Enterprise gas consumption decision-making method and device

Abstract

The invention provides an enterprise gas consumption decision-making method and device. The method comprises the following steps: receiving related data of enterprise gas consumption; taking minimization of carbon emission and maximization of enterprise profit as targets, constructing a multi-target planning model based on the first enterprise gas consumption cost, the carbon tax under the stepped pricing condition and the second enterprise gas consumption cost, and solving the multi-target planning model based on the related data to obtain an optimal gas consumption decision; generating a gas use decision suggestion according to the optimal gas use decision; according to the gas consumption decision making method, the situation that an existing enterprise often makes a gas consumption decision through experience or manual judgment is considered, the modes of collecting the gas consumption information of the enterprise, constructing a mathematical model and outputting gas consumption suggestions are utilized, the enterprise is guided in a scientific mode to make gas consumption decision making modes, the gas consumption cost of the enterprise is reduced, and the environmental protection level of the enterprise is improved.

Description

Technical Field

[0001] This application relates to the technical field of natural gas, and in particular, to a method and device for enterprise gas use decision-making. Background Art

[0002] The proposal of the "dual carbon" goal has made natural gas, as a clean and low-carbon fossil energy, play a more important role in the energy structure. How enterprises handle the relationship between carbon emission reduction and their own economic development and make scientific natural gas use decisions has become an important issue currently faced by enterprises. The company urgently needs to develop an enterprise gas use decision-making model considering the substitution effect of carbon emission tax and carbon trading, and accurately depict the impact of the step height of the tax rate and the carbon trading price on the substitution effect, the impact of the substitution effect on the enterprise's carbon emissions, and how the substitution effect acts on the enterprise's gas use decision. At the same time, the size of the natural gas market demand is also the basic basis for natural gas supply. By reasonably, efficiently, and accurately calculating the enterprise's gas use demand, precise measures can be taken in resource allocation, sales arrangement, marketing channel establishment, and customer service. Therefore, accurately predicting the customer's gas use decision is of great significance for natural gas supply engineering projects. It is necessary to study the gas use decisions of various users in the natural gas market on the premise of considering the substitution effect of carbon emission tax and carbon trading.

[0003] Currently, in the field of natural gas, the mutual influence of the two emission reduction policies of carbon tax and carbon trading has not been considered, and there is almost no scientific or reasonable model available for the gas use-related decision-making problems of emission reduction entities under the interaction of carbon tax and carbon trading. The prediction and deduction of the customer's gas use decision are mostly based on the self-investigation situation, and the subjectivity of individuals is relatively strong. Summary of the Invention

[0004] The purpose of this application is to provide a method for enterprise gas use decision-making to solve the above problems.

[0005] The purpose of this application is achieved by the following technical solutions:

[0006] In a first aspect, this application provides a method for enterprise gas use decision-making, including the following steps:

[0007] Step 1: Receive relevant data on enterprise gas use;

[0008] Step 2: With the goal of minimizing carbon emissions and maximizing enterprise profits, construct a multi-objective programming model based on the first enterprise gas use cost, carbon tax under stepped pricing, and the second enterprise gas use cost, and solve the multi-objective programming model based on the relevant data to obtain the optimal gas use decision;

[0009] Step 3: Generate gas use decision suggestions based on the optimal gas use decision;

[0010] The beneficial effects of this technical solution are as follows: Through the calculations in Step 2, this method helps enterprises minimize their carbon emissions, which means that enterprises can better fulfill their environmental protection responsibilities, reduce the negative impact on the environment, and at the same time comply with relevant regulations and policies to promote sustainable development. At the same time, the calculations in Step 2 also help enterprises achieve maximum profit. By optimizing gas usage decisions, enterprises can more effectively control costs and resource utilization, improve economic efficiency, and thus increase their profitability. Step 3 generates gas usage decision-making suggestions, enabling enterprises to make decisions based on quantitative data and multi-objective optimization methods. This scientific decision-making support helps enterprises avoid relying on subjective opinions or experience, improving the accuracy and reliability of decision-making; The gas usage decision-making suggestions generated in Step 3 of the method can be adjusted and optimized over time to adapt to changes in market and enterprise needs. This real-time feedback contributes to the continuous improvement and optimization of gas usage decisions.

[0011] In another embodiment, the receiving of relevant data on enterprise gas usage includes:

[0012] Receiving the enterprise gas consumption, natural gas unit price, transaction fees, carbon emissions that the enterprise needs to pay, unit carbon emission tax rate, carbon emission rights trading unit price, product unit environmental cost, enterprise initial carbon quota, and the step height of carbon tax in enterprise gas usage;

[0013] The beneficial effects of this technical solution are as follows: Using data-driven decision-making instead of experience or manual methods to drive decision-making, these data provide the key information required in the decision-making process, enabling enterprises to make decisions based on actual situations. By using data such as enterprise gas consumption and natural gas unit price, the decision-making process is more objective and scientific, helping to avoid subjective and empiricist decision-making; At the same time, multiple factors in the data (such as carbon emissions, costs, environmental costs, etc.) can be used for multi-objective optimization to balance different goals. This helps enterprises achieve the best gas usage decisions while considering multiple key performance indicators.

[0014] This technical solution takes into account both the stepped carbon tax payment and the purchase of carbon emission rights as two ways to obtain emissions, bringing multiple advantages compared to the traditional single payment of stepped carbon tax or direct purchase of carbon emission rights: First, by comprehensively considering the two ways to obtain emissions, enterprises can more flexibly respond to different market conditions and policy requirements. This means that enterprises are no longer limited to a single emission reduction approach, but can flexibly choose the most economical emission reduction method based on the relative advantages of carbon tax and carbon emission rights trading prices, thereby reducing the emission reduction cost. Second, this method provides more comprehensive decision-making support. By considering the two ways to obtain emissions simultaneously, enterprises can achieve more comprehensive optimization in carbon emission management, not only reducing carbon emissions but also maximizing enterprise profits. This comprehensive optimization method helps enterprises strike a balance between environmental protection compliance and economic benefits and achieve dual goals. In addition, by considering the mutual influence of the two ways to obtain emissions, enterprises can better understand the substitution effect under different policies and market conditions, which helps enterprises better plan future emission reduction strategies and reduce decision-making risks.

[0015] In another embodiment, aiming at minimizing carbon emissions and maximizing enterprise profits, a multi-objective programming model based on the gas consumption cost of the first enterprise, the carbon tax under the stepped pricing situation, and the gas consumption cost of the second enterprise is constructed, and the optimal gas consumption decision is obtained by solving the multi-objective programming model based on the relevant data, including:

[0016] Step 2.1, calculate the gas consumption cost of the first enterprise;

[0017] Step 2.2, calculate the carbon tax under the stepped pricing situation;

[0018] Step 2.3, calculate the gas consumption cost of the second enterprise;

[0019] Step 2.4, construct a multi-objective programming model based on the gas consumption cost of the first enterprise, the carbon tax under the stepped pricing situation, and the gas consumption cost of the second enterprise;

[0020] Step 2.5, obtain the optimal gas consumption decision for minimizing carbon emissions and maximizing enterprise profits based on the relevant data and the multi-objective programming model;

[0021] The beneficial effects of this technical solution are as follows: Through steps 2.1 and 2.3, the gas consumption costs of the first and second enterprises are first calculated, differentiating the "non-environmental costs" of enterprise gas consumption, i.e., the costs without considering carbon emissions when purchasing carbon emission rights or paying progressive carbon taxes (when the emissions are less than the carbon emission quota, the enterprise can also profit by selling carbon emission rights, which is not considered in the gas consumption cost of the first enterprise), and the "environmental costs", i.e., the costs that the enterprise needs to pay for carbon emission behaviors, which refers to the cost of purchasing carbon emission rights or the cost of paying progressive carbon taxes in the present invention; this enables the enterprise to accurately understand the impact of different gas consumption methods on costs, thereby better planning resources and making decisions. Step 2.2 involves calculating the carbon tax under the progressive pricing situation, which is beneficial for the enterprise to clarify how the tax rates of different tiers affect costs and decisions under complex carbon tax policies; through step 2.4, a multi-objective programming model is introduced, which considers two important objectives of carbon emissions and enterprise profits, and this helps the enterprise to maximize economic benefits while achieving environmental protection goals; step 2.5 provides the optimal carbon emission decision based on the multi-objective programming model to achieve the minimization of carbon emissions and the maximization of enterprise profits. This method helps the enterprise to comprehensively consider multiple key factors when making gas consumption decisions to achieve the best balance, and at the same time reserves design space for subsequent training with corresponding models for long-term guidance;

[0022] Generally speaking, by refining step 2, it provides the advantages of more accurate cost calculation, the ability to consider progressive pricing, multi-objective optimization, and providing the best decision, which helps the enterprise to better manage gas consumption decisions to achieve the dual goals of environmental protection and economic benefits.

[0023] In another embodiment, the gas consumption cost of the first enterprise is the gas consumption cost of the enterprise without considering environmental factors, the gas consumption cost of the second enterprise is the gas consumption cost of the enterprise paid for the environment, and the sum of the gas consumption cost of the first enterprise and the gas consumption cost of the second enterprise is equal to the actual gas consumption cost of the enterprise.

[0024] In another embodiment, the method for calculating the gas consumption cost of the first enterprise in step 2.1 is as follows:

[0025] MC1(q) = a + b·q

[0026]

[0027] where MC1(q) is the product marginal cost without considering carbon emissions, q is the gas consumption of the enterprise, a and b are undetermined parameters greater than zero, the undetermined parameters are user-defined, and C1 is the gas consumption cost of the first enterprise;

[0028] The beneficial effects of this technical solution are as follows: By allowing users to customize parameters a and b, this method can adapt to the specific situations of different enterprises and markets. This personalized parameter setting helps to more accurately reflect the gas consumption costs of enterprises; different enterprises have different basic costs, so a is different, and different unit gas consumption corresponds to different costs, so b is different. The customized a and b can adapt to the enterprise situations under different real conditions. It provides an adjustable model that allows enterprises to set parameters according to their specific situations to better meet their needs.

[0029] In another embodiment, the method for calculating the carbon tax in the case of stepped pricing in step 2.2 is as follows:

[0030] Δf(ρ) = f(ρ i+1 ) - f(ρ i )

[0031] T(ρ) = ∑f(ρ i )·ρ i (i = 0, 1,..., n)

[0032] where, Δf(ρ) is the step height of the stepped carbon tax, i is the number of steps, T(ρ) is the total value of the stepped carbon tax, and ρ is the carbon emissions that the enterprise needs to pay;

[0033] The beneficial effects of this technical solution are as follows: By calculating and adding the carbon tax prices of different steps segment by segment, this method can adapt to multi-level stepped carbon tax policies, which is beneficial for enterprises to accurately calculate their carbon tax burdens under complex carbon tax policies; as the carbon tax policy changes, the tax rates of different steps may be updated continuously. Through this method, enterprises can reflect the policy changes in a timely manner and ensure that their gas consumption decisions are based on the latest regulations; the beneficial effect of this calculation method is to provide a flexible and easy-to-operate way to calculate the stepped carbon tax, which helps enterprises better understand and manage their carbon tax costs.

[0034] In another embodiment, the way to calculate the gas consumption cost of the second enterprise in step 2.3 is as follows:

[0035]

[0036] where, C2 is the gas consumption cost of the second enterprise, (1 - μ) is the proportion of carbon emissions by the enterprise in the way of paying carbon tax, μ is the proportion of carbon emissions by the enterprise in the way of purchasing carbon emission rights in the trading market, is the transaction fee, ρ is the carbon emissions that the enterprise needs to pay, and σ is the unit price of carbon emission rights trading;

[0037] The beneficial effects of this technical solution are as follows: By setting the proportion of a company's carbon emissions through paying carbon taxes and the proportion of a company's carbon emissions through purchasing carbon emission rights in the trading market, the gas usage cost of the second company is calculated. In step 2.3, the gas usage cost of the second company is variable. Introducing the above variable helps to accurately obtain the gas usage cost of the second company under actual conditions.

[0038] In another embodiment, the model constructed in step 2.4 is as follows:

[0039]

[0040]

[0041] Min is the function for minimizing the carbon emission cost per unit of the company, and Max is the function for maximizing the economic benefit of the company on the basis of achieving the optimal carbon emission cost of the company;

[0042] At this time, with the highest carbon emission reduction efficiency Minγ(μ) as the main indicator and the maximization of the company's profit MaxΠ(q,μ) = P·q - C(q,μ) as the secondary indicator, a model is constructed:

[0043]

[0044] where σ is the unit price of carbon emission rights trading, σ m is the minimum unit price of carbon emission rights trading, σ M is the maximum unit price of carbon emission rights trading, is the minimum trading fee, is the maximum trading fee, q max is the maximum gas usage of the company;

[0045] The beneficial effects of this technical solution are as follows: This model comprehensively considers the trade-off relationship between the company's gas usage decision-making, carbon emission reduction efficiency, and the company's profit. This helps the company balance the emission reduction target and economic benefit in decision-making, thereby formulating a more comprehensive strategy; By considering the two main indicators, the model helps the company achieve an optimized decision. It can help the company find the best balance point between carbon emission reduction and profitability and achieve the optimization of dual goals; Since the company not only focuses on emission reduction efficiency but also on profitability, it can better manage potential economic risks, such as the increase in carbon tax expenditure. By taking the carbon emission reduction efficiency as the first priority goal, this model helps the company make a more sustainable gas usage decision, which is crucial for the company to play an active role in responding to climate change and environmental protection regulations.

[0046] In another embodiment, the method for obtaining the carbon emission decision-making that minimizes carbon emissions and maximizes the company's profit in step 2.5 is as follows:

[0047]

[0048] where, when 0 < (1 - μ) < ρ -1 ·f -1 (σ), f((1 - μ)ρ) - σ > 0, and the profit of an enterprise with ∏ μ ≥ 0 increases with the increase of the substitution effect; when ρ -1 ·f -1 (σ) < (1 - μ) < 1, f((1 - μ)ρ) - σ < 0, and the profit of an enterprise with ∏ μ < 0 decreases with the increase of the substitution effect; when (1 - μ) = ρ -1 ·f -1 (σ), f((1 - μ)ρ) - σ = 0, and ∏ μ = 0, at this time the profit value of the enterprise reaches the maximum;

[0049] The beneficial effects of this technical solution are as follows: by clarifying the profit change trend of the enterprise under different substitution effects, this method helps to determine the optimal carbon emission decision, and the enterprise can accurately determine how to achieve the best balance between emission reduction and profit according to the magnitude of the substitution effect; it points out the changes in the enterprise's profit under different substitution effect conditions, which helps the enterprise identify potential risks and opportunities to better manage economic risks, especially in the case of frequent changes in carbon tax and carbon trading policies.

[0050] In another embodiment, an enterprise gas use decision-making device includes:

[0051] A receiving module for receiving relevant data on the enterprise's gas use;

[0052] A solving module for constructing a multi-objective programming model based on the first enterprise gas use cost, carbon tax under stepped pricing, and the second enterprise gas use cost with the goal of minimizing carbon emissions and maximizing the enterprise's profit, and solving the multi-objective programming model based on the relevant data to obtain the optimal gas use decision;

[0053] An output module for generating gas use decision-making suggestions according to the optimal gas use decision.

[0054] The beneficial effects of this technical solution are as follows: The device allows users to set different optimization goals and the weights of the optimization goals, which means that enterprises can consider multiple goals, such as the trade-off between minimizing carbon emissions and maximizing profits. This helps enterprises consider different goals more comprehensively to make more reasonable gas-using decisions. Through the receiving module, the device can obtain data related to the gas use of the enterprise. This makes the decision-making more objective and based on the actual situation, thus improving the accuracy and practicality of the decision-making. The device is built-in with a solving module that can automatically calculate the optimal gas-using decision to balance different goals, which reduces the burden of decision-making for enterprises and improves efficiency. The output module generates a gas-using decision report, including the recommended gas consumption and relevant data, enabling users to clearly understand the optimal gas-using decision and easily apply these recommendations to actual operations. At the same time, users can adjust the settings according to different situations and optimization goals to meet the different needs of enterprises.

[0055] In another embodiment, a computer-readable storage medium stores a computer program. When the program is executed by a processor, the processor executes the enterprise gas-using decision-making method as described above.

[0056] In another embodiment, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the enterprise gas-using decision-making method as described above. Description of the Drawings

[0057] The present application will be further described below with reference to the drawings and embodiments.

[0058] Figure 1 It is a schematic flowchart of an enterprise gas-using decision-making method provided by an embodiment of the present application;

[0059] Figure 2 It is a schematic diagram of detailed steps of an enterprise gas-using decision-making method provided by an embodiment of the present application;

[0060] Figure 3 It is a schematic diagram of a stepped carbon tax rate provided by an embodiment of the present application;

[0061] Figure 4 It is a schematic diagram of a stepped carbon tax rate after taking the limit provided by an embodiment of the present application;

[0062] Figure 5 It is a schematic structural diagram of an enterprise gas-using decision-making device provided by an embodiment of the present application;

[0063] Figure 6 It is a schematic structural diagram of a device for an enterprise gas-using decision-making method provided by an embodiment of the present application;

[0064] Figure 7 This is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0065] Next, in combination with the accompanying drawings and specific implementation manners, the present application will be further described. It should be noted that, on the premise of no conflict, the following-described embodiments or technical features can be arbitrarily combined with each other to form new embodiments.

[0066] In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary" or "for example" aims to present relevant concepts in a specific manner.

[0067] Next, first, a simple description will be given of one application scenario of the embodiments of the present application (a stepped carbon tax and carbon trading composite emission reduction policy and the initial carbon emission quota of an enterprise is difficult to meet the carbon emissions required for the enterprise's production).

[0068] Stepped carbon tax. A stepped carbon tax is a carbon pricing policy aimed at encouraging the reduction of greenhouse gas emissions, especially carbon dioxide (CO2), to address climate change. It is different from traditional carbon taxes. Traditional carbon taxes usually have a constant tax rate, while a stepped carbon tax is graded according to the quantity or intensity of emissions and sets different tax rates for different levels of emissions.

[0069] Carbon emission rights. Carbon emission rights refer to the permits or rights to emit a certain amount of greenhouse gases. Usually, these emission rights are measured in tons of carbon dioxide equivalent (CO2e), indicating the equivalent amount of greenhouse gas emissions that can be emitted.

[0070] The substitution effect of carbon tax on carbon trading: Both carbon tax and carbon emission trading are effective paths to achieve carbon emission reduction. There is an interaction effect between them, and the substitution effect between them can be refined by determining the optimal sharing ratio of stepped carbon tax and carbon trading.

[0071] The present invention aims to determine the substitution effect between stepped carbon tax and carbon trading, and further study the gas usage decision-making problem of micro emission reduction entity enterprises under the influence of the substitution effect. The invention constructs a multi-objective programming model with the carbon emission reduction efficiency of emission reduction entity enterprises as the first priority goal and the optimal enterprise profit as the second priority goal, refines the substitution effect between them based on the optimal sharing ratio of stepped carbon tax and carbon trading, and on this basis, studies the optimal gas usage decision of micro emission reduction entity enterprises to achieve more accurate analysis and measurement of customer gas usage decisions.

[0072] See Figure 1, an embodiment of the present application provides an enterprise gas use decision-making method, including:

[0073] Step 1: Receive relevant data on enterprise gas use; Step 2: Construct a multi-objective programming model based on the first enterprise gas use cost, carbon tax under the stepped pricing situation, and the second enterprise gas use cost with the goal of minimizing carbon emissions and maximizing enterprise profit, and solve the multi-objective programming model based on the relevant data to obtain the optimal gas use decision; Step 3: Generate a gas use decision recommendation according to the optimal gas use decision. The optimal gas use decision includes the optimal gas consumption, and the gas use decision recommendation includes the recommended optimal gas consumption and relevant data. The relevant data refers to the data related to the gas consumption, such as GDP, enterprise production capacity, etc., and these relevant data can be obtained from the enterprise's database.

[0074] When an enterprise faces a composite emission reduction policy of stepped carbon tax and carbon trading and the initial carbon emission quota of the enterprise is difficult to meet the carbon emissions required for enterprise production, the enterprise has three options: pay the carbon tax, purchase carbon emission rights, or partially pay the carbon tax and partially purchase carbon emissions. In the traditional mode, enterprises are used to using a single method of paying the carbon tax or purchasing carbon emission rights to meet the carbon emissions required for production. Due to the lack of scientific guidance, there is no effective decision-making method for obtaining carbon emissions in the traditional mode, and it is often impossible to balance maximizing energy conservation and emission reduction to the greatest extent and maximizing enterprise profit on this basis. At the same time, it is also impossible to find the balance point of maximizing profit between paying the carbon tax and purchasing carbon emission rights.

[0075] Based on considering the substitution effect between stepped carbon tax and carbon trading, the present invention constructs a two-level multi-objective programming model, and focuses on analyzing the influence of the step height of the tax rate and the carbon trading price on the substitution effect, the influence of the substitution effect on the enterprise's carbon emissions, and how the substitution effect acts on the enterprise's gas use decision. Specifically, based on the optimal sharing ratio of stepped carbon tax and carbon trading, the substitution effect between the two is refined, and on this basis, the gas use decision of the enterprise in the natural gas market is determined to achieve accurate control of the customer's gas use demand.

[0076] Among them, the substitution effect between carbon tax and carbon trading mainly refers to that under the composite emission reduction policy of stepped carbon tax and carbon trading, the enterprise can achieve the emission reduction goal by choosing to pay the carbon tax, purchase carbon emission rights, or partially pay the carbon tax and partially purchase carbon emission rights. The stepped carbon tax and carbon trading policies will produce a substitution effect on the emission reduction entity, and different step heights of the carbon tax will produce different substitution effects on carbon trading; the action mechanism of the substitution effect mainly refers to that under the dual goals of maximizing enterprise profit and minimizing carbon emissions, the way the enterprise chooses to meet its production requirements will not only change with the changes in the stepped carbon tax rate and carbon trading price, but also be affected by the substitution effect between carbon tax and carbon trading.

[0077] Therefore, an enterprise gas use decision-making method is designed. In step 1, the device or system will first collect various data related to the enterprise's gas use. These data include, but are not limited to: the enterprise's gas consumption, which represents the amount of natural gas used by the enterprise during a specific period; the unit price of natural gas, which represents the price per unit of natural gas, usually expressed in currency units; transaction costs, including any related costs required to purchase carbon emission rights, such as the cost per unit of carbon emission rights and transaction handling fees, etc.; the carbon emissions that the enterprise needs to pay: which represents the total greenhouse gas emissions generated by the enterprise during production and operation, usually measured in tons of carbon dioxide equivalent (CO2e); the unit carbon emission tax rate: which represents the tax rate per unit of carbon emission; the unit price of carbon emission rights trading: which represents the price of purchasing or selling carbon emission rights in the carbon market; the enterprise's initial carbon quota: which represents the enterprise's initial carbon emission quota during a certain period, etc.

[0078] After obtaining the relevant various data, the device or system will build a model with the obtained data. There are various choices for the model here. It can be a multi-objective linear programming (MOLP) model. This model is suitable for problems with linear objective functions and linear constraint conditions. In this case, the objective functions of carbon emissions and enterprise profit can be linearly modeled to achieve minimizing carbon emissions and maximizing enterprise profit; it can be a multi-objective integer programming (MOIP) model. If the problem contains integer or binary variables, multi-objective integer programming can be used. This model is suitable for more complex problems where the decision variables have discrete properties; it can also be a multi-objective nonlinear programming (MONLP) model. When the objective function or constraint conditions of the problem are nonlinear, multi-objective nonlinear programming can be used. This model is suitable for more complex problems where the relationship between carbon emissions and enterprise profit may not be linear; or, a multi-objective evolutionary algorithm model, such as a multi-objective genetic algorithm (MOGA) model or a multi-objective particle swarm optimization (MOPSO) model, etc. Through model operation on the above data, the best gas use decision is obtained to achieve the purpose of minimizing carbon emissions and maximizing enterprise profit. Finally, gas use decision-making suggestions are generated based on the best gas use decision.

[0079] To sum up, this design is different from traditional decision-making methods that rely on experience and manpower. Through scientific model algorithms, the two main goals, namely minimizing emissions and maximizing profits, are taken as the objectives, and relevant constraint conditions are considered, such as the upper and lower limits of gas consumption, the upper and lower limits of emissions, etc. The optimal point with the least emissions and the greatest profit is obtained through calculation. Finally, gas use decision-making suggestions are given based on the calculation results.

[0080] Specifically in this application, the relevant data obtained in this embodiment are: the enterprise's gas consumption, the unit price of natural gas, transaction costs, the carbon emissions that the enterprise needs to pay, the unit carbon emission tax rate, the unit price of carbon emission rights trading, the unit environmental cost of products, the enterprise's initial carbon quota, and the step height of the carbon tax.

[0081] See Figure 2 , the embodiment of the present application provides an enterprise gas use decision-making method. On the basis of the above steps, step 2 is divided into: step 2.1, calculating the first enterprise gas use cost; step 2.2, calculating the carbon tax under the stepped pricing situation; step 2.3, calculating the second enterprise gas use cost; step 2.4, constructing a multi-objective programming model based on the first enterprise gas use cost, the carbon tax under the stepped pricing situation, and the second enterprise gas use cost; step 2.5, obtaining a carbon emission decision that minimizes carbon emissions and maximizes enterprise profits based on the relevant data and the multi-objective programming model.

[0082] Specifically in the present application, the first enterprise gas use cost is the enterprise gas use cost without considering environmental factors, and the second enterprise gas use cost is the enterprise gas use cost paid for the environment. The sum of the first enterprise gas use cost and the second enterprise gas use cost is equal to the actual gas use cost of the enterprise;

[0083] The method for calculating the first enterprise gas use cost in step 2.1 is as follows: Assume that the enterprise is in a perfectly competitive market, and the price P of natural gas is determined by the total output Q of the entire industry. The enterprise can only control costs by adjusting the gas consumption q to maximize enterprise profits; the cost C of the enterprise using natural gas can be split into two parts, the first enterprise gas use cost C1 (the cost without considering environmental factors) and the second enterprise gas use cost C2 (the part paid for the environment). Among them, in addition to the absolute increase in the total carbon emissions caused by the increase in gas consumption, the second enterprise gas use cost is also affected by how the enterprise plans the carbon tax and carbon emission rights trading; then the actual gas use cost C of the enterprise = C1 + C2, and the marginal cost without considering environmental factors is: MC1(q) = a + b·q; where MC1(q) is the product marginal cost without considering carbon emissions, q is the gas consumption of the enterprise, a and b are undetermined parameters greater than zero, and the undetermined parameters are defined by the user. C1 is the first enterprise gas use cost. The magnitude of a depends on the basic cost of the enterprise. Here, the basic cost refers to the basic cost that the enterprise has to pay even if it does not use gas. The magnitude of b depends on the price of natural gas. Then the first enterprise gas use cost without considering environmental factors at this time:

[0084] See Figure 3 The tax rate of the stepped carbon tax is as Figure 3 shown. The tax rate of the stepped carbon tax increases in a stepped manner as the carbon emissions increase. The step height of the stepped carbon tax is the difference between adjacent tax rates Δf(ρ) = f(ρ i+1 ) - f(ρ i ), and the carbon tax: T(ρ) = ∑f(ρ i )·ρ i(i = 0, 1, …, n), where Δf(ρ) is the step height of the stepped carbon tax, i is the number of steps, T(ρ) is the total value of the stepped carbon tax, and ρ is the carbon emissions that the enterprise needs to pay; however, since the stepped carbon tax is represented as a discontinuous function in the function graph, for the convenience of model solving, in step 2.2 of this embodiment, the interval of the stepped carbon tax is taken to the limit, so that the steps of the carbon tax tend to be progressive, and the tax rate function of the carbon tax tends to be a continuous function. Assume that the carbon tax interval is small enough (limΔρ→0), see Figure 4 , the total value of the stepped carbon tax:

[0085] The method for calculating the gas consumption cost of the second enterprise in step 2.3 is as follows: First, define that the proportion μ (0 < μ < 1) of the carbon emissions of the enterprise is used to purchase carbon emission rights in the carbon trading market, and the rest is used to pay the carbon tax. Then, the following formula is constructed: Among them, the carbon emissions that the enterprise needs to pay: ρ = θ - q·β, where θ represents the initial carbon quota of the enterprise, and β represents the carbon emissions per unit of commodity such as natural gas; if ρ < 0, it means that the carbon emissions required by the enterprise are less than the carbon quota allocated by the government, and the excess carbon quota can be sold for profit; if ρ > 0, it means that the carbon emissions required by the enterprise are greater than the carbon quota allocated by the government, and additional purchases or carbon tax payments are required. Among them, C2 is the gas consumption cost of the second enterprise, (1 - μ) is the proportion of the enterprise's carbon emissions by paying the carbon tax, μ is the proportion of the enterprise's carbon emissions by purchasing carbon emission rights in the trading market, is the transaction cost, ρ is the carbon emissions that the enterprise needs to pay, and σ is the unit price of carbon emission rights trading.

[0086] Under the consideration of the substitution effect between the stepped carbon tax and carbon trading, this invention adopts the operation research goal programming technology to construct a multi-objective programming model with the carbon emission reduction efficiency of the emission reduction main body enterprise as the first priority goal and the enterprise profit as the second priority goal. Based on the optimal sharing ratio of the stepped carbon tax and carbon trading, the substitution effect between the two is refined, and on this basis, the gas consumption decision of the micro emission reduction main body enterprise is determined. The influencing factors include the starting point and step height of the carbon tax rate, the trading price of carbon emission rights, the cost of natural gas, the cost of other fossil energy sources (such as coal, etc.), the carbon emissions of the enterprise, the cost and unit price of the products produced by the enterprise, the production volume of the enterprise, the smoothness of the supply chain, etc.

[0087] Step 2.4, the method for constructing the multi-objective programming model is as follows: In order to maximize the carbon emission efficiency of the enterprise, with the unit carbon emission cost γ(μ) = C2·ρ of the enterprise -1 representing the carbon emission efficiency, construct a function to minimize the unit carbon emission cost of the enterprise: On the basis of achieving the optimal carbon emission cost of the enterprise, construct a function to maximize the economic benefit of the enterprise: Construct a multi-objective programming model according to the above two functions. Set the first-priority objective as the highest carbon emission reduction efficiency Minγ(μ), and set the second-priority objective as the maximization of enterprise profit MaxΠ(q,μ) = P·q - C(q,μ). Thus, we can obtain:

[0088]

[0089] (1) The relationship between the carbon tax ladder and the substitution effect between carbon tax and carbon trading (solving for the highest carbon emission reduction efficiency) is as follows:

[0090] According to C(q,μ) = C1 + C2, the carbon emission efficiency can be obtained:

[0091] γ(μ) is a continuous concave function with a minimum point, and the solution is: μ0 = 1 - ρ -1 ·f -1 (σ),

[0092]

[0093] Since μ0 = 1 - ρ -1 ·f -1 (σ), taking the first and second derivatives of σ in μ0 respectively gives:

[0094]

[0095] Since the larger the first derivative f′(σ) of the carbon tax rate function, the larger it will be, that is, the higher the ladder, the larger μ0 will be. However, as the tax rate increases, it will decrease, that is, the increasing speed of μ0 will decline. Furthermore, it can be known that the higher the carbon tax ladder, the smaller the substitution effect of the stepped carbon tax on carbon trading, and as the tax rate increases, the substitution effect will slow down the decline speed;

[0096] (2) The relationship between the substitution effect between carbon tax and carbon trading and the optimal gas consumption of the enterprise is as follows:

[0097] Substitute ρ = θ·q - β into μ0 = 1 - ρ -1 ·f -1 (σ)

[0098] to get μ0 = 1 - (θ·q - β) -1 ·f -1 (σ)

[0099] So q0(μ0) = θ -1 ·[(1 - μ0) -1 ·f -1 (σ) + β]

[0100] The actual carbon emission ρ *(μ0) = (1 - μ0) -1 ·f -1 (σ) + β

[0101] q0(μ0), ρ * Taking the first derivative of q0(μ0) with respect to μ0 gives:

[0102]

[0103] The smaller the optimal proportion μ0 of the carbon emission rights purchased in the total emissions, the greater the substitution effect (1 - μ0) of the stepped carbon tax on carbon trading, the smaller the optimal gas consumption q0 of the enterprise, and the smaller the actual carbon emissions ρ of the enterprise * the smaller;

[0104] (3) The relationship between the optimal profit of the enterprise and the substitution effect between the carbon tax and carbon trading (solving for the maximization of enterprise profit) is as follows:

[0105] The profit function of the enterprise to be solved is:

[0106]

[0107] Taking the first derivative and the second derivative of (1 - μ) in Π(q, μ) respectively gives:

[0108]

[0109] The optimal profit of the enterprise first increases and then decreases with the increase of the substitution effect between the carbon tax and carbon trading, showing an inverted U shape. When 0 < (1 - μ) < ρ -1 ·f -1 (σ), f((1 - μ)ρ) - σ > 0, Π μ ≥0 The profit of the enterprise increases with the increase of the substitution effect; when ρ -1 ·f -1 (σ) < (1 - μ) < 1, f((1 - μ)ρ) - σ < 0, Π μ < 0 The profit of the enterprise decreases with the increase of the substitution effect; when (1 - μ) = ρ -1 ·f -1 (σ), f((1 - μ)ρ) - σ = 0, Π μ = 0, and at this time the profit value of the enterprise reaches the maximum; where σ is the unit price of carbon emission rights trading, σ m is the minimum unit price of carbon emission rights trading, σ M is the maximum unit price of carbon emission rights trading, is the minimum trading cost, is the maximum trading cost, q max is the maximum gas consumption of the enterprise.

[0110] See Figure 5, an embodiment of the present application provides an enterprise gas use decision-making device, including:

[0111] A receiving module 301, configured to receive relevant data of enterprise gas use;

[0112] A solving module 302, configured to construct a multi-objective programming model based on the first enterprise gas use cost, carbon tax under the stepped pricing situation, and the second enterprise gas use cost with the goal of minimizing carbon emissions and maximizing enterprise profits, and solve the multi-objective programming model based on the relevant data to obtain the optimal gas use decision;

[0113] An output module 303, configured to generate a gas use decision-making suggestion according to the optimal gas use decision.

[0114] See Figure 6 As shown, an embodiment of the present application provides a device capable of implementing an enterprise gas use decision-making method, including: a data receiving module, configured to receive data related to enterprise gas use; a target setting module, allowing users to set different optimization goals and weights, where the weight refers to the priority of each optimization goal. In this embodiment, the enterprise carbon emission reduction efficiency is the first priority goal, and the optimal enterprise profit is the second priority goal; an optimization algorithm module, configured to calculate the optimal gas use decision to achieve the balance of multiple goals; a decision output module, generating a gas use decision report, including the recommended gas consumption and relevant data;

[0115] Specifically for the present application, the data receiving module includes multiple interfaces, such as an enterprise gas consumption acquisition interface, a natural gas unit price database interface, a carbon emission tax rate database interface, etc. The data receiving module receives the subsequent required data and packs and sends the data to the optimization algorithm module. The target setting module is a part of the software in the embodiment of the present application. Users can set it through a computer, a smart phone, or a dedicated setting controller. At the same time, the target setting module has a user interface to facilitate users to define different optimization goals and set parameters. The optimization algorithm module is software in the embodiment of the present application, including a mathematical model and an optimization algorithm, and can run on the enterprise's server or a cloud server, while the decision output module can run on a computer, a mobile device, or a dedicated display screen and has an export function.

[0116] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored. When the program is executed by a processor, the processor is caused to execute the enterprise gas use decision-making method as described above.

[0117] Its specific embodiments are consistent with the embodiments described in the above method embodiments and the achieved technical effects, and some contents will not be repeated.

[0118] The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the steps of any of the above methods are implemented or the functions of any of the above devices are implemented.

[0119] In another embodiment, an electronic device, as Figure 7 shown, the electronic device includes a memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402. When the processor 402 executes the program, the enterprise gas decision-making method as described above is implemented.

[0120] The specific embodiments thereof are consistent with the embodiments described in the above method embodiments and the achieved technical effects, and some contents will not be elaborated again.

[0121] This application is described from the perspectives of purpose of use, efficacy, progress, and novelty, and has met the functional enhancement and use requirements emphasized by the patent law. The above description and accompanying drawings of this application are only preferred embodiments of this application, and do not limit this application thereto. Therefore, all those that are similar or identical to the structure, device, features, etc. of this application, that is, all equivalent replacements or modifications made according to the scope of the patent application of this application, shall fall within the scope of protection of the patent application of this application.

Claims

1. An enterprise gas usage decision-making method, characterized in that, It includes the following steps: Receiving relevant data on enterprise gas consumption; Taking minimizing carbon emissions and maximizing enterprise profits as the goals, constructing a multi-objective programming model based on the first enterprise gas consumption cost, carbon tax under stepped pricing, and the second enterprise gas consumption cost, and solving the multi-objective programming model based on the relevant data to obtain the optimal gas consumption decision; Generating gas consumption decision suggestions according to the optimal gas consumption decision.

2. The enterprise gas use decision-making method according to claim 1, characterized in that The receiving of relevant data on enterprise gas consumption includes: Receiving the enterprise gas consumption volume, natural gas unit price, transaction cost, carbon emissions to be paid by the enterprise, unit carbon emission tax rate, carbon emission rights trading unit price, product unit environmental cost, enterprise initial carbon quota, and the step height of the carbon tax in enterprise gas consumption.

3. The enterprise gas consumption decision-making method according to claim 1, characterized in that The construction of a multi-objective programming model based on the first enterprise gas consumption cost, carbon tax under stepped pricing, and the second enterprise gas consumption cost, taking minimizing carbon emissions and maximizing enterprise profits as the goals, and solving the multi-objective programming model based on the relevant data to obtain the optimal gas consumption decision includes: Calculating the first enterprise gas consumption cost; Calculating the carbon tax under stepped pricing; Calculating the second enterprise gas consumption cost; Constructing a multi-objective programming model based on the first enterprise gas consumption cost, the carbon tax under stepped pricing, and the second enterprise gas consumption cost; Obtaining the optimal gas consumption decision for minimizing carbon emissions and maximizing enterprise profits based on the relevant data and the multi-objective programming model.

4. The enterprise gas consumption decision-making method according to claim 1, characterized in that The method for calculating the first enterprise gas consumption cost is: MC1(q) = a + b·q where MC1(q) is the product marginal cost without considering carbon emissions, q is the enterprise gas consumption volume, a and b are undetermined parameters greater than zero, the undetermined parameters are user-defined, and C1 is the first enterprise gas consumption cost.

5. The enterprise gas consumption decision-making method according to claim 4, characterized in that The method for calculating the carbon tax under stepped pricing is: Δf(ρ) = f(ρ i+1 ) - f(ρ i ) T(ρ) = ∑f(ρ i )·ρ i (i = 0, 1,..., n) where Δf(ρ) is the step height of the stepped carbon tax, i is the number of steps, T(ρ) is the total value of the stepped carbon tax, and ρ is the carbon emissions to be paid by the enterprise.

6. The enterprise gas consumption decision-making method according to claim 5, characterized in that The method for calculating the second enterprise gas consumption cost is: Among them, C2 is the gas consumption cost of the second enterprise, (1-μ) is the proportion of the enterprise's carbon emissions by paying carbon tax, μ is the proportion of the enterprise's carbon emissions by purchasing carbon emission rights in the trading market, is the transaction cost, ρ is the carbon emissions that the enterprise needs to pay, and σ is the unit price of carbon emission rights trading.

7. The enterprise gas consumption decision-making method according to claim 6, characterized in that The constructed multi-objective programming model is: Min is the function for minimizing the enterprise unit carbon emission cost, and Max is the function for maximizing the economic benefits of the enterprise on the basis of achieving the optimal carbon emission cost of the enterprise; At this time, taking the highest carbon emission reduction efficiency Minγ(μ) as the main index and the maximization of enterprise profit MaxΠ(q, μ) = P·q - C(q, μ) as the secondary index, a model is constructed: Among them, σ is the unit price of carbon emission rights trading, σ m is the minimum unit price of carbon emission rights trading, σ M is the maximum unit price of carbon emission rights trading, is the minimum transaction cost, is the maximum transaction cost, q max is the maximum gas consumption of the enterprise, γ(μ) is the carbon emission reduction efficiency, and P is the unit price of natural gas.

8. The enterprise gas consumption decision-making method according to claim 7, characterized in that The method for obtaining the carbon emission decision for minimizing carbon emissions and maximizing enterprise profits is: where, when 0 < (1 - μ) < ρ -1 ·f -1 (σ), f((1 - μ)ρ) - σ > 0, and the profit of the enterprise with ∏ μ ≥ 0 increases with the increase of the substitution effect; when ρ -1 ·f -1 (σ) < (1 - μ) < 1, f((1 - μ)ρ) - σ < 0, and Π μ < 0, the profit of the enterprise decreases with the increase of the substitution effect; when (1 - μ) = ρ -1 ·f -1 (σ), f((1 - μ)ρ) - σ = 0, and ∏ μ = 0, at this time the profit value of the enterprise reaches the maximum.

9. An enterprise gas use decision-making device, characterized in that, It includes: A receiving module for receiving relevant data on enterprise gas consumption; A solution module, configured to construct a multi-objective programming model based on the gas consumption cost of the first enterprise, the carbon tax under the stepped pricing situation, and the gas consumption cost of the second enterprise, with the goal of minimizing carbon emissions and maximizing the enterprise profit, and solve the multi-objective programming model based on the relevant data to obtain the optimal gas consumption decision; An output module, configured to generate a gas consumption decision-making suggestion according to the optimal gas consumption decision.

10. A computer-readable storage medium, characterized in that, A computer program is stored in a computer-readable storage medium. When the program is executed by a processor, the processor is caused to execute the enterprise gas consumption decision-making method according to any one of claims 1-8.

11. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the enterprise gas consumption decision-making method according to any one of claims 1-8 is implemented.

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