factor model construction: Based on the pre-processed multi-source heterogeneous data, a carbon emission reduction factor model is constructed;

[0028] S32, Carbon emission reduction benefit calculation: Calculate the carbon emission reduction benefit of clean energy in inter-regional power transmission through the constructed carbon emission reduction factor model;

[0029] S33, comprehensive carbon emission reduction benefit output: carbon emission reduction benefit calculation results, output of carbon emission reduction benefits of cross-regional transmission of clean energy.

[0030] Optionally, the S4 includes:

[0031] S41, integration and preparation of input data: Based on the pre-processed 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;

[0032] S42, Simulation algorithm selection and application: Select the simulated annealing algorithm as the multi-objective simulation algorithm, and perform simulation calculations based on the carbon emission reduction benefits under different clean energy configuration schemes.

[0033] S43, simulation result 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 processing during simulation: during the simulation calculation process, constraints are imposed on 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;

[0036] S45, Calculation and evaluation of carbon emission reduction benefits: During the simulation process, 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 result output and scheme evaluation: After the simulation is completed, the carbon emission reduction benefit simulation results of each clean energy configuration scheme are output, and different schemes are compared and analyzed through multi-dimensional evaluation indicators (such as cost-effectiveness ratio, carbon emission reduction, resource utilization efficiency, etc.), and the optimal configuration scheme is identified through the evaluation results.

[0038] Optionally, the S5 includes:

[0039] S51, optimization target setting: according to the simulation results of carbon emission reduction benefits, set the optimization target function, which includes maximizing carbon emission reduction benefits and minimizing transmission losses;

[0040] S52, Selection and implementation of genetic algorithm: Select genetic algorithm as the optimization algorithm and implement the optimization of clean energy inter-regional transmission configuration 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 the transmission lines and changes in environmental parameters;

[0042] S54, optimization result output: After multiple generations of iterations, the optimal clean energy cross-regional transmission configuration plan is output.

[0043] Optionally, the S6 includes:

[0044] S61, preparation of evaluation input data: 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 the 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 benefit of the optimized clean energy configuration scheme based on the carbon emission reduction factor model;

[0046] S63, multi-dimensional analysis of evaluation indicators: after calculating the carbon emission reduction benefits, generate multi-dimensional evaluation indicators.

[0047] S64, Carbon emission reduction report generation: Generate a detailed carbon emission reduction report based on the results of carbon emission reduction benefit calculation and evaluation index analysis.

[0048] Optionally, the S7 includes:

[0049] S71, Decision support input preparation: Based on the carbon emission reduction effect evaluation report, collect and prepare the input data required for decision support, including carbon emission reduction benefit data, energy configuration plan data, environmental parameter data and electricity demand data;

[0050] S72, decision suggestion generation: based on the prepared input data, using the decision support algorithm to generate decision suggestions for inter-regional power transmission and energy configuration scheduling;

[0051] S73, decision support output: generating an energy scheduling plan based on the generated decision suggestions;

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

[0053] Beneficial effects of the present invention:

[0054] This invention, through the method of calculating carbon emission reduction in cross-regional power transmission based on clean energy configuration, adopts the precise collection and preprocessing of multi-source heterogeneous data, combined with the carbon emission reduction factor model and optimization algorithm, can accurately evaluate the contribution of clean energy configuration to carbon emission reduction. By optimizing energy configuration, maximizing carbon emission reduction benefits, and reducing the use of traditional fossil energy, it will help promote the development of green and low-carbon power grids and provide strong support for achieving the goal of carbon neutrality.

[0055] The present invention uses a multi-objective simulation algorithm and a genetic algorithm to optimize the cross-regional transmission configuration scheme of clean energy, which can reduce transmission losses while ensuring power supply, and respond to environmental changes and fluctuations in power demand by dynamically adjusting the optimization strategy. This not only improves the efficiency of energy use and reduces system operating costs, but also enhances the adaptability and flexibility of the cross-regional transmission system under different environmental and load conditions, ensuring the economy and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0058] Figure 2 Schematic diagram of S4 process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and 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 references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

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

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

[0063] S1, data collection: collect multi-source heterogeneous data in each cross-region area in real time through monitoring equipment and sensors. The multi-source heterogeneous data includes clean energy production data, operation status data of cross-regional transmission lines, electricity demand data and environmental parameter data, among which;

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

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

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

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

[0068] S2, data preprocessing: preprocess the collected multi-source heterogeneous data, including data cleaning, outlier removal and normalization;

[0069] S3, calculation of carbon emission reduction factor: Based on the pre-processed multi-source heterogeneous data, a carbon emission reduction factor model is constructed. The carbon emission reduction benefit of clean energy in inter-regional power transmission is calculated through the carbon emission reduction factor model. 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 the pre-processed multi-source heterogeneous data and carbon emission reduction factor model, a multi-objective simulation algorithm is used to simulate the carbon emission reduction benefits under different clean energy configuration schemes, and the carbon emission reduction simulation results of each scheme are output;

[0071] S5, Optimization of carbon emission reduction effect: Based on the simulation results of carbon emission reduction benefits, the genetic algorithm is used to optimize the cross-regional transmission configuration plan 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 to output the optimal configuration plan;

[0072] S6, Carbon emission reduction effect evaluation: Evaluate the carbon emission reduction effect of the optimized clean energy configuration plan and generate a corresponding carbon emission reduction report;

[0073] S7, Decision support and feedback: Based on the evaluation results, provide decision suggestions for cross-regional power transmission and energy configuration scheduling, and feedback to the power grid operation scheduling platform through the scheduling system to implement optimal energy scheduling.

[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 per unit time of wind farms and photovoltaic power stations, ensuring real-time reflection of the clean energy production status;

[0076] S12, data collection of the operation status of inter-regional transmission lines: by installing monitoring sensors at various key nodes of the 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: collect the electricity load characteristics and fluctuations of each cross-regional area in real time through regional power demand monitoring equipment, which includes load monitors, power meters and data recording devices;

[0078] S14, Environmental parameter data collection: 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, elimination of invalid data and duplicate data, and ensuring the accuracy and completeness of the data, including:

[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 whether the data recorded by the sensor or monitoring equipment has obvious collection errors, such as abnormal conditions such as power values ​​that are too high or too low, and mark or remove them;

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

[0084] S22, outlier removal: The Z-Score method is used to detect outliers in the preliminarily cleaned multi-source heterogeneous data. The specific methods include:

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

[0086]

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

[0088] Set a reasonable threshold (such as 3) as the elimination standard. When the Z-Score of a data item exceeds the set threshold, the data is considered to be an outlier and is eliminated.

[0089] Smoothing the data set after removing outliers to ensure data stability and consistency;

[0090] S23, normalization: The minimum-maximum normalization method is used to normalize data from different sources and dimensions, and unify the data into a dimensionless range. The specific methods include:

[0091] The clean energy production data (such as wind power and photovoltaic power station power data), transmission line data (such as current, voltage, etc.) and environmental data (such as temperature, humidity, etc.) are normalized to the minimum and maximum values. The normalization formula is:

[0092]

[0093] Among them, X is the original data, X min and X max are the minimum and maximum values ​​in the data set, respectively, ′ is the normalized data value, ranging from [0,1];

[0094] Check the normalized data to ensure that the normalization process does not cause information loss and ensure the validity of the data.

[0095] S3 includes:

[0096] S31, Carbon emission reduction factor model construction: Based on the pre-processed multi-source heterogeneous data, a carbon emission reduction factor model is constructed. The carbon emission reduction factor model takes into account the carbon emission intensity of different types of energy, the substitution effect of clean energy, and the impact of power transmission loss on carbon emission reduction benefits. The specific steps include:

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

[0098]

[0099] Among them, C energy is the carbon emission intensity of energy, E CO2 The carbon dioxide emissions generated by energy consumption, E energy is the 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 stations) and traditional energy power generation, a clean energy substitution effect model is constructed. The clean energy substitution effect model considers the substitution effect of different clean energy on traditional energy under different time periods and load conditions, and defines the coefficient of clean energy substitution for traditional energy, which is expressed as:

[0101]

[0102] Among them, P clean energy is the power generated by clean energy, P total energy is the total energy demand power;

[0103] (3) Power transmission loss modeling: During the transmission process, power will experience loss, which will in turn affect the carbon emission reduction benefits. The power transmission loss model is used to calculate the loss ratio of power during the transmission process and correct the clean energy substitution effect. The power loss calculation formula is expressed as:

[0104]

[0105] Among them, L loss is the transmission loss ratio, P input is the input power, P output is the output power;

[0106] S32, Carbon emission reduction benefit calculation: Calculate the carbon emission reduction benefits of clean energy in inter-regional power transmission through the constructed carbon emission reduction factor model, 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, calculate the contribution of clean energy in reducing carbon emissions from traditional energy. The calculation formula for carbon emission reduction benefits is:

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

[0109] Among them, C reduction For carbon reduction benefits, P clean energy is the power generated by clean energy, C energy is the carbon emission intensity of traditional energy, Substitution Effect is the substitution effect coefficient;

[0110] (2) Carbon emission reduction benefits after transmission loss adjustment: According to the power transmission loss model, the carbon emission reduction benefits of clean energy are adjusted to obtain the final carbon emission reduction amount, which is expressed as:

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

[0112] Among them, C adjusted is the adjusted carbon reduction benefit, L loss is the power transmission loss ratio;

[0113] S33, comprehensive carbon emission reduction benefit output: carbon emission reduction benefit calculation results, output of carbon emission reduction benefits of cross-regional transmission of clean energy.

[0114] S4 includes:

[0115] S41, Integration and preparation of input data: Based on the pre-processed multi-source heterogeneous data and carbon reduction factor model, clean energy production data, transmission line status data, electricity demand data, environmental parameter data and the calculation results of the carbon reduction factor model are integrated to provide input data for the multi-objective simulation algorithm, including:

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

[0117] Combine the capacity constraints, load capacity and power flow data of transmission lines with carbon reduction benefits to form a transmission network characteristics dataset;

[0118] Combine the load curve, fluctuation characteristics and environmental parameter data of electricity demand to prepare the time series data required for input simulation;

[0119] S42, Simulation algorithm selection and application: Select the simulated annealing algorithm as the multi-objective simulation algorithm, and perform simulation calculations based on the carbon emission reduction benefits under different clean energy configuration schemes. The simulated annealing algorithm can optimize multiple objective functions at the same time, considering 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 changes: the impact of dynamically changing environmental parameters (such as temperature and humidity) on clean energy generation capacity and demand fluctuations;

[0122] Demand fluctuations: Fluctuations in regional electricity demand, especially changes in load peaks and valleys.

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

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

[0125] (2) Definition of energy function: Define the energy function, take carbon emission reduction benefit as the main optimization goal, and consider the impact of factors such as capacity constraints, environmental changes and demand fluctuations. The energy function is:

[0126]

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

[0128] (3) Acceptance and updating of solutions: Through the temperature update strategy of the simulated annealing algorithm, new solutions are accepted at a certain "temperature" or worse solutions are accepted according to the set probability, gradually approaching the optimal solution.

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

[0130] S4 also includes:

[0131] S44, constraint processing during simulation: during the simulation calculation process, constraints are imposed on 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, among which;

[0132] Capacity constraint: According to the actual capacity limit of the transmission line, the power output of each configuration scheme is limited during the simulation process to prevent exceeding the load capacity of the transmission line and ensure that the simulation results are actually operational;

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

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

[0135] S45, Calculation and evaluation of carbon emission reduction benefits: During the simulation process, 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. The specific calculations include:

[0136] Calculate the carbon reduction for each clean energy configuration, using a carbon reduction factor combined with clean energy generation;

[0137] Adjust the carbon reduction effect of each solution under the influence of capacity constraints and load fluctuations to ensure the practical feasibility of the optimization results;

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

[0139] S5 includes:

[0140] S51, optimization target setting: according to the simulation results of carbon emission reduction benefits, set the optimization target function, the optimization target function includes maximizing carbon emission reduction benefits and minimizing transmission losses, where;

[0141] Maximize carbon emission reduction benefits: that is, maximize the carbon emission reduction benefits of each configuration scheme through reasonable configuration of clean energy;

[0142] Minimize transmission losses: optimize the clean energy inter-regional transmission plan and reduce power losses during transmission;

[0143] The optimization objective function is expressed as:

[0144]

[0145] Among them, Obj is the optimization objective function, C reduction (i) is the carbon emission reduction benefit of the i-th configuration plan, L loss (i) is the transmission loss of the i-th configuration scheme, w1 and w2 are weight 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: Select genetic algorithm as the optimization algorithm and implement the optimization of clean energy inter-regional transmission configuration based on genetic algorithm. The main steps of genetic algorithm include:

[0147] Initialize the population: randomly generate multiple initial configuration schemes to form the initial population of the optimization algorithm, where each individual represents a clean energy configuration scheme;

[0148] Fitness evaluation: The fitness of each individual is evaluated. The fitness function is the aforementioned optimization objective function. The advantages and disadvantages of each scheme are evaluated based on the carbon emission reduction benefits and transmission losses.

[0149] Selection operation: select parent individuals according to their fitness values, using methods such as roulette selection and tournament selection to select individuals with higher fitness as parents for crossover and mutation operations;

[0150] Crossover operation: Generate new configuration schemes through crossover operation, simulate the gene recombination process, and produce better solutions. Crossover operation can be performed in single-point crossover, multi-point crossover or uniform crossover mode;

[0151] Mutation operation: Introduce mutation operation to increase the diversity of the population by randomly changing some parameters of the configuration scheme and avoid falling into the local optimal solution;

[0152] Termination conditions: Set the termination conditions of the algorithm. Common conditions include the maximum number of iterations, the fitness reaching the set threshold, the population fitness tending to be stable, etc.

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

[0154] Capacity constraint processing: adjust each optimization solution according to the real-time transmission line load and capacity constraints to ensure that the transmission line is not overloaded;

[0155] Environmental parameter adaptation: Dynamically adjust clean energy power generation capacity based on real-time meteorological data (such as temperature and humidity) and optimize configuration plans to adapt to environmental changes;

[0156] S54, optimization result output: After multiple generations of iterations, the optimal clean energy inter-regional transmission configuration plan is output. The plan should be able to maximize the carbon emission reduction benefits while minimizing transmission losses and meet the transmission line capacity and environmental constraints. The output results include:

[0157] The carbon reduction benefits of the optimal configuration scheme,

[0158] Transmission loss of the optimal configuration solution,

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

[0160] S6 includes:

[0161] S61, Preparation of evaluation input data: 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 the clean energy configuration data, environmental parameter data and carbon emission reduction factor model data of the optimized scheme, including:

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

[0163] Environmental parameter data: such as real-time meteorological data (temperature, humidity, air pressure, etc.), 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 benefit of the optimized clean energy configuration scheme is calculated based on the carbon emission reduction factor model. The calculation process takes into account the substitution benefit of clean energy power generation, transmission loss and the impact of environmental changes on carbon emission reduction. The calculation formula is:

[0166]

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

[0168] S63, multi-dimensional analysis of evaluation indicators: after calculating the carbon emission reduction benefits, generate multi-dimensional evaluation indicators, including:

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

[0170] Carbon emission reduction efficiency: the carbon emission reduction effect per unit of electricity, which evaluates the efficiency of clean energy utilization;

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

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

[0173] S64, Carbon emission reduction report generation: Generate a detailed carbon emission reduction report based on the results of carbon emission reduction benefit calculation and evaluation index analysis. The report content includes:

[0174] Carbon reduction benefits overview: summarize the carbon reduction benefits of each clean energy configuration scheme and give the total carbon reduction amount;

[0175] Environmental impact assessment: Based on environmental parameters and carbon reduction factors, evaluate the performance of each scheme under different environmental conditions and provide an analysis of environmental adaptability;

[0176] Economic benefit analysis: Evaluate the relationship between carbon emission reduction and cost, and provide economic benefit analysis results;

[0177] Optimization plan suggestions: Provide further optimization suggestions to help decision makers choose the plan with the greatest environmental and economic benefits.

[0178] S7 includes:

[0179] S71, Decision support input preparation: Based on the carbon emission reduction effect evaluation report, collect and prepare the input data required for decision support. The input data includes carbon emission reduction benefit data, energy configuration plan data, environmental parameter data and power demand data, including:

[0180] Carbon reduction benefit data: total carbon reduction from the assessment results, carbon reduction efficiency of each plan, environmental adaptability and other indicators;

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

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

[0183] Power demand data: cross-regional power demand forecast data, including the power load characteristics and fluctuations of each region;

[0184] S72, decision suggestion generation: based on the prepared input data, using the decision support algorithm to generate decision suggestions for inter-regional power transmission and energy configuration scheduling, the decision suggestions include;

[0185] Energy configuration optimization suggestions: Based on the carbon emission reduction benefits and power demand fluctuations, the optimal clean energy cross-regional transmission configuration plan is given to ensure the maximum carbon emission reduction benefits and the highest energy utilization efficiency;

[0186] Power dispatch optimization suggestions: Provide dispatch suggestions based on the optimized energy configuration plan and power demand forecast to ensure the balance of cross-regional power flow and the stability of power supply. Dispatching suggestions include the optimal power generation and transmission load allocation plan to ensure that power supply between different regions can be allocated on demand;

[0187] Dynamic adjustment suggestions: According to the real-time environmental parameter changes and power demand fluctuations, dynamic adjustment suggestions are put forward to help the power grid operation system respond to external environmental changes in a timely manner and ensure the flexibility and stability of the dispatching plan;

[0188] S73, decision support output: based on the generated decision suggestions, generate an energy scheduling plan, which includes:

[0189] Optimal inter-regional transmission plan: Detailed list of clean energy generation capacity, power flow and load distribution of transmission lines in each region;

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

[0191] Environmental adaptability adjustment: Suggestions for flexible adjustment of scheduling plans based on environmental changes;

[0192] S74, feedback to the grid operation and dispatching platform: Feedback the generated energy dispatching plan to the grid operation and dispatching platform to execute the optimal energy dispatching plan. The feedback process includes:

[0193] Receiving on the dispatching platform: The power grid operation dispatching platform receives the generated energy dispatching plan, including the specific plan of energy configuration and power flow;

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

[0195] Real-time monitoring and adjustment: Through the real-time monitoring system feedback of operating status data, monitor the implementation of scheduling, and dynamically adjust energy scheduling to cope with sudden changes in demand or environmental changes.

[0196] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0197] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as 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 configuration, characterized in that: The following steps are involved: S1, data collection: collect multi-source heterogeneous data in each cross-region area in real time through monitoring equipment and sensors. The multi-source heterogeneous data includes clean energy production data, operation status data of cross-regional transmission lines, electricity demand data and environmental parameter data, among which; Clean energy production data includes the power generation capacity and real-time output power of wind farms and photovoltaic power plants; The operating status data of inter-regional transmission lines include line losses, load capacity and power flow direction; Electricity demand data include regional electricity load characteristics and fluctuations; Environmental parameter data include air temperature, humidity and air pressure; S2, data preprocessing: preprocess the collected multi-source heterogeneous data, including data cleaning, outlier removal and normalization; S3, carbon emission reduction factor calculation: Based on the pre-processed 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 the pre-processed multi-source heterogeneous data and carbon emission reduction factor model, a multi-objective simulation algorithm is used to simulate the carbon emission reduction benefits under different clean energy configuration schemes, and the carbon emission reduction simulation results of each scheme are output; S5, Optimization of carbon emission reduction effect: Based on the simulation results of carbon emission reduction benefits, the genetic algorithm is used to optimize the cross-regional transmission configuration plan 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 to output the optimal configuration plan; S6, Carbon emission reduction effect evaluation: Evaluate the carbon emission reduction effect of the optimized clean energy configuration plan and generate a corresponding carbon emission reduction report; S7, decision support and feedback: Based on the evaluation results, provide decision suggestions for cross-regional power transmission and energy configuration scheduling, and feedback to the power grid operation scheduling platform through the scheduling system.

2. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy configuration according to claim 1 is characterized in that: The 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 per unit time of wind farms and photovoltaic power stations; S12, collecting operation status data of inter-regional transmission lines: by installing monitoring sensors at various nodes of the inter-regional transmission lines, real-time collection of operation status data of the lines, 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: collect the electricity load characteristics and fluctuations of each cross-regional area in real time through regional power demand monitoring equipment, which includes load monitors, power meters and data recording devices; S14, environmental parameter data collection: by deploying environmental monitoring equipment, the temperature, humidity and air pressure of each cross-region 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 of inter-regional power transmission based on clean energy configuration according to claim 2 is characterized in that: The S2 includes: S21, data cleaning: preliminary cleaning of the collected multi-source heterogeneous data to remove invalid data and duplicate data; S22, outlier removal: The Z-Score method is used to detect outliers in the multi-source heterogeneous data after preliminary cleaning; S23, normalization: The minimum-maximum normalization method is used to standardize data from different sources and dimensions, and unify the data into a dimensionless range.

4. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy configuration according to claim 3 is characterized in that: The S3 includes: S31, Carbon emission reduction factor model construction: Based on the pre-processed multi-source heterogeneous data, a carbon emission reduction factor model is constructed; S32, Carbon emission reduction benefit calculation: Calculate the carbon emission reduction benefit of clean energy in inter-regional power transmission through the constructed carbon emission reduction factor model; S33, comprehensive carbon emission reduction benefit output: carbon emission reduction benefit calculation results, output of carbon emission reduction benefits of cross-regional transmission of clean energy.

5. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy configuration according to claim 4 is characterized in that: The S4 includes: S41, integration and preparation of input data: Based on the pre-processed 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; S42, Simulation algorithm selection and application: Select the simulated annealing algorithm as the multi-objective simulation algorithm, and perform simulation calculations based on the carbon emission reduction benefits under different clean energy configuration schemes; S43, simulation result 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 configuration according to claim 5 is characterized in that: The S4 further comprises: S44, constraint processing during simulation: during the simulation calculation process, constraints are imposed on each clean energy configuration scheme, and the 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 process, 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 result output and scheme evaluation: After the simulation is completed, the carbon emission reduction benefit simulation results of each clean energy configuration scheme are output, and different schemes are compared and analyzed through multi-dimensional evaluation indicators. The optimal configuration scheme is identified through the evaluation results.

7. The method for calculating carbon emission reduction of inter-regional power transmission based on clean energy configuration according to claim 1 is characterized in that: The S5 includes: S51, optimization target setting: according to the simulation results of carbon emission reduction benefits, set the optimization target function, which includes maximizing carbon emission reduction benefits and minimizing transmission losses; S52, Selection and implementation of genetic algorithm: Select genetic algorithm as the optimization algorithm and implement the optimization of clean energy inter-regional transmission configuration 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 the transmission lines and changes in environmental parameters; S54, optimization result output: After multiple generations of iterations, the optimal clean energy cross-regional transmission configuration plan is output.

8. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy configuration according to claim 7 is characterized in that: The S6 includes: S61, preparation of evaluation input data: prepare the input data required for the evaluation, including the clean energy configuration data, environmental parameter data and carbon emission reduction factor model data of the optimization scheme; S62, Carbon emission reduction benefit calculation: Using the prepared input data, calculate the carbon emission reduction benefit of the optimized clean energy configuration scheme based on the carbon emission reduction factor model; S63, multi-dimensional analysis of evaluation indicators: after calculating the carbon reduction benefits, generate multi-dimensional evaluation indicators; S64, Carbon emission reduction report generation: Generate a carbon emission reduction report based on the results of carbon emission reduction benefit calculation and evaluation index analysis.

9. The method for calculating carbon emission reduction in inter-regional power transmission based on clean energy configuration according to claim 8 is characterized in that: The S7 includes: S71, Decision support input preparation: Based on the carbon emission reduction effect evaluation report, collect and prepare the input data required for decision support, including carbon emission reduction benefit data, energy configuration plan data, environmental parameter data and electricity demand data; S72, decision suggestion generation: based on the prepared input data, using the decision support algorithm to generate decision suggestions for inter-regional power transmission and energy configuration scheduling; S73, decision support output: generating an energy scheduling plan based on the generated decision suggestions; S74, feedback to the power grid operation and dispatching platform: the generated energy dispatching plan is fed back to the power grid operation and dispatching platform to execute the optimal energy dispatching plan.

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