An electric-enterprise-carbon market coupling modeling method, device and storage medium

Abstract

The method comprises the following steps: acquiring relevant data in a current simulation period; inputting the relevant data into a preset coupling model; analyzing the relevant data to determine a current core control signal; determining a current electricity price initialization demand prediction curve, an initial production cost, and an external environment corresponding to the current core control signal; performing simulation to obtain a first simulation result; acquiring hard adjustment parameters; inputting the hard adjustment parameters into the coupling model; performing second simulation based on the first simulation result to obtain a second simulation result; and determining an electricity-enterprise-carbon market coupling situation of the current simulation period according to the first simulation result and the second simulation result. The method realizes endogenous driving and overall coupling simulation of the core control signal, can quantitatively evaluate the comprehensive influence of the adjustment of the hard adjustment parameters on the power generation structure, enterprise behavior, and carbon price dynamics, and supports regional heterogeneity analysis.

Description

Technical Field

[0001] This disclosure relates to the field of electricity market technology, and in particular to a method, device and storage medium for coupled modeling of electricity-enterprise-carbon markets. Background Technology

[0002] As a major source of carbon emissions, the power sector is a crucial link in achieving carbon reduction. Currently, it primarily employs a combination of the electricity market and the carbon emissions trading market, introducing carbon price signals to adjust power generation structures and corporate production behaviors towards low-carbon practices. However, the electricity market and the carbon market are highly coupled and exhibit significant nonlinear characteristics. The system's overall behavior is complex, influenced by multiple factors such as power generation costs, demand changes, the proportion of renewable energy, and carbon quotas. Furthermore, current methods lack a comprehensive understanding of the coupling between the electricity market, corporate production behaviors, and the carbon market; they lack a three-dimensional linkage framework based on price-driven mechanisms; they cannot simulate the comprehensive impact of hard parameter adjustments on the economy and energy system; and they are significantly affected by geographical location. Summary of the Invention

[0003] To solve the above-mentioned technical problems, or at least partially solve them, this disclosure provides a method, device, and storage medium for coupled modeling of the electricity-enterprise-carbon market.

[0004] This disclosure provides a method for coupled modeling of the electricity-enterprise-carbon market, the method comprising: Acquire relevant data for the current simulation period and input the relevant data into a preset coupling model. The relevant data includes power data, enterprise data, and carbon data. Based on the coupling model, the relevant data is analyzed to determine the current core control signal. The coupling model is used to determine the current electricity price initial demand forecast curve, initial production cost, and external environment corresponding to the current core control signal. The current electricity price initial demand forecast curve, initial production cost, and external environment are simulated respectively to obtain the first simulation result. Obtain the hard adjustment parameters, input the hard adjustment parameters into the coupling model, and perform a second simulation based on the first simulation result to obtain the second simulation result; The coupling of the electricity-enterprise-carbon market in the current simulation period is determined based on the first and second simulation results.

[0005] The method provided in this disclosure, which determines the current core control signal based on the coupling model and the relevant data, includes: The theoretical electricity price is determined by analyzing the relevant data based on the coupling model, and the current electricity price is determined based on the theoretical electricity price and the upper and lower limits of the fluctuation of the theoretical electricity price. The theoretical electricity price is obtained by weighting the electricity price in the previous simulation period in the electricity data with the ideal electricity price; The theoretical carbon price is determined by analyzing the carbon data in the relevant data based on the coupling model, and the current carbon price is determined based on the theoretical carbon price and the upper and lower limits of the fluctuation of the theoretical carbon price. The theoretical carbon price is obtained by weighting the carbon price of the previous simulation period, the marginal cost of emission reduction, and the expected value of the standardized emission gap in the carbon data. The current core control signal is determined based on the current carbon price and the current electricity price.

[0006] The method provided in this disclosure uses the coupling model to determine the current electricity price initial demand forecast curve, initial production cost, and external environment corresponding to the current core control signal, and simulates the current electricity price initial demand forecast curve, initial production cost, and external environment respectively to obtain a first simulation result, including: The current core control signal is input into the coupling model, the initial demand forecast curve is determined based on the current electricity price in the current control signal, the initial production cost is calculated based on the current control signal, and the external environment is determined based on the current control signal; Using the initial demand forecast curve, initial production cost, and external environment as the initial state, the dynamics of power supply and demand are simulated based on the initial state to obtain dynamic simulation results; enterprise production decisions are simulated based on the initial state to obtain decision simulation results; and generator investment is simulated based on the initial state to obtain investment simulation results. The first simulation result is obtained by combining the dynamic simulation results, decision simulation results, and investment simulation results.

[0007] The method provided in this disclosure, which obtains hard adjustment parameters, inputs the hard adjustment parameters into the coupling model, performs a second simulation based on the first simulation result, and obtains a second simulation result, includes: Analyze the first simulation results, obtain hard adjustment parameters based on the analysis results, and input the hard adjustment parameters into the coupling model; The first simulation result is used as the initial state for the second simulation. The second simulation is performed based on the hard adjustment parameters using the coupling model to obtain the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results. The second simulation result is obtained by combining the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results.

[0008] The method provided in this disclosure, which determines the coupling of the electricity-enterprise-carbon market in the current simulation period based on the first simulation result and the second simulation result, includes: If the hard adjustment parameter is 0, the coupled model outputs the first simulation result; If the hard adjustment parameter is not 0, the coupled model outputs a second simulation result; The core indicators of the electricity-enterprise-carbon market are extracted from the first and second simulation results. These core indicators include signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators. The coupling of the electricity-enterprise-carbon market is determined based on the aforementioned signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators.

[0009] This disclosure also provides an electricity-enterprise-carbon market coupling modeling device, the device comprising: The acquisition module is used to acquire relevant data during the current simulation period and input the relevant data into a preset coupling model. The relevant data includes power data, enterprise data, and carbon data. The first simulation module is used to analyze the relevant data based on the coupling model to determine the current core control signal, use the coupling model to determine the current electricity price initial demand forecast curve, initial production cost, and external environment corresponding to the current core control signal, and simulate the current electricity price initial demand forecast curve, initial production cost, and external environment respectively to obtain the first simulation result; The second simulation module is used to acquire hard adjustment parameters, input the hard adjustment parameters into the coupled model, perform a second simulation based on the first simulation result, and obtain a second simulation result. The determination module is used to determine the coupling status of the electricity-enterprise-carbon market in the current simulation period based on the first simulation result and the second simulation result.

[0010] The device provided in this disclosure embodiment, wherein the acquisition module is specifically used for: The theoretical electricity price is determined by analyzing the relevant data based on the coupling model, and the current electricity price is determined based on the theoretical electricity price and the upper and lower limits of the fluctuation of the theoretical electricity price. The theoretical electricity price is obtained by weighting the electricity price in the previous simulation period in the electricity data with the ideal electricity price; The theoretical carbon price is determined by analyzing the carbon data in the relevant data based on the coupling model, and the current carbon price is determined based on the theoretical carbon price and the upper and lower limits of the fluctuation of the theoretical carbon price. The theoretical carbon price is obtained by weighting the carbon price of the previous simulation period, the marginal cost of emission reduction, and the expected value of the standardized emission gap in the carbon data. The current core control signal is determined based on the current carbon price and the current electricity price.

[0011] The device provided in this embodiment of the disclosure, wherein the first simulation module is specifically used for: The current core control signal is input into the coupling model, and the new electricity price initial demand forecast curve, initial production cost, and external environment are determined based on the coupling model. Based on the demand forecast curve initialized by the new electricity price, the dynamics of electricity supply and demand are simulated to obtain dynamic simulation results; Based on the initial production cost, the enterprise's production decisions are simulated to obtain the decision simulation results; Based on the enterprise data in the current core control signal, generator investment is simulated to obtain investment simulation results; The first simulation result is obtained by combining the dynamic simulation results, decision simulation results, and investment simulation results.

[0012] The device provided in this disclosure embodiment, wherein the second simulation module is specifically used for: Analyze the first simulation results, obtain hard adjustment parameters based on the analysis results, and input the hard adjustment parameters into the coupling model; The first simulation result is used as the initial state for the second simulation. The second simulation is performed based on the hard adjustment parameters using the coupling model to obtain the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results. The second simulation result is obtained by combining the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results.

[0013] The device provided in this disclosure embodiment, wherein the determining module is specifically used for: If the hard adjustment parameter is 0, the coupled model outputs the first simulation result; If the hard adjustment parameter is not 0, the coupled model outputs a second simulation result; The core indicators of the electricity-enterprise-carbon market are extracted from the first and second simulation results. These core indicators include signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators. The coupling of the electricity-enterprise-carbon market is determined based on the aforementioned signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators.

[0014] This disclosure also provides a computer-readable storage medium storing a computer program for executing the electricity-enterprise-carbon market coupling modeling method provided in this disclosure.

[0015] The technical solution provided in this disclosure has the following advantages compared with the prior art: The electricity-enterprise-carbon market coupling modeling method provided in this disclosure involves inputting electricity, enterprise, and carbon data into a coupling model. The coupling model intrinsically calculates and obtains core signals of electricity and carbon prices, driving the first round of simulation to obtain the first simulation result. After inputting hard adjustment parameters, a second simulation is performed with the first simulation result as the initial state to obtain the second simulation result. By analyzing the changes in indicators of the first and second simulation results, the changes in the market coupling state under the intervention of hard adjustment parameters are analyzed, thus constructing a three-dimensional linkage framework of electricity, enterprise, and carbon. This enables the intrinsic driving force of core control signals and overall coupling simulation, and can quantitatively evaluate the comprehensive impact of hard adjustment parameter adjustments on power generation structure, enterprise behavior, and carbon price dynamics, supporting regional heterogeneity analysis. Attached Figure Description

[0016] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0017] Figure 1 A flowchart illustrating the electricity-enterprise-carbon market coupling modeling method provided in this embodiment of the disclosure; Figure 2 This is a schematic diagram of the core variable relationships in the coupling model provided in the embodiments of this disclosure; Figure 3 A schematic diagram showing the comparison between the second simulation results provided in this embodiment and the actual installed capacity of renewable energy. Figure 4 A schematic diagram showing the comparison between the second simulation results provided in this embodiment and the actual power supply. Figure 5 A schematic diagram showing the comparison between the second simulation results provided in this embodiment and the actual monthly operating hours of thermal power plants; Figure 6 A schematic diagram of the structure of the electricity-enterprise-carbon market coupling modeling device provided in this embodiment of the disclosure. Detailed Implementation

[0018] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0019] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0020] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0021] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different modules or units, and are not used to limit the order of the functions performed by these modules or units or their interdependencies.

[0022] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0023] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0024] To address the aforementioned issues, this disclosure provides a method for coupled modeling of the electricity-enterprise-carbon market. The method will be described below with reference to specific embodiments.

[0025] Figure 1 This is a flowchart illustrating a method for coupled modeling of the electricity-enterprise-carbon market provided in an embodiment of the present disclosure. The method can be executed by an electricity-enterprise-carbon market coupled modeling device, which can be implemented in software and / or hardware and is generally integrated into an electronic device.

[0026] Example 1: This disclosure provides a method for coupled modeling of the electricity-enterprise-carbon market, including: S101: Obtain relevant data during the current simulation period and input the relevant data into a preset coupling model. The relevant data includes power data, enterprise data, and carbon data. S102: Based on the coupling model, analyze the relevant data to determine the current core control signal, use the coupling model to determine the current electricity price initial demand forecast curve, initial production cost, and external environment corresponding to the current core control signal, and simulate the current electricity price initial demand forecast curve, initial production cost, and external environment respectively to obtain the first simulation result; S103: Obtain the hard adjustment parameters, input the hard adjustment parameters into the coupling model, perform a second simulation based on the first simulation result, and obtain the second simulation result; S104: Determine the coupling status of the electricity-enterprise-carbon market in the current simulation period based on the first simulation results and the second simulation results.

[0027] In this embodiment, the simulation period is the basic time unit for the operation of the coupled model, and each simulation period completes a complete cycle of data input, calculation and state update.

[0028] In this embodiment, the coupled model is a system dynamics simulation platform integrating modules of the electricity market, enterprise production, and carbon market. It is used to simulate the dynamic feedback and hard adjustment parameter shocks among these three entities. The inputs are the external information required for the model, including electricity data, enterprise data, carbon data, and various system parameters, serving as the initial conditions and boundaries for the simulation. The outputs are the system state and key indicator set generated after the model runs, as well as the current electricity-enterprise-carbon market coupling situation during the simulation period, obtained from the analysis of electricity data, enterprise data, and carbon data. Figure 2 The diagram illustrates the relationships between core variables in the coupled model.

[0029] In this embodiment, power data is a set of parameters describing the state and characteristics of the power system, including historical electricity prices, generation costs, unit capacity, fuel prices, utilization hours, and load levels.

[0030] In this embodiment, carbon data is a set of parameters describing the state of the carbon market and hard adjustment parameters, including historical carbon prices, total emission allowances, actual emissions, and marginal emission reduction cost curves.

[0031] In this embodiment, enterprise data refers to the set of parameters used to characterize and simulate enterprise production behavior in the input coupling model. These parameters mainly include historical and current industry production scale, energy consumption intensity, carbon emission intensity, product prices, capacity utilization rate, technological composition, and short-term demand elasticity to electricity and carbon prices.

[0032] In this embodiment, the current core control signal is a key system instruction composed of the current electricity price and the current carbon price, which drives the current electricity supply and demand, enterprise production and investment decisions.

[0033] In this embodiment, the initial demand forecast curve is a basic curve generated by using the current electricity price as the key input parameter and combining historical demand data with price elasticity and other relationships to predict the total amount and pattern of electricity demand in the current period.

[0034] In this embodiment, the initial production cost is calculated based on the current electricity price and the current carbon price, representing the unit product cost that the company is expected to face when it starts production activities at the beginning of the simulation period.

[0035] In this embodiment, the external environment is the carbon market trading background and conditions jointly defined by the current carbon price, the total amount of carbon emission allowances in the current period, and market rules, which constitute the initial external constraint framework affecting corporate compliance and trading decisions.

[0036] In this embodiment, the first simulation result is a systematic output formed by the coupled model after summarizing and integrating the dynamic simulation results, decision simulation results and investment simulation results under the baseline scenario (without applying hard adjustment parameters), which is used to describe the baseline development path and state of the system.

[0037] In this embodiment, the hard adjustment parameters are directly set based on the relevant hard adjustment parameters of the power, enterprise, and carbon parties and input into the exogenous control variables of the coupled model. They are used to characterize the intervention of specific hard adjustment parameters. The hard adjustment parameters include the total amount of carbon market quotas, the weighting coefficient of carbon cost transmission to electricity price, etc.

[0038] In this embodiment, the second simulation result is a complete output dataset formed by the coupled model after summarizing and integrating the simulation results of dynamic trajectory, power sector operation, and efficiency under a scenario where hard adjustment parameters are applied. Figure 3 As shown in the figure, the comparison between the second simulation results and the actual installed capacity of renewable energy is illustrated. Figure 4 As shown in the diagram, the comparison between the second simulation results and the actual power supply is illustrated. Figure 5 As shown, this diagram illustrates the comparison between the second simulation results and the actual monthly operating hours of thermal power plants.

[0039] In this embodiment, the coupling status of the electricity-enterprise-carbon market is a qualitative judgment and quantitative description of the overall state of interaction and mutual influence among the electricity market, enterprise production, and carbon market. Specifically, it is reflected in whether market signal transmission is smooth, whether physical flow coordination is efficient, and whether the main body responses are coordinated, ultimately determining whether the system is in a tightly coupled, weakly coupled, or disconnected state.

[0040] The working principle and beneficial effects of this embodiment are as follows: Inputting electricity, enterprise, and carbon data into the coupled model, the coupled model endogenously calculates and obtains the core signals of electricity price and carbon price, driving the first round of simulation to obtain the first simulation result. After inputting hard adjustment parameters, a second simulation is performed with the first simulation result as the initial state to obtain the second simulation result. By analyzing the changes in indicators of the first and second simulation results, the changes in the market coupling state under the intervention of hard adjustment parameters are analyzed, and a three-dimensional linkage framework of electricity-enterprise-carbon is constructed. This realizes the endogenous driving of core regulation signals and the overall coupled simulation, which can quantitatively evaluate the comprehensive impact of hard adjustment parameter adjustment on power generation structure, enterprise behavior, and carbon price dynamics, and supports regional heterogeneity analysis.

[0041] Example 2: The method provided in this embodiment of the present disclosure, which determines the current core control signal based on the coupling model and the relevant data, includes: The theoretical electricity price is determined by analyzing the relevant data based on the coupling model, and the current electricity price is determined based on the theoretical electricity price and the upper and lower limits of the fluctuation of the theoretical electricity price. The theoretical electricity price is obtained by weighting the electricity price in the previous simulation period in the electricity data with the ideal electricity price; The theoretical carbon price is determined by analyzing the carbon data in the relevant data based on the coupling model, and the current carbon price is determined based on the theoretical carbon price and the upper and lower limits of the fluctuation of the theoretical carbon price. The theoretical carbon price is obtained by weighting the carbon price of the previous simulation period, the marginal cost of emission reduction, and the expected value of the standardized emission gap in the carbon data. The current core control signal is determined based on the current carbon price and the current electricity price.

[0042] In this embodiment, the theoretical carbon price is a base carbon price value calculated by the model according to the pricing formula. It is a weighted combination of the previous carbon price, marginal emission reduction cost, and expected emission gap.

[0043] In this embodiment, the upper and lower limits of the theoretical carbon price fluctuation are preset limits of the allowable range of carbon price changes, which are used to constrain the final market performance of the theoretical carbon price and usually reflect the market stabilization mechanism.

[0044] In this embodiment, the current carbon price is the actual price of carbon emission rights executed in the current carbon market, determined by adjusting the theoretical carbon price for upper and lower fluctuation limits. The specific formula is as follows: ,in, Represents the carbon price in period t; This indicates the lower limit of the theoretical carbon price; This indicates the upper limit of the theoretical carbon price; The theoretical carbon price is expressed by the following formula: , This represents the marginal emission reduction cost in period t. This represents the standardized emission gap value in period t. ,in, This represents the expected value of the standardized emissions gap. ,in, Weighting coefficients representing ideal emissions; This represents the standardized emission gap value for period t-1. Indicates total emissions; Indicates the total quota; Indicates ideal emissions; The weighting coefficient represents the marginal cost of emissions reduction; Weighting coefficients representing the standardized emissions gap; The weighting coefficient represents the expected value of the gap.

[0045] In this embodiment, the marginal emission reduction cost is the additional economic cost required to reduce carbon dioxide emissions by one unit based on a specific technology level and emission levels.

[0046] In this embodiment, the standardized emission gap expectation is the market's expectation of the relative difference between emissions and quotas. It is usually calculated based on a weighted average of historical gaps and hard adjustment parameters, and is used to guide carbon price formation.

[0047] The working principle and beneficial effects of this embodiment are as follows: Electricity and carbon data are read through a coupled model, and theoretical electricity price and theoretical carbon price are calculated respectively. The theoretical electricity price is generated by weighting the previous period's electricity price and the current period's ideal electricity price, and the theoretical carbon price is generated by weighting the previous period's carbon price, marginal emission reduction cost, and expected emission gap value. The theoretical value is compared with the preset upper and lower limits of fluctuation to determine the current period's electricity price and carbon price. The two together constitute the current core control signal, realizing the endogenous linkage and range stability of electricity price and carbon price. The signal generation process simultaneously considers historical inertia and current cost and emission pressure, providing a driving core that conforms to the real pricing logic for subsequent simulations.

[0048] Example 3: The method provided in this embodiment of the present disclosure uses the coupling model to determine the current electricity price initial demand forecast curve, initial production cost, and external environment corresponding to the current core control signal, and simulates the current electricity price initial demand forecast curve, initial production cost, and external environment respectively to obtain a first simulation result, including: The current core control signal is input into the coupling model, the initial demand forecast curve is determined based on the current electricity price in the current control signal, the initial production cost is calculated based on the current control signal, and the external environment is determined based on the current control signal; Using the initial demand forecast curve, initial production cost, and external environment as the initial state, the dynamics of power supply and demand are simulated based on the initial state to obtain dynamic simulation results; enterprise production decisions are simulated based on the initial state to obtain decision simulation results; and generator investment is simulated based on the initial state to obtain investment simulation results. The first simulation result is obtained by combining the dynamic simulation results, decision simulation results, and investment simulation results.

[0049] In this embodiment, the dynamics of power supply and demand are the continuous process of mutual adjustment and interaction between power demand and power supply over time to achieve real-time equilibrium during the simulation period.

[0050] In this embodiment, the specific formula corresponding to the electricity demand is: ,in, Represents the electricity demand in period t; This indicates the expected growth rate of energy demand; Indicates the number of months within the period; Indicates a reference electricity price; Indicates the reference carbon price; This represents the electricity price in period t-1; This represents the carbon price in period t-1; The elasticity coefficient representing the magnitude of changes in electricity prices; The elasticity coefficient representing the magnitude of carbon price changes; This represents the amount of electricity generated by renewable energy in period t. ,in, This indicates the effective power generation hours of renewable energy sources; This indicates the utilization rate of renewable energy generation. Indicates weather fluctuation factor; Let represent the power generation of the thermal power generator in period t. ,in, This represents the maximum power generation of the thermal power generator in period t. Let represent the theoretical power generation of the thermal power generator in period t. ,in, Indicates network loss rate; This represents the minimum power generation of the thermal power generator in period t. This represents the installed capacity of renewable energy in period t. ,in, This represents the new investment in renewable energy in period t-1; Indicates the depreciation rate; ,in, This indicates the effective generating hours of a thermal power generator. This indicates the utilization rate of the power generation capacity of thermal power generators; ,in, This represents the installed capacity of thermal power generators in period t. This represents the installed capacity of thermal power generators in period t-1.

[0051] In this embodiment, the specific formula corresponding to the power supply is: ,in, This represents the electricity supply in period t.

[0052] In this embodiment, the dynamic simulation results are quantitative results output after simulating the dynamic process of power supply and demand, mainly including operational data such as power generation dispatch plans, unit output, system load and real-time electricity prices for each time period.

[0053] In this embodiment, enterprise production decision-making refers to the optimization calculations and behavioral choices made by an enterprise in pursuit of specific goals, given initial production costs and product market conditions, regarding output, factor inputs, and technology selection.

[0054] In this embodiment, the decision simulation results are quantitative results output after simulating the enterprise's production decision-making process, mainly including data such as the output level of various industries, energy consumption, carbon emissions, and profit changes.

[0055] In this embodiment, generator investment is the process by which power generation companies or investors evaluate and make capital allocation decisions on new, expanded, or decommissioned projects using different power generation technologies based on their expectations of the environmental impact of future electricity prices, carbon prices, and hard adjustment parameters.

[0056] In this embodiment, the investment simulation results are quantitative results output after simulating generator investment behavior, mainly including data such as the new and retired installed capacity of various power generation technologies, investment flow and capital cost during the planning period.

[0057] In this embodiment, ,in, This represents the total revenue of the power generation sector in period t. This indicates the amount of reinvestment in renewable energy; Indicates the reinvestment ratio; This represents the fuel consumption for thermal power generation in period t. Indicates the fuel price coefficient; ,in, This represents the carbon emissions from thermal power generators in period t. Indicates the emission factor of thermal power generators; This represents the firm's production output in period t; Indicates electricity productivity; This represents the carbon emissions from the consumption side in period t. This indicates the intensity of emissions from the consumption side.

[0058] The working principle and beneficial effects of this embodiment are as follows: Using the current core control signal as input, the demand curve, production costs, and market environment are initialized. Starting from this point, the dynamics of power supply and demand, enterprise production decisions, and generator investment behavior are simulated in parallel. The results are then aggregated to form the first simulation result, achieving system-level transmission and concurrent simulation from the core control signal to the behavior of multiple entities, accurately depicting the dynamic response and overall equilibrium of the three major links of power, production, and investment under signal-driven conditions.

[0059] Example 4: The method provided in this embodiment of the present disclosure obtains hard adjustment parameters, inputs the hard adjustment parameters into the coupling model, performs a second simulation based on the first simulation result, and obtains a second simulation result, including: Analyze the first simulation results, obtain hard adjustment parameters based on the analysis results, and input the hard adjustment parameters into the coupling model; The first simulation result is used as the initial state for the second simulation. The second simulation is performed based on the hard adjustment parameters using the coupling model to obtain the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results. The second simulation result is obtained by combining the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results.

[0060] In this embodiment, the dynamic trajectory simulation results are the path data of the core regulatory signal output by the model over time under a hard adjustment parameter intervention scenario. Specifically, this includes the electricity price sequence and carbon price sequence at each time point throughout the simulation period.

[0061] In this embodiment, the analysis results are an evaluation conclusion of the first simulation results, specifically identifying the quantitative gap between the current status of the system and the target corresponding to the hard adjustment parameters and its core causes, such as the extent of carbon emission exceeding the standard, the degree of insufficient carbon price incentives, or the reasons for the lag in clean investment.

[0062] In this embodiment, the power sector operation simulation results are a set of data on the physical operation and techno-economic status of the power system output by the model under a scenario of hard parameter adjustment intervention. These mainly include indicators such as total power generation, the proportion of power generation from various sources, unit utilization hours, system carbon emissions, and power supply costs.

[0063] In this embodiment, the performance simulation results are a set of non-economic indicators output by the model simulation under a scenario of hard parameter adjustment intervention, used to measure the overall performance of the system. These mainly include measures reflecting technological, environmental, and social performance, such as energy conversion efficiency, carbon emission intensity, clean energy penetration rate, and system reliability.

[0064] The working principle and beneficial effects of this embodiment are as follows: Hard adjustment parameters are obtained by analyzing the first simulation results and input into the model. Starting from the first result state, a second simulation is run to obtain price trajectory, power operation, and efficiency results. These are then combined to form the second simulation result, enabling continuous dynamic evaluation of hard adjustment parameter intervention. This accurately quantifies the recursive impact of hard parameter adjustments on price paths, power system operation, and overall efficiency, supporting iterative optimization of the strategy.

[0065] Example 5: The method provided in this embodiment of the present disclosure determines the coupling situation of the electricity-enterprise-carbon market in the current simulation period based on the first simulation result and the second simulation result, including: If the hard adjustment parameter is 0, the coupled model outputs the first simulation result; If the hard adjustment parameter is not 0, the coupled model outputs a second simulation result; The core indicators of the electricity-enterprise-carbon market are extracted from the first and second simulation results. These core indicators include signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators. The coupling of the electricity-enterprise-carbon market is determined based on the aforementioned signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators.

[0066] In this embodiment, the core indicators are a representative set of metrics used to quantitatively evaluate the key states and behaviors of the electricity-enterprise-carbon coupling system. These indicators are extracted from simulation results and can systematically reflect the performance of dimensions such as market linkages, physical transformation, and agent response.

[0067] In this embodiment, the signal coordination index is a quantitative measure of the linkage between electricity price signals and carbon price signals. It mainly includes the correlation coefficient, transmission elasticity, and fluctuation synchronicity of the two types of signals, used to assess the tightness and efficiency of information transmission between markets.

[0068] In this embodiment, the material flow and conversion efficiency indicators are quantitative measures of energy and material flow and their conversion processes within the system. These mainly include current carbon intensity, energy conversion efficiency, and clean energy penetration rate, used to assess the physical conversion efficiency from energy consumption to economic output and emissions.

[0069] In this embodiment, the principal action and structural change indicators are quantitative measures of the behavioral responses of key decision-makers within the system and the evolution of the system's composition. These mainly include the proportion of clean technology in power generation investment, the intensity of corporate emission reduction actions, and the rate of change in energy consumption structure, used to assess the momentum of behavioral adjustments and system transformation driven by hard adjustment parameters.

[0070] The working principle and beneficial effects of this disclosure are as follows: By comparing the simulation results of benchmark and hard adjustment parameter scenarios, three core indicators—signal coordination, material flow efficiency, and principal action—are extracted. Based on the indicator difference analysis, the overall coupling status of the electricity-enterprise-carbon market is determined. Through quantitative comparison, the impact of hard adjustment parameters on the strength of inter-market correlation and coordination efficiency is objectively assessed, clarifying whether the coupling mechanism is strengthened, weakened, or reversed, thus providing a basis for precise regulation.

[0071] To implement the above embodiments, this disclosure also proposes an electricity-enterprise-carbon market coupling modeling device.

[0072] Figure 6 This is a schematic diagram of the structure of the electricity-enterprise-carbon market coupling modeling device provided in an embodiment of this disclosure. The device 200 can be implemented by software and / or hardware, and is generally integrated into an electronic device. For example... Figure 6 As shown, the device 200 includes: an acquisition module 201, a first simulation module 202, a second simulation module 203, and a determination module 204, wherein, The acquisition module 201 is used to acquire relevant data during the current simulation period and input the relevant data into a preset coupling model. The relevant data includes power data, enterprise data, and carbon data. The first simulation module 202 is used to analyze the relevant data based on the coupling model to determine the current core control signal, use the coupling model to determine the current electricity price initial demand forecast curve, initial production cost, and external environment corresponding to the current core control signal, and simulate the current electricity price initial demand forecast curve, initial production cost, and external environment respectively to obtain the first simulation result; The second simulation module 203 is used to acquire hard adjustment parameters, input the hard adjustment parameters into the coupling model, perform a second simulation based on the first simulation result, and obtain a second simulation result. The determination module 204 is used to determine the coupling status of the electricity-enterprise-carbon market in the current simulation period based on the first simulation result and the second simulation result.

[0073] The device provided in this embodiment of the disclosure, wherein the acquisition module 201 is specifically used for: The theoretical electricity price is determined by analyzing the relevant data based on the coupling model, and the current electricity price is determined based on the theoretical electricity price and the upper and lower limits of the fluctuation of the theoretical electricity price. The theoretical electricity price is obtained by weighting the electricity price in the previous simulation period in the electricity data with the ideal electricity price; The theoretical carbon price is determined by analyzing the carbon data in the relevant data based on the coupling model, and the current carbon price is determined based on the theoretical carbon price and the upper and lower limits of the fluctuation of the theoretical carbon price. The theoretical carbon price is obtained by weighting the carbon price of the previous simulation period, the marginal cost of emission reduction, and the expected value of the standardized emission gap in the carbon data. The current core control signal is determined based on the current carbon price and the current electricity price.

[0074] The device provided in this embodiment of the disclosure, wherein the first simulation module 202 is specifically used for: The current core control signal is input into the coupling model, and the new electricity price initial demand forecast curve, initial production cost, and external environment are determined based on the coupling model. Based on the demand forecast curve initialized by the new electricity price, the dynamics of electricity supply and demand are simulated to obtain dynamic simulation results; Based on the initial production cost, the enterprise's production decisions are simulated to obtain the decision simulation results; Based on the enterprise data in the current core control signal, generator investment is simulated to obtain investment simulation results; The first simulation result is obtained by combining the dynamic simulation results, decision simulation results, and investment simulation results.

[0075] The device provided in this embodiment of the disclosure, wherein the second simulation module 203 is specifically used for: Analyze the first simulation results, obtain hard adjustment parameters based on the analysis results, and input the hard adjustment parameters into the coupling model; The first simulation result is used as the initial state for the second simulation. The second simulation is performed based on the hard adjustment parameters using the coupling model to obtain the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results. The second simulation result is obtained by combining the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results.

[0076] The device provided in this embodiment of the disclosure, wherein the determining module 204 is specifically used for: If the hard adjustment parameter is 0, the coupled model outputs the first simulation result; If the hard adjustment parameter is not 0, the coupled model outputs a second simulation result; The core indicators of the electricity-enterprise-carbon market are extracted from the first and second simulation results. These core indicators include signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators. The coupling of the electricity-enterprise-carbon market is determined based on the aforementioned signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators.

[0077] The electricity-enterprise-carbon market coupling modeling device provided in this disclosure can execute the electricity-enterprise-carbon market coupling modeling method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0078] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program / instruction, which, when executed by a processor, implements the electricity-enterprise-carbon market coupling modeling method in the above embodiments.

[0079] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0080] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0081] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0082] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the aforementioned electricity-enterprise-carbon market coupling modeling method.

[0083] Electronic devices can be programmed with computer program code in one or more programming languages ​​or combinations thereof to perform the operations of this disclosure. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0085] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0086] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0087] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0088] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0089] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0090] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for coupled modeling of the electricity-enterprise-carbon market, characterized in that, include: Acquire relevant data for the current simulation period and input the relevant data into a preset coupling model. The relevant data includes power data, enterprise data, and carbon data. Based on the coupling model, the relevant data is analyzed to determine the current core control signal. The coupling model is used to determine the current electricity price initial demand forecast curve, initial production cost, and external environment corresponding to the current core control signal. The current electricity price initial demand forecast curve, initial production cost, and external environment are simulated respectively to obtain the first simulation result. Obtain the hard adjustment parameters, input the hard adjustment parameters into the coupling model, and perform a second simulation based on the first simulation result to obtain the second simulation result; The coupling of the electricity-enterprise-carbon market in the current simulation period is determined based on the first and second simulation results.

2. The method according to claim 1, characterized in that, Based on the coupling model, the relevant data is analyzed to determine the current core control signal, including: The theoretical electricity price is determined by analyzing the relevant data based on the coupling model, and the current electricity price is determined based on the theoretical electricity price and the upper and lower limits of the fluctuation of the theoretical electricity price. The theoretical electricity price is obtained by weighting the electricity price in the previous simulation period in the electricity data with the ideal electricity price; The theoretical carbon price is determined by analyzing the carbon data in the relevant data based on the coupling model, and the current carbon price is determined based on the theoretical carbon price and the upper and lower limits of the fluctuation of the theoretical carbon price. The theoretical carbon price is obtained by weighting the carbon price of the previous simulation period, the marginal cost of emission reduction, and the expected value of the standardized emission gap in the carbon data. The current core control signal is determined based on the current carbon price and the current electricity price.

3. The method according to claim 1, characterized in that, Using the coupling model, the initial demand forecast curve, initial production cost, and external environment corresponding to the current core control signal are determined. Simulations are then performed on the initial demand forecast curve, initial production cost, and external environment to obtain a first simulation result, including: The current core control signal is input into the coupling model, the initial demand forecast curve is determined based on the current electricity price in the current control signal, the initial production cost is calculated based on the current control signal, and the external environment is determined based on the current control signal; Using the initial demand forecast curve, initial production cost, and external environment as the initial state, the dynamics of power supply and demand are simulated based on the initial state to obtain dynamic simulation results; enterprise production decisions are simulated based on the initial state to obtain decision simulation results; and generator investment is simulated based on the initial state to obtain investment simulation results. The first simulation result is obtained by combining the dynamic simulation results, decision simulation results, and investment simulation results.

4. The method according to claim 1, characterized in that, Obtain hard adjustment parameters, input the hard adjustment parameters into the coupled model, perform a second simulation based on the first simulation result, and obtain the second simulation result, including: Analyze the first simulation results, obtain hard adjustment parameters based on the analysis results, and input the hard adjustment parameters into the coupling model; The first simulation result is used as the initial state for the second simulation. The second simulation is performed based on the hard adjustment parameters using the coupling model to obtain the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results. The second simulation result is obtained by combining the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results.

5. The method according to claim 1, characterized in that, Based on the first and second simulation results, the coupling of the electricity-enterprise-carbon market in the current simulation period is determined, including: If the hard adjustment parameter is 0, the coupled model outputs the first simulation result; If the hard adjustment parameter is not 0, the coupled model outputs a second simulation result; The core indicators of the electricity-enterprise-carbon market are extracted from the first and second simulation results. These core indicators include signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators. The coupling of the electricity-enterprise-carbon market is determined based on the aforementioned signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators.

6. A coupling modeling device for the electricity-enterprise-carbon market, the device comprising: The acquisition module is used to acquire relevant data during the current simulation period and input the relevant data into a preset coupling model. The relevant data includes power data, enterprise data, and carbon data. The first simulation module is used to analyze the relevant data based on the coupling model to determine the current core control signal, use the coupling model to determine the current electricity price initial demand forecast curve, initial production cost, and external environment corresponding to the current core control signal, and simulate the current electricity price initial demand forecast curve, initial production cost, and external environment respectively to obtain the first simulation result; The second simulation module is used to acquire hard adjustment parameters, input the hard adjustment parameters into the coupled model, perform a second simulation based on the first simulation result, and obtain a second simulation result. The determination module is used to determine the coupling status of the electricity-enterprise-carbon market in the current simulation period based on the first simulation result and the second simulation result.

7. The device according to claim 6, characterized in that, The acquisition module is specifically used for: The theoretical electricity price is determined by analyzing the relevant data based on the coupling model, and the current electricity price is determined based on the theoretical electricity price and the upper and lower limits of the fluctuation of the theoretical electricity price. The theoretical electricity price is obtained by weighting the electricity price in the previous simulation period in the electricity data with the ideal electricity price; The theoretical carbon price is determined by analyzing the carbon data in the relevant data based on the coupling model, and the current carbon price is determined based on the theoretical carbon price and the upper and lower limits of the fluctuation of the theoretical carbon price. The theoretical carbon price is obtained by weighting the carbon price of the previous simulation period, the marginal cost of emission reduction, and the expected value of the standardized emission gap in the carbon data. The current core control signal is determined based on the current carbon price and the current electricity price.

8. The device according to claim 6, characterized in that, The first simulation module is specifically used for: The current core control signal is input into the coupling model, and the new electricity price initial demand forecast curve, initial production cost, and external environment are determined based on the coupling model. Based on the demand forecast curve initialized by the new electricity price, the dynamics of electricity supply and demand are simulated to obtain dynamic simulation results; Based on the initial production cost, the enterprise's production decisions are simulated to obtain the decision simulation results; Based on the enterprise data in the current core control signal, generator investment is simulated to obtain investment simulation results; The first simulation result is obtained by combining the dynamic simulation results, decision simulation results, and investment simulation results.

9. The device according to claim 6, characterized in that, The second simulation module is specifically used for: Analyze the first simulation results, obtain hard adjustment parameters based on the analysis results, and input the hard adjustment parameters into the coupling model; The first simulation result is used as the initial state for the second simulation. The second simulation is performed based on the hard adjustment parameters using the coupling model to obtain the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results. The second simulation result is obtained by combining the dynamic trajectory simulation results of the current electricity price and the current carbon price, the power sector operation simulation results, and the efficiency simulation results.

10. The device according to claim 6, characterized in that, The determining module is specifically used for: If the hard adjustment parameter is 0, the coupled model outputs the first simulation result; If the hard adjustment parameter is not 0, the coupled model outputs a second simulation result; The core indicators of the electricity-enterprise-carbon market are extracted from the first and second simulation results. These core indicators include signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators. The coupling of the electricity-enterprise-carbon market is determined based on the aforementioned signal coordination indicators, material flow and conversion efficiency indicators, and principal action and structural change indicators.

11. A computer-readable storage medium, characterized in that, It stores a computer program / instruction thereon, which, when executed by a processor, implements the steps of the method described in any one of claims 1-5.

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