Industrial low-carbon demand response method considering electric quantity-carbon quota excitation cross coupling

Abstract

The invention discloses an industry low-carbon demand response method considering electric quantity-carbon quota excitation cross coupling, and relates to the technical field of demand response, and the method comprises the following steps: building an excitation cross elastic matrix of electric quantity and carbon quota, analyzing a driving mechanism of carbon quota to power demand transfer, and dynamically adjusting a demand elastic coefficient; constructing a price demand response model based on the dynamically adjusted electric quantity-electricity price elastic matrix; the method comprises the following steps of: constructing an active low-carbon demand response model based on'electricity price-carbon factor 'dual excitation by taking minimization of total cost and carbon emission of key control and emission industries as optimization targets and combining time-of-use electricity price and a dynamic carbon emission factor, and carrying out iterative solution on the model to obtain an energy consumption optimization curve of each key control and emission industry in one day. According to the invention, through a multi-level and dynamically-adjusted incentive mechanism, the energy consumption management level of the key emission control industry under a low-carbon target is improved, and the dynamic balance of economy and environmental benefits is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of demand response, and more specifically, to an industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives. Background Art

[0002] Traditional demand response mechanisms mainly guide users to adjust their electricity consumption behaviors through electricity price signals or incentives to achieve safe and economical operation of the power system. However, in this mechanism, electricity prices are usually regarded as the only means of regulation, thus ignoring the carbon emissions caused by energy consumption. With the proposal of the "dual carbon" goals, the effectiveness of demand response mechanisms that rely solely on electricity price signals in promoting low-carbon transformation is limited. Existing demand response strategies lack sufficient consideration of the carbon market and fail to form an effective linkage between prices and carbon emissions, making it difficult to balance carbon emission reduction and economic benefits while achieving stable operation of the power system.

[0003] In the context of low-carbon economy, the carbon quota system has been widely used as a means to guide carbon emission reduction through market mechanisms. The allocation of carbon quotas in different industries will have a significant impact on electricity behavior, but the carbon quota element has not been effectively introduced into the traditional demand response model. The electricity demand of power users under the carbon quota restriction has become more complicated, and relying solely on electricity price regulation can no longer fully reflect the actual energy demand and carbon emission pressure of industry users. Therefore, it is urgent to construct an incentive cross-elasticity matrix of carbon quota and electricity volume, quantify the driving effect of carbon quota changes on electricity demand transfer, and dynamically adjust the elasticity coefficient of traditional demand response. In addition, the existing demand response mechanism has also failed to fully consider the dynamic characteristics of carbon emissions. The carbon emission intensity of the power system fluctuates with the change of power supply structure in different time periods, and the traditional model cannot reflect the dynamic relationship between user electricity behavior and carbon emissions. Therefore, introducing dynamic carbon emission factors into the demand response model can better capture the changes in carbon emissions on the power supply side, and then promote more accurate low-carbon energy behavior on the demand side.

[0004] Based on the above background, an incentive cross-coupling mechanism combining carbon quota and electricity is proposed, which can effectively analyze the impact of carbon quota changes on electricity demand transfer, and dynamically adjust the elasticity coefficient in the price demand response model accordingly. This mechanism not only overcomes the limitation of traditional demand response that does not consider carbon emissions enough, but also introduces dynamic carbon emission factors to construct a low-carbon demand response model with dual incentives of "electricity price-carbon factor". This innovative mechanism can not only effectively guide industry users to achieve carbon emission reduction in electricity consumption behavior, but also promote the coordinated optimization of the economic efficiency and environmental benefits of the power system. .

[0005] The above disclosed technical solutions have at least the following technical problems: the existing demand response strategy lacks sufficient consideration of the carbon market and fails to form an effective linkage between price and carbon emissions, which makes it difficult to balance carbon emission reduction and economic benefits while achieving stable operation of the power system. In addition, the existing demand response mechanism also fails to fully consider the dynamic characteristics of carbon emissions. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an industry low-carbon demand response method that considers the cross-coupling of electricity quantity-carbon quota incentives, and solves the problems raised in the above-mentioned background technology through an incentive cross-coupling mechanism that combines carbon quota and electricity quantity.

[0007] To achieve the above object, the present invention provides the following technical solutions: The industry low-carbon demand response method considering the cross-coupling of electricity quantity-carbon quota incentives includes the following steps: establishing an incentive cross-elasticity matrix between electricity quantity and carbon quota in key emission-controlled industries, analyzing the driving mechanism of carbon quota on electricity demand transfer, and dynamically adjusting the demand elasticity coefficient; constructing a price demand response model based on the dynamically adjusted electricity quantity-electricity price elasticity matrix to quantify the impact of electricity transfer on the comprehensive cost of key emission-controlled industries; taking the minimization of total cost and carbon emissions in key emission-controlled industries as the optimization goal, combining time-of-use electricity prices and dynamic carbon emission factors, and constructing an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor"; iteratively solving the active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor" to obtain the energy consumption optimization curve of each key emission-controlled industry for one day.

[0008] In a preferred embodiment, the incentive cross-elasticity matrix of electricity consumption and carbon quota in key emission-controlled industries is established to analyze the driving mechanism of carbon quota on electricity demand transfer, specifically: obtaining carbon quota and electricity consumption data of different key emission-controlled industries in different time periods; calculating the self-elasticity coefficient and cross-elasticity coefficient respectively based on the calculation formula of the elasticity coefficient; filling the calculated self-elasticity coefficient and cross-elasticity coefficient into the matrix to form an incentive cross-elasticity matrix; constructing a heat map of the influence relationship between electricity consumption and carbon quota based on the carbon quota and electricity consumption data and dividing its values ​​into four intervals, namely strong correlation, medium correlation, weak correlation and almost irrelevant; and evaluating the impact of different carbon quota allocation schemes on electricity demand transfer based on the incentive cross-elasticity matrix.

[0009] In a preferred embodiment, the dynamic adjustment of the demand elasticity coefficient is specifically as follows: through the electricity-carbon quota incentive cross matrix, the impact of the carbon quota on the industry's electricity demand is identified; based on the impact of the carbon quota on the industry's electricity demand, the demand elasticity coefficient is adjusted in real time. When the industry's carbon emission pressure increases, the price elasticity coefficient is increased to enhance the industry's sensitivity to electricity prices, and vice versa, the elasticity coefficient is reduced, thereby dynamically adjusting the balance between the industry's electricity costs and carbon emissions.

[0010] In a preferred embodiment, the price demand response model is constructed based on the dynamically adjusted electricity quantity-electricity price elasticity matrix to quantify the impact of electricity transfer on the comprehensive cost of key control industries, specifically: collect real-time load data in the microgrid system and external network time-of-use electricity price data; perform feature extraction and preprocessing on the real-time load data and external network time-of-use electricity price data, and the preprocessing includes data cleaning, missing value processing, and data smoothing; calculate the electricity price elasticity coefficient based on the preprocessed real-time load data and external network time-of-use electricity price data and construct an electricity quantity-electricity price elasticity matrix; construct a price demand response model based on the electricity quantity-electricity price elasticity matrix; collect and organize electricity price data for each time period, including peak, normal and valley electricity prices; calculate the electricity cost saved due to electricity transfer based on the scale of electricity transfer and time-of-use electricity prices; use production efficiency indicators to evaluate the impact of electricity transfer on production efficiency; and calculate the cost of increased or reduced production efficiency due to electricity transfer by comparing the changes in production efficiency before and after electricity transfer.

[0011] In a preferred embodiment, the minimization of total cost and carbon emissions in key emission-controlled industries is taken as the optimization goal, and time-of-use electricity prices and dynamic carbon emission factors are combined to construct an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor", specifically: collecting electricity consumption data, carbon emission data, and time-of-use electricity price data in key emission-controlled industries; constructing an objective function with the minimization of total cost and carbon emissions in key emission-controlled industries as the optimization goal; setting carbon quota constraints, daily total load constraints, transferable load constraints, and reducible load constraints for the objective function; when the carbon emissions of an industry exceed a preset threshold, a penalty price is added to the time-of-use electricity price of the price demand response, and the penalty price is calculated by the dynamic average carbon emission factor over a certain period of time; according to the objective function, the time-of-use electricity price and the dynamic carbon emission factor are combined to obtain an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor".

[0012] In a preferred embodiment, the active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor" has a specific mechanism: guiding key emission control industries to optimize load curves through time-of-use electricity prices, and using dynamic carbon emission factors as penalty factors to form an electricity price mechanism with carbon attributes, so that users can effectively perceive the carbon emission levels generated by their own electricity consumption behavior, thereby driving users to respond to low-carbon demand through electricity prices with carbon attributes, thereby achieving electricity optimization and carbon emission control.

[0013] The technical effects and advantages of the industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives of the present invention are as follows: 1. The present invention optimizes the energy consumption behavior of industries and effectively reduces greenhouse gas emissions in key emission-controlled industries through an active low-carbon demand response mechanism with dual incentives of "electricity price-carbon factor", thereby promoting the realization of the country's low-carbon goals.

[0014] 2. This invention establishes an incentive cross elasticity matrix between electricity and carbon quotas, clarifies the impact of carbon quotas on electricity demand transfer, and then dynamically adjusts the price elasticity coefficient, so as to more scientifically evaluate demand response capabilities. It helps industry users optimize energy costs, improve resource utilization efficiency, and ultimately improve the overall economic benefits of the industry, reduce operating costs, and enhance market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the structure of the industry low-carbon demand response method considering the cross-coupling of electricity-carbon quota incentives in the present invention.

[0016] Figure 2 Diagram of the active low-carbon demand response mechanism based on the dual incentives of "electricity price-carbon factor".

[0017] Figure 3 The solution process of the active low-carbon demand response model; Figure 4 These are the load curves for three types of key emission-controlled industries.

[0018] Figure 5 Before and after load curves for scenario 2 response.

[0019] Figure 6 The load curve before and after the response for scenario 3. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1, Figure 1 The present invention provides an industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives, comprising the following steps: S1. Establish an incentive cross-elasticity matrix between electricity consumption and carbon quotas in key emission-controlled industries, analyze the driving mechanism of carbon quotas on electricity demand transfer, and dynamically adjust the demand elasticity coefficient; S2, based on the dynamically adjusted electricity quantity-price elasticity matrix, build a price demand response model to quantify the impact of power transfer on the comprehensive cost of key emission control industries; S3, with the minimization of total cost and carbon emissions in key emission-controlled industries as optimization goals, combines time-of-use electricity prices with dynamic carbon emission factors, and constructs an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor" to optimize industry energy consumption behavior and promote the dual reduction of electricity demand and carbon emissions.

[0022] For the government, this project, by implementing a low-carbon demand response method that takes electricity demand shifts into consideration, will help promote proactive emission reductions in key emission-controlled industries, enhance their enthusiasm for market competition, and provide policy support for achieving energy conservation, emission reduction, and sustainable development. For power companies, by dynamically adjusting the electricity price elasticity coefficient, the project can optimize electricity demand management, ensure the safe and reliable operation of the power grid, and improve resource allocation efficiency. For the electricity market, this project provides an innovative demand response mechanism that aims to improve the economic benefits of the industry and promote the healthy development of the market. For electricity users, the project allows electricity consumption patterns to be adjusted according to demand response, thereby improving energy efficiency and reducing electricity costs. For the carbon market, by optimizing electricity demand and carbon emission behavior, this project can promote the development of the carbon market, promote carbon emission reduction, and contribute to achieving carbon neutrality goals. In this context, the present invention proposes a method for low-carbon demand response in key emission-controlled industries that takes into account the cross-coupling of electricity-carbon quota incentives, improves the energy management level of key emission-controlled industries under low-carbon goals, and achieves a balance between economic and environmental benefits. Figure 1 As shown in the figure, first, an incentive cross-elasticity matrix of electricity and carbon quotas is established, the driving mechanism of carbon quotas on electricity demand transfer is analyzed, and the electricity price elasticity coefficient is dynamically adjusted based on the differences in carbon quota changes among industries. Secondly, based on the driving effect of carbon quotas on electricity transfer, combined with the dynamically adjusted electricity-electricity price elasticity matrix, a price demand response model is constructed to quantify the impact of electricity transfer on the comprehensive cost of the industry. Finally, with the minimization of total cost and carbon emissions in key emission-controlled industries as the optimization goal, combined with time-of-use electricity prices and dynamic carbon emission factors, an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor" is constructed to optimize industry energy consumption behavior and promote the dual reduction of electricity demand and carbon emissions.

[0023] S1. Establish an incentive cross-elasticity matrix between electricity consumption and carbon quotas in key emission-controlled industries, analyze the driving mechanism of carbon quotas on electricity demand transfer, and dynamically adjust the demand elasticity coefficient; The above mentioned establishment of an incentive cross elasticity matrix between electricity consumption and carbon quotas in key emission-controlled industries and analysis of the driving mechanism of carbon quotas on electricity demand transfer are as follows: Obtain carbon quota and electricity consumption data for different key emission-controlled industries in different time periods; Based on the calculation formula of elasticity coefficient, the self-elasticity coefficient (the degree to which the change in electricity quantity responds to the change in its own price) and the cross-elasticity coefficient (the degree to which the change in electricity quantity responds to the change in other time periods or carbon quotas) are calculated respectively; Fill the calculated elastic coefficients into the matrix to form an excitation cross elastic matrix; The heat map of the relationship between electricity consumption and carbon quotas is constructed using the carbon quota and electricity consumption data, and its values ​​are divided into four intervals: strong correlation, medium correlation, weak correlation, and almost irrelevant. The impact of different carbon quota allocation schemes on electricity demand shift is evaluated based on the incentive cross elasticity matrix.

[0024] The larger the value in the heat map, the greater the growth rate of electricity consumption is than the growth rate of carbon quotas, that is, the smaller the impact of the increase in carbon quotas on electricity consumption. The smaller the value in the heat map, the smaller the growth rate of electricity consumption is than the growth rate of carbon quotas, that is, the greater the impact of the increase in carbon quotas on electricity consumption. A negative value in the heat map indicates that an increase in carbon quotas leads to a decrease in electricity consumption in a certain industry, or a decrease in carbon quotas leads to an increase in electricity consumption in a certain industry, which is manifested as an inverse impact of carbon quotas on electricity consumption.

[0025] The dynamic adjustment of demand elasticity coefficient is specifically: Through the electricity-carbon quota incentive cross matrix, the impact of carbon quotas on industry electricity demand is identified, and the demand elasticity coefficient is adjusted in real time based on this result. Specifically, as the carbon quota changes, when the industry's carbon emission pressure increases, the price elasticity coefficient is increased to enhance the industry's sensitivity to electricity prices, and vice versa, the elasticity coefficient is reduced, thereby dynamically adjusting the balance between the industry's electricity costs and carbon emissions, ensuring the coordinated optimization of electricity demand response and carbon emission targets among key emission-controlled industries.

[0026] The incentive cross elasticity matrix of electricity and carbon quota includes: (1) in, represents the cross elasticity coefficient, Q represents electricity consumption, C represents carbon quota, Represents the change of electricity consumption and carbon quota. The electricity consumption is generally affected by the carbon quota in different periods, and its expression is shown in formula (2): (2) In the formula, electricity consumption is generally affected by the adjustment and allocation of carbon quotas. and represents the carbon quota and electricity consumption in the i-th and j-th time periods, and n represents the total number of time periods in a given time period, which is generally related to the divided time periods. It represents the cross elasticity coefficient of carbon quota adjustment to the electricity consumption of a certain industry in different time periods, that is, the response of carbon quota to a certain electricity consumption in different time periods. Divide a day into n time periods and establish an n-dimensional elasticity matrix E as shown in formula (3): (3) Substituting into formula (1), the relationship between the carbon quota change rate and the electricity consumption change rate is shown in formula (4): (4) S2, based on the dynamically adjusted electricity quantity-price elasticity matrix, build a price demand response model to quantify the impact of power transfer on the comprehensive cost of key emission control industries; Based on the dynamically adjusted electricity quantity-price elasticity matrix, a price demand response model is constructed to quantify the impact of power transfer on the comprehensive cost of key emission control industries, specifically: Collect real-time load data in the microgrid system and external network time-of-use electricity price data; Feature extraction and preprocessing of real-time load data and external network time-of-use electricity price data, including data cleaning, missing value processing, and data smoothing; Calculate the electricity price elasticity coefficient based on the pre-processed real-time load data and external network time-of-use electricity price data and construct the electricity price elasticity matrix; Construct a price demand response model based on the electricity quantity-price elasticity matrix; Collect and organize electricity price data for each time period, including peak, normal and off-peak hours; Calculate the electricity cost saved due to power transfer based on the scale of power transfer and time-of-use electricity price Use production efficiency indicators (e.g., energy consumption per unit of output, equipment utilization, etc.) to assess the impact of power shifts on production efficiency; By comparing the changes in production efficiency before and after the power transfer, the cost of the increase or decrease in production efficiency due to the power transfer is calculated.

[0027] The specific expression of the electricity price elasticity coefficient is:

[0028] In the formula, is the electricity price elasticity coefficient, and are the initial charge and its change respectively; and are the initial electricity price and its change respectively.

[0029] Divide a day into n time periods, and the expression of the n-dimensional electricity price elasticity matrix F is:

[0030] Self-elastic coefficient Indicates the impact of the change in electricity price in period i on the demand for electricity in period i; cross elasticity coefficient It represents the impact of the change in electricity price in time period j on the demand for electricity in time period i.

[0031] According to the electricity price elasticity matrix F, the expression of electricity consumption at each finger after response can be obtained as follows:

[0032] In the formula, , Respectively represent the power consumption before and after the response; represents the power consumption in the period t before the response; represents the traditional fixed electricity price for period t before the response.

[0033] S3, taking the minimization of total cost and carbon emissions of key emission-controlled industries as the optimization goal, combining time-of-use electricity prices with dynamic carbon emission factors, and constructing an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor"; The optimization goal is to minimize the total cost and carbon emissions of key emission-controlled industries, combine time-of-use electricity prices with dynamic carbon emission factors, and build an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor". Specifically: Collect power consumption data, carbon emission data, and time-of-use electricity price data for key emission-controlled industries; Taking the minimization of total cost and carbon emissions of key emission-controlled industries as the optimization goal, the objective function is constructed; Set carbon quota constraints, daily total load constraints, transferable load constraints, and reducible load constraints for the objective function; When the carbon emissions of an industry exceed a preset threshold, a penalty price is added to the time-of-use electricity price based on the price demand response. The penalty price is calculated based on the dynamic average carbon emission factor over a certain period of time. According to the objective function, the time-of-use electricity price and the dynamic carbon emission factor are combined to obtain an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor".

[0034] Active low-carbon demand response drives the industry to change its electricity consumption mode and transfer some flexible loads through interests. Therefore, the industry demand response objective function is to minimize the comprehensive cost and carbon emissions. While ensuring the reduction of emission reduction costs, the industry's carbon emissions are minimized. The active low-carbon demand response model is:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] In the formula, for the cost of carbon emissions; Benefits of shifting loads for users; Revenue from load reduction for users; The cost of electricity after low-carbon demand response; Compensation factor for shifting loads for industrial users; is the load transferred in at time t; is the load transferred out at time t; Compensation factor for load reduction for industrial users; Reduce the load for industry users at time t; is the carbon emissions before low-carbon demand response; is the time-of-use electricity price at time t before low-carbon demand response; is the carbon emission after low-carbon demand response; c is the average carbon emission factor in the region; Allocate a quota for carbon emissions per unit of electricity; is the carbon price; Carbon emissions after low-carbon demand response; Free carbon allowances for industry; is the electricity price at time t after low-carbon demand response; is the electricity load at time t after low-carbon demand response.

[0042]

[0043]

[0044]

[0045] In the formula, , are the maximum values ​​of the load that can be transferred and the load that can be reduced at time t respectively; , They are the loads that can be reduced and transferred after low-carbon demand response; , They are the price demand elasticity matrix of curtailable load and the price demand elasticity matrix of transferable load respectively; , They are the percentage of load that can be removed and the percentage of load that can be added, respectively.

[0046] The active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor" is specifically: Through time-of-use electricity prices, key emission-controlled industries are guided to optimize load curves, and dynamic carbon emission factors are used as penalty factors to form an electricity price mechanism with carbon attributes, so that users can effectively perceive the carbon emission levels generated by their own electricity usage behavior, thereby driving users to respond to low-carbon demand through electricity prices with carbon attributes, thereby achieving electricity optimization and carbon emission control.

[0047] Low-carbon demand response must meet multiple constraints, including carbon quota constraints, daily total load constraints, transferable load constraints, and reducible load constraints.

[0048] When the industry's carbon emissions exceed a certain threshold, the industry will add a penalty price on top of the time-of-use electricity price that responds to price demand, that is, an electricity price with carbon attributes.

[0049]

[0050] In the formula, is the carbon emissions of the industry in period t; Carbon emissions thresholds set for implementation.

[0051] In order to better indicate whether the industry's carbon emissions exceed the set threshold, a state variable is used for unified description:

[0052] is the state variable of the industry.

[0053] In order to ensure the fairness of user response and facilitate unified management, the non-clean energy supply ratio of a certain regional system is used to calculate the 24-hour dynamic average carbon emission factor with a time scale of 1 hour as a unified additional penalty factor for electricity prices with carbon attributes. The specific calculation of the dynamic carbon emission factor on the user side is as follows:

[0054] In the formula, is the dynamic carbon emission factor; is the carbon emission intensity of thermal power unit i; is the net output of the i-th thermal power unit in period t; , are the on-grid power of wind power and photovoltaic power in period t respectively.

[0055]

[0056] In the formula, is the dynamic carbon emission factor after dimensionless processing; is the minimum value of the dynamic carbon emission factor; is the maximum value of the dynamic carbon emission factor.

[0057] Therefore, the electricity price with carbon attributes under low-carbon demand response can be calculated. The calculation principle is as follows:

[0058] In the formula, The electricity price for each period after low-carbon demand response; It is the electricity price for each period after price demand response.

[0059] This section first analyzes the time-of-use electricity price and the electricity price model with carbon attributes to obtain the load curve after low-carbon demand response; then, carbon emissions and costs are considered, and the comprehensive cost minimization and carbon emission reduction minimization are taken as components of the objective function; finally, under the conditions of meeting the carbon quota constraints, the total load constraints for the whole day, and the industry transfer load constraints, the CPLEX solver is called based on the MATLAB platform to solve. The solution process is as follows: Figure 3 shown.

[0060] S4, iteratively solves the active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor" to obtain the energy consumption optimization curve of each key emission control industry in one day.

[0061] Embodiment 2, the following is a best embodiment of the method of the present invention.

[0062] (1) Model parameters The typical daily load curve of a key emission control industry in a certain region in September 2022 is selected for analysis, with 24 hours as an operation cycle and a unit operation time of 1 hour. Figure 4 shown.

[0063] In order to verify the low-carbon economic characteristics of the proposed model and the carbon reduction benefits of the industry, this section compares and analyzes the following three scenarios. Scenario 3 is the low-carbon demand response scenario proposed in this paper, and the rest are conventional scenarios. The information of each scenario is shown in Table 1.

[0064] Table 1 Information of each scene

[0065] (2) Results analysis The benefits, costs and actual carbon emissions of each scenario are shown in Table 2.

[0066] Table 2 Cost and carbon emissions of each scenario

[0067] As shown in Table 2, compared with scenario 1, the costs and carbon emissions of the three industries in scenario 2 and scenario 3 have been significantly reduced. For example, in the industry, the comprehensive costs of scenario 2 and scenario 3 have been reduced by 38.246 million yuan and 31.976 million yuan respectively. This is because scenario 2 and scenario 3 have initial carbon emission quotas, which can offset part of the carbon cost, and scenario 2 and scenario 3 can get certain compensation for shifting and reducing loads when implementing demand response. The total operating cost of scenario 3 is higher than that of scenario 2, because scenario 3 takes into account the emission reduction cost. Compared with scenario 1, carbon emissions in scenario 2 have not been reduced. This is because users cannot perceive carbon emission information in each period and will only pursue more economical electricity purchase methods, and cannot respond to carbon reduction. Among the three industries, the industrial sector has the most significant response to the low-carbon demand response mechanism proposed in this section, because it has the greatest effect in reducing comprehensive costs and carbon emissions. The main reason is that industry is easier to adjust production plans and process flows and flexibly respond to electricity price signals. In contrast, policy incentives and support are relatively weak in the construction and transportation industries, and the scale effect is not as significant as that in the industrial sector.

[0068] Combine Table 2 with Figure 5 It can be seen that the cost of the three industries in scenario 2 is lower than that in scenario 3. This is because the price demand response aims to minimize the comprehensive cost and ignores the environmental benefits. The electricity price is higher during the 10:00-12:00 and 20:00-22:00 periods. Therefore, after price demand response, the load is reduced during the high electricity price period, and the load is appropriately increased during the low electricity price period.

[0069] Scenario 3 is a low-carbon demand response based on Scenario 2. Figure 6 (a) It can be seen that the carbon emissions in scenario 3 during the period of 9:00-17:00 are too high, so low-carbon demand response is carried out to promote carbon reduction in the industry. The industry in scenario 3 uses the dynamic carbon emission factor as a penalty electricity price to obtain a time-of-use electricity price with carbon attributes. This electricity price allows industry users to perceive the size of carbon emissions in each period and actively change their electricity consumption behavior, thereby reducing carbon emissions. Compared with the peak-flat-valley distribution of the original load, the load of the three types of industry users in this area is reduced during peak hours and increased during valley hours, achieving the goal of "peak shaving and valley filling" of the daily load curve. The industry load decreases during periods with high electricity prices, indicating that by implementing a time-of-use electricity price with carbon attributes, the price difference between peak and valley periods is effectively converted into the transfer amount of peak and valley load, proving that price incentives are an effective means of demand-side response.

[0070] Effect evaluation: Table 3 Evaluation indicators of load curves for key emission-controlled industries in this region

[0071] Table 3 shows the load changes in the industrial, construction and transportation sectors under the two mechanisms of price demand response and low-carbon demand response, and specifically analyzes the impact of daily maximum load, daily minimum load and peak-to-valley difference. From the data, price demand response has a significant effect on the peak load of each industry. The daily maximum load of the industrial, construction and transportation industries increased by 10.69%, 11.24% and 11.63% respectively. This increase reflects that the price mechanism stimulates users to increase electricity consumption during peak hours. Although higher electricity prices may be used to guide users to use electricity during off-peak hours, the peak load suppression effect is limited, resulting in an increase in the overall load peak. In addition, price demand response expands the peak-to-valley difference of the load. The peak-to-valley difference of the industrial and transportation industries increased by 42.42% and 20.46% respectively, indicating that the load is further concentrated during peak hours, which may aggravate the pressure and imbalance of the power grid, resulting in greater pressure on the power grid's carrying capacity.

[0072] In contrast, low-carbon demand response demonstrates a more effective load peak-shaving and valley-filling capability. Data show that under low-carbon demand response, the daily maximum loads of the industrial, construction and transportation sectors decreased by 2.31%, 2.05% and 1.70% respectively, reflecting its load reduction effect during peak hours. At the same time, the low-carbon response mechanism significantly reduced the peak-to-valley difference, with the peak-to-valley difference in the industrial and transportation sectors reduced by 14.13% and 13.66% respectively, indicating that the mechanism not only reduces peak loads, but also promotes load recovery during valley hours, optimizes the load distribution of the power grid, and enhances the carrying capacity and operating efficiency of the large power grid. This load adjustment helps reduce the pressure on the power grid during peak hours, improves the reliability and stability of the power system, and achieves better results in energy conservation and emission reduction.

[0073] In general, price demand response mainly relies on economic means to adjust user behavior, but its effect on suppressing peak load is relatively limited. Low-carbon demand response effectively balances the load distribution of the power system through active load management and carbon management strategies, further improving the grid's carrying and regulation capabilities under load fluctuations.

[0074] The main conclusions are as follows: 1) From the perspective of power grid operation: The low-carbon demand response mechanism proposed in this study can accurately capture the dynamic changes of carbon emissions by introducing dynamic carbon emission factors. This mechanism effectively reduces the peak load of the power grid and fills the valley, achieving the "peak shaving and valley filling" effect of the load, alleviating the power supply pressure during peak hours and improving the carrying capacity of the power grid. At the same time, the effect of reducing the peak-valley difference of the load is significant, especially in the application of the industrial and transportation industries, the peak-valley difference is reduced by 14.13% and 13.66%, indicating that low-carbon response plays an important role in optimizing the operation of the power grid. This not only improves the stability of the power grid, but also reduces the comprehensive cost and additional electricity demand caused by load fluctuations in the industry. 2) From the perspective of industry users: The low-carbon demand response mechanism guides key emission-controlled industry users to actively participate in demand-side management through dynamic electricity prices. In particular, after considering the dynamic carbon emission factors, the setting of electricity prices can more accurately reflect the actual situation of carbon emissions, prompting users to reduce electricity demand during high-carbon emission periods and transfer production activities to low-carbon emission periods. For industry users, this mechanism effectively guides reasonable peak-shifting production, helping companies to significantly reduce carbon emissions while reducing electricity costs. The daily maximum load of the industrial and construction industries decreased by 2.31% and 2.05% respectively, achieving optimization of electricity costs and control of carbon emissions while maintaining production capacity. In addition, this mechanism also promotes intelligent electricity management, improves electricity efficiency and production flexibility, and enables companies to plan production in a more scientific, orderly and economical manner.

[0075] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0076] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0077] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0078] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0079] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0080] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives, characterized in that: The following steps are involved: Establish an incentive cross-elasticity matrix between electricity and carbon quotas in key emission-controlled industries, analyze the driving mechanism of carbon quotas on electricity demand transfer, and dynamically adjust the demand elasticity coefficient; Based on the dynamically adjusted electricity volume-price elasticity matrix, a price demand response model is constructed to quantify the impact of power transfer on the comprehensive cost of key emission control industries; Taking the minimization of total cost and carbon emissions of key emission-controlled industries as the optimization goal, combining time-of-use electricity prices with dynamic carbon emission factors, an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor" is constructed; The active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor" is iteratively solved to obtain the energy consumption optimization curve of each key emission control industry in one day.

2. The industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives according to claim 1 is characterized in that: The above mentioned establishment of an incentive cross elasticity matrix between electricity consumption and carbon quotas in key emission-controlled industries and analysis of the driving mechanism of carbon quotas on electricity demand transfer are as follows: Obtain carbon quota and electricity consumption data for different key emission-controlled industries in different time periods; The self-elastic coefficient and the cross-elastic coefficient are calculated based on the calculation formula of the elastic coefficient; Fill the calculated self-elastic coefficient and cross-elastic coefficient into the matrix to form an excitation cross-elastic matrix; The heat map of the relationship between electricity consumption and carbon quotas is constructed using the carbon quota and electricity consumption data, and its values ​​are divided into four intervals: strong correlation, medium correlation, weak correlation, and almost irrelevant. The impact of different carbon quota allocation schemes on electricity demand shift is evaluated based on the incentive cross elasticity matrix.

3. The industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives according to claim 2 is characterized in that: The dynamic adjustment of demand elasticity coefficient is specifically: Through the electricity volume-carbon quota incentive cross matrix, the impact of carbon quotas on the industry's electricity demand is identified; based on the impact of carbon quotas on the industry's electricity demand, the demand elasticity coefficient is adjusted in real time. When the industry's carbon emission pressure increases, the price elasticity coefficient is increased to enhance the industry's sensitivity to electricity prices, and vice versa, the elasticity coefficient is reduced, thereby dynamically adjusting the balance between the industry's electricity costs and carbon emissions.

4. The industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives according to claim 3 is characterized in that: Based on the dynamically adjusted electricity quantity-price elasticity matrix, a price demand response model is constructed to quantify the impact of power transfer on the comprehensive cost of key emission control industries, specifically: Collect real-time load data in the microgrid system and external network time-of-use electricity price data; Feature extraction and preprocessing of real-time load data and external network time-of-use electricity price data, including data cleaning, missing value processing, and data smoothing; Calculate the electricity price elasticity coefficient based on the pre-processed real-time load data and external network time-of-use electricity price data and construct the electricity price elasticity matrix; Construct a price demand response model based on the electricity quantity-price elasticity matrix; Collect and organize electricity price data for each time period, including peak, normal and off-peak hours; Calculate the electricity cost saved due to power transfer based on the scale of power transfer and time-of-use electricity price; Use production efficiency indicators to assess the impact of power shifts on production efficiency; By comparing the changes in production efficiency before and after the power transfer, the cost of the increase or decrease in production efficiency due to the power transfer is calculated.

5. The industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives according to claim 4 is characterized in that: The optimization goal is to minimize the total cost and carbon emissions of key emission-controlled industries, combine time-of-use electricity prices with dynamic carbon emission factors, and build an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor". Specifically: Collect power consumption data, carbon emission data, and time-of-use electricity price data for key emission-controlled industries; Taking the minimization of total cost and carbon emissions of key emission-controlled industries as the optimization goal, the objective function is constructed; Set carbon quota constraints, daily total load constraints, transferable load constraints, and reducible load constraints for the objective function; When the carbon emissions of an industry exceed a preset threshold, a penalty price is added to the time-of-use electricity price based on the price demand response. The penalty price is calculated based on the dynamic average carbon emission factor over a certain period of time. According to the objective function, the time-of-use electricity price and the dynamic carbon emission factor are combined to obtain an active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor".

6. The industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives according to claim 5 is characterized in that: The active low-carbon demand response model based on the dual incentives of "electricity price-carbon factor" has the following specific mechanisms: Through time-of-use electricity prices, key emission-controlled industries are guided to optimize load curves, and dynamic carbon emission factors are used as penalty factors to form an electricity price mechanism with carbon attributes, so that users can effectively perceive the carbon emission levels generated by their own electricity usage behavior, thereby driving users to respond to low-carbon demand through electricity prices with carbon attributes, thereby achieving electricity optimization and carbon emission control.

7. The industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives according to claim 6 is characterized in that: The incentive cross elasticity matrix of electricity and carbon quota includes: Electricity consumption is generally affected by carbon quotas in different periods, specifically: in, represents the cross elasticity coefficient, Q represents electricity consumption, C represents carbon quota, represents the change in electricity consumption and carbon quota, and represents the carbon quota and electricity consumption in the i-th and j-th time periods, n represents the total number of time periods in a given time period, It represents the cross elasticity coefficient of carbon quota adjustment to industry electricity consumption in different time periods, that is, the response of carbon quota to a certain electricity consumption in different time periods; Divide a day into n time periods and establish an n-dimensional elasticity matrix E, specifically: The relationship between the carbon quota change rate and the electricity consumption change rate is as follows: 。 8. The industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives according to claim 7 is characterized in that: The price demand response model is specifically: The specific expression of the electricity price elasticity coefficient is: Divide a day into n time periods, and the expression of the n-dimensional electricity price elasticity matrix F is: According to the electricity price elasticity matrix F, the expression of electricity consumption at each finger after response can be obtained as follows: in, is the electricity price elasticity coefficient, and are the initial charge and its change respectively; and are the initial electricity price and its change; the self-elasticity coefficient Indicates the impact of the change in electricity price in period i on the demand for electricity in period i; cross elasticity coefficient It represents the impact of the change in electricity price in period j on the demand for electricity in period i, , Respectively represent the power consumption before and after the response; represents the power consumption in the period t before the response; represents the traditional fixed electricity price for period t before the response.

9. The industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives according to claim 8 is characterized in that: The active low-carbon demand response model is specifically: In the formula, for the cost of carbon emissions; Benefits of shifting loads for users; Revenue from load reduction for users; The cost of electricity after low-carbon demand response; Compensation factor for shifting loads for industrial users; is the load transferred in at time t; is the load transferred out at time t; Compensation factor for load reduction for industrial users; Reduce the load for industry users at time t; is the carbon emissions before low-carbon demand response; is the time-of-use electricity price at time t before low-carbon demand response; is the carbon emission after low-carbon demand response; c is the average carbon emission factor in the region; Allocate a quota for carbon emissions per unit of electricity; is the carbon price; Carbon emissions after low-carbon demand response; Free carbon allowances for industry; is the electricity price at time t after low-carbon demand response; is the electricity load at time t after low-carbon demand response.

10. The industry low-carbon demand response method considering the cross-coupling of electricity and carbon quota incentives according to claim 9 is characterized in that: The electricity price with carbon attributes is specifically: The specific calculation of the dynamic carbon emission factor is as follows: The calculation formula for the electricity price with carbon attributes under low-carbon demand response is as follows: In the formula, is the dynamic carbon emission factor; is the carbon emission intensity of thermal power unit i; is the net output of the i-th thermal power unit in period t; , are the on-grid power of wind power and photovoltaic power in period t, is the dynamic carbon emission factor after dimensionless processing; is the minimum value of the dynamic carbon emission factor; is the maximum value of the dynamic carbon emission factor, The electricity price for each period after low-carbon demand response; It is the electricity price for each period after price demand response.

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