Method and system for analyzing influence of differential electricity price mechanism on steel industry

Abstract

The invention discloses a method and system for analyzing the influence of a differential electricity price mechanism on the iron and steel industry, and the method comprises the following steps: constructing a dual difference model of a cluster cost per kilowatt-hour in the iron and steel industry in response to a differential electricity price policy, and obtaining a policy effect coefficient representing the net influence of the differential electricity price policy on the iron and steel industry; in a time-sharing differential electricity price scene, a stepped differential electricity price scene and an incentive differential electricity price scene, the obtained policy effect coefficients are decoupled respectively, and analysis models used for representing implementation effects of time-sharing, stepped and incentive differential electricity price policies are established respectively; and utilizing the established analysis model to obtain the influence of the time-sharing differential electricity price, the stepped differential electricity price and the exciting differential electricity price on the kilowatt-hour cost of the iron and steel industry. According to the method, the deviation of the differential electricity price on the analysis of the electricity utilization cost can be reduced, and the policy influence effect of the time-sharing, stepped and excited differential electricity prices on the iron and steel industry can be accurately analyzed.

Description

Technical Field

[0001] The present invention relates to the technical field of analysis of the impact of electricity price policies on the steel industry, and in particular to a method and system for analyzing the impact of a differential electricity price mechanism on the steel industry. Background Art

[0002] The implementation and adjustment of the differentiated electricity price policy directly affects the electricity costs and corporate energy consumption of users in the steel industry. For different differentiated electricity price scenarios, the degree to which users in the steel industry respond to differentiated electricity prices is different, and the changes in electricity costs and corporate energy consumption will also be different. At the same time, the adjustment of differentiated electricity prices is a continuous and iterative process. Insufficient understanding of load elasticity, the mismatch between electricity price adjustments and actual conditions, etc., may lead to a sharp increase in users' electricity costs and high load peaks during the adjustment of differentiated electricity prices. Therefore, power grid companies and government price regulatory departments need to promptly evaluate and analyze the adjustment of time-of-use electricity prices, adjust and optimize differentiated electricity price policies in a timely manner, and steel companies also need to actively adjust their energy use behaviors in response to changes in electricity prices.

[0003] When evaluating the impact of the differential electricity price policy on the electricity costs of users in the steel industry and the energy consumption of enterprises, the existing technology uses a series of quantitative and qualitative methods to fully and deeply understand the policy effects. These methods include but are not limited to cost-benefit analysis, regression analysis, simulation analysis, and the double difference method discussed in this paper. Cost-benefit analysis is a basic method for evaluating the economic rationality of policies. It analyzes the policy

[0004] The direct costs of electricity costs and energy consumption costs of steel enterprises before and after implementation are quantitatively compared, while considering the indirect benefits such as energy saving and consumption reduction, and production efficiency improvement that the policy may bring, and expressing the overall economic effect of the policy in monetary form. However, cost-benefit analysis often fails to accurately capture the comprehensive impact of the policy on all stakeholders, especially those social and environmental benefits that are difficult to quantify. Regression analysis is a more sophisticated statistical method used to explore the quantitative relationship between electricity price changes and variables such as energy consumption and costs of steel enterprises. By establishing a multivariate regression model, the direct impact of electricity price changes on various economic indicators and the differences in these impacts in different enterprises and different time periods can be analyzed. The advantage of regression analysis is that it can reveal the causal relationship between variables, but its effectiveness is highly dependent on the accuracy of the data and the applicability of the model. Simulation analysis simulates the possible results under different electricity price policy scenarios by constructing an operating model of the steel industry or enterprise. This method can predict the long-term impact of the policy, including changes in the production cost, market competitiveness, energy consumption structure, etc. of the enterprise. However, the results of simulation analysis are often affected by subjective factors such as model assumptions and parameter settings, so its accuracy needs to be combined with other evaluation methods for verification. The double difference method is a statistical method used to evaluate the effect of policy intervention. Its core idea is to estimate the net impact of the policy on the target indicator by comparing the changes in the same group of individuals before and after the policy is implemented, and the changes when the policy is not implemented. Compared with other methods, it is more suitable for studying the impact of the differential electricity price policy on the steel industry.

[0005] As an effective policy evaluation tool, the double difference method has a wide range of applications, covering multiple industries and fields. In many fields such as public health, education, transportation, and environmental protection, the double difference method is used to evaluate the impact of government policies, project interventions, or regulatory changes on socioeconomic indicators. In the field of public health, the double difference method is often used to evaluate the improvement effect of policies such as medical reform and vaccination programs on indicators such as residents' health status and the efficiency of medical resource utilization. In the field of education, the double difference method can be used to analyze the impact of educational policy adjustments on students' academic performance and educational resource allocation. In the field of transportation, the double difference method can evaluate the changes in traffic flow, travel mode selection, etc. caused by policies such as traffic congestion fees and public transportation subsidies. In the field of environmental protection, the double difference method can be used to evaluate the regulatory effects of environmental policies such as carbon taxes and emission rights trading on pollutant emissions and energy consumption structure.

[0006] However, although the double difference method has been widely used in many fields, it is relatively less used in the field of differential electricity pricing policy evaluation. Therefore, a method for analyzing the impact of the differential electricity pricing mechanism on the steel industry based on the double difference model is needed. Summary of the invention

[0007] In order to address the deficiencies in the prior art, the present invention provides a method and system for analyzing the impact of a differential electricity price mechanism on the steel industry. Based on the double difference method, a double difference model of the net impact of the cluster electricity cost response of the steel industry to the differential electricity price policy is constructed. The model can reduce the deviation in the analysis of the differential electricity price on the electricity cost, and more accurately analyze the policy impact of the three types of differential electricity prices, namely time-of-use, step-by-step and incentive, on the steel industry.

[0008] The present invention adopts the following technical solution.

[0009] In a first aspect, the present invention provides a method for analyzing the impact of a differential electricity price mechanism on the steel industry, comprising the following steps: constructing a double difference model of the steel industry cluster's electricity cost per kilowatt-hour response to a differential electricity price policy, and obtaining a policy effect coefficient representing the net impact of the differential electricity price policy on the steel industry; constructing a time-of-use differential electricity price scenario, a step-differentiated electricity price scenario, and an incentive differential electricity price scenario; in the time-of-use differential electricity price scenario, the step-differentiated electricity price scenario, and the incentive differential electricity price scenario, respectively decoupling the obtained policy effect coefficients, and establishing analysis models for representing the implementation effects of the time-of-use, step-differentiated electricity price, and incentive differential electricity price policies, respectively; using the established analysis models, obtaining the impact of the time-of-use differential electricity price, the step-differentiated electricity price, and the incentive differential electricity price on the electricity cost per kilowatt-hour of the steel industry.

[0010] Preferably, in the step of constructing a double difference model of the electricity cost per kWh of the steel industry cluster responding to the differential electricity price policy and obtaining a policy effect coefficient representing the net impact of the differential electricity price policy on the steel industry, the constructed double difference model of the electricity cost per kWh of the steel industry cluster responding to the differential electricity price policy is:

[0011] P i,Δt =δ 0,Δt +δ 1,Δt D iΔt +ε i,Δt

[0012] Where: Δt is the time span of one year; P i,Δt is the electricity cost of the steel industry cluster in one year; 0,Δt The external changes caused by the non-differential electricity price policy adjustment factors in the steel industry that implements or does not implement time-sharing in one year;δ 1,Δt is the policy effect of the steel industry implementing time-sharing in one year, and is the cost change; i,Δt is the error of the annual change model of the unit; D iΔt It is expressed as the interaction term between policy implementation status and policy implementation time.

[0013] Preferably, in the scenario of time-of-use differential electricity prices, the obtained policy effect coefficient is decoupled, and an analysis model for representing the implementation effect of the time-of-use differential electricity price policy is established, comprising the following steps: in the scenario of time-of-use differential electricity prices, the obtained policy effect coefficient is decoupled to obtain a policy effect coefficient decoupling model for time-of-use electricity prices; a quadratic multivariate regression fitting method is used to combine the independent variables in the policy effect coefficient decoupling model for time-of-use electricity prices to form a multivariate linear regression analysis model for representing the implementation effect of the time-of-use differential electricity price policy.

[0014] Preferably, the obtained policy effect coefficient decoupling model of time-of-use electricity price is as follows:

[0015]

[0016] In the formula, δ 1,Δt,v is the policy effect of the double difference model in the time-of-use electricity price policy; δ 0,Δt The external factors changes caused by the non-differential electricity price policy adjustment factors in the steel industry that implements or does not implement time-sharing in one year; f , P p , P g These are the peak, flat and valley electricity prices for the steel enterprise cluster respectively.

[0017] Preferably, the multivariate linear regression analysis model formed to represent the implementation effect of the time-of-use differential electricity price policy is as follows:

[0018]

[0019] in,

[0020]

[0021] Where: X, They are δ 0,Δt , and The primary form, secondary form and mutual inductance form of these three independent variables; Ω, Ψ, φ are X, In the linear coefficient matrix, quadratic coefficient matrix and mutual inductance coefficient matrix, C is a constant; m and n are positive integers.

[0022] Preferably, in the scenario of step-differential electricity prices, the obtained policy effect coefficients are decoupled, and an analysis model for representing the implementation effect of the step-differential electricity price policy is established, including the following steps: in the scenario of step-differential electricity prices, the obtained policy effect coefficients are decoupled to obtain a policy effect coefficient decoupling model for step electricity prices; a quadratic multivariate regression fitting method is used to combine the independent variables in the policy effect coefficient decoupling model for step electricity prices to form a multivariate linear regression analysis model for representing the implementation effect of the step-differential electricity price policy.

[0023] Preferably, the obtained policy effect coefficient decoupling model of the tiered electricity price is as follows:

[0024]

[0025] In the formula, δ 1,Δt,sf is the policy effect of the double difference model under the stepped electricity price scenario; δ 0,Δt,sf The external factors changes caused by the non-differential electricity price policy adjustment factors in the steel industry that implements or does not implement the tiered electricity price in one year P sf , P sν , P sg They are the tiered electricity price surcharge for the steel industry cluster, the tiered electricity price surcharge for enterprises that exceed the limit by up to twice, and the tiered electricity price surcharge for enterprises that exceed the limit by more than twice.

[0026] Preferably, the multivariate linear regression analysis model formed to represent the implementation effect of the step-by-step differential electricity price policy is as follows:

[0027]

[0028] in,

[0029]

[0030] Where: X sf , They are δ 0,Δt,sf , The primary form, secondary form and mutual inductance form of these three independent variables; Ω sf , sf ,φ sf X sf , The first-order coefficient matrix and the second-order coefficient matrix in C sf is a constant, m and n are positive integers.

[0031] Preferably, in the incentive differential electricity pricing scenario, the obtained policy effect coefficient is decoupled, and an analysis model for representing the implementation effect of the incentive differential electricity pricing policy is established, including the following steps: in the incentive differential electricity pricing scenario, the obtained policy effect coefficient is decoupled to obtain a policy effect coefficient decoupling model for incentive electricity prices; a quadratic multivariate regression fitting method is used to combine the independent variables in the policy effect coefficient decoupling model for incentive electricity prices to form a multivariate linear regression analysis model for representing the implementation effect of the incentive differential electricity pricing policy.

[0032] Preferably, the obtained policy effect coefficient decoupling model of the incentive electricity price is as follows:

[0033]

[0034] In the formula, δ 1,Δt,si is the policy effect of the incentive-based electricity price double difference model; δ 0,Δt,si The external factors changes caused by the non-differential electricity price policy adjustment factors in the steel industry that implements or does not implement incentive electricity prices in one year; si , P sn They are the electricity prices that receive incentive electricity price subsidies and the electricity prices that do not participate in the subsidy because they do not meet the requirements of the incentive electricity price subsidy research.

[0035] Preferably, the multivariate linear regression analysis model formed to represent the implementation effect of the incentive differential electricity price policy is as follows:

[0036]

[0037] in,

[0038]

[0039] Where: X si , They are δ 0,Δt,si , The first and mutual inductance forms of these two independent variables; Ω si , si ,φ si For X si , In the linear coefficient matrix, quadratic coefficient matrix and mutual inductance coefficient matrix, Csi is a constant, m and n are positive integers.

[0040] In a second aspect, the present invention provides a system for analyzing the impact of the aforementioned differential electricity price mechanism on the steel industry, the system comprising: a double difference model construction module, a decoupling module and a regression analysis model construction module; the double difference model construction module is used to construct a double difference model of the cluster electricity cost response differential electricity price policy in the steel industry, and obtain a policy effect coefficient representing the net impact of the differential electricity price policy on the steel industry; the decoupling module is used to decouple the obtained policy effect coefficient in the time-of-use differential electricity price scenario, the step-by-step differential electricity price scenario and the incentive differential electricity price scenario, and obtain a policy effect coefficient decoupling model of time-of-use electricity price, step-by-step electricity price and incentive electricity price; the regression analysis model construction module is used to perform regression analysis on the policy effect coefficient decoupling model of time-of-use electricity price, step-by-step electricity price and incentive electricity price, and establish an analysis model for representing the implementation effect of time-of-use, step-by-step and incentive differential electricity price policies.

[0041] The beneficial effect of the present invention is that, compared with the prior art, the present invention can separate the "non-differential electricity price policy adjustment factors" from the "differential electricity price policy adjustment factors" by constructing a double difference model of the net impact of the steel industry cluster electricity cost response to the differential electricity price policy, and obtain the net impact value of the differential electricity price policy, thereby reducing the deviation in the analysis of the differential electricity price on the electricity cost, making the analysis result more accurate. At the same time, by decoupling the policy effect coefficient obtained by the double difference method, and obtaining the policy impact effect analysis model of the three types of differential electricity price mechanisms, namely time-sharing, step-by-step and incentive, on the electricity cost of the steel industry through the multivariate linear regression method, it can reveal the mechanism of action of the differential electricity price mechanism on the electricity cost, provide important guidance for the formulation of the differential electricity price mechanism policy and the sustainable development of the steel industry, and help optimize the allocation of power resources and achieve a win-win situation of economic benefits and environmental protection goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of the method for analyzing the impact of the differential electricity price mechanism on the steel industry in the present invention;

[0043] Figure 2 The electricity consumption changes of steel industry users in Jiangsu Province on a certain day in 2023 when the time-of-use electricity price policy is implemented;

[0044] Figure 3 The change in electricity cost per kilowatt-hour for users in the steel industry in Jiangsu Province on a certain day in 2023 when the time-of-use electricity price policy is implemented;

[0045] Figure 4 The policy effect of the 2023 time-of-use electricity price adjustment in Jiangsu Province on the electricity cost per kilowatt-hour in the steel industry;

[0046] Figure 5 The policy effect of the time-based differential electricity price adjustment in Jiangsu Province in 2023 on the daily electricity cost of the steel industry;

[0047] Figure 6 The cost change effect of implementing tiered electricity prices for steel enterprises in Jiangsu Province in 2023;

[0048] Figure 7 The policy effect of the tiered electricity price adjustment in Jiangsu Province on the annual electricity cost of the steel industry;

[0049] Figure 8 The effect of changes in the cost of implementing incentive electricity prices for steel enterprises in Jiangsu Province in 2023;

[0050] Fig. 9 This is the policy effect of the incentive electricity price adjustment in Jiangsu Province on the annual electricity cost of the steel industry. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0052] like Figure 1 As shown, embodiment 1 of the present invention provides a method for analyzing the impact of a differential electricity price mechanism on the steel industry based on a double difference model, which specifically includes the following steps:

[0053] Step 1: Construct a double difference model of the steel industry cluster electricity cost response to the differentiated electricity price policy, separate the "non-differential electricity price policy adjustment factor" from the "differential electricity price policy adjustment factor", and obtain the policy effect coefficient representing the net impact of the differentiated electricity price policy on the steel industry.

[0054] The double difference method has great potential in analyzing the changes in the power cost of users due to differential electricity prices. The main purpose of the double difference method (Difference-in-Difference Method, DID) used in this invention is to eliminate the impact of non-differential electricity price policy factors (external changes such as economic development level, climate change, changes in power generation costs, changes in transmission and distribution prices, etc.) on the electricity cost and energy consumption of the steel industry. The specific steps of the DID method modeling are to set up the steel industry that implements the differential electricity price policy (intervention group) and the steel industry that does not implement the differential electricity price (control group), and then control other influencing factors, and compare the differences between the differential electricity price group and the control group before and after the policy is implemented, so as to evaluate the effect of policy implementation. The present invention uses the "power consumption intensity" of the steel industry as the explained variable; the variable "differential electricity price group" indicates whether the industry is subject to the differential electricity price policy. The variable is a binary variable. When the variable value is 1, it means that the industry implements the differential electricity price policy, and when the variable value is 0, it means that the industry does not implement the differential electricity price policy.

[0055] In addition, the use of the double difference method requires robustness analysis through parallel trend tests, that is, it is necessary to prove that the change trends of the control group and the intervention group are consistent before policy intervention. In the present invention, the control group and the intervention group are steel users who have not implemented and have implemented differential electricity prices in various regions, categories, and voltage levels. For these users, before the time-of-use electricity price adjustment, since they belong to the same province, the same category, and the same voltage level of electricity users, they implement the same electricity price policy, so the changes in the cost per kilowatt-hour are basically the same, which meets the parallel trend test.

[0056] In this model study, the year 2023 is taken as an example to study the annual change relationship, that is, the net impact of the differentiated electricity price policy on the electricity costs of users in the steel industry and the energy consumption of enterprises in a unit year.

[0057] Specifically, the electricity cost per kilowatt-hour in the steel industry (P i,t ) is used as the explained variable, i.e. the core indicator of policy analysis. Based on the basic logic of the double difference method, the double difference model of this indicator responding to the differential electricity price policy is constructed as follows:

[0058] P i,t =β 0 +β 1 M i,t +δ 0,t T i,t +δ 1,t D i,t +ε i,t (1)

[0059] Where: P i,t is the electricity cost of the steel industry cluster at time t; i is a binary variable, the variable value of 1 indicates that the steel industry cluster has implemented differential electricity prices, and the variable value of 0 indicates that the steel industry cluster has not implemented differential electricity prices; M i,t Indicates the policy implementation status, 1 for implementation and 0 for non-implementation; T i,t Indicates the policy implementation time, 0 before implementation and 1 after implementation; D i,t It is represented as the interaction term between policy implementation and policy implementation time. It is only when both of the above two are 1 that D i,t The value is 1, and the value is 0 in other cases, which is used to judge the impact of differential electricity prices on electricity costs; β 0 is the electricity cost of the steel industry cluster before the implementation of the policy without differential electricity prices, δ 0,t The joint effect of policy implementation time on the steel industry clusters that have and have not implemented time-sharing, β 1 is the internal difference of the steel industry cluster with and without time-sharing, δ 1,t is the policy implementation effect, which is the core variable of the double difference method. i,t is the model error.

[0060] The present invention studies the effect of the implementation of the policy for the steel industry cluster in 2022-2023. 1 The electricity cost at the time of the experiment is compared with the electricity cost of the steel industry cluster after the implementation of differential electricity prices in 2022. 2 By subtracting the electricity cost at the time, the double difference model can be rewritten as:

[0061]

[0062] Where: t 1 ,t 2 Both are after the implementation of differential electricity prices, which are the same months in 2023 and 2022 respectively; Indicates the t of the steel industry cluster after the implementation of differential electricity prices in 2023 1 The electricity cost at the time; Indicates the t of the steel industry cluster after the implementation of differential electricity prices in 2022 2 The electricity cost at the time; It indicates the joint effect of the policy implementation time in 2023 on the steel industry clusters that implemented and did not implement differential electricity prices; It represents the effect of policy implementation in 2023 and is the core variable of the double difference method; It indicates the joint effect of the policy implementation time in 2022 on the steel industry clusters that implemented and did not implement differential electricity prices; Indicates the policy implementation time in 2023, 0 before implementation and 1 after implementation; Indicates the policy implementation time in 2022, 0 before implementation and 1 after implementation; The interaction term between the policy implementation in 2023 and the policy implementation time is only valid when both of the above are 1. The value is 1, and the value is 0 in other cases, which is used to judge the impact of differential electricity prices on electricity costs; represents the interaction term between the policy implementation in 2022 and the policy implementation time; represents the model error in 2023; Represents the model error in 2022.

[0063] It is not difficult to find that, 0 With β 1 Since it is not affected by the policy implementation time, it is eliminated after the difference is made, and because t 1 ,t 2 All are after the implementation of differential electricity prices. and The value of is 1.

[0064] Based on formula (2), since the time span from 2022 to 2023 is one year, t 1 ,t 2 The sub-items are directly expressed by Δt. By simplifying and integrating equation (2), the double difference model can be further simplified and rewritten as:

[0065] P i,Δt =δ 0,Δt +δ 1,Δt D iΔt +ε i,Δt (3)

[0066] Where: Δt is the time span of one year; P i,Δt is the electricity cost of the steel industry cluster in one year; 0,Δt The external factors changes caused by the non-differential electricity price policy adjustment factors in the steel industry that implements or does not implement time-sharing in one year, including economic development level, climate change, and changes in power generation costs; 1,Δt is the policy effect of the steel industry implementing time-sharing in one year, and is the cost change; i,Δt is the error of the annual change model of the unit; D iΔt It is expressed as the interaction term of policy implementation status and policy implementation time. It takes the value of 1 only when both of the above are 1, and takes the value of 0 in other cases. It is used to judge the impact of differential electricity prices on electricity costs.

[0067] The double difference model uses δ to represent the external changes caused by the adjustment factors of the non-differential electricity price policy. 0,Δt The policy effect of the differential electricity price mechanism on the steel industry is expressed as δ 1,Δt It means that it is possible to separate the "non-differential electricity price policy adjustment factors" from the "differential electricity price policy adjustment factors" and obtain the net impact value of the differentiated electricity price policy, thereby more accurately highlighting the policy effect of the differentiated electricity price itself.

[0068] Step 2: Construct three typical differential electricity price scenarios to analyze the impact of differential electricity price policy adjustment factors on the steel industry. The three typical differential electricity price scenarios are: time-of-use differential electricity price scenario, step-by-step differential electricity price scenario, and incentive differential electricity price scenario.

[0069] Based on the analysis of the impact of differential electricity price factors on the steel industry, steel users account for the largest proportion of electricity consumption among all types of users, and are also the user category with the most widespread differential electricity price mechanism. Among them, electricity costs are one of the main production costs of some high-energy-consuming industries, which has a great impact on their profitability. Therefore, it is necessary to analyze the impact of the differential electricity price policy implemented by users in the steel industry on electricity user costs. This model is based on the data of electricity users in the steel industry in 2023 in some areas of Jiangsu Province, and conducts a comparative study according to three types of differential electricity price policy scenarios: time-of-use electricity prices, tiered electricity prices, and incentive electricity prices.

[0070] Step 3: Under the scenarios of time-of-use differential electricity prices, step-by-step differential electricity prices, and incentive-based differential electricity prices, the obtained policy effect coefficients are decoupled respectively, and analytical models are established to represent the implementation effects of time-of-use, step-by-step, and incentive-based differential electricity prices.

[0071] Step 4: Using the established analysis model, obtain the impact of time-of-use differential electricity prices, step differential electricity prices and incentive differential electricity prices on the electricity cost per kilowatt-hour in the steel industry.

[0072] Step 3 specifically includes the following steps:

[0073] Step 3.1: Analyze the impact of the time-of-use electricity price policy on the electricity cost per kilowatt-hour in the steel industry, decouple the obtained policy effect coefficient in the time-of-use differential electricity price scenario, and establish an analytical model to represent the implementation effect of the time-of-use differential electricity price policy.

[0074] Based on the double difference method of the unit annual change model, the impact of the time-of-use electricity price policy on the electricity cost of the steel industry is analyzed. Figure 2 , Figure 3 As shown in the figure, taking the policy effects of electricity consumption and kilowatt-hour cost of the steel industry under the change of differential electricity prices on a certain day in 2023 as an example, it can be analyzed that time-of-use electricity prices, as a price mechanism, correspond to different electricity prices according to the electricity demand and supply conditions in different time periods of the day (peak, peak, normal, and valley). This mechanism guides steel enterprise users to adjust their electricity consumption behavior through price differences, thereby optimizing the allocation of power resources and achieving the "peak shaving and valley filling" of electricity demand. Under the guidance of time-of-use electricity prices, the steel industry adjusts the electricity consumption during peak electricity price and peak electricity price periods to normal and valley periods according to the demand ratio, with the main purpose of reducing electricity costs. In addition, the change in electricity consumption behavior of steel enterprises guided by time-of-use electricity prices can reduce electricity consumption during peak periods, thereby alleviating the pressure on the power grid and reducing power supply risks, while enterprise users can increase electricity consumption during normal and valley periods to avoid the waste of power resources and promote the effective consumption of electricity.

[0075] The change in the cost per kilowatt-hour is affected by both the time-of-use electricity price and the adjustment of electricity consumption. It is manifested in that the cost per kilowatt-hour of steel enterprises has decreased under the electricity price in the off-peak period, and the cost per kilowatt-hour of steel enterprises has increased during the peak period, peak period, and normal period, among which the increase in the normal period is larger. Since the adjustment range of electricity prices in different time periods is different compared with the time-of-use electricity price policy, and the electricity consumption of steel enterprises also shifts at different times, the impact mechanism of time-of-use electricity prices on the cost per kilowatt-hour is multi-dimensional. Overall, the average cost per kilowatt-hour per hour is still on an upward trend.

[0076] Based on the double difference method of the unit annual change model, the impact of the time-of-use electricity price policy on the energy consumption of steel industry enterprises is analyzed. The policy effect of the steel industry energy consumption under the change of differential electricity prices from January 2023 to December 2023 shows that the energy consumption of key steel enterprises in Jiangsu Province shows a fluctuating trend. In 2023, the annual comprehensive energy consumption of key steel enterprises in Jiangsu Province decreased by 5.32%. The time-of-use electricity price policy usually imposes higher electricity prices on the steel industry, thereby encouraging these industries to carry out technological transformation and energy conservation and consumption reduction. The steel industry may invest in more efficient production facilities and improve energy efficiency due to rising electricity prices.

[0077] Based on the decoupling model of the policy effect coefficient of time-of-use electricity price, a regression analysis of the time-of-use electricity price adjustment on electricity cost was conducted. The survey found that there is a close connection between the policy effects of different steel enterprise clusters and the external changes caused by the non-differential electricity price policy adjustment factors of the steel enterprise cluster, as well as the peak and valley electricity prices being implemented by the steel enterprise cluster.

[0078] Specifically, exogenous changes include changes in the external environment in which the steel industry cluster is located, such as climate change. When the weather is hot, the electricity cost of the steel industry may rise due to the tiered electricity price, but at the same time, the cost per kilowatt-hour of the time-sharing steel industry with less elasticity may rise more due to the existence of peak electricity prices. Therefore, the changing trend of exogenous changes can also reflect the changing trend of policy effects to a certain extent. In addition, peak and valley electricity prices directly affect the effect of policies on the cost per kilowatt-hour in terms of policy mechanism, and there is also a strong correlation. Therefore, based on the double difference model, the present invention converts the coefficient δ representing the policy effect in the time-sharing electricity price difference model 1,Δt,v Further decoupling is performed, and it is believed that there is a functional relationship between policy effect and external factors, peak-valley price ratio, etc., and the policy effect coefficient decoupling model of time-of-use electricity price can be expressed as:

[0079]

[0080] In the formula, δ 1,Δt,v is the policy effect of the double difference model in the time-of-use electricity price policy; P f , P p , P g These are the peak, flat and valley electricity prices for the steel enterprise cluster respectively.

[0081] The policy effect has an obvious quadratic function relationship with each variable, and the product of each variable, that is, the mutual inductance between variables, also has a strong correlation. Therefore, the present invention adopts a quadratic multiple regression fitting method to combine the independent variables in the policy effect coefficient decoupling model of time-of-use electricity prices to form a multiple linear regression analysis model for representing the implementation effect of the time-of-use differential electricity price policy, which is expressed as:

[0082]

[0083] in,

[0084]

[0085] Where: X, They are δ 0,Δt , and The primary form, secondary form and mutual inductance form of these three independent variables; Ω, Ψ, φ are X, In the linear coefficient matrix, quadratic coefficient matrix and mutual inductance coefficient matrix, C is a constant; m and n are positive integers.

[0086] Due to the small number of changes in the differential electricity price policies of various enterprises and the limited data available, it is difficult for the data set to support the regression analysis of the differential electricity price mechanism of the entire steel industry in Jiangsu Province. Therefore, the present invention conducts a unified regression analysis on the effects of differential electricity price policies of different voltage levels, and obtains the average trend of Jiangsu Province based on the current status of the industry. Although the results are affected by the differences in enterprise operations, they can also reflect the impact of the differential electricity price mechanism on the electricity cost of the steel industry under the premise of ensuring certain operability.

[0087] Based on the coefficient decoupling model, the policy effects of various types of users in the double difference method are decoupled and fitted with a multivariate regression model. Considering that the electricity consumption of users in the steel industry is relatively dispersed at various voltage levels, the electricity cost of users in the steel industry at different voltage levels is regressed and fitted, and the results of the correlation coefficient r2 are shown in the following table.

[0088] Table 1 Correlation test results of various regression models

[0089]

[0090] With the addition of quadratic terms and mutual inductance terms, the fitting accuracy gradually improved. Finally, the regression model including all the first-order, second-order and mutual inductance terms achieved the highest degree of fit. The r2 test values ​​were all above 0.95, and the fitting test values ​​of the policy effects in the steel industry were generally close to 1, showing a strong correlation.

[0091] An analysis of the time-of-use differential electricity price mechanism and policy adjustment expansion research was conducted for users in the steel industry, and the fitting results of each coefficient are shown in the following table.

[0092] Table 2 Fitting results of coefficients in regression model

[0093]

[0094] The relationship between the policy effect and the change in the peak floating ratio can be expressed through regression results (the coefficients are rounded off because only the trend of change is concerned and the constant term is ignored):

[0095] δ 1,Δt,v =x 2 (2.3+4x 1 +0.5x 2 -3x 3 ) (7)

[0096] When The policy effect is positively correlated with the change in the peak floating ratio. The determination coefficient is generally slightly smaller than x in each scenario. 2, it can be considered that the policy effect is positively correlated with the change in the floating ratio of peak electricity prices. That is, the greater the floating ratio during peak hours, the greater the increase in the cost per kilowatt-hour due to the policy effect, such as Figure 4 This conclusion is consistent with the previous trend analysis, that is, the current steel industry user flexibility is limited, and the increase in peak electricity prices is more likely to lead to an increase in electricity costs. For the steel industry, And at x 2 =1.1936, the two are equal. This means that under the current policy conditions and external changes, users in the steel industry still have available demand elasticity, such as Figure 5 As shown in the figure, the cost per kilowatt-hour can be further reduced before the peak electricity price rises to 1.1936 (about 20%), and then the cost per kilowatt-hour increases with the increase in the peak electricity price. According to the decoupling model and the peak electricity price adjustment method, the historical peak, flat and valley electricity prices are increased by 20% respectively, and the peak electricity price is increased by 10%. The policy effect of differential electricity price adjustment on electricity consumption in the steel industry is predicted and analyzed.

[0097] Step 3.2: Analyze the impact of the tiered electricity price policy on the electricity cost per kilowatt-hour in the steel industry, decouple the obtained policy effect coefficient in the tiered differential electricity price scenario, and establish an analytical model to represent the implementation effect of the tiered differential electricity price policy.

[0098] The policy effect of the tiered electricity price is to reduce the cost of electricity per kilowatt-hour for the steel industry in Jiangsu Province at the overall average level of each voltage level. The implementation of the tiered electricity price in my country is mainly divided into two stages. The first is the price increase for eliminated and restricted high-energy-consuming enterprises implemented since 2004; and the second is the differentiated electricity price policy related to ultra-low emissions implemented for specific industries such as steel and coking from 2021.

[0099] Under the ultra-low emission differentiated electricity price scenario, starting from 2021, Jiangsu Province, as a major steel-producing province in China, has adopted strict policies for environmental governance in the steel industry to cope with increasingly severe pollution problems. Ultra-low emission transformation aims to reduce energy consumption and exhaust emissions, improve air quality, and meet national environmental protection requirements. The steel industry in Jiangsu Province was selected as the research object to explore the impact of the implementation of the carbon emission price increase standard of "organized emissions, unorganized emissions, and clean transportation" on its electricity costs and energy consumption levels. At the same time, typical enterprises in the non-ferrous metal rolling processing industry in Jiangsu Province were selected as the control group to analyze the impact and changing trend of the carbon emission price increase policy on the two types of enterprises.

[0100] Since the implementation of ultra-low emission differential electricity prices varies among different enterprises, three typical enterprises, including Jiangsu Delong Nickel Industry Co., Ltd., Yangzhou Huahang Special Steel Co., Ltd., and Jiangsu Binxin Steel Group Co., Ltd., were selected as examples for analysis in the steel industry; two typical enterprises, including Jiangsu Yichu New Materials Co., Ltd. and Jiangsu Fengyuan Aluminum Materials Technology Co., Ltd., were selected as examples for analysis in the non-ferrous metal rolling processing industry. Studies have shown that for steel enterprises and non-ferrous metal rolling processing industries that have not implemented tiered electricity prices, steel enterprises are more sensitive to electricity prices, and the characteristics of adjusting electricity consumption behavior under the incentive of electricity prices are more obvious, and the changes in electricity costs are more significant. When steel enterprises implement tiered electricity prices, after the ultra-low emission price increase in the short term, due to the lag effect of electricity response, their electricity costs will rise sharply in the first quarter of 2023, mainly due to the initial additional cost pressure and insufficient short-term adjustments. With technological progress, management optimization and policy adaptation, enterprises have gradually found ways to reduce electricity costs and achieve long-term cost control.

[0101] At the same time, from January 1, 2021 to December 31, 2025, Jiangsu Province will implement a full-process ultra-low emission policy of "organized emissions, unorganized emissions, and clean transportation" throughout the province, and implement phased and tiered price increases. Since its implementation, three ultra-low emission price increase standards have been issued. The impact of the three carbon emission electricity price increase standards that have been issued on the electricity costs of the steel industry is explored. There are differences in the implementation of steel enterprises in Jiangsu Province. Some enterprises only implement some of the price increase requirements of "organized emissions, unorganized emissions, and clean transportation". According to the current status of the policy implementation of steel enterprises, the impact and changing trend of the carbon emission price increase policy on the electricity costs of enterprises are explored.

[0102] In terms of enterprise energy consumption, since the steel industry and the nonferrous metal industry have common characteristics, in order to accurately evaluate the actual effect of the tiered electricity price policy, the monthly average comprehensive energy consumption of key steel enterprises in Jiangsu Province that implemented the policy was compared with those that did not implement it and those that did not implement it. It was found that the monthly average comprehensive energy consumption of steel enterprises that implemented the tiered electricity price policy decreased significantly, indicating the effectiveness of the policy in promoting energy conservation and emission reduction in enterprises.

[0103] The tiered electricity price scenario mainly sets penalty electricity price surcharge standards for over-limit enterprises to punish related enterprises. The exploration refers to the historical policy of tiered electricity prices implemented in my country, that is, the "Notice of the Provincial Development and Reform Commission and the Provincial Department of Industry and Information Technology on Improving Differentiated Electricity Price Policies to Promote Green Development" issued in January 2020, for exceeding the limit standard by one times (including one times), a surcharge will be imposed on the basis of the current catalog sales electricity price or market transaction electricity price (the same below), with a surcharge of 0.10 yuan per kilowatt-hour; if it exceeds the limit standard by more than one times, a surcharge of 0.30 yuan per kilowatt-hour. Therefore, based on the tiered electricity prices currently implemented in Jiangsu Province, the impact of the tiered electricity price policy on the steel industry is studied.

[0104] There is a close relationship between the policy effects of different steel enterprise clusters and the external changes caused by the non-differential electricity price policy adjustment factors of the steel enterprise cluster and the tiered electricity price increase standards currently being implemented by the steel industry cluster. The tiered electricity price increase standards directly affect the effect of the policy on the cost of electricity in terms of policy mechanism, and there is also a strong correlation. Therefore, referring to the decoupling method of time-of-use differential electricity prices, the coefficient δ representing the policy effect in the tiered electricity price difference model is 1,Δt,sf Further decoupling is performed, and it is believed that there is a functional relationship between the policy effect and the changes in external factors and the tiered electricity price increase standards. The decoupling model of the policy effect coefficient of the tiered electricity price can be expressed as:

[0105]

[0106] In the formula, δ 1,Δt,sf is the policy effect of the double difference model under the stepped electricity price scenario; P sf , P sν , P sg They are the tiered electricity price surcharge for the steel industry cluster, the tiered electricity price surcharge for enterprises that exceed the limit by less than one time, and the tiered electricity price surcharge for enterprises that exceed the limit by more than one time. Therefore, the quadratic multiple regression fitting method is used to decouple the policy effect coefficient of the tiered electricity price and conduct regression analysis on the tiered electricity price adjustment on the electricity cost, forming a multiple linear regression analysis model for representing the implementation effect of the tiered differential electricity price policy, which is expressed as:

[0107] in,

[0108]

[0109] Where: X sf , They are δ 0,Δt,sf , The primary form, secondary form and mutual inductance form of these three independent variables; Ω sf , sf ,φ sf X sf , The first-order coefficient matrix and the second-order coefficient matrix in C sf is a constant, m and n are positive integers.

[0110] Based on the coefficient decoupling model, the policy effects of various types of users in the double difference method are decoupled and fitted with a multivariate regression model. Considering that the steel industry users exceed the limit standard and implement different tiered electricity price surcharge standards, the cost per kilowatt-hour of different types of steel industry users is regressed and fitted, and the results of the correlation coefficient r2 are shown in the following table.

[0111] Table 3 Correlation test results of various regression models

[0112]

[0113] With the addition of quadratic terms, the fitting accuracy gradually improved. Finally, the regression model including all linear and quadratic terms achieved the highest degree of fit. The r2 test values ​​were all above 0.92, and the fitting test values ​​of industrial and commercial policy effects were generally close to 1, showing a strong correlation.

[0114] An analysis of the tiered electricity price mechanism and policy adjustment expansion research was conducted for users in the steel industry, and the fitting results of each coefficient are shown in the following table.

[0115] Table 4 Fitting results of coefficients in regression model

[0116] Variable Types Regression coefficient Variable Types Regression coefficient <![CDATA[C sf ]]> -3.12 <![CDATA[x 2 2 ]]> 0.51 <![CDATA[x 1 ]]> 0.02 <![CDATA[x 3 2 ]]> 2.56 <![CDATA[x 2 ]]> 2.93 <![CDATA[x 12 ]]> 3.47 <![CDATA[x 3 ]]> -5.12 <![CDATA[x 13 ]]> -0.05 <![CDATA[x 1 2 ]]> -8.27 <![CDATA[x 23 ]]> -2.56

[0117] The relationship between the policy effect and the change in the floating rate of the tiered electricity price increase standard can be expressed through regression results (the coefficients are rounded off because only the trend of change is concerned and the constant term is ignored)

[0118] δ 1,Δt,sf =x 2 (2.1+0.6x 1 +2x 2 -1.5x 3 ) (11)

[0119] When The policy effect is positively correlated with the increase in the tiered electricity price increase standard. That is, the greater the increase in the tiered electricity price increase standard, the greater the increase in the cost per kilowatt-hour due to the policy effect. The relationship curve is as follows: Figure 6 As for the steel industry, And at x 2 =0.92, the two are equal. The steel industry can partially offset the impact of rising electricity prices by adjusting production plans and electricity consumption strategies, making the increase in electricity cost smaller. However, when the floating rate of the tiered electricity price standard exceeds a certain threshold (about 8%), the electricity cost of the steel industry will increase significantly, and as the floating rate increases further, the increase in electricity cost will also accelerate. Figure 6 As shown in the figure. The impact of the tiered electricity price policy on the electricity cost per kilowatt-hour in the steel industry is not linear. Under different floating ratios, the response strategies and cost changes of the steel industry are significantly different. Therefore, when formulating relevant policies, it is necessary to take into account the characteristics of the industry and the bearing capacity of enterprises to achieve a balance between policy effects and enterprise development.

[0120] like Figure 7As shown in the figure, according to the decoupling model and referring to the adjustment method of the tiered electricity price increase standard, the historical tiered electricity price increase standard is increased by 10%, 15%, and 20% respectively. It is predicted that the policy effect of the tiered electricity price adjustment on the electricity cost of the steel industry is that the electricity cost of users in the steel industry has increased. Specifically, in the scenario of a 20% increase, the average electricity cost of the industry is 21.35 yuan / MW*h higher than when it was increased by 10%, and 14.32 yuan / MW*h higher than when it was increased by 15%. It shows that the higher the increase ratio of the tiered electricity price increase standard, the more obvious the impact on the overall electricity consumption behavior of the steel industry. The reason for this impact lies in the characteristics of the steel industry itself.

[0121] Step 3.3: Analyze the impact of the incentive electricity price policy on the electricity cost per kilowatt-hour in the steel industry, decouple the obtained policy effect coefficient in the incentive differential electricity price scenario, and establish an analytical model to represent the implementation effect of the incentive differential electricity price policy.

[0122] The implementation of the incentive electricity price policy is mainly to support the production capacity of steel enterprises that have reached the benchmark level of energy efficiency and the A-level environmental performance, and to accelerate the transformation and upgrading of enterprises below the benchmark level of energy efficiency. Encourage all regions to improve the level of refinement in the management of high-energy-consuming and high-emission projects. For steel enterprises in Jiangsu Province that have reached the benchmark level of energy efficiency and the A-level environmental performance, a subsidy of 0.04 yuan per kilowatt-hour of electricity consumption will be provided, and the maximum subsidy for a single enterprise at the municipal level will be 500,000 yuan. Enterprises are required to conduct energy efficiency audits first and achieve the predetermined energy-saving targets within one year after the audit.

[0123] There is a close relationship between the policy effects of different steel enterprise clusters and the external changes caused by the non-differential electricity price policy adjustment factors of the steel enterprise cluster and the tiered electricity price increase standards currently being implemented by the steel industry cluster. The incentive electricity price subsidy policy directly affects the effect of the policy on the cost of electricity in terms of policy mechanism, and there is also a strong correlation. Therefore, referring to the decoupling method of time-of-use differential electricity prices, the coefficient δ representing the policy effect in the incentive electricity price difference model is 1,Δt,si Further decoupling is performed, and it is believed that there is a functional relationship between policy effect and external changes, incentive electricity price subsidies, etc., and the policy effect coefficient decoupling model of incentive electricity price can be expressed as:

[0124]

[0125] In the formula, δ 1,Δt,si is the policy effect of the double difference model of incentive electricity price; P si , P snThey are respectively the electricity price of incentive electricity price subsidies in the steel industry cluster and the electricity price that does not participate in the subsidy because it does not meet the incentive electricity price subsidy research. Therefore, the quadratic multivariate regression fitting method is used to decouple the policy effect coefficient of the incentive electricity price and the regression analysis of the incentive electricity price adjustment on the electricity cost, forming a multivariate linear regression analysis model for representing the implementation effect of the incentive differential electricity price policy, which is expressed as:

[0126]

[0127] in,

[0128]

[0129] Where: X si , They are δ 0,Δt,si , The first and mutual inductance forms of these two independent variables; Ω si , si ,φ si For X si , In the linear coefficient matrix, quadratic coefficient matrix and mutual inductance coefficient matrix, Csi is a constant, m and n are positive integers.

[0130] Based on the coefficient decoupling model, the policy effects of various types of users in the double difference method are decoupled and fitted with a multivariate regression model. Considering that steel enterprises whose energy efficiency reaches the benchmark level and whose environmental protection performance reaches the A level are subject to incentive electricity price subsidies, the electricity cost per kilowatt-hour of different types of steel industry users is regressed and fitted, and the results of the correlation coefficient r2 are shown in the following table.

[0131] Table 2.3.3 Correlation test results of various regression models

[0132]

[0133] With the addition of quadratic terms, the fitting accuracy gradually improved. Finally, the regression model including all linear and quadratic terms achieved the highest degree of fit. The r2 test values ​​were all above 0.92, and the fitting test values ​​of industrial and commercial policy effects were generally close to 1, showing a strong correlation.

[0134] An analysis of incentive electricity price subsidies and policy adjustment expansion research was conducted for users in the steel industry, and the fitting results of each coefficient are shown in the following table.

[0135] Table 2.3.4 Fitting results of coefficients in regression model

[0136] Variable Types Regression coefficient Variable Types Regression coefficient <![CDATA[C si ]]> -2.28 <![CDATA[x 1 2 ]]> -5.63 <![CDATA[x 1 ]]> 0.48 <![CDATA[x 2 2 ]]> 0.78 <![CDATA[x 2 ]]> -3.57 <![CDATA[x 12 ]]> 6.31

[0137] The relationship between the policy effect and the change in the floating rate of incentive electricity price subsidies can be expressed through regression results as follows (the coefficients are rounded off because only the trend of change is concerned and the constant term is ignored)

[0138] δ 1,Δt,si =x 2 (3.5-0.52x 1 +3.85x 2 )

[0139] When The policy effect is positively correlated with the increase in incentive electricity price subsidies. That is, the greater the increase in incentive electricity price subsidies, the less the policy effect increases the cost per kilowatt-hour. The relationship curve is as follows: Figure 8 As shown. For steel enterprises, And at x 2 =0.04, under the condition of low subsidy ratio, the decline of electricity price is limited, and the electricity cost of steel enterprises is only slightly reduced, which may have little impact on the overall profit margin of enterprises. However, when the floating ratio of incentive electricity price subsidy exceeds a certain threshold (about 4%), the electricity cost of steel enterprises will drop significantly, and as the floating ratio further increases, the rate of decline of electricity cost will also accelerate. However, due to the maximum subsidy amount limit for single-household enterprises at the municipal level, when the floating ratio of incentive electricity price subsidy exceeds 15%, the rate of decline of electricity cost of steel enterprises slows down significantly. When the floating ratio exceeds 20%, the electricity cost of steel enterprises no longer changes. Fig. 9 shown.

[0140] The increase in the incentive electricity price subsidy surcharge standard has had a significant impact on the electricity cost per kilowatt-hour in the steel industry. According to the data, when the surcharge standard was increased by 10%, 15%, and 20%, the electricity cost per kilowatt-hour of users in the steel industry decreased. Specifically, in the scenario of a 20% increase, the average electricity cost per kilowatt-hour in the industry decreased by 19.71 yuan / MW*h compared with the 10% increase, and decreased by 11.43 yuan / MW*h compared with the 15% increase. This data shows that the higher the increase in the incentive electricity price subsidy standard, the more obvious the impact on the overall electricity consumption behavior of the steel industry. The reason for this impact lies in the characteristics of the steel industry itself. As an industry with high energy consumption and high electricity consumption, the consumption of electricity energy in the steel production process is huge. Therefore, the adjustment of electricity prices will directly affect its production costs. When the incentive electricity price subsidy standard is increased, eligible steel companies receive subsidies and their electricity costs decrease. This behavior may also prompt companies to further optimize their production plans, thereby having a greater impact on the electricity consumption behavior of the entire industry.

[0141] Taking the increase of 20% in the incentive electricity price subsidy standard as an example, the impact of the increase in the incentive electricity price subsidy standard on the change in the cost per kilowatt-hour of the steel industry is explored. Through data analysis, it is predicted that after the price increase, the cost per kilowatt-hour of the steel industry will show a significant decline in the next few quarters. The change in the cost per kilowatt-hour caused by the policy requires steel companies to ventilate for a certain period of time to respond and adjust. Therefore, there is a trend of climbing in the first and second quarters, and it is basically stable in the third and fourth quarters.

[0142] Embodiment 2 of the present invention provides a system for analyzing the impact of the differential electricity price mechanism on the steel industry in any one of the above embodiments, and the system includes: a double difference model construction module, a decoupling module and a regression analysis model construction module.

[0143] Among them, the double difference model construction module is used to construct a double difference model of the cluster electricity cost response to the differentiated electricity price policy in the steel industry, and obtain the policy effect coefficient representing the net impact of the differentiated electricity price policy on the steel industry; the decoupling module is used to decouple the obtained policy effect coefficient in the time-of-use differentiated electricity price scenario, the step-by-step differentiated electricity price scenario and the incentive differentiated electricity price scenario, and obtain the policy effect coefficient decoupling model of time-of-use electricity price, step-by-step electricity price and incentive electricity price; the regression analysis model construction module is used to perform regression analysis on the policy effect coefficient decoupling model of time-of-use electricity price, step-by-step electricity price and incentive electricity price, and establish an analysis model for representing the implementation effect of time-of-use, step-by-step and incentive differential electricity price policies.

[0144] The beneficial effect of the present invention is that, compared with the prior art, the present invention can separate the "non-differential electricity price policy adjustment factors" from the "differential electricity price policy adjustment factors" by constructing a double difference model of the net impact of the steel industry cluster electricity cost response to the differential electricity price policy, and obtain the net impact value of the differential electricity price policy, thereby reducing the deviation in the analysis of the differential electricity price on the electricity cost, making the analysis result more accurate. At the same time, by decoupling the policy effect coefficient obtained by the double difference method, and obtaining the policy impact effect analysis model of the three types of differential electricity price mechanisms, namely time-sharing, step-by-step and incentive, on the electricity cost of the steel industry through the multivariate linear regression method, it can reveal the mechanism of action of the differential electricity price mechanism on the electricity cost, provide important guidance for the formulation of the differential electricity price mechanism policy and the sustainable development of the steel industry, and help optimize the allocation of power resources and achieve a win-win situation of economic benefits and environmental protection goals.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing the impact of differential electricity price mechanism on the steel industry, characterized in that: The following steps are involved: A double difference model of the electricity cost of the steel industry cluster responding to the differential electricity price policy is constructed, and the policy effect coefficient representing the net impact of the differential electricity price policy on the steel industry is obtained; Construct time-of-use differential electricity price scenarios, step-by-step differential electricity price scenarios, and incentive differential electricity price scenarios; Under the scenarios of time-of-use differential electricity prices, step-by-step differential electricity prices, and incentive differential electricity prices, the obtained policy effect coefficients are decoupled respectively, and analytical models are established to represent the implementation effects of time-of-use, step-by-step, and incentive differential electricity price policies; Using the established analytical model, the effects of time-of-use differential electricity prices, step-by-step differential electricity prices and incentive differential electricity prices on the electricity cost per kilowatt-hour in the steel industry are obtained.

2. The method for analyzing the impact of the differential electricity price mechanism on the steel industry according to claim 1 is characterized by: In the step of constructing a double difference model of the electricity cost per kWh of the steel industry cluster responding to the differential electricity price policy, and obtaining the policy effect coefficient representing the net impact of the differential electricity price policy on the steel industry, the constructed double difference model of the electricity cost per kWh of the steel industry cluster responding to the differential electricity price policy is: P i,Δt =d 0,Δt +d 1,Δt D iΔt +e i,Δt Where: Δt is the time span of one year; P i,Δt is the electricity cost of the steel industry cluster in one year; 0,Δt The external changes caused by the non-differential electricity price policy adjustment factors in the steel industry that implements or does not implement time-sharing in one year;δ 1,Δt is the policy effect of the steel industry implementing time-sharing in one year, and is the cost change; i,Δt is the error of the annual change model of the unit; D iΔt It is expressed as the interaction term between policy implementation status and policy implementation time.

3. The method for analyzing the impact of the differential electricity price mechanism on the steel industry according to claim 1 is characterized by: In the scenario of time-of-use differential electricity prices, the obtained policy effect coefficient is decoupled, and an analytical model for representing the implementation effect of the time-of-use differential electricity price policy is established, including the following steps: In the time-of-use differential electricity price scenario, the obtained policy effect coefficient is decoupled to obtain the policy effect coefficient decoupling model of time-of-use electricity price; The quadratic multiple regression fitting method is used to decouple the independent variable combination in the model of the policy effect coefficient of time-of-use electricity price, forming a multiple linear regression analysis model for representing the implementation effect of the time-of-use differential electricity price policy.

4. The method for analyzing the impact of the differential electricity price mechanism on the steel industry according to claim 3 is characterized by: The obtained policy effect coefficient decoupling model of time-of-use electricity price is as follows: In the formula, δ 1,Δt,v is the policy effect of the double difference model in the time-of-use electricity price policy; δ 0,Δt The external factors changes caused by the non-differential electricity price policy adjustment factors in the steel industry that implements or does not implement time-sharing in one year; f , P p , P g These are the peak, flat and valley electricity prices for the steel enterprise cluster respectively.

5. The method for analyzing the impact of the differential electricity price mechanism on the steel industry according to claim 4 is characterized by: The multivariate linear regression analysis model formed to represent the implementation effect of the time-of-use differential electricity price policy is as follows: in, Where: X, They are δ 0,Δt , and The primary form, secondary form and mutual inductance form of these three independent variables; Ω, Ψ, φ are X, In the linear coefficient matrix, quadratic coefficient matrix and mutual inductance coefficient matrix, C is a constant; m and n are positive integers.

6. The method for analyzing the impact of the differential electricity price mechanism on the steel industry according to claim 1 is characterized by: In the scenario of step-by-step differential electricity prices, the obtained policy effect coefficient is decoupled, and an analytical model for representing the implementation effect of the step-by-step differential electricity price policy is established, including the following steps: In the scenario of stepped differential electricity prices, the obtained policy effect coefficients are decoupled to obtain a decoupling model of the policy effect coefficients of stepped electricity prices; The quadratic multiple regression fitting method is used to decouple the independent variable combination in the policy effect coefficient of the ladder electricity price, forming a multiple linear regression analysis model for representing the effect of the implementation of the ladder differential electricity price policy.

7. The method for analyzing the impact of the differential electricity price mechanism on the steel industry according to claim 6 is characterized by: The obtained decoupling model of the policy effect coefficient of the tiered electricity price is as follows: In the formula, δ 1,Δt,sf is the policy effect of the double difference model under the stepped electricity price scenario; δ 0,Δt,sf The external factors changes caused by the non-differential electricity price policy adjustment factors in the steel industry that implements or does not implement the tiered electricity price in one year P sf , P sν , P sg They are the tiered electricity price surcharge for the steel industry cluster, the tiered electricity price surcharge for enterprises that exceed the limit by up to twice, and the tiered electricity price surcharge for enterprises that exceed the limit by more than twice.

8. The method for analyzing the impact of the differential electricity price mechanism on the steel industry according to claim 7 is characterized by: The multivariate linear regression analysis model formed to represent the implementation effect of the step-by-step differential electricity price policy is as follows: in, Where: X sf , They are δ 0,Δt,sf , The primary form, secondary form and mutual inductance form of these three independent variables; Ω sf , sf ,φ sf X sf , The first-order coefficient matrix and the second-order coefficient matrix in C sf is a constant, m and n are positive integers.

9. The method for analyzing the impact of the differential electricity price mechanism on the steel industry according to claim 1 is characterized by: In the incentive differential electricity price scenario, the obtained policy effect coefficient is decoupled, and an analytical model for representing the implementation effect of the incentive differential electricity price policy is established, including the following steps: In the incentive differential electricity price scenario, the obtained policy effect coefficient is decoupled to obtain the policy effect coefficient decoupling model of the incentive electricity price; The quadratic multiple regression fitting method is used to decouple the independent variable combination in the policy effect coefficient of the incentive electricity price, forming a multiple linear regression analysis model for representing the implementation effect of the incentive differential electricity price policy.

10. The method for analyzing the impact of the differential electricity price mechanism on the steel industry according to claim 9, characterized in that: The obtained policy effect coefficient decoupling model of the incentive electricity price is as follows: In the formula, δ 1,Δt,si is the policy effect of the incentive-based electricity price double difference model; δ 0,Δt,si The external factors changes caused by the non-differential electricity price policy adjustment factors in the steel industry that implements or does not implement incentive electricity prices in one year; si , P sn They are the electricity prices that receive incentive electricity price subsidies and the electricity prices that do not participate in the subsidy because they do not meet the requirements of the incentive electricity price subsidy research.

11. The method for analyzing the impact of the differential electricity price mechanism on the steel industry according to claim 10, characterized in that: The multivariate linear regression analysis model formed to represent the implementation effect of the incentive differential electricity price policy is as follows: in, Where: X si , They are δ 0,Δt,si , The first and mutual inductance forms of these two independent variables; Ω si , si ,φ si For X si , In the linear coefficient matrix, quadratic coefficient matrix and mutual inductance coefficient matrix, Csi is a constant, m and n are positive integers.

12. A system for analyzing the impact of the differential electricity price mechanism on the steel industry using any one of claims 1-11, characterized in that: The system comprises: a double difference model building module, a decoupling module and a regression analysis model building module; The double difference model building module is used to build a double difference model of the cluster electricity cost response to the differential electricity price policy in the steel industry, and obtain a policy effect coefficient representing the net impact of the differential electricity price policy on the steel industry; The decoupling module is used to decouple the obtained policy effect coefficients in the time-of-use differential electricity price scenario, the ladder differential electricity price scenario and the incentive differential electricity price scenario, respectively, to obtain a policy effect coefficient decoupling model of the time-of-use electricity price, the ladder electricity price and the incentive electricity price; The regression analysis model building module is used to perform regression analysis on the policy effect coefficient decoupling model of time-of-use electricity price, ladder electricity price and incentive electricity price respectively, and establish an analysis model for representing the implementation effect of time-of-use, ladder and incentive differential electricity price policies.

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