Transformer low-carbon operation control method and system considering carbon emission characteristics

Abstract

The invention relates to the field of carbon emission, and discloses a transformer low-carbon operation control method and system considering carbon emission characteristics, and the method comprises the steps: obtaining power flow sample data in an analogue simulation stage; constructing a neural network probabilistic power flow model according to the power flow sample data, and obtaining 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, correcting the calculated initial carbon emission result of each power transmission transformer in the full life cycle to obtain a carbon emission accounting result of each power transmission transformer; and in an actual low-carbon operation control stage, controlling an energy unit corresponding to the determined target power transmission transformer to transmit electric energy to a power grid supply side. According to the low-carbon operation control method, accurate accounting is carried out on the carbon emission of the transformer, so that accurate data support is provided for actual low-carbon operation control, and the intelligent, green and healthy development process of the carbon emission is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission, and in particular to a transformer low-carbon operation control method and system taking carbon emission characteristics into consideration. Background Art

[0002] Accurate and comprehensive carbon emission measurement and accounting is the basis for the low-carbon transformation of the power energy system. By calculating the carbon emissions of the power system, we can accurately understand the carbon emissions generated by the power system during operation and provide data support.

[0003] As an important energy conversion device in power transmission and transformation, transformers are designed and constructed with large volume and high energy consumption, and are high carbon emission devices among electrical devices. In the operation and control process of today's distributed energy grid system, the impact of transformer carbon emissions is not taken into account, 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 transformer low-carbon operation control method and system taking into account the carbon emission characteristics, taking into account the impact of fluctuations of different energy units on the transformer, and accurately calculating the carbon emissions of the transformer, thereby providing accurate data support for actual low-carbon operation control, and promoting the intelligent, green and healthy development of low-carbon operation control.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a transformer low-carbon operation control method considering carbon emission characteristics, which is applied to a distributed energy grid system composed 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 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.

[0006] As one of the preferred solutions, the process of constructing 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.

[0007] As one preferred solution, before outputting the probability distribution interval of the node voltage in the power flow result, 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.

[0008] As one of the preferred solutions, 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 transmission transformer are corrected to obtain the carbon emission accounting results of each transmission transformer, including: 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.

[0009] As one of the preferred solutions, 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.

[0010] Another embodiment of the present invention provides a transformer low-carbon operation control system considering carbon emission characteristics, which is applied to a distributed energy grid system composed 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 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 power 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, 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 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.

[0011] As one of the preferred solutions, the process of constructing 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.

[0012] As one preferred solution, the system further includes: 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.

[0013] As one preferred solution, the system further includes: 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.

[0014] As one preferred solution, the system further includes: 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.

[0015] Compared with the prior art, the embodiments of the present invention have the following advantages: (1) Considering that the intermittent and volatile nature of energy units will bring many uncertainties to the power grid, thereby reducing the safety and reliability of the entire distributed energy power grid system, the present invention uses the Monte Carlo algorithm to perform power flow calculations. The Monte Carlo method can handle these uncertainties by introducing randomness. This enables the Monte Carlo method to more realistically reflect the actual operation of the system in the power system power flow calculation and improve the accuracy of the final calculation; (2) Due to the uncertainty of the output of energy units, the supply side of the transmission line will fluctuate, which will in turn affect the carbon emissions of the transmission transformer throughout its life cycle, which is not conducive to the comprehensive management of the distributed energy grid system. The embodiment of the present invention accurately calculates the probabilistic flow of each energy unit, analyzes its impact on the carbon emissions of the respective supply-side transmission transformers, and performs targeted control on the distributed energy grid system to avoid selecting transmission lines with excessively high transformer carbon emissions due to line problems, thereby achieving a carbon emission balance for the distributed energy grid system, providing an accurate control basis for the access position of the energy unit on the supply side, and promoting the intelligent and green and healthy development of low-carbon operation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic diagram of the architecture of a distributed energy grid system in one embodiment of the present invention; Figure 2 It is a flow chart of a transformer low-carbon operation control method considering carbon emission characteristics in one embodiment of the present invention; Figure 3 It is a structural block diagram of a transformer low-carbon operation control system considering carbon emission characteristics in one embodiment of the present invention; Reference numerals: 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. Flow sample module; 12. Flow model module; 13. Carbon emission accounting module; 14. Control module. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0019] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0020] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. 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 this application can be understood by specific circumstances.

[0021] It should be noted in advance that the embodiments of the present invention are applied to a distributed energy grid system. For details, please refer to Figure 1 , Figure 1 The schematic diagram of the architecture of the distributed energy grid system in the embodiment of the present invention is shown. The distributed energy grid system is composed of a number of energy units on the power generation side, including wind turbines 1, photovoltaic units 2, and biomass units 3. After the energy units on the power generation side produce electric energy, they transmit the electric energy to the power supply side through long-distance power transmission lines. In the power system, the electric energy generated by the power generation side is usually 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 the user end through the principle of electromagnetic induction, thereby realizing the transmission and distribution of electric energy. Therefore, the distributed energy grid system is also provided with a first transmission transformer A connected to the wind turbine 1, a second transmission transformer B connected to the photovoltaic unit, and a third transmission transformer C connected to the biomass unit.

[0022] Generally speaking, a transmission transformer needs to be installed near the power generation side of the transmission line, and also needs to be installed near the supply side of the transmission line. The embodiments of the present invention mainly improve the transmission transformer on the power generation side, which will not be further elaborated here.

[0023] An embodiment of the present invention provides a transformer low-carbon operation control method considering carbon emission characteristics. For details, see Figure 2 , Figure 2 The figure shows a flow chart of a transformer low-carbon operation control method considering carbon emission characteristics in one embodiment of the present invention, which includes steps S1 to S4: S1. 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; S2. 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 perform relaxation processing on 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; S3. 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; S4. 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.

[0024] Probabilistic power flow is an important tool for uncertainty analysis of power systems. It obtains the distribution information of system power flow through probabilistic statistical methods, can take into account various random factor scenarios, comprehensively reflect the operating conditions of the power grid, and facilitate dispatchers to discover system weaknesses and potential risks.

[0025] The Monte Carlo method can provide relatively accurate numerical solutions for complex systems and high-dimensional space problems that are difficult to solve with traditional numerical methods. In power system flow calculations, 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 is fluctuation or uncertainty in the input data, the Monte Carlo method can give relatively stable output results.

[0026] In the above step S1, a simulation model needs to be performed to obtain the flow sample data first, so that the subsequent probability flow model can be constructed. Of course, the flow calculation process and the Monte Carlo algorithm are common means in this field, and will not be further described in the embodiment of the present invention. In addition, the flow sample data needs to go through a series of steps such as normalization processing, which will not be further described in the embodiment of the present invention.

[0027] In the process of constructing the neural network probability power flow model in step S2, the RBF neural network probability power flow model is preferably used. Specifically, the embodiment of the present invention adopts a probability power flow based on the random 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 configuration points, which can effectively improve the calculation efficiency. The specific process is as follows: (1) Input the load data of energy units, system topology data and probability distribution parameters of system input random variables (such as wind farms, photovoltaic power stations and loads) as the basic parameters of the RBF neural network model; (2) Select M configuration points of standard normal variables ξ, and the second-order polynomial model is as follows: M = 1 + 2n +n (n-1) / 2 Among them, the kth configuration point is denoted as: k = [ξk1 ,ξ k2 ,…,ξ kn ], k = 1, 2, … , M.

[0028] (3) The Nataf transformation is used to transform all configuration points from the standard normal space to the original space to obtain a series of sample points associated with the input variables.

[0029] (4) Use the Matpower toolbox to perform deterministic power flow calculations on each input sample point to obtain the corresponding sample points of the output response quantity Y (including node voltage, branch power, network loss, etc.).

[0030] (5) Solve the unknown coefficient a of the polynomial by configuring the points and their corresponding output response samples i , and obtain the second-order cone polynomial expansion expression of the output variable Y.

[0031] Furthermore, in the above embodiment, before the probability distribution interval of the node voltage in the output power flow result is calculated, the voltage interval needs to be calculated, that is, the result of the power flow calculation includes a variety of indicators, and in the embodiment of the present invention, it is necessary to consider the impact on the transformer. In the actual power grid architecture, as mentioned above, transformers are distributed at both ends of the transmission line. The embodiment of the present invention considers the transformer of the node close to the energy unit. The specific process is: using 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, the probability distribution interval of each node voltage in the distributed energy grid system is calculated.

[0032] The inventors have found through a large number of experimental analyses that voltage fluctuations can cause varying degrees of losses to the transformer, which are mainly reflected in the following aspects: Increased 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 a saturated state, the eddy current and hysteresis inside it will intensify, resulting in more energy loss.

[0033] Copper loss changes: Although voltage fluctuations directly affect the magnetic flux in the core, they also indirectly affect the current in the transformer windings. Under the condition of constant load, if the voltage is reduced, the current in the winding will increase accordingly to maintain the output power unchanged. This will cause the loss (copper loss) on the winding resistance to increase. However, if the voltage is too high, although the winding current will decrease, the excessive voltage may cause excessive stress on the insulation material, causing other faults.

[0034] Accelerated insulation aging: Voltage fluctuations may also lead to accelerated aging of transformer insulation materials. In particular, when the voltage increases, the insulation material may be subjected to higher electric field strength, which accelerates its aging and damage. This may lead to a decrease in insulation performance and even cause faults such as short circuits.

[0035] Harmonic loss: When the voltage waveform is distorted (such as containing harmonics), the transformer will also produce harmonic loss. Harmonic current will cause additional losses in the transformer windings, and will also pollute the power grid and affect the normal operation of other equipment.

[0036] In addition to the above-mentioned major losses, voltage fluctuations may also cause some additional losses, such as mechanical vibration and noise 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.

[0037] Transformers play a vital role in the power system. The losses in their operation process include the above-mentioned iron loss (hysteresis loss and eddy current loss) and copper loss (resistance loss). When the transformer loss increases, it means that more electric energy is needed to maintain the same output power, which will lead to changes in carbon emissions. Therefore, the embodiment of the present invention adopts effective measures to screen transformers with losses and determine the energy unit corresponding to this transformer as a high-carbon emission unit, thereby avoiding the subsequent selection of high-carbon emission units leading to increased carbon emissions.

[0038] Of course, the above content only involves the carbon emissions of the equipment operation stage in the whole life cycle of the transmission transformer. The calculation of the carbon emissions of the transformer in the whole life cycle involves all stages. You can refer to the whole life cycle evaluation index system shown in the following table: After correcting the carbon emission calculation results of the equipment operation stage in the entire life cycle of each of the transmission transformers, the carbon emission accounting results of each transmission transformer connected to each energy unit can be obtained.

[0039] In the actual low-carbon operation and control stage, the carbon emission accounting results of each of the above are analyzed, and the energy units corresponding to the transmission transformers with lower losses are selected and used as the main energy supply end to transmit electricity to the power grid supply side, thereby effectively realizing low-carbon control of the distributed energy power grid system and promoting the intelligent, green and healthy development of low-carbon operation control.

[0040] Furthermore, in the above embodiment, 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.

[0041] The carbon reduction evaluation index system can be referred to as shown in the following table: 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 and guide enterprises and regions to take effective measures to reduce carbon emissions. It is of great significance to promote green and low-carbon transformation, promote policy formulation and optimization, enhance corporate competitiveness, achieve sustainable development, and enhance social cognition and participation.

[0042] For details, see Figure 3 , Figure 3 The structure block diagram of a transformer low-carbon operation control system considering carbon emission characteristics in one embodiment of the present invention is shown, which is applied to a distributed energy grid system composed of a number of energy units, each of which transmits electric energy to the grid supply side through a corresponding transmission transformer. The low-carbon operation control system considering the carbon emission characteristics of the transformer includes: The power flow sample module 11 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 12, used to construct 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; The carbon emission accounting module 13 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 14 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 result to transmit electric energy to the power grid supply side.

[0043] Furthermore, in the above embodiment, the process of constructing 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.

[0044] Furthermore, in the above embodiment, the system further includes: 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.

[0045] Furthermore, in the above embodiment, the system further includes: 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.

[0046] Furthermore, in the above embodiment, the system further includes: 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.

[0047] The transformer low-carbon operation control method and system considering carbon emission characteristics provided by the embodiment of the present invention have the beneficial effects of at least one of the following: (1) Considering that the intermittent and volatile nature of energy units will bring many uncertainties to the power grid, thereby reducing the safety and reliability of the entire distributed energy power grid system, the present invention uses the Monte Carlo algorithm to perform power flow calculations. The Monte Carlo method can handle these uncertainties by introducing randomness. This enables the Monte Carlo method to more realistically reflect the actual operation of the system in the power system power flow calculation and improve the accuracy of the final calculation; (2) Due to the uncertainty of the output of energy units, the supply side of the transmission line will fluctuate, which will in turn affect the carbon emissions of the transmission transformer throughout its life cycle, which is not conducive to the comprehensive management of the distributed energy grid system. The embodiment of the present invention accurately calculates the probabilistic flow of each energy unit, analyzes its impact on the carbon emissions of the respective supply-side transmission transformers, and performs targeted control on the distributed energy grid system to avoid selecting transmission lines with excessively high transformer carbon emissions due to line problems, thereby achieving a carbon emission balance for the distributed energy grid system, providing an accurate control basis for the access position of the energy unit on the supply side, and promoting the intelligent and green and healthy development of low-carbon operation control.

[0048] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached 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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