A Low-Carbon Operation Control Method and System for Transformers Considering Carbon Emission Characteristics

Abstract

The present invention relates to the field of carbon emissions, and discloses a low-carbon operation control method and system for a transformer considering carbon emission characteristics. The method includes, in the simulation stage, obtaining power flow sample data; constructing a neural network probabilistic power flow model with the power flow sample data, and obtaining the power flow results output by the neural network probabilistic power flow model based on the stochastic response surface method; based on the probability distribution interval of the node voltage in the output power flow results, correcting the initial carbon emission results of the entire life cycle of each calculated transmission transformer to obtain the carbon emission accounting results of each transmission transformer; in the actual low-carbon operation control stage, controlling the energy unit corresponding to the determined target transmission transformer to perform power transmission to the power grid supply side. The low-carbon operation control method of the present invention accurately accounts for the carbon emissions of the transformer, thereby providing accurate data support for the actual low-carbon operation control, and promoting the intelligent and green and healthy development process of carbon emissions.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emissions, and particularly to a low-carbon operation control method and system for a transformer considering carbon emission characteristics. Background Art

[0002] Accurate and comprehensive carbon emission measurement and accounting are the basis for the low-carbon transformation of the power energy system. By calculating the carbon emissions of the power system, the carbon emissions generated during the operation of the power system can be accurately understood, providing data support.

[0003] As an important energy conversion device in power transmission and transformation, the transformer is large in design and construction volume and high in energy consumption, and is a high-carbon emission device among electrical devices. In the current distributed energy grid system during the operation control process, the impact of transformer carbon emissions is not considered, which is not conducive to the low-carbon transformation and healthy development of the power industry. Summary of the Invention

[0004] The present invention provides a low-carbon operation control method and system for a transformer considering carbon emission characteristics, which considers the impact of fluctuations of different energy units on the transformer, accurately calculates the carbon emissions of the transformer, thereby providing accurate data support for actual low-carbon operation control, and promoting the intelligent and green and healthy development process of low-carbon operation control.

[0005] To solve the above technical problems, an embodiment of the present invention provides a low-carbon operation control method for a transformer considering carbon emission characteristics, which is applied to a distributed energy grid system composed of a plurality of energy units, and each of the energy units transmits electric energy to the grid supply side through a corresponding power transmission transformer. The low-carbon operation control method for a transformer considering carbon emission characteristics includes:

[0006] In the simulation and simulation stage, based on the Monte Carlo algorithm, the load data of each of the energy units and the system topology structure data of the distributed energy grid system are processed to obtain power flow sample data;

[0007] A neural network probabilistic power flow model is constructed with the power flow sample data, wherein the neural network probabilistic power flow model is configured to perform relaxation processing on the neural network probabilistic power flow model according to the mixed integer second-order cone programming method, and obtain the power flow result output by the neural network probabilistic power flow model based on the stochastic response surface method;

[0008] Based on the probability distribution interval of the node voltage in the output power flow result, the initial carbon emission result of the whole life cycle of each of the power transmission transformers calculated is corrected to obtain the carbon emission accounting result of each of the power transmission transformers;

[0009] In the actual low-carbon operation control stage, analyze each of the carbon emission accounting results, and control the power transmission of the energy units corresponding to the target power transmission transformers determined according to the analysis results to the power grid supply side.

[0010] As one of the preferred solutions, the construction process of the neural network probabilistic power flow model includes:

[0011] Take the load data of the energy units input, the system topology structure data, and the probability distribution parameters of the set system input random variables as the basic parameters of the RBF neural network model;

[0012] Select the collocation points of the standard normal variables and formulate the second-order cone polynomial;

[0013] Based on the Nataf transformation method, transform all the collocation points from the standard normal space to the original space to obtain the sample points associated with the power flow sample data;

[0014] Perform the power flow calculation by expanding the second-order cone polynomial for each of the sample points, so that the neural network probabilistic power flow model outputs the corresponding power flow results.

[0015] As one of the preferred solutions, before the probability distribution interval of the node voltage in the output power flow results, the method further includes:

[0016] Use the empirical distribution function to perform discrete fitting on the output power flow results to obtain the probability distribution functions reflecting different random variables;

[0017] According to the probability distribution functions, calculate the probability distribution intervals of the node voltages in each distributed energy grid system.

[0018] As one of the preferred solutions, based on the probability distribution interval of the node voltage in the output power flow results, correct the initial carbon emission results of the entire life cycle of each of the power transmission transformers calculated to obtain the carbon emission accounting results of each of the power transmission transformers, including:

[0019] Analyze the probability distribution interval to obtain the voltage fluctuation information of each of the power transmission transformers;

[0020] According to the voltage fluctuation information, determine the loss index corresponding to each of the power transmission transformers, where the loss index at least includes the iron loss index, copper loss index, insulation aging index, harmonic index, or magnetic flux index;

[0021] Based on the loss index, correct the carbon emission calculation results in the equipment operation stage of the entire life cycle of each of the power transmission transformers to obtain the corrected carbon emission accounting results of each of the power transmission transformers.

[0022] As one of the preferred solutions, after controlling the power transmission of the energy unit corresponding to the target power transmission transformer determined according to the analysis result to the grid supply side, the method further includes:

[0023] Calculating the carbon emission of energy supply of the energy unit corresponding to the target power transmission transformer, and optimizing the weight of the constructed digital carbon reduction evaluation index system with the carbon emission of energy supply.

[0024] Another embodiment of the present invention provides a low-carbon operation control system for transformers considering carbon emission characteristics, which is applied to a distributed energy grid system composed of a number of energy units. Each energy unit transmits electric energy to the grid supply side through a corresponding power transmission transformer. The low-carbon operation control of the transformer considering carbon emission characteristics includes:

[0025] A power flow sample module, configured to process the load data of each energy unit and the system topology structure data of the distributed energy grid system based on the Monte Carlo algorithm during the simulation stage to obtain power flow sample data;

[0026] A power flow model module, configured to construct a neural network probabilistic power flow model with the power flow sample data. The neural network probabilistic power flow model is configured to perform relaxation processing on the neural network probabilistic power flow model according to the mixed integer second-order cone programming method, and obtain the power flow result output by the neural network probabilistic power flow model based on the stochastic response surface method;

[0027] A carbon emission accounting module, configured to correct the initial carbon emission results of the entire life cycle of each power transmission transformer calculated based on the probability distribution interval of the node voltage in the output power flow result to obtain the carbon emission accounting result of each power transmission transformer;

[0028] A control module, configured to analyze the carbon emission accounting results during the actual operation control stage, and control the power transmission of the energy unit corresponding to the target power transmission transformer determined according to the analysis result to the grid supply side.

[0029] As one of the preferred solutions, the construction process of the neural network probabilistic power flow model includes:

[0030] Taking the load data of the energy unit input, the system topology structure data, and the probability distribution parameters of the set system input random variables as the basic parameters of the RBF neural network model;

[0031] Selecting the collocation points of the standard normal variable and formulating the second-order cone polynomial;

[0032] Based on the Nataf transformation method, all the configuration points are transformed from the standard normal space to the original space to obtain sample points associated with the power flow sample data;

[0033] Perform power flow calculation by expanding the second-order cone polynomial for each of the sample points, so that the neural network probabilistic power flow model outputs the corresponding power flow results.

[0034] As one of the preferred solutions, the system further includes:

[0035] A discrete fitting module, which is used to perform discrete fitting on the output power flow results by using the empirical distribution function to obtain the probability distribution functions reflecting different random variables;

[0036] A calculation module, which is used to calculate the probability distribution intervals of the voltages of each node in the distributed energy power grid system according to the probability distribution functions.

[0037] As one of the preferred solutions, the system further includes:

[0038] A voltage fluctuation unit, which is used to analyze the probability distribution intervals to obtain the voltage fluctuation information of each transmission transformer;

[0039] A loss index unit, which is used to determine the loss index corresponding to each transmission transformer according to the voltage fluctuation information, where the loss index at least includes an iron loss index, a copper loss index, an insulation aging index, a harmonic index or a magnetic flux index;

[0040] A correction unit, which is used to correct the carbon emission calculation results in the equipment operation stage of the entire life cycle of each transmission transformer based on the loss index to obtain the corrected carbon emission accounting results of each transmission transformer.

[0041] As one of the preferred solutions, the system further includes:

[0042] A weight optimization module, which is used to calculate the carbon emissions of the energy supply of the energy units corresponding to the target transmission transformer, and optimize the weights of the constructed digital carbon reduction evaluation index system with the carbon emissions of the energy supply.

[0043] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0044] (1) Considering that the intermittency and volatility of energy units will bring many uncertain factors to the power grid, thereby reducing the security and reliability of the entire distributed energy power grid system, the present invention selects the Monte Carlo algorithm for power flow calculation. The Monte Carlo method can handle these uncertainty factors by introducing randomness. This enables the Monte Carlo method to more realistically reflect the actual operation of the system in power system power flow calculation and improve the accuracy of the final calculation;

[0045] (2) Due to the uncertainty of the output of energy units, it will cause fluctuations on the supply side of transmission lines, which will in turn affect the carbon emissions throughout the life cycle of transmission transformers and is not conducive to the comprehensive management of distributed energy power grid systems. The embodiments of the present invention accurately calculate the probabilistic power flow of each energy unit, analyze its impact on the carbon emissions of the respective supply-side transmission transformers, and conduct targeted control of the distributed energy power grid system to avoid selecting transmission lines with excessive carbon emissions of transformers due to line problems. Thus, the carbon emission balance of the distributed energy power grid system is achieved, providing an accurate control basis for the access position of energy units on the supply side and promoting the intelligent and green and healthy development process of low-carbon operation control. Brief Description of the Drawings

[0046] Figure 1 is a schematic diagram of the architecture of a distributed energy power grid system in one embodiment of the present invention;

[0047] Figure 2 is a schematic flowchart of a low-carbon operation control method for a transformer considering carbon emission characteristics in one embodiment of the present invention;

[0048] Figure 3 is a structural block diagram of a low-carbon operation control system for a transformer considering carbon emission characteristics in one embodiment of the present invention;

[0049] Reference Numerals:

[0050] Among them, 1, wind turbine; 2, photovoltaic unit; 3, biomass energy unit; A, first transmission transformer; B, second transmission transformer; C, third transmission transformer; 11, power flow sample module; 12, power flow model module; 13, carbon emission accounting module; 14, control module. Detailed Embodiments

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0053] In the description of the present application, it should be noted that, unless otherwise clearly defined and limited, the terms "installed", "connected", "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration and do not indicate or imply that the indicated device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0054] In the description of the present application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0055] It should be noted in advance that the embodiments of the present invention are applied to a distributed energy grid system. Specifically, please refer to Figure 1 , Figure 1The architecture schematic diagram of the distributed energy grid system in the embodiment of the present invention is shown. The distributed energy grid system consists of a number of energy units on the power generation side, including a wind turbine unit 1, a photovoltaic unit 2, and a biomass energy unit 3. After each energy unit on the power generation side generates electric energy, it transmits the electric energy to the grid supply side through a long-distance grid transmission line. In the power system, the electric energy generated on the power generation side is usually at a high voltage, while the supply side (such as households, industrial equipment, etc.) requires low-voltage electric energy. The transformer converts the high voltage into a low voltage suitable for use by the user side through the principle of electromagnetic induction, realizing the transmission and distribution of electric energy. Therefore, in the distributed energy grid system, there is also a first transmission transformer A connected to the wind turbine unit 1, a second transmission transformer B connected to the photovoltaic unit, and a third transmission transformer C connected to the biomass energy unit.

[0056] Generally speaking, a transmission transformer needs to be set at the position of the transmission line close to the power generation side, and a transmission transformer also needs to be set at the position of the transmission line close to the supply side. The embodiment of the present invention mainly improves the transmission transformer on the power generation side, and will not be elaborated additionally here.

[0057] An embodiment of the present invention provides a low-carbon operation control method for a transformer considering carbon emission characteristics. Specifically, please refer to Figure 2 , Figure 2 The flow schematic diagram of the low-carbon operation control method for a transformer considering carbon emission characteristics in one of the embodiments of the present invention is shown, which includes steps S1 to S4:

[0058] S1. In the simulation stage, based on the Monte Carlo algorithm, process the load data of each energy unit and the system topology structure data of the distributed energy grid system to obtain power flow sample data;

[0059] S2. Construct a neural network probabilistic power flow model with the power flow sample data. Among them, the neural network probabilistic power flow model is configured to perform relaxation processing on the neural network probabilistic power flow model according to the mixed integer second-order cone programming method, and obtain the power flow result output by the neural network probabilistic power flow model based on the stochastic response surface method;

[0060] S3. Based on the probability distribution interval of the node voltage in the output power flow result, correct the initial carbon emission results of the whole life cycle of each calculated transmission transformer to obtain the carbon emission accounting results of each transmission transformer;

[0061] S4. In the actual low-carbon operation control stage, analyze the carbon emission accounting results, and control the energy unit corresponding to the target transmission transformer determined according to the analysis results to transmit electric energy to the grid supply side.

[0062] Probabilistic power flow is an important tool for the uncertainty analysis of power systems. It obtains the distribution information of the system power flow through probabilistic and statistical methods, can take into account various stochastic factor scenarios, comprehensively reflect the operating conditions of the power grid, and is convenient for dispatchers to discover the weak links and potential risks of the system.

[0063] For complex systems and high-dimensional space problems that are difficult to solve by traditional numerical methods, the Monte Carlo method can provide relatively accurate numerical solutions. In the power flow calculation of power systems, the Monte Carlo method can handle complex systems containing a large number of random variables (such as wind speed, light intensity, etc.). Since the Monte Carlo method is based on the statistical results of a large number of random samples, its calculation results have good stability. Even when there are fluctuations or uncertainties in the input data, the Monte Carlo method can give relatively stable output results.

[0064] In the above step S1, a simulation model needs to be carried out. First, the power flow sample data is obtained, and then the subsequent probabilistic power flow model can be constructed. Of course, the process of power flow calculation and the Monte Carlo algorithm are common means in this field and will not be elaborated additionally in the embodiments of the present invention. In addition, the power flow sample data needs to go through a series of normalization processing steps and will not be described additionally in the embodiments of the present invention.

[0065] In the process of constructing the neural network probabilistic power flow model in step S2, it is preferably an RBF neural network probabilistic power flow model. Specifically, the embodiments of the present invention adopt the probabilistic power flow based on the stochastic response surface method (SRSM) to obtain the power flow distribution of the power grid containing multiple associated energy units. SRSM only needs to perform deterministic power flow calculations on a small number of collocation points, which can effectively improve the calculation efficiency. The specific process is as follows:

[0066] (1) Input the load data of the energy units, the system topology structure data, and the probability distribution parameters of the system input random variables (such as wind farms, photovoltaic power stations, and loads) as the basic parameters of the RBF neural network model;

[0067] (2) Select M collocation points of the standard normal variable ξ. For the second-order polynomial model, as follows:

[0068] M = 1 + 2n + n(n - 1) / 2

[0069] Among them, the kth collocation point is denoted as: ξ k = [ξ k1 , ξ k2 , …, ξ kn , k = 1, 2, …, M.

[0070] (3) Use the Nataf transformation to transform all collocation points from the standard normal space to the original space to obtain a series of sample points of associated input variables.

[0071] (4) Use the Matpower toolbox to perform deterministic power flow calculations on each input sample point to obtain sample points of the corresponding output response quantities Y (including node voltages, branch powers, network losses, etc.).

[0072] (5) Solve the unknown coefficients a of the polynomial through the collocation points and their corresponding output response samples i to obtain the second-order cone polynomial expansion expression of the output variable Y.

[0073] Further, in the above embodiment, before the probability distribution interval of the node voltage in the power flow result based on the output, it is also necessary to calculate the voltage interval. That is, the results of the power flow calculation include various indicators, and in the embodiment of the present invention, the impact on the transformer needs to be considered. In the actual power grid architecture, as described above, transformers are distributed at both ends of the transmission line. The embodiment of the present invention considers the transformers of the nodes on the side close to the energy unit. The specific process is as follows: Use the empirical distribution function to perform discrete fitting on the output power flow result to obtain the probability distribution function reflecting different random variables; According to the probability distribution function, calculate the probability distribution interval of the node voltages in the distributed energy grid system.

[0074] The inventor found through a large number of experimental analyses that voltage fluctuations will cause different degrees of losses to the transformer, which are mainly reflected in the following points:

[0075] Increase in iron loss: When the voltage increases, the magnetic flux density in the transformer core will also increase accordingly. If the magnetic flux density exceeds the saturation point of the core, the magnetic permeability of the core will decrease, resulting in a significant increase in iron loss (also known as no-load loss). This is because when the core is in the saturated state, the eddy current and hysteresis phenomena inside it will intensify, resulting in more energy loss.

[0076] Change in copper loss: Although voltage fluctuations directly affect the magnetic flux in the core, they will also indirectly affect the current in the transformer winding. Under the condition of constant load, if the voltage decreases, the current in the winding will increase accordingly to maintain the output power unchanged. This will lead to an increase in the loss (copper loss) on the winding resistance. However, if the voltage is too high, although the winding current will decrease, the excessive voltage may cause the insulating material to bear too much pressure, thus triggering other faults.

[0077] Accelerated insulation aging: Voltage fluctuations may also cause accelerated aging of the transformer insulation material. Especially when the voltage increases, the insulating material may bear a higher electric field strength, thus accelerating its aging and damage. This may lead to a decrease in insulation performance and even trigger faults such as short circuits.

[0078] Harmonic loss: In the case of distorted voltage waveforms (such as containing harmonics), transformers will also generate harmonic losses. Harmonic currents will generate additional losses in the transformer windings, and at the same time, they will also pollute the power grid and affect the normal operation of other equipment.

[0079] In addition to the above several main losses, voltage fluctuations may also cause some additional losses, such as mechanical vibrations and noises caused by magnetic flux fluctuations. Although these losses are relatively small, they will also have a certain impact on the overall performance of the transformer.

[0080] Transformers play a crucial role in the power system. The losses during their operation, including the above-mentioned iron losses (hysteresis loss and eddy current loss) and copper losses (resistance loss), since when the transformer losses increase, it means that more electrical energy needs to be consumed to maintain the same output power, which will lead to changes in the carbon emission results. Therefore, the embodiments of the present invention adopt effective measures to screen out the transformers with losses and determine the energy units corresponding to these transformers as high-carbon emission units, thereby avoiding the increase in carbon emissions caused by subsequent selection of high-carbon emission units.

[0081] Of course, the above content only involves the carbon emissions during the equipment operation stage in the whole life cycle of the power transmission transformer. The calculation of the whole life cycle carbon emissions of the transformer involves each stage, and the whole life cycle assessment index system shown in the following table can be referred to:

[0082]

[0083] After correcting the carbon emission calculation results of the equipment operation stage in the whole life cycle of each power transmission transformer, the carbon emission accounting results of each power transmission transformer connected to each energy unit can be obtained.

[0084] In the actual low-carbon operation control stage, analyze each of the above carbon emission accounting results, select the energy units corresponding to the power transmission transformers with lower losses, and use them as the main power supply ends of the energy to transmit electrical energy to the power grid supply side, thereby effectively realizing the low-carbon control of the distributed energy power grid system and promoting the intelligent and green and healthy development process of low-carbon operation control.

[0085] Further, in the above embodiment, after controlling the energy unit corresponding to the target power transmission transformer determined according to the analysis result to transmit electrical energy to the power grid supply side, the method further includes:

[0086] Calculate the energy supply carbon emissions of the energy unit corresponding to the target power transmission transformer, and optimize the weights of the constructed digital carbon reduction evaluation index system with the energy supply carbon emissions.

[0087] The carbon reduction evaluation index system can refer to the following table:

[0088]

[0089] Through the digital carbon reduction evaluation index system, the carbon emission data of the distributed energy grid system can be monitored in real time to ensure the accuracy and timeliness of the data. The evaluation index system can also clarify the goals and directions of the green and low-carbon transformation, guiding enterprises and regions to take effective measures to reduce carbon emissions. It is of great significance for promoting the green and low-carbon transformation, facilitating policy formulation and optimization, enhancing the competitiveness of enterprises, achieving sustainable development, and increasing social awareness and participation.

[0090] Specifically, please refer to Figure 3 , Figure 3 FIG. shows a structural block diagram of a low-carbon operation control system for a transformer considering carbon emission characteristics in one embodiment of the present invention, which is applied to a distributed energy grid system composed of a plurality of energy units. Each of the energy units transmits electric energy to the grid supply side through a corresponding power transmission transformer. The low-carbon operation control system considering the carbon emission characteristics of the transformer includes:

[0091] A power flow sample module 11, configured to process the load data of each of the energy units and the system topology structure data of the distributed energy grid system based on the Monte Carlo algorithm during the simulation stage to obtain power flow sample data;

[0092] A power flow model module 12, configured to construct a neural network probabilistic power flow model with the power flow sample data. Among them, the neural network probabilistic power flow model is configured to perform relaxation processing on the neural network probabilistic power flow model according to the mixed-integer second-order cone programming method, and obtain the power flow result output by the neural network probabilistic power flow model based on the stochastic response surface method;

[0093] A carbon emission accounting module 13, configured to correct the initial carbon emission results of the entire life cycle of each of the power transmission transformers calculated based on the probability distribution interval of the node voltage in the output power flow result to obtain the carbon emission accounting result of each of the power transmission transformers;

[0094] A control module 14, configured to analyze the carbon emission accounting results during the actual low-carbon operation control stage, and control the energy units corresponding to the target power transmission transformers determined according to the analysis results to transmit electric energy to the grid supply side.

[0095] Further, in the above embodiment, the construction process of the neural network probabilistic power flow model includes:

[0096] Take the load data of the input energy unit, the system topology structure data, and the probability distribution parameters of the set system input random variables as the basic parameters of the RBF neural network model;

[0097] Select the collocation points of the standard normal variable and formulate the second-order cone polynomial;

[0098] Based on the Nataf transformation method, transform all the collocation points from the standard normal space to the original space to obtain the sample points associated with the power flow sample data;

[0099] Perform power flow calculation with second-order cone polynomial expansion for each of the sample points, so that the neural network probabilistic power flow model outputs the corresponding power flow results.

[0100] Furthermore, in the above embodiment, the system further includes:

[0101] A discrete fitting module, which is used to perform discrete fitting on the output power flow results by using the empirical distribution function to obtain the probability distribution functions reflecting different random variables;

[0102] A calculation module, which is used to calculate the probability distribution intervals of the voltages of each node in the distributed energy grid system according to the probability distribution functions;

[0103] Furthermore, in the above embodiment, the system further includes:

[0104] A voltage fluctuation unit, which is used to analyze the probability distribution intervals to obtain the voltage fluctuation information of each transmission transformer;

[0105] A loss index unit, which is used to determine the loss index corresponding to each transmission transformer according to the voltage fluctuation information, where the loss index at least includes iron loss index, copper loss index, insulation aging index, harmonic index or magnetic flux index;

[0106] A correction unit, which is used to correct the carbon emission calculation results in the equipment operation stage of the whole life cycle of each transmission transformer based on the loss index to obtain the corrected carbon emission accounting results of each transmission transformer.

[0107] Furthermore, in the above embodiment, the system further includes:

[0108] A weight optimization module, which is used to calculate the carbon emissions of the energy supply of the energy unit corresponding to the target transmission transformer, and optimize the weights of the constructed digital carbon reduction evaluation index system with the carbon emissions of the energy supply;

[0109] The transformer low-carbon operation control method and system considering carbon emission characteristics provided by the embodiments of the present invention have at least one of the following beneficial effects:

[0110] (1) Considering that the intermittency and volatility of energy units will bring many uncertain factors to the power grid, thereby reducing the security and reliability of the entire distributed energy power grid system, the present invention selects the Monte Carlo algorithm for power flow calculation. The Monte Carlo method can handle these uncertain factors by introducing randomness. This enables the Monte Carlo method to more realistically reflect the actual operation of the system in power system power flow calculation and improve the accuracy of the final calculation;

[0111] (2) Due to the uncertainty of the output of energy units, it will cause fluctuations on the supply side of transmission lines, which will in turn affect the carbon emissions throughout the life cycle of transmission transformers and is not conducive to the comprehensive management of distributed energy power grid systems. The embodiments of the present invention accurately calculate the probabilistic power flow of each energy unit, analyze its impact on the carbon emissions of the respective supply-side transmission transformers, and conduct targeted control of the distributed energy power grid system to avoid selecting transmission lines with excessive carbon emissions of transformers due to line problems. Thus, the carbon emission balance of the distributed energy power grid system is achieved, providing an accurate control basis for the access position of energy units on the supply side and promoting the intelligent and green and healthy development process of low-carbon operation control.

[0112] The above-described embodiments merely represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the present invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A transformer low-carbon operation control method considering carbon emission characteristics, characterized in that: Applied to a distributed energy grid system composed of a number of energy units, each of the energy units transmits electric energy to the grid supply side through a corresponding transmission transformer, and the transformer low-carbon operation control method considering carbon emission characteristics includes: In the simulation stage, based on the Monte Carlo algorithm, the load data of each energy unit and the system topology data of the distributed energy grid system are processed to obtain power flow sample data; Constructing a neural network probabilistic power flow model with the power flow sample data, wherein the neural network probabilistic power flow model is configured to relax the neural network probabilistic power flow model according to a mixed integer second-order cone programming method, and obtain a power flow result output by the neural network probabilistic power flow model based on a random response surface method; Based on the probability distribution interval of the node voltage in the output power flow result, the calculated initial carbon emission results of the entire life cycle of each of the transmission transformers are corrected to obtain the carbon emission accounting results of each of the transmission transformers; In the actual low-carbon operation control stage, each of the carbon emission accounting results is analyzed, and the energy unit corresponding to the target transmission transformer determined according to the analysis results is controlled to transmit electric energy to the grid supply side.

2. The transformer low-carbon operation control method considering carbon emission characteristics according to claim 1 is characterized in that: The construction process of the neural network probability power flow model includes: The load data of the energy unit, the system topology data and the probability distribution parameters of the set system input random variables are used as the basic parameters of the RBF neural network model; Select the collocation points of the standard normal variable and formulate the second-order cone polynomial; Based on the Nataf transformation method, all the configuration points are converted from the standard normal space to the original space to obtain sample points associated with the power flow sample data; A second-order cone polynomial expansion power flow calculation is performed on each of the sample points so that the neural network probabilistic power flow model outputs a corresponding power flow result.

3. The transformer low-carbon operation control method considering carbon emission characteristics according to claim 1 is characterized in that: Before the probability distribution interval of the node voltage in the power flow result is output, the method further includes: Using the empirical distribution function to perform discrete fitting on the output power flow results, to obtain probability distribution functions reflecting different random variables; According to the probability distribution function, the probability distribution interval of each node voltage in the distributed energy grid system is calculated.

4. The transformer low-carbon operation control method considering carbon emission characteristics according to claim 1 is characterized in that: The method of correcting the calculated initial carbon emission results of the entire life cycle of each of the transmission transformers based on the probability distribution interval of the node voltage in the output power flow result to obtain the carbon emission accounting results of each of the transmission transformers includes: Analyzing the probability distribution interval to obtain voltage fluctuation information of each of the transmission transformers; Determine the loss index corresponding to each of the transmission transformers according to the voltage fluctuation information, wherein the loss index at least includes an iron loss index, a copper loss index, an insulation aging index, a harmonic index or a magnetic flux index; Based on the loss index, the carbon emission calculation results of the equipment operation stage in the entire life cycle of each of the transmission transformers are corrected to obtain a corrected carbon emission accounting result of each of the transmission transformers.

5. The transformer low-carbon operation control method considering carbon emission characteristics according to claim 1 is characterized in that: After controlling the energy unit corresponding to the target transmission transformer determined according to the analysis result to transmit electric energy to the grid supply side, the method further includes: The energy supply carbon emissions of the energy unit corresponding to the target transmission transformer are calculated, and the constructed digital carbon reduction evaluation index system is weighted optimized based on the energy supply carbon emissions.

6. A transformer low-carbon operation control system considering carbon emission characteristics, characterized in that: Applied to a distributed energy grid system consisting of a number of energy units, each of which transmits electric energy to the grid supply side through a corresponding transmission transformer, the transformer low-carbon operation control system considering carbon emission characteristics includes: A power flow sample module is used to process the load data of each energy unit and the system topology data of the distributed energy grid system based on the Monte Carlo algorithm in the simulation stage to obtain power flow sample data; A power flow model module, used for constructing a neural network probabilistic power flow model with the power flow sample data, wherein the neural network probabilistic power flow model is configured to relax the neural network probabilistic power flow model according to a mixed integer second-order cone programming method, and obtain a power flow result output by the neural network probabilistic power flow model based on a random response surface method; A carbon emission accounting module, which is used to correct the calculated initial carbon emission results of the entire life cycle of each of the transmission transformers based on the probability distribution interval of the node voltage in the output power flow result, so as to obtain the carbon emission accounting results of each of the transmission transformers; The control module is used to analyze each of the carbon emission accounting results in the actual low-carbon operation control stage, and control the energy unit corresponding to the target transmission transformer determined according to the analysis results to transmit electric energy to the power grid supply side.

7. The transformer low-carbon operation control system considering carbon emission characteristics according to claim 6, characterized in that: The construction process of the neural network probability power flow model includes: The load data of the energy unit, the system topology data and the probability distribution parameters of the set system input random variables are used as the basic parameters of the RBF neural network model; Select the collocation points of the standard normal variable and formulate the second-order cone polynomial; Based on the Nataf transformation method, all the configuration points are converted from the standard normal space to the original space to obtain sample points associated with the power flow sample data; A second-order cone polynomial expansion power flow calculation is performed on each of the sample points so that the neural network probabilistic power flow model outputs a corresponding power flow result.

8. The transformer low-carbon operation control system considering carbon emission characteristics according to claim 6, characterized in that: The system further comprises: A discrete fitting module is used to perform discrete fitting on the output power flow results using an empirical distribution function to obtain probability distribution functions reflecting different random variables; The calculation module is used to calculate the probability distribution interval of the voltage of each node in the distributed energy grid system according to the probability distribution function.

9. The transformer low-carbon operation control system considering carbon emission characteristics according to claim 6, characterized in that: The system further comprises: A voltage fluctuation unit, used for analyzing the probability distribution interval to obtain voltage fluctuation information of each of the transmission transformers; A loss index unit, used to determine the loss index corresponding to each of the transmission transformers according to the voltage fluctuation information, wherein the loss index at least includes an iron loss index, a copper loss index, an insulation aging index, a harmonic index or a magnetic flux index; A correction unit is used to correct the carbon emission calculation results of the equipment operation stage in the entire life cycle of each of the transmission transformers based on the loss index to obtain a corrected carbon emission accounting result of each of the transmission transformers.

10. The transformer low-carbon operation control system considering carbon emission characteristics according to claim 6, characterized in that: The system further comprises: The weight optimization module is used to calculate the energy supply carbon emissions of the energy unit corresponding to the target transmission transformer, and to perform weight optimization on the constructed digital carbon reduction evaluation index system based on the energy supply carbon emissions.

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