Power dispatching management system

Abstract

The invention relates to the technical field of electric power, and discloses an electric power dispatching management system, and the system comprises a data collection module which is used for obtaining real-time data, and the real-time data comprises one or more of wind-solar power prediction data, power generation source information, load demand information and power grid power generation equipment operation parameters; the preliminary scheduling module is used for selecting a matched scheduling model from a model library according to the real-time data and determining a target scheduling strategy corresponding to an optimal solution of the scheduling model; the control module is used for issuing a scheduling strategy to power equipment, and the power equipment comprises one or more of distributed photovoltaic, an electric vehicle charging pile and a user energy storage end; and the real-time optimization module is used for increasing and adjusting constraint conditions in the scheduling model in real time according to the influence factors, and adjusting a target scheduling strategy according to the constraint conditions. According to the system, the renewable energy consumption capability of the power grid is remarkably improved, and the adaptability of the power grid to the fluctuation and complex load requirements is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of power technology, and particularly to a power dispatching management system. Background Art

[0002] The power dispatching system undertakes the key task of ensuring the safe, stable and economic operation of the power system, and its functions cover data acquisition, power generation control, transmission management, and power consumption control. Data acquisition and monitoring require real-time and accurate acquisition of electrical parameters of each node in the whole network, such as voltage, current, power, etc., to comprehensively control the operation status of the system. Power generation control needs to finely adjust the processing of generating units according to load forecasting and system operation conditions to ensure the balance between supply and demand. Transmission management needs to closely monitor and dispatch transmission lines and substations to ensure efficient power transmission and avoid abnormalities such as line overload. Therefore, there are many factors to be considered in power dispatching, and how to reasonably, safely and efficiently dispatch power resources is the key to the normal operation of the power system.

[0003] In the process of implementing the embodiments of the present application, it is found that there are at least the following problems in the related technologies: The existing power dispatching methods usually formulate dispatching strategies by integrating various factors and data. However, during the power consumption process, the dispatching strategies are often static and immutable. The dispatching strategies relying on static rules lack dynamic adaptability and intelligent decision-making level, and it is difficult to effectively meet the increasingly complex and changeable power grid dispatching requirements.

[0004] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] To have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important elements or delineate the protection scope of these embodiments, but rather serves as a preface to the subsequent detailed description.

[0006] The embodiments of the present application provide a power dispatching management system to improve the efficiency and rationality of power dispatching.

[0007] In some embodiments, the power dispatching management system includes: A data acquisition module for acquiring real-time data, where the real-time data includes one or more of wind and light power prediction data, power source information, load demand information, and operating parameters of grid power generation equipment; A preliminary dispatching module for selecting a matching dispatching model from a model library according to the real-time data and determining the target dispatching strategy corresponding to the optimal solution of the dispatching model; A control module for sending the target scheduling strategy to power equipment, where the power equipment includes one or more of distributed photovoltaics, electric vehicle chargers, and user energy storage terminals; A real-time optimization module for adding and adjusting constraint conditions in the scheduling model in real time according to impact factors, and adjusting the target scheduling strategy according to the constraint conditions.

[0008] Optionally, the wind and light power prediction data includes weather prediction data, and the weather prediction data is obtained by the following method: constructing an LSTM prediction model; training the LSTM prediction model using historical wind speed, wind direction, irradiance, cloud cover, and temperature information; and updating the LSTM prediction model at preset time intervals.

[0009] Optionally, the preliminary scheduling module includes: a robust optimization module that establishes an objective function composed of curtailment cost, load shedding penalty, and reserve capacity cost; a generator that generates typical scenarios for new energy processing under extreme weather conditions based on a GAN network; and a security checking unit that quickly calculates the line overlimit risk under N-1 faults using an improved DC power flow model.

[0010] Optionally, the impact factors include curtailment cost and voltage deviation, and the real-time optimization module includes adding the following constraint condition to the objective function: K1*α(curtailment cost) + K2*β(voltage deviation) ≤ threshold, where α and β are different risk confidence level parameters.

[0011] Optionally, the impact factors include market price and season parameters, and the real-time optimization module includes adding the following constraint condition to the objective function: G(X)=α*(P_market - P_threshold)² + β*|S_season - S_optimal| + γ*max(0, E_actual - E_limit) ≤ C_tolerance; where P_market is the current market price fluctuation coefficient, dynamically reflecting the market premium level of raw materials / products, P_threshold represents the price trigger threshold, and strong constraints are activated when the market price exceeds this value. S_season is the quantified value of season parameters (such as the normalization result of environmental factors such as temperature and humidity), S_optimal is the optimal operating condition reference value of the equipment under these season parameters, E_actual represents the real-time environmental protection index (such as carbon emissions per unit of output), E_limit is the emission upper limit required by environmental protection regulations, α, β, and γ are dynamic weight coefficients automatically adjusted according to the importance of the operating conditions by an online learning algorithm, and C_tolerance is the constraint deviation tolerance allowed by the system.

[0012] Optionally, the impact factor includes a real-time carbon price signal, and the real-time optimization module includes adding a carbon quota constraint condition to the objective function, and the expression is: min Σ(λ1*power generation cost + λ2*risk cost + λ3*carbon trading cost); where the regional carbon quota ≥ actual carbon emissions × (1 - green power offset ratio).

[0013] Optionally, each constraint condition in the real-time optimization module further includes an admission parameter, and the admission parameter is the difference between the current value and the preset value of the impact factor. When the difference is less than zero, the influence of the constraint condition corresponding to the impact factor in the scheduling model is excluded.

[0014] Optionally, the system further includes a carbon flow tracking visualization platform, including: a carbon flow calculation module for generating the carbon flow density corresponding to the power of each branch in real time according to the power flow tracking algorithm; a binding module for on-chain confirmation of the rights of the wind and solar power generation and carbon emission reduction through a smart contract; a display module including a 3D power grid carbon flow heat map for displaying the clean energy penetration rate of different regional power grids according to the color gradient.

[0015] The power dispatching management system provided by the embodiments of the present application can achieve the following technical effects: By collecting multi-dimensional data such as wind and solar power predictions, power source information, load demands, and power grid equipment parameters in real time, and combining the matching optimization and dynamic constraint adjustment of the intelligent dispatching model library, the system realizes the precise coordinated control of power equipment such as distributed photovoltaics, electric vehicle charging piles, and user energy storage terminals, significantly improves the power grid's consumption capacity of renewable energy, dispatching response speed, and system operation economy, and at the same time enhances the power grid's adaptability to fluctuations and complex load demands.

[0016] The above general description and the following description are only exemplary and explanatory, and are not used to limit the present application. Description of the Drawings

[0017] One or more embodiments are exemplarily illustrated by the corresponding drawings. These exemplary illustrations and the drawings do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a proportional limitation, and among them: Figure 1 is a schematic diagram of the composition structure of a power system; Figure 2 is a schematic diagram of the structure of a power dispatching management system according to an embodiment of the present application; Figure 3 is a schematic diagram of the structure of a power dispatching optimization management system provided by an embodiment of the present application. Detailed Embodiments

[0018] In order to understand the features and technical content of the embodiments of the present application in more detail, the implementation of the embodiments of the present application will be described in detail below with reference to the accompanying drawings. The attached drawings are only for reference and explanation, and are not used to limit the embodiments of the present application. In the following technical description, for the sake of explanation, numerous details are provided to give a thorough understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be shown in a simplified manner to simplify the drawings.

[0019] In the description of the embodiments of the present application, the terms "first", "second", etc. in the specification, claims and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so as to implement the embodiments of the present application described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion.

[0020] Unless otherwise specified, the term "plurality" means two or more.

[0021] In the embodiments of the present application, the character " / " indicates that the front and rear objects are in an "or" relationship. For example, A / B means: A or B.

[0022] The term "and / or" is an associative relationship describing an object, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B, these three relationships.

[0023] As shown in combination with Figure 1 The power plant dispatching system generally includes a master station and substation equipment. The master station includes core components such as a computer and a dual-machine switching component, an external memory (disk drive / tape drive), input / output devices (console terminal / printer), a data transmission channel interface, a dispatching console, and a user terminal. The substation is configured with a microcomputer telecontrol device (RTU), a special telecontrol transmitter, a power summator and its cabinet, and data acquisition and execution devices such as a remote control / remote signal execution relay cabinet.

[0024] The system also relies on a dedicated communication infrastructure, such as a telecontrol channel composed of power line carrier, optical fiber or digital microwave, and an uninterruptible power supply (UPS) to ensure continuous power supply. Data interaction between the master station and the substation is realized through a standardized protocol (such as CDT or POLLING protocol), and at the same time, an analog panel and a recording and printing device are equipped for real-time status visualization.

[0025] As shown in combination with Figure 2As shown in the figure, a power dispatching management system 200 provided by an embodiment of the present application includes: a data acquisition module 201, a preliminary dispatching module 202, a control module 203, and a real-time optimization module 204. Among them, the data acquisition module 201 is used to obtain real-time data, and the real-time data includes one or more of wind-solar power prediction data, power generation source information, load demand information, and grid power generation equipment operation parameters; the preliminary dispatching module 202 is used to select a matching dispatching model from the model library according to the real-time data, and calculate the target dispatching strategy corresponding to the optimal solution of the dispatching model; the control module 203 is used to send the target dispatching strategy to power equipment, and the power equipment includes one or more of distributed photovoltaics, electric vehicle chargers, and user energy storage terminals; the real-time optimization module 204 is used to add and adjust constraint conditions in real time in the dispatching model according to impact factors, and adjust the target dispatching strategy according to the constraint conditions. This system can dynamically add constraint conditions according to impact factors. For example, constraint conditions related to weather data, economic costs and benefits, and geographical information can be added. It can also remove certain constraint conditions or dynamically adjust the weights of constraint conditions according to the actual application scenario. The system realizes a dynamic adjustment mechanism of constraint conditions through the real-time optimization module, and flexibly increases or decreases or re-weights constraint conditions (such as curtailment penalty coefficients, voltage deviation thresholds, etc.). For example, safety constraints are automatically strengthened in extreme weather scenarios, and economic constraints are relaxed during low electricity price periods, so that the dispatching strategy always fits the actual operation requirements, and the new energy consumption rate can be effectively improved compared with traditional static dispatching models.

[0026] As an example, the initial state of the dynamic constraint conditions can be expressed as: ;

[0027] Among them, represents the total system load demand at time t, which is the core input parameter for dispatching optimization. represents the sensitivity of the nodal marginal price (LMP) to load changes, reflecting the change trend of electricity price when the load increases (economic constraint). represents the power correction amount dynamically adjusted according to the changes in load and electricity price. α and β are adjustment coefficients.

[0028] During the control process, the adjustment coefficients can be dynamically adjusted, or constraint terms can be dynamically added. For example:

[0029] Among them, P base is the baseline load, P weather is the weather-sensitive load, P event is the special event load.

[0030] Furthermore, the above-mentioned wind and light power prediction data includes weather prediction data, and the weather prediction data is obtained by the following method: constructing an LSTM prediction model; training the LSTM prediction model using historical wind speed, wind direction, irradiance, cloud cover, and temperature information; and updating the LSTM prediction model at preset time intervals.

[0031] Among them, the LSTM (Long Short-Term Memory) prediction model is constructed through the following steps: 1) Data processing: Collect historical weather data, and exclude abnormal data, incomplete data, and outliers according to data similarity; 2) Data classification: Perform cluster analysis on the data and sort out the data types; 3) Model training: Use historical data to train the LSTM model, and obtain the prediction results based on evaluation indicators. Input historical meteorological data (wind speed, irradiance, cloud cover, etc.) and corresponding power generation data. Train the LSTM model using the sliding window method, and update the model weights every 6 hours to adapt to weather mutations.

[0032] To more comprehensively evaluate the experimental prediction effect, the embodiments of this application use multi-dimensional errors to construct an evaluation function, where the one-dimensional error is E1, the two-dimensional error is E2, the determination coefficient is R, and the three-dimensional error is weighted and summed to determine the evaluation function value. The specific formula is as follows:

[0033]

[0034]

[0035] Among them, N is the number of samples, and are the predicted power and actual power at the i-th point on the day to be measured, respectively, is the average value of the actual power.

[0036] Through the above model, weather information can be accurately predicted, and the weather information can be used as a constraint condition to update the target scheduling strategy.

[0037] For a power dispatching optimization management system 300 provided by the embodiments of this application, in Figure 2Based on the system shown, the preliminary scheduling module 202 of the system further includes: a robust optimization module 2001 that establishes an objective function composed of curtailment cost of wind and light, load shedding penalty, and reserve capacity cost; a generator 2002 that generates new energy power application scenarios under extreme weather conditions based on the GAN network; a security checking unit 2003 that quickly calculates the line overlimit risk under N-1 faults using an improved DC power flow model. The security checking process is a calculation module in the power system used to evaluate the operating security of the power grid, mainly checking for risks such as overload and voltage overlimit. The method of the embodiment of the present application uses an optimized simplified model to quickly predict whether other lines will be overloaded when any component in the power grid fails. Compared with traditional methods, the embodiment of the present application combines a robust optimization objective function, GAN-generated extreme scenarios, and fast security checking to significantly improve the economy and reliability of high-proportion new energy grid scheduling, while reducing the curtailment cost of wind and light and ensuring system security under N-1 faults.

[0038] Among them, the objective function is designed to minimize the total cost: min(C 弃风 +C 切负荷 +C 备用 ) Among them, C 弃风 =K1×curtailed wind volume, C 切负荷 =K2×load shedding volume, C 备用 =K3×reserve capacity, and K1, K2, and K3 are weight coefficients.

[0039] Further, the influencing factors include curtailment cost of wind and voltage deviation. The real-time optimization module includes adding constraint conditions to the objective function, and the constraint conditions include: K1*α + K2*β≤ threshold;

[0040] Among them, α is a risk confidence level parameter, reflecting the tolerance for the probability of wind curtailment. For example, α = 95% means allowing a 5% wind curtailment risk. β is a confidence parameter for the voltage safety margin, related to the N-1 fault check of the power grid. K1 and K2 are weight coefficients used to adjust the priorities of curtailment cost of wind and voltage deviation in the total objective.

[0041] In some embodiments, the influencing factors include market price, season parameter, and environmental protection requirement. The real-time optimization module includes adding constraint conditions to the objective function, and the constraint conditions include: G(X)=α*(P_market - P_threshold)²+β*|S_season - S_optimal|+γ*max(0, E_actual - E_limit)≤C_tolerance Among them, P_market is the current market price fluctuation coefficient, dynamically reflecting the market premium level of raw materials or products. P_threshold represents the price trigger threshold, and when the market price exceeds this value, strong constraints are initiated. S_season is the seasonal parameter quantization value (such as the normalization result of environmental factors such as temperature and humidity). S_optimal is the optimal operating condition benchmark value of the equipment under this seasonal parameter. E_actual represents the real-time environmental protection index (such as carbon emissions per unit of output). E_limit is the emission upper limit required by environmental protection regulations. α, β, γ are dynamic weight coefficients, which are automatically adjusted according to the importance of the operating conditions through an online learning algorithm. C_tolerance is the constraint deviation tolerance allowed by the system.

[0042] In some embodiments, the real-time optimization module includes carbon quota constraint conditions, and embeds a real-time carbon price signal in the objective function, and the expression is as follows: Real-time carbon price signal = min Σ(λ1 * power generation cost + λ2 * risk cost + λ3 * carbon trading cost).

[0043] Among them, it is satisfied that the regional carbon quota ≥ actual carbon emissions * (1 - green power deduction ratio).

[0044] In some embodiments, each constraint condition in the real-time optimization module further includes an admission parameter. The admission parameter includes the difference between the current value and the preset value of the influencing factor. When the difference between the current value and the preset value is less than zero, the influence of the constraint item corresponding to this influencing factor in the scheduling model is excluded. For example, if the difference Δ between the current value and the preset value of the influencing factor < 0, then this constraint item is excluded to avoid over-optimization.

[0045] In some embodiments, the system further includes a carbon flow tracking visualization platform, which includes: a carbon flow calculation module, used to generate the carbon flow density corresponding to the power of each branch in real time according to the power flow tracking algorithm; a binding module, which conducts on-chain confirmation of the wind and solar power generation and carbon emission reduction through a smart contract; a display module, including a three-dimensional power grid carbon flow heat map, used to display the clean energy penetration rate of different regional power grids according to the color gradient. This system has a visualization platform that can monitor the environmental protection situation of the power grid in real time like a "carbon emission map".

[0046] Specifically, through the carbon flow calculation module, the "carbon concentration" of each line of the power grid is calculated. When wind energy and solar energy are generated, the system will automatically calculate and display the carbon emissions reduced by this clean power using blockchain technology. Finally, in the form of a heat map display, the color depth (for example, the darker the green, the more clean energy) is used to intuitively show which areas are more environmentally friendly on the three-dimensional power grid map. In this way, the pollution source can be accurately calculated, and the distribution effect of green energy can be intuitively seen.

[0047] The above description and the accompanying drawings fully illustrate the embodiments of the present application, enabling those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, process, and other changes. Embodiments only represent possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terms used in this application are only for describing embodiments and do not limit the claims. As used in the description of embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to also include the plural forms. Similarly, as used in this application, the term "and / or" refers to any and all possible combinations including one or more of the associated listed items. Additionally, when used in this application, the term "comprise" and its variants "comprises" and / or "comprising" etc. mean the presence of the stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groups of these. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, or apparatus comprising the element. Herein, what each embodiment focuses on can be the differences from other embodiments, and the same or similar parts among the embodiments can be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method parts disclosed in the embodiments, the relevant parts can refer to the description of the method parts.

[0048] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner can depend on the specific application and design constraints of the technical solution. The skilled person can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the embodiments of the present application. The skilled person can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0049] In the embodiments disclosed in this document, the disclosed methods, products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units can be merely a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Additionally, the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms. The units described as separate components can be or can not be physically separated. The components displayed as units can be or can not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to implement this embodiment. Additionally, in the embodiments of this application, each functional unit can be integrated in a processing unit, or each unit can physically exist alone, or two or more units can be integrated in one unit.

[0050] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to the embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the blocks can also occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks can also occur in a different order than that disclosed in the description. Sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, which can depend on the functions involved. Each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A power dispatching management system, characterized in that, Including: A data acquisition module for obtaining real-time data, where the real-time data includes one or more of wind and light power prediction data, power generation source information, load demand information, and operation parameters of power grid power generation equipment; A preliminary scheduling module for selecting a matching scheduling model from a model library according to the real-time data and determining a target scheduling strategy corresponding to the optimal solution of the scheduling model; A control module for sending the target scheduling strategy to power equipment, where the power equipment includes one or more of distributed photovoltaics, electric vehicle charging piles, and user energy storage terminals; A real-time optimization module for adding and adjusting constraint conditions in the scheduling model in real time according to impact factors and adjusting the target scheduling strategy according to the constraint conditions.

2. The system according to claim 1, wherein The wind and light power prediction data includes weather prediction data, and the weather prediction data is obtained by the following method: Construct an LSTM prediction model; Train the LSTM prediction model using historical wind speed, wind direction, irradiance, cloud cover, and temperature information; Update the LSTM prediction model at preset time intervals.

3. The system according to claim 1, characterized in that, The preliminary scheduling module includes: A robust optimization module that establishes an objective function composed of curtailment cost, load shedding penalty, and reserve capacity cost; A generator that generates typical scenarios for new energy processing under extreme weather conditions based on a GAN network; A security checking unit that quickly calculates the line overlimit risk under N-1 faults using an improved DC power flow model.

4. The system according to claim 3, characterized in that The impact factors include market price and season parameters, and the real-time optimization module includes adding the following constraint conditions to the objective function: K1*α (curtailment cost) + K2*β (voltage deviation) ≤ threshold; Where α and β are different risk confidence level parameters.

5. The system according to claim 3, wherein According to the impact factors including market price and season parameters, the real-time optimization module includes adding the following constraint conditions to the objective function: G(X) = α*(P_market - P_threshold)² + β*|S_season - S_optimal| + γ*max(0, E_actual - E_limit) ≤ C_tolerance; Where P_market is the current market price fluctuation coefficient, which dynamically reflects the market premium level of raw materials or products, P_threshold represents the price trigger threshold, and strong constraints are activated when the market price exceeds the P_threshold value. S_season is the quantification value of season parameters (such as the normalization result of environmental factors such as temperature and humidity), S_optimal is the optimal operating condition reference value of the equipment under the season parameters, E_actual represents the real-time environmental protection index (such as carbon emissions per unit output), E_limit is the emission upper limit required by environmental protection regulations, α, β, and γ are dynamic weight coefficients, which are automatically adjusted according to the importance of the working conditions through an online learning algorithm, and C_tolerance is the constraint deviation tolerance allowed by the system.

6. The system according to claim 3, wherein The impact factors include real-time carbon price signals, and the real-time optimization module includes adding the following carbon quota constraint conditions to the objective function: min Σ(λ1 * power generation cost + λ2 * risk cost + λ3 * carbon trading cost); wherein, regional carbon quota ≥ actual carbon emissions × (1 - green power offset ratio).

7. The system according to claim 1, wherein Each constraint condition in the real-time optimization module further includes an access parameter, which is the difference between the current value and the preset value of the influencing factor. When the difference is less than zero, the influence of the constraint condition corresponding to the influencing factor in the scheduling model is excluded.

8. The system according to claim 1, wherein The system further includes a carbon flow tracking visualization platform, including: a carbon flow calculation module for generating the carbon flow density corresponding to the power of each branch in real time according to the power flow tracking algorithm; a binding module for on-chain confirmation of the wind and solar power generation and carbon emission reduction through a smart contract; a display module including a 3D power grid carbon flow heat map for displaying the clean energy penetration rate of different regional power grids according to the color gradient.

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