Regional power grid carbon peak reaching prediction method, device and equipment based on system dynamics, storage medium and program product
By decomposing the carbon emissions of the power grid using system dynamics methods and constructing a dynamic model, the problem of low accuracy in predicting peak carbon emissions in traditional methods is solved, and accurate prediction of peak carbon emissions of the power grid and scientific decision support are achieved.
Patent Information
- Application Number
- CN202511594830.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-03
- Publication Date
- 2026-02-10
AI Technical Summary
Traditional methods for predicting peak carbon emissions in the power sector struggle to fully capture the multi-stage feedback effects, dynamic coupling, and nonlinear relationships between the generation end, the grid side, and the consumption end. They also fail to adequately reflect the impact of policy adjustments, technological advancements, and load regulation on the evolution path of peak carbon emissions, resulting in low accuracy in calculations.
A system dynamics-based approach is adopted, which decomposes the carbon emissions of the power grid using the LMDI decomposition method, constructs an initial dynamic model covering both the generation and consumption ends, and uses differential equations and integral relationships to dynamically express the core state variables, thereby constructing a system dynamics model to predict the peak carbon emissions of the power grid.
It enables accurate prediction of peak carbon emissions from the power grid, improves the scientific nature and operability of the prediction, supports customized modeling of power grid systems of different regions and scales, and provides systematic support for phased emission reduction path optimization and power dispatch strategies.
Smart Images
Figure CN121504485A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of carbon emission prediction technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting peak carbon emissions in regional power grids based on system dynamics. Background Technology
[0002] In recent years, with the diversification of the power structure, the composition of carbon emissions from the power grid has exhibited complex characteristics of multi-source coupling and temporal dynamic evolution. The proportion of renewable energy sources such as hydropower, wind power, and photovoltaic power in the power grid has been continuously increasing. These power sources generate almost no direct carbon emissions during the power generation process, while coal-fired power remains the main source of carbon emissions from the power grid. Its carbon emission intensity is significantly affected by multiple factors, including power generation load, unit efficiency, and fuel structure, resulting in significant volatility and dynamism. At the same time, fluctuations in electricity demand on the user side, differences in peak and valley loads, and the introduction of new power system elements such as inter-regional power transmission and energy storage regulation have further exacerbated the complexity of the spatiotemporal distribution of carbon emissions from the power grid.
[0003] Traditional methods for predicting peak carbon emissions in the power sector often employ trend extrapolation, factor decomposition, or regression analysis based on annual statistical data. These methods typically use key drivers such as population, economic growth, and energy structure as inputs, combining the Kaya identity or a single regression model to calculate total carbon emissions. However, most traditional methods rely on static parameter settings and unidirectional causal chains, making it difficult to comprehensively capture the feedback effects, dynamic coupling, and nonlinear relationships among multiple stages between the generation, grid, and consumption ends. They also fail to fully reflect the impact of policy adjustments, technological advancements, and load regulation on the evolution path of peak carbon emissions under different influences, thus limiting the scientific rigor and operability of peak carbon emission prediction and resulting in low accuracy. Summary of the Invention
[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting the peak carbon emissions of a regional power grid based on system dynamics, in order to address the above-mentioned technical problems.
[0005] Firstly, this application provides a method for predicting peak carbon emissions in regional power grids based on system dynamics, including:
[0006] Obtain historical datasets corresponding to multiple different fields in the target area, perform data cleaning and normalization on the historical datasets, and obtain multi-source historical datasets within a preset interval;
[0007] Based on the multi-source historical dataset, the LMDI decomposition method is used to decompose the power grid carbon emissions in the target area, thereby obtaining multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target area.
[0008] An initial dynamic model covering both the power generation and consumption ends is constructed, and the core state variables of the initial dynamic model are set according to multiple carbon emission influencing factors.
[0009] Based on preset changing parameters and external driving parameters, each core state variable is dynamically expressed through differential equations and integral relationships to obtain the state variable integral equation corresponding to each core state variable.
[0010] A stock-flow diagram of each of the core state variables is constructed. Based on the stock-flow diagram and the integral equation of the state variables, the initial dynamic model is updated to obtain a system dynamic model. The system dynamic model is used to predict the peak carbon emissions of the power grid in the target area.
[0011] In one embodiment, the carbon emission influencing factors include emission factor effects, power generation coal consumption rate effects, energy structure effects, power structure effects, residential electricity intensity effects, economic effects, and population size effects. The process involves decomposing the grid carbon emissions of the target region using the LMDI decomposition method based on the multi-source historical dataset to obtain multiple carbon emission influencing factors corresponding to the grid carbon emissions in the target region, including:
[0012] Based on the multi-source historical dataset, the carbon emissions of the power grid are decomposed into the contributions of multiple carbon emission influencing factors using the logarithmic mean difference method, and the contribution differences of each carbon emission influencing factor are determined.
[0013] In one embodiment, the core state variables include the target area's thermal power generation, carbon emissions per unit of coal consumption, GDP, total population, and residential electricity consumption. The construction of the stock-flow graph for each of the core state variables includes:
[0014] The core state variables are treated as stock variables, and the annual changes of the core state variables are treated as flow variables. Based on the stock variables and the flow variables, a stock-flow diagram for each of the core state variables is constructed.
[0015] In one embodiment, the step of dynamically expressing each of the core state variables using differential equations and integral relationships based on preset change parameters and external driving parameters to obtain the state variable integral equations corresponding to each of the core state variables includes:
[0016] The core state variables are set to correspond to the input flow, output flow, and time step in the integral update formula. Based on the preset change parameters, the external driving parameters, the input flow, the output flow, and the time step, the integral update formula is used to dynamically express the state variables to construct the thermal power generation, the carbon emission per unit of coal consumption, the regional GDP, the total population, and the residential electricity consumption.
[0017] In one embodiment, after setting the core state variables of the initial kinetic model based on the multiple carbon emission influencing factors, the method further includes:
[0018] Causal relationship analysis is performed on the core state variables to identify the causal paths between them; positive and negative feedback relationships are determined based on the causal paths, and the core state variables are associated through these positive and negative feedback relationships to form a feedback network.
[0019] In one embodiment, the data cleaning and normalization process for the historical dataset includes:
[0020] Outliers and missing values in the historical dataset are detected, outliers are removed and missing values are filled in to obtain the cleaned current historical dataset; based on the maximum and minimum values in the current historical dataset and the original data, the current historical dataset is normalized using the range standardization method.
[0021] Secondly, this application also provides a regional power grid carbon peak prediction device based on system dynamics, comprising:
[0022] The data acquisition module is used to acquire historical datasets corresponding to multiple different fields in the target area, perform data cleaning and normalization on the historical datasets, and obtain multi-source historical datasets within a preset range.
[0023] The factor decomposition module is used to decompose the power grid carbon emissions of the target area according to the multi-source historical dataset using the LMDI decomposition method, and obtain multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target area.
[0024] The model building module is used to build an initial dynamic model covering both the power generation and power consumption ends, and to set the core state variables of the initial dynamic model according to multiple carbon emission influencing factors.
[0025] The dynamic expression module is used to dynamically express each of the core state variables through differential equations and integral relationships based on preset changing parameters and external driving parameters, so as to obtain the state variable integral equations corresponding to each of the core state variables.
[0026] The model update module is used to construct a stock-flow diagram of each of the core state variables, and update the initial dynamic model based on the stock-flow diagram and the integral equation of the state variables to obtain a system dynamic model; the system dynamic model is used to predict the peak carbon emissions of the power grid in the target area.
[0027] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0028] Historical datasets corresponding to multiple different fields in the target area are acquired, and the historical datasets are cleaned and normalized to obtain multi-source historical datasets within a preset interval. Based on the multi-source historical datasets, the power grid carbon emissions in the target area are decomposed using the LMDI decomposition method to obtain multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target area. An initial dynamic model covering both the generation and consumption ends is constructed, and core state variables of the initial dynamic model are set according to the multiple carbon emission influencing factors. Each core state variable is dynamically expressed using differential equations and integral relationships based on preset changing parameters and external driving parameters to obtain the state variable integral equations corresponding to each core state variable. A stock-flow graph of each core state variable is constructed, and the initial dynamic model is updated based on the stock-flow graph and the state variable integral equations to obtain a system dynamic model. The system dynamic model is used to predict the peak power grid carbon emissions in the target area.
[0029] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:
[0030] Historical datasets corresponding to multiple different fields in the target area are acquired, and the historical datasets are cleaned and normalized to obtain multi-source historical datasets within a preset interval. Based on the multi-source historical datasets, the power grid carbon emissions in the target area are decomposed using the LMDI decomposition method to obtain multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target area. An initial dynamic model covering both the generation and consumption ends is constructed, and core state variables of the initial dynamic model are set according to the multiple carbon emission influencing factors. Each core state variable is dynamically expressed using differential equations and integral relationships based on preset changing parameters and external driving parameters to obtain the state variable integral equations corresponding to each core state variable. A stock-flow graph of each core state variable is constructed, and the initial dynamic model is updated based on the stock-flow graph and the state variable integral equations to obtain a system dynamic model. The system dynamic model is used to predict the peak power grid carbon emissions in the target area.
[0031] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:
[0032] Historical datasets corresponding to multiple different fields in the target area are acquired, and the historical datasets are cleaned and normalized to obtain multi-source historical datasets within a preset interval. Based on the multi-source historical datasets, the power grid carbon emissions in the target area are decomposed using the LMDI decomposition method to obtain multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target area. An initial dynamic model covering both the generation and consumption ends is constructed, and core state variables of the initial dynamic model are set according to the multiple carbon emission influencing factors. Each core state variable is dynamically expressed using differential equations and integral relationships based on preset changing parameters and external driving parameters to obtain the state variable integral equations corresponding to each core state variable. A stock-flow graph of each core state variable is constructed, and the initial dynamic model is updated based on the stock-flow graph and the state variable integral equations to obtain a system dynamic model. The system dynamic model is used to predict the peak power grid carbon emissions in the target area.
[0033] The aforementioned regional power grid carbon peak prediction method, device, computer equipment, computer-readable storage medium, and computer program product based on system dynamics use system dynamics as the core modeling principle. It constructs a multi-level, multi-variable coupled feedback network, incorporating multiple carbon emission influencing factors into a unified dynamic framework. Through causal loop diagrams and stock-flow diagrams, it visually depicts the complex positive and negative feedback and time-delay effects between various stages, achieving full-cycle simulation and peak identification of the dynamic evolution of total power grid carbon emissions and carbon factors. During the modeling process, an innovative dynamic parameter adjustment mechanism is introduced, allowing parameters to be updated in real time with policy disturbances, technological changes, and load variations, effectively improving the model's adaptability to changes in the external environment and its prediction accuracy. Furthermore, the model design supports the integration of multi-source data input, enabling customized modeling for power grid systems of different regions and scales. This application can reflect the impact of policy adjustments, technological progress, and load regulation on the evolution path of carbon emission peaks under different factors. It can output the timing, scale, and contribution rate of the main driving factors of the peak carbon emissions in the power grid, effectively improving the scientificity, operability, and accuracy of carbon emission peak prediction. It provides systematic and quantitative support for the optimization of phased emission reduction paths, the formulation of power dispatch strategies, and the decomposition of regional carbon peaking targets. Attached Figure Description
[0034] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0035] Figure 1 This is an application environment diagram of a regional power grid carbon peak prediction method based on system dynamics in one embodiment;
[0036] Figure 2 This is a flowchart illustrating a regional power grid carbon peak prediction method based on system dynamics in one embodiment;
[0037] Figure 3 This is a schematic diagram illustrating the simulation results of electricity carbon emissions and electricity carbon emission coefficient in one embodiment;
[0038] Figure 4 This is a diagram of the power grid carbon peaking stock flow based on system dynamics in one embodiment.
[0039] Figure 5 This is a causal loop diagram of grid carbon peaking based on system dynamics in one embodiment;
[0040] Figure 6This is a flowchart illustrating a regional power grid carbon peak prediction method based on system dynamics in a specific embodiment.
[0041] Figure 7 This is a structural block diagram of a regional power grid carbon peak prediction device based on system dynamics in one embodiment;
[0042] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0044] With the increasing digitalization and informatization of power systems, big data-based monitoring and modeling methods have provided a data foundation for dynamic analysis of carbon emissions. However, black-box prediction models that rely solely on data-driven approaches still have shortcomings in terms of interpretability and modeling of system feedback mechanisms, making it difficult to effectively support macro-level carbon peaking path projection and policy regulation verification. Therefore, there is an urgent need for an analytical tool that can integrate macro-level driving factors and micro-level feedback mechanisms, possessing both systematic and dynamic characteristics.
[0045] System dynamics, as a modeling method capable of depicting the causal loops, information feedback, and temporal evolution relationships within complex systems, has been widely applied in the fields of energy, environment, and sustainable development. By dividing the power grid carbon emission system into multi-level subsystems (such as the power generation structure subsystem, load demand subsystem, and economic and policy subsystem), system dynamics models can intuitively describe the interactions and dynamic feedback paths between key factors, providing theoretical support for the dynamic analysis of power grid carbon emission peaks. However, existing research mostly focuses on the measurement of total carbon emissions from regional energy systems or industries, lacking detailed modeling and empirical evidence for peak carbon emissions at the power grid level, and has not yet formed a systematic analytical method that can be directly used to guide the low-carbon operation and dispatch optimization of the power grid.
[0046] The regional power grid carbon peak prediction method based on system dynamics provided in this application can be applied to, for example... Figure 1 The application environment shown illustrates this. In this environment, the terminal can communicate with the server via a network. The data storage system can store the data that the server needs to process. The data storage system can be integrated onto the server or located on the cloud or other network servers. In situations such as... Figure 1 In the application environment shown, the terminal can be, but is not limited to, various personal computers, laptops, smartphones, and tablets. The server can be implemented using a standalone server or a server cluster consisting of multiple servers.
[0047] In one embodiment, such as Figure 2 As shown, a method for predicting peak carbon emissions in regional power grids based on system dynamics is presented. This method can be applied to... Figure 1 In the terminal, the method may include the following steps:
[0048] Step S201: Obtain historical datasets corresponding to multiple different fields in the target area, perform data cleaning and normalization on the historical datasets, and obtain multi-source historical datasets within the preset interval.
[0049] The historical datasets corresponding to different fields may include the electricity consumption of the industry ( Coal consumption for thermal power ( ), Gross Regional Product ( ), total population ( Population size, residential electricity consumption ( Energy structure parameters and policy factors, etc.
[0050] Specifically, in response to the received regional power grid carbon peak prediction command, the terminal obtains historical datasets corresponding to multiple different fields in the target area, cleans the historical datasets, normalizes the main continuous variables using the range standardization method, and obtains multi-source historical datasets within a preset interval.
[0051] Step S202: Based on the multi-source historical dataset, the LMDI decomposition method is used to decompose the power grid carbon emissions in the target area to obtain multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target area.
[0052] The LMDI (Log-Mean Dijkstra Index) decomposition method is a commonly used method for analyzing the contributions of various factors to changes in a variable in fields such as energy, environment, and economy. The LMDI decomposition method can break down a complex change into the contributions of multiple factors, thereby helping researchers and policymakers understand the mechanisms by which these factors act.
[0053] Specifically, the terminal uses the difference method of log-means to decompose the carbon emissions of the power grid in the target area into the contributions of multiple carbon emission influencing factors based on the multi-source historical dataset, and determines the contribution weight of each carbon emission influencing factor.
[0054] Step S203: Construct an initial dynamic model covering the power generation end, the grid side, and the power consumption end, and set the core state variables of the initial dynamic model according to multiple carbon emission influencing factors.
[0055] Specifically, taking a regional power grid in a certain province as the research object, the system boundary of the initial dynamic model covers the power generation end (thermal power and non-fossil energy) and the power consumption end (industrial electricity consumption and residential electricity consumption). It also comprehensively considers key aspects such as energy consumption structure, economy and population, policy and technology to ensure that all major carbon emission influencing factors are effectively incorporated into the modeling system.
[0056] Step S204: Based on the preset changing parameters and external driving parameters, the core state variables are dynamically expressed through differential equations and integral relationships to obtain the state variable integral equations corresponding to each core state variable.
[0057] Among them, the preset change parameters may include the power generation change rate. Residential electricity consumption change rate and the rate of change of regional GDP wait.
[0058] External driving parameters may include policy factors, technological progress factors, and structural adjustment factors.
[0059] Specifically, the construction of the state variable integral equation is based on the differential-integral idea of system dynamics and the rigorous quantification of multiple carbon emission influencing factors by the LMDI decomposition method. All core state variables are dynamically expressed through differential equations and integral relationships to construct the state variable integral equation corresponding to each core state variable, ensuring that the model accurately describes the evolution of the peak carbon emission of the power grid.
[0060] Step S205: Construct the stock-flow diagram of each core state variable, update the initial dynamic model based on the stock-flow diagram and the state variable integral equation, and obtain the system dynamic model; the system dynamic model is used to predict the peak carbon emissions of the power grid in the target area.
[0061] Specifically, the terminal updates the model parameters (biases and weights, etc.) of the initial dynamic model based on the stock-flow diagram and state variable integral equations to obtain the system dynamic model. Based on the simulation output results of the system dynamic model (such as...), Figure 3As shown in the figure, carbon emissions from electricity generation have increased year by year over time, reaching a peak around 2030, followed by a slight decline, indicating that the system has a relatively clear characteristic of carbon peaking. Thermal power generation and total electricity consumption have maintained an overall upward trend, driving a continuous increase in total carbon emissions. However, the proportion of thermal power in the structure is gradually decreasing, while the proportion of new energy sources is increasing, gradually showing a restraining effect on the peak carbon emissions. The overall trend of the carbon emission coefficient of electricity generation is downward, indicating an effective reduction in carbon intensity per unit of electricity generation, with significant effects from technological progress and structural adjustments. Although per capita electricity emissions have continued to rise slightly, the pressure of peak emissions has been significantly alleviated. Emission reduction costs decreased rapidly before reaching the peak and tended to stabilize afterward, improving the economic feasibility of subsequent in-depth emission reduction. By comparing simulation curves with historical data, it can be seen that the model performs well in fitting historical behavior and has high predictive and decision-making support capabilities. Combined with sensitivity analysis of different variables, the results show that accelerating the reduction of the proportion of thermal power, continuously promoting technological progress and energy efficiency improvement, and optimizing electricity demand management are key paths to achieving carbon peaking ahead of schedule and reducing peak levels. Based on the above simulation results, a systematic and engineering-based decision-making basis can be provided for local departments and power companies to formulate scientific carbon peaking routes and structural optimization policies.
[0062] This embodiment uses system dynamics as the core of modeling, constructing a multi-level, multi-variable coupled feedback network to incorporate multiple carbon emission influencing factors into a unified dynamic framework. Causal loop diagrams and stock-flow diagrams visually depict the complex positive and negative feedback and time-delay effects between each stage, enabling full-cycle simulation and peak identification of the dynamic evolution of total grid carbon emissions and carbon factors. During modeling, an innovative dynamic parameter adjustment mechanism is introduced, allowing parameters to be updated in real time with policy disturbances, technological changes, and load variations, effectively improving the model's adaptability and prediction accuracy to changes in the external environment. Furthermore, the model design supports the integration of multi-source data input, enabling customized modeling for grid systems of different regions and scales. This application reflects the impact of policy adjustments, technological advancements, and load adjustments on the evolution path of carbon emission peaks under different factors, outputting the peak occurrence time, peak size, and contribution rate of major driving factors. This effectively improves the scientific rigor, operability, and accuracy of carbon emission peak prediction, providing systematic and quantitative support for phased emission reduction path optimization, power dispatch strategy formulation, and regional carbon peak target decomposition.
[0063] In one embodiment, the carbon emission influencing factors include emission factor effects, power generation coal consumption rate effects, energy structure effects, power structure effects, residential electricity consumption intensity effects, economic effects, and population size effects. In step S202 above, the carbon emissions of the power grid in the target area are decomposed using the LMDI decomposition method based on multi-source historical datasets to obtain multiple carbon emission influencing factors corresponding to the carbon emissions of the power grid in the target area. This may include the following steps:
[0064] Based on multi-source historical datasets, the carbon emissions of the power grid are decomposed into the contributions of multiple carbon emission influencing factors using the difference method of log-means, and the differences in the contributions of each carbon emission influencing factor are determined.
[0065] The difference in contribution of carbon emission influencing factors is the weight of each carbon emission influencing factor.
[0066] Specifically, in the LMDI modeling phase, the decomposition formula for carbon emissions C follows the structure below:
[0067]
[0068] get
[0069]
[0070] In the above formula, It is the emission factor effect. It is the effect of coal consumption rate in power generation. It is an energy structure effect. It is a power structure effect. It is the effect of residential electricity intensity. It's an economic effect. It is the population size effect.
[0071] After rigorous processing, the continuity and accuracy of all variables' data can serve as the basic input for LMDI decomposition and system dynamic parameter initialization, enabling quantitative decomposition of carbon emission structure, driving factors, and historical variation patterns. This provides a scientific and reliable data foundation and engineering support for subsequent grid carbon peaking system simulation analysis.
[0072] In one embodiment, the core state variables include the target area's thermal power generation, carbon emissions per unit of coal consumption, regional GDP, total population, and residential electricity consumption. Step S205 above, which involves constructing a stock-flow diagram for each core state variable, may include the following steps:
[0073] The core state variables are treated as stock variables, and the annual changes of the core state variables are treated as flow variables. Based on the stock variables and flow variables, a stock-flow diagram of each core state variable is constructed.
[0074] Specifically, the terminal combines the LMDI decomposition model with actual business scenarios to scientifically select the system's core state variables (stock variables), flow variables, and key parameters. The model covers major stock variables such as thermal power generation, carbon emissions per unit of coal consumption, regional GDP, total population, and residential electricity consumption, using their annual changes as corresponding flow variables to reflect the dynamic evolution of each element over time. The stock-flow diagram is based on a "state quantity-rate quantity" structure, clearly defining the coupling relationships between energy, carbon emissions, and socio-economic variables in each link of the system. Each stock variable is dynamically updated through integral input and output rates. Driving flow variables, such as changes in regional GDP, thermal power generation, and carbon emissions per unit of coal consumption, are obtained from statistical data and LMDI decomposition factors, ensuring a rigorous and traceable structural logic. The system's time boundary is set from 2005 to 2035, with a step size DT of 1 year, adapting to industry data granularity. All variables and structural relationships are represented by the stock-flow diagram (e.g., ...). Figure 4 (As shown) serves as a blueprint, facilitating engineering implementation and software modeling.
[0075] In one embodiment, step S204 above, which dynamically expresses each core state variable through differential equations and integral relationships based on preset change parameters and external driving parameters to obtain the state variable integral equations corresponding to each core state variable, may include the following steps:
[0076] The input flow, output flow, and time step corresponding to each core state variable are set in the integral update formula. Based on the preset changing parameters, external driving parameters, input flow, output flow, and time step, the integral update formula is used to dynamically express the state variables to construct the integral equations corresponding to thermal power generation, carbon emissions per unit of coal consumption, regional GDP, total population, and residential electricity consumption.
[0077] The construction of the state variable integral equation is based on the differential-integral idea of system dynamics and the rigorous quantification of carbon emission driving factors by the LMDI decomposition method. All core variables are dynamically expressed through differential equations and integral relationships to ensure that the model accurately describes the evolution of peak carbon emissions from the power grid.
[0078] Specifically, the following integral-type update formula is used for each major state variable (such as thermal power generation, carbon emissions per unit of coal consumption, regional GDP, total population, and residential electricity consumption):
[0079]
[0080] In the above formula, For state variables, For input traffic, To output flow, For time step.
[0081] Based on actual business needs and decomposition requirements, the main variable equations are specified as follows:
[0082] Thermal power generation ( (Unit: 100 million kilowatt-hours)
[0083]
[0084] In the above formula, This represents the change in thermal power generation. For the proportion of thermal power in the structure, For the rate of change in power generation, As a policy adjustment factor for thermal power, As a policy factor.
[0085] Carbon emissions per unit of coal consumption ( (Unit: 10,000 tons / 10,000 tons of standard coal)
[0086]
[0087] In the above formula, It is the change in carbon emissions per unit of coal consumption. It is a power supply structure influencing factor. It is a factor influencing technological progress.
[0088] Regional Gross Domestic Product (GDP, unit: trillion yuan):
[0089]
[0090] In the above formula, It is the change in GDP. It is the rate of change in GDP.
[0091] Total population ( (Unit: 10,000 people)
[0092]
[0093] In the above formula, For population increase, For population growth For birth rate, The mortality rate.
[0094] Residential electricity consumption ( (Unit: 100 million kilowatt-hours)
[0095]
[0096] In the above formula, This refers to changes in residential electricity consumption. It is the rate of change in residential electricity consumption.
[0097] The formulas for LMDI and auxiliary parameters are as follows:
[0098] Coal consumption ( (Unit: 10,000 tons)
[0099]
[0100] Carbon emissions from electricity ( (Unit: 100 million tons)
[0101]
[0102] Carbon emission factor of electricity ( (Unit: 100 million tons / 100 million kilowatt-hours)
[0103]
[0104] In the above formula, This is the total electricity consumption, measured in hundreds of millions of kilowatt-hours.
[0105] The parameters and boundary conditions are input as follows:
[0106] Variation parameters (e.g.) (etc.) The input is segmented annually, i.e., entered in the system dynamics model in the form of WITH LOOKUP. External driving parameters such as policy factors, technological progress factors, and structural adjustment factors are set according to forecasted needs or filled in with reference to industry trends.
[0107] In one embodiment, after setting the core state variables of the initial kinetic model based on multiple carbon emission influencing factors, the method of this application further includes the following steps:
[0108] Causal relationship analysis is performed based on the core state variables to identify the causal paths between them; positive and negative feedback relationships are determined based on these causal paths, and the core state variables are linked together through these positive and negative feedback relationships to form a feedback network.
[0109] Specifically, in the causal relationship analysis phase, referring to the various carbon emission influencing factors decomposed by LMDI, the variables within the system are linked through positive and negative feedback relationships. Using regional GDP, population size, energy structure, carbon emission factor per unit of electricity generation, power generation efficiency, electricity consumption structure, and residential electricity intensity as core variables, the following causal path is identified at the terminal level:
[0110] 1) Regional GDP growth drives up electricity demand, leading to an increase in total power generation and consequently increasing total carbon emissions;
[0111] 2) Optimizing industrial and energy structures can reduce energy consumption per unit of output and carbon emission intensity during power generation;
[0112] 3) Technological advancements reduce coal consumption for power generation and increase the proportion of new energy sources, thereby achieving a dynamic reduction in carbon emission factors per unit.
[0113] 4) Policy regulation affects the proportion of thermal power structure and the development speed of renewable energy, which plays a key constraining and guiding role in the overall carbon peaking path;
[0114] 5) Residential electricity consumption intensity and population size together affect the total electricity consumption of the whole society, becoming another important driving factor influencing changes in carbon emissions;
[0115] 6) Environmental feedback and policy response mechanisms can form a closed-loop regulation system through carbon pricing, subsidies, investment, and other means.
[0116] Then, based on the above causal path, the positive and negative feedback relationships between each core state variable are determined, and the core state variables are associated through positive and negative feedback relationships to form a pattern as follows: Figure 5 The feedback network is shown.
[0117] In one embodiment, step S201 above, which involves cleaning and normalizing the historical dataset, may include the following steps:
[0118] Outliers and missing values in the historical dataset are detected, outliers are removed and missing values are filled in to obtain the cleaned current historical dataset. Based on the maximum and minimum values in the current historical dataset and the original data, the current historical dataset is normalized using the range standardization method.
[0119] Specifically, in the data acquisition and preprocessing stage, based on the requirements of the LMDI (Logarithmic Mean Dijkstra Index) decomposition method for carbon emission influencing factors, the system collected multi-source historical data covering the power grid and related economic and social sectors of a certain province. The main input data included: electricity consumption of industries (…). Coal consumption for thermal power ( ), GDP ), total population ( Population size, residential electricity consumption ( Energy structure parameters, policy factors, etc.
[0120] The data primarily comes from a provincial statistical yearbook, annual power balance sheets published by the energy bureau, power company operation reports, relevant industry annual reports, and authoritative public databases. In the data preprocessing stage, to ensure the accuracy of LMDI decomposition and subsequent system dynamics modeling, a comprehensive quality review of the original dataset was first conducted. For missing or outlier data points, industry-standard interpolation and mean methods were used for correction, and extreme values inconsistent with physical or economic meaning were removed. For variables with inconsistent units between years, necessary unit conversions were performed (such as standard coal equivalent conversion, carbon emission equivalent conversion, etc.), and the main continuous variables were normalized using the range standardization method, as shown in the following formula:
[0121]
[0122] In the above formula, The original data, , These are the minimum and maximum values, respectively. For the normalized data, the data continuity, logarithmic additivity, and decomposition suitability of all key indicators are verified at this stage.
[0123] In one embodiment, such as Figure 6 As shown, a method for predicting peak carbon emissions in a regional power grid based on system dynamics is provided in a specific embodiment, which includes the following steps:
[0124] Step S601: Obtain historical datasets corresponding to multiple different domains in the target area, detect outliers and missing values in the historical datasets, remove outliers and fill in missing values to obtain the cleaned current historical dataset; normalize the current historical dataset using the range standardization method based on the maximum and minimum values in the current historical dataset and the original data.
[0125] Step S602: Based on the multi-source historical dataset, the power grid carbon emissions in the target area are decomposed using the LMDI decomposition method to obtain multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target area; an initial dynamic model covering the generation end, the power grid side and the power consumption end is constructed, and the core state variables of the initial dynamic model are set according to multiple carbon emission influencing factors.
[0126] Step S603: Perform causal relationship analysis based on the core state variables to identify the causal paths between the core state variables; determine the positive and negative feedback relationships between the core state variables based on the causal paths, and associate the core state variables through positive and negative feedback relationships to form a feedback network.
[0127] Step S604: Set the input flow, output flow, and time step corresponding to each core state variable in the integral update formula; based on the preset change parameters, external driving parameters, input flow, output flow, and time step, dynamically express them through the integral update formula to construct the state variable integral equations corresponding to the target area's thermal power generation, carbon emissions per unit of coal consumption, GDP, total population, and residential electricity consumption.
[0128] Step S605: Treat the core state variables as stock variables and the annual changes of the core state variables as flow variables, and construct the stock-flow diagram for each core state variable based on the stock variables and flow variables.
[0129] Step S606: Update the initial dynamic model based on the stock-flow diagram and the state variable integral equation to obtain the system dynamic model; the system dynamic model is used to predict the peak carbon emissions of the power grid in the target area.
[0130] The beneficial effects of the above embodiments are as follows:
[0131] 1) This application uses system dynamics as the core of its modeling, constructing a multi-level, multi-variable coupled feedback network. Multiple carbon emission influencing factors are incorporated into a unified dynamic framework. Causal loop diagrams and stock-flow diagrams are used to visually depict the complex positive and negative feedback and time-delay effects between each stage, enabling full-cycle simulation and peak identification of the dynamic evolution of total grid carbon emissions and carbon factors. During the modeling process, an innovative dynamic parameter adjustment mechanism is introduced, allowing parameters to be updated in real time with policy disturbances, technological changes, and load variations, effectively improving the model's adaptability to changes in the external environment and its prediction accuracy.
[0132] 2) This application can reflect the impact of policy adjustments, technological progress and load regulation on the evolution path of carbon emission peak under different factors. It can output the occurrence time, peak size and contribution rate of main driving factors of grid carbon emission peak, effectively improving the scientificity, operability and accuracy of carbon emission peak prediction, and providing systematic and quantitative support for the optimization of phased emission reduction path, the formulation of power dispatch strategy and the decomposition of regional carbon peak target.
[0133] 3) This application breaks through the limitations of traditional methods, such as single feedback link, insufficient real-time performance and adaptability, and weak interpretability of results. It has good scalability and implementation capabilities, and can provide a scientific theoretical basis and decision-making technical means for building a low-carbon and efficient power system and promoting the realization of the "dual carbon" strategic goal.
[0134] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0135] Based on the same inventive concept, this application also provides a system dynamics-based regional power grid carbon peak prediction device for implementing the aforementioned system dynamics-based regional power grid carbon peak prediction method. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more system dynamics-based regional power grid carbon peak prediction device embodiments provided below can be found in the above-described limitations of the system dynamics-based regional power grid carbon peak prediction method, and will not be repeated here.
[0136] In one exemplary embodiment, such as Figure 7 As shown, a regional power grid carbon peak prediction device based on system dynamics is provided, which may include:
[0137] The data acquisition module 701 is used to acquire historical datasets corresponding to multiple different fields in the target area, perform data cleaning and normalization on the historical datasets, and obtain multi-source historical datasets within a preset interval.
[0138] The factor decomposition module 702 is used to decompose the power grid carbon emissions of the target area according to the multi-source historical dataset using the LMDI decomposition method, and obtain multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target area.
[0139] The model building module 703 is used to build an initial dynamic model covering the power generation end and the power consumption end, and to set the core state variables of the initial dynamic model according to multiple carbon emission influencing factors.
[0140] The dynamic expression module 704 is used to dynamically express each of the core state variables through differential equations and integral relationships according to preset changing parameters and external driving parameters, so as to obtain the state variable integral equations corresponding to each of the core state variables.
[0141] The model update module 705 is used to construct a stock-flow diagram of each of the core state variables, and update the initial dynamic model based on the stock-flow diagram and the state variable integral equation to obtain a system dynamic model; the system dynamic model is used to predict the peak carbon emissions of the power grid in the target area.
[0142] In one embodiment, carbon emission influencing factors include emission factor effect, power generation coal consumption rate effect, energy structure effect, power structure effect, residential electricity intensity effect, economic effect, and population size effect. The factor decomposition module 702 is further configured to decompose the power grid carbon emissions into the contributions of multiple carbon emission influencing factors based on the multi-source historical dataset using the difference method of logarithmic mean, and determine the contribution differences of each of the carbon emission influencing factors.
[0143] In one embodiment, the core state variables include the target area's thermal power generation, carbon emissions per unit of coal consumption, regional GDP, total population, and residential electricity consumption. The model update module 705 is further configured to treat the core state variables as stock variables and the annual changes of the core state variables as flow variables, and construct the stock-flow diagram of each of the core state variables based on the stock variables and the flow variables.
[0144] In one embodiment, the dynamic expression module 704 is further configured to set the input flow, output flow, and time step corresponding to each of the core state variables in the integral update formula; and to dynamically express the state variables according to the preset change parameters, the external driving parameters, the input flow, the output flow, and the time step through the integral update formula, so as to construct the integral equations of the state variables corresponding to the thermal power generation, the carbon emission per unit of coal consumption, the regional GDP, the total population, and the residential electricity consumption, respectively.
[0145] In one embodiment, the device may further include: a feedback association module, configured to perform causal relationship analysis based on the core state variables to identify causal paths between the core state variables; determine positive and negative feedback relationships between the core state variables based on the causal paths; and associate the core state variables through the positive and negative feedback relationships to form a feedback network.
[0146] In one embodiment, the data acquisition module 701 is further configured to detect outliers and missing values in the historical dataset, remove the outliers and fill in the missing values to obtain the cleaned current historical dataset; and normalize the current historical dataset using the range standardization method based on the maximum and minimum values in the current historical dataset and the original data.
[0147] The modules in the aforementioned regional power grid carbon peak prediction device based on system dynamics can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.
[0148] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a regional power grid carbon peak prediction method based on system dynamics. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.
[0149] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0150] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.
[0151] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.
[0152] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.
[0153] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0154] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.
[0155] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0156] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A method for predicting peak carbon emissions in regional power grids based on system dynamics, characterized in that, The method includes: Obtain historical datasets corresponding to multiple different fields in the target area, perform data cleaning and normalization on the historical datasets, and obtain multi-source historical datasets within a preset interval; Based on the multi-source historical dataset, the LMDI decomposition method is used to decompose the power grid carbon emissions in the target area, thereby obtaining multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target area. An initial dynamic model covering the power generation end, the grid side, and the power consumption end is constructed, and the core state variables of the initial dynamic model are set according to multiple carbon emission influencing factors. Based on preset changing parameters and external driving parameters, each core state variable is dynamically expressed through differential equations and integral relationships to obtain the state variable integral equation corresponding to each core state variable. A stock-flow diagram of each of the core state variables is constructed. Based on the stock-flow diagram and the integral equation of the state variables, the initial dynamic model is updated to obtain a system dynamic model. The system dynamic model is used to predict the peak carbon emissions of the power grid in the target area.
2. The method according to claim 1, characterized in that, The carbon emission influencing factors include emission factor effects, power generation coal consumption rate effects, energy structure effects, power structure effects, residential electricity consumption intensity effects, economic effects, and population size effects. The method of decomposing the power grid carbon emissions of the target region using the LMDI decomposition method based on the multi-source historical dataset yields multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target region, including: Based on the multi-source historical dataset, the carbon emissions of the power grid are decomposed into the contributions of multiple carbon emission influencing factors using the logarithmic mean difference method, and the contribution differences of each carbon emission influencing factor are determined.
3. The method according to claim 1, characterized in that, The core state variables include the target area's thermal power generation, carbon emissions per unit of coal consumption, GDP, total population, and residential electricity consumption. The construction of the stock-flow diagram for each of these core state variables includes: The core state variables are treated as stock variables, and the annual changes of the core state variables are treated as flow variables. Based on the stock variables and the flow variables, a stock-flow diagram for each of the core state variables is constructed.
4. The method according to claim 3, characterized in that, The process of dynamically expressing each core state variable using differential equations and integral relationships based on preset changing parameters and external driving parameters to obtain the state variable integral equation corresponding to each core state variable includes: Set the input flow, output flow, and time step of each of the core state variables in the integral update formula; Based on the preset change parameters, the external driving parameters, the input flow rate, the output flow rate, and the time step, the integral update formula is used to dynamically express the state variable integral equations corresponding to the thermal power generation, the carbon emission per unit of coal consumption, the regional GDP, the total population, and the residential electricity consumption, respectively.
5. The method according to claim 1, characterized in that, After setting the core state variables of the initial dynamic model based on multiple carbon emission influencing factors, the method further includes: A causal relationship analysis is performed based on the core state variables to identify the causal paths between them. Based on the causal path, the positive and negative feedback relationships between the core state variables are determined, and the core state variables are associated through the positive and negative feedback relationships to form a feedback network.
6. The method according to any one of claims 1 to 5, characterized in that, The data cleaning and normalization process for the historical dataset includes: Outliers and missing values in the historical dataset are detected, outliers are removed and missing values are filled in to obtain the cleaned current historical dataset. Based on the maximum and minimum values in the current historical dataset and the original data, the current historical dataset is normalized using the range standardization method.
7. A regional power grid carbon peak prediction device based on system dynamics, characterized in that, The device includes: The data acquisition module is used to acquire historical datasets corresponding to multiple different fields in the target area, perform data cleaning and normalization on the historical datasets, and obtain multi-source historical datasets within a preset range. The factor decomposition module is used to decompose the power grid carbon emissions of the target area according to the multi-source historical dataset using the LMDI decomposition method, and obtain multiple carbon emission influencing factors corresponding to the power grid carbon emissions in the target area. The model building module is used to build an initial dynamic model covering both the power generation and power consumption ends, and to set the core state variables of the initial dynamic model according to multiple carbon emission influencing factors. The dynamic expression module is used to dynamically express each of the core state variables through differential equations and integral relationships based on preset changing parameters and external driving parameters, so as to obtain the state variable integral equations corresponding to each of the core state variables. The model update module is used to construct a stock-flow diagram of each of the core state variables, and update the initial dynamic model based on the stock-flow diagram and the integral equation of the state variables to obtain a system dynamic model; the system dynamic model is used to predict the peak carbon emissions of the power grid in the target area.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.