Carbon peak-reaching path planning method, system and equipment considering energy conservation and carbon reduction cost

Abstract

The invention provides a carbon peak-reaching path planning method, system and equipment considering energy conservation and carbon reduction cost, and is applied to the technical field of energy conservation and environmental protection. The method comprises the following steps: for a carbon peak-reaching planning period, considering the energy activity of a planning area and the carbon emission influence of industrial production, and constructing a carbon emission model; considering the influence of energy conservation and carbon reduction on the economy of the planning area, and constructing a carbon emission economic disturbance model; taking minimization of carbon emission of the planning area and economic disturbance of the carbon emission as targets, and based on a carbon emission model of the planning area, a carbon emission economic disturbance model and a coupling relation between the carbon emission and economic indexes, constructing a carbon peak multi-target optimization model; based on an improved multi-target firefly algorithm, carrying out optimization solution on the carbon peak reaching multi-target optimization model of the planning area; and planning a carbon peak-reaching path of the planning area in the carbon peak-reaching planning period based on a solving result. According to the invention, the problem of poor implementability of the existing carbon-to-peak path is solved.

Description

Technical Field

[0001] The present invention relates to the field of energy conservation and environmental protection technology, and specifically to a carbon peak path planning method, system and equipment that take energy conservation and carbon reduction costs into consideration. Background Art

[0002] Since the 21st century, extreme weather and natural disasters closely related to global climate change have occurred frequently. Countries around the world have set carbon peak and carbon neutrality climate goals. Accelerating the transformation to clean and low-carbon energy and actively responding to climate change have become common global issues.

[0003] At present, my country is still in the stage of industrialization and urbanization, and economic development is still my country's top priority. In addition, there are differences in the development of various industries in various regions of my country, and the effective peak methods and peak paths are also different. Therefore, how to implement carbon targets with the least impact on industry and life has become a key issue. Traditional research on carbon peak paths often only focuses on energy and carbon emissions, ignoring the impact of energy conservation and carbon reduction on other aspects, making the carbon peak path difficult to implement and poorly implementable. Summary of the invention

[0004] In order to overcome the problem of poor feasibility of the above-mentioned carbon peak path, the present invention provides a carbon peak path planning method, system and equipment that takes energy saving and carbon reduction costs into consideration.

[0005] On the one hand, the present invention provides a carbon peak path planning method considering energy conservation and carbon reduction costs, comprising:

[0006] For a carbon peak planning period, the carbon emission model of the planning area is constructed by considering the carbon emission impact of energy activities and industrial production in the planning area; the carbon emission economic disturbance model of the planning area is constructed by considering the impact of energy conservation and carbon reduction on the economy of the planning area;

[0007] With the goal of minimizing carbon emissions in the planning area and minimizing the economic disturbance of carbon emissions in the planning area, a multi-objective optimization model for carbon peak in the planning area is constructed based on the carbon emission model of the planning area, the carbon emission economic disturbance model of the planning area, and the coupling relationship between carbon emissions and economic indicators in the planning area.

[0008] Based on the improved multi-objective firefly algorithm, the carbon peak multi-objective optimization model of the planning area is optimized and solved, and the optimization results of energy consumption of each energy category, the output optimization results of each industrial product and the optimization results of economic indicators of the planning area at each planning time point during the carbon peak planning period are obtained;

[0009] Based on the optimization results of energy consumption of each energy category at each planning time point, the optimization results of output of each industrial product and the optimization results of economic indicators, the carbon peak path of the planning area during the carbon peak planning period is planned.

[0010] Optionally, the carbon emission model of the planning area is constructed by considering the carbon emission impact of energy activities and industrial production in the planning area, including:

[0011] Determine the energy carbon emission model within the planning area based on the energy categories within the planning area and the carbon emission impact factors of each energy type;

[0012] Determine the industrial production carbon emission model within the planning area based on the types of industrial products within the planning area and the carbon emission impact factor of each industrial product;

[0013] Based on the energy carbon emission model of the planning area and the industrial production carbon emission model of the planning area, a carbon emission model of the planning area is constructed.

[0014] Optionally, the considering the impact of energy conservation and carbon reduction on the economy of the planning area and constructing a carbon emission economic disturbance model for the planning area includes:

[0015] For the planning area, determining the relationship between the corresponding carbon emissions and economic growth rate based on historical carbon emissions, historical labor, historical production factors and historical economic data;

[0016] Determine the consumption utility discount factor at each planning time node during the carbon peak planning period based on the relationship between the carbon emissions and the economic growth rate and the economic data at each planning time node during the carbon peak planning period;

[0017] For each planned time node within the carbon peak planning period, based on the consumption utility discount factor and the total consumption in the planning area, determine the total discounted utility of consumption at each planned time node;

[0018] The carbon emission economic disturbance at each planning time node is reversely represented by the total discounted utility of consumption at each planning time node, and the carbon emission economic disturbance model of the planning area is obtained.

[0019] Optionally, the carbon peak multi-objective optimization model of the planning area is constructed based on the carbon emission model of the planning area, the carbon emission economic disturbance model of the planning area, and the coupling relationship between carbon emissions and economic indicators of the planning area, with the goal of minimizing the carbon emissions of the planning area and minimizing the economic disturbance of the carbon emissions of the planning area, including:

[0020] The objective function of the model is to minimize the carbon emissions in the planning area and maximize the total discounted utility of consumption in the planning area;

[0021] The model constraints are the coupling relationship constraints between carbon emissions and economic indicators in the planning area, the structural change constraints of various energy types in the planning area, the constraints of various industrial products in the planning area, and the economic indicator constraints in the planning area;

[0022] Based on the model objective function and the model constraints, a multi-objective optimization model for carbon peak in the planning area is obtained.

[0023] Optionally, the expression of the model objective function is:

[0024]

[0025] Among them, τ and t are the years in the carbon peak planning period, t∈T, T is the carbon peak planning period, d is the index of energy type, μ dt is the discount coefficient of the dth energy source in year t, X dt is the consumption of the d-th energy in year t, λ t is the carbon emission factor of standard coal in year t; e is the index of industrial product types, is the carbon emission factor of the e-th industrial product in year t; P et is the output of the e-th industrial product in year t; U(t) is the total discounted utility of consumption in the planning area in year t, udf(t) is the utility discount factor in the planning area in year t; C(t) is the total consumption in the planning area in year t, udr(τ) is the utility discount rate in the planning area in year τ, udr(t) is the utility discount rate in the planning area in year t; kpvs(t) is the proportion of the added value of capital in the planning area in year t to the regional GDP, kgdp is the ratio of the capital stock in the base year to the regional GDP in the planning area; depr is the annual depreciation rate of fixed assets in the planning area; grow(t) is the potential economic growth rate of the planning area in year t.

[0026] Optionally, the constraints on various industrial products in the planning area include constraints on the output variation range of each industrial product and constraints on the total energy consumption of the industrial production process;

[0027] The economic indicator constraints within the planning area include the relationship constraints between the total consumption, total output and total investment in the planning area at each planning time node during the carbon peak planning period, the change range constraints of the economic potential growth rate of the planning area, the change range constraints of the total output of the planning area and the change range constraints of the total investment in the planning area.

[0028] Optionally, the coupling relationship constraint between the carbon emissions of the planning area and the economic indicators is a correlation relationship between the carbon emissions of the planning area and the consumption and investment of the planning area.

[0029] Optionally, the expression for the coupling relationship constraint between the carbon emissions and economic indicators of the planning area is:

[0030]

[0031] Among them, t is the year within the carbon peak planning period, t∈T, T is the carbon peak planning period, d is the index of energy type, μ dt is the discount coefficient of the dth energy source in year t, X dt is the consumption of the d-th energy in year t, λ t is the carbon emission factor of standard coal in year t; e is the index of industrial product types, is the carbon emission factor of the e-th industrial product in year t; P et is the output of the e-th industrial product in year t; C(t) is the total consumption in the planning area in year t, I(t) is the total investment in the planning area in year t, and A, α, and β are all correlation parameters between carbon emissions and economic indicators.

[0032] Optionally, the carbon peak multi-objective optimization model of the planning area is optimized and solved based on the improved multi-objective firefly algorithm to obtain the optimized results of energy consumption of each energy category in the planning area at each planning time point during the carbon peak planning period, the optimized results of the output of each industrial product and the optimized results of economic indicators, including:

[0033] The energy consumption of each energy category, the output of each industrial product, the total consumption and the total discounted utility of consumption are respectively used as decision variables, a decision vector consisting of a group of decision variables in the feasible solution region of the decision variables is used as a firefly, the value of the objective function corresponding to each decision variable in the decision vector is used as the brightness of the firefly, and the position of the firefly is searched in the feasible solution region based on the brightness of the firefly;

[0034] Determine the current moving step length of the firefly based on the Levy flight disturbance, and update the position and brightness of the firefly based on the current moving step length and the position search result;

[0035] Performing individual mutation of the fireflies based on a mutation operation of the decision variables in the decision vector to obtain the mutated positions of the fireflies;

[0036] The firefly position search, the updating of the position and brightness of the fireflies, and the individual variation of the fireflies are repeated until the iteration termination condition is reached to obtain an optimized decision vector, i.e., the optimized results of energy consumption of each energy category at each planning time point, the optimized results of the output of each industrial product, the optimized results of total consumption, and the optimized results of the total discounted utility of consumption.

[0037] On the other hand, the present invention also provides a carbon peak path planning system that takes energy saving and carbon reduction costs into consideration, including:

[0038] The carbon emission economic model construction module is used to construct a carbon emission model for a planning area during a carbon peak planning period, taking into account the carbon emission impact of energy activities and industrial production in the planning area; and to construct a carbon emission economic disturbance model for the planning area by taking into account the impact of energy conservation and carbon reduction on the economy of the planning area;

[0039] The multi-objective optimization model building module is used to build a multi-objective optimization model for carbon peak in the planning area based on the carbon emission model of the planning area, the carbon emission economic disturbance model of the planning area, and the coupling relationship between carbon emissions and economic indicators in the planning area, with the goal of minimizing carbon emissions in the planning area and minimizing the economic disturbance of carbon emissions in the planning area.

[0040] An optimization solution module is used to optimize and solve the carbon peak multi-objective optimization model of the planning area based on the improved multi-objective firefly algorithm, and obtain the optimization results of energy consumption of each energy category in the planning area at each planning time point during the carbon peak planning period, the optimization results of the output of each industrial product, and the optimization results of economic indicators;

[0041] The path planning module is used to plan the carbon peak path for the planning area during the carbon peak planning period based on the optimization results of energy consumption of each energy category at each planning time point, the optimization results of output of each industrial product and the optimization results of economic indicators.

[0042] On the other hand, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0043] The memory is used to store one or more programs;

[0044] When the one or more programs are executed by the at least one processor, any one of the above-mentioned carbon peak path planning methods considering energy saving and carbon reduction costs is implemented.

[0045] On the other hand, the present invention also provides a readable storage medium having an execution program stored thereon. When the execution program is executed, the carbon peak path planning method taking into account energy saving and carbon reduction costs as described in any one of the above items is implemented.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention provides a carbon peak path planning method, system and equipment that considers energy conservation and carbon reduction costs. On the one hand, in terms of carbon emissions, it considers the impact of both energy and industrial production, and the impact of energy conservation and carbon reduction on the regional economy. Based on this, a carbon peak multi-objective optimization model is constructed with the goal of minimizing carbon emissions in the planning area and minimizing the economic disturbance of carbon emissions in the planning area, so that the optimization target simultaneously considers multiple carbon emissions and the economic cost of energy conservation and carbon reduction, and minimizes the impact of the implementation of the carbon peak path on the economy, thereby improving the feasibility of the carbon peak path. On the other hand, the multi-objective optimization model also considers the coupling relationship between carbon emissions and economic indicators in the planning area, realizes the coupling coordination of carbon emissions and economic indicators, is conducive to achieving a win-win situation of carbon reduction and efficiency improvement, and improves the rationality and feasibility of the carbon peak path. The carbon peak multi-objective optimization model of the planning area is optimized and solved by an improved multi-objective firefly algorithm, thereby ensuring the accuracy of the optimization results. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic flow chart of a carbon peak path planning method considering energy conservation and carbon cost reduction of the present invention;

[0049] Figure 2 A schematic diagram of the solution process of a carbon peak multi-objective optimization model using an improved multi-objective firefly algorithm of the present invention;

[0050] Figure 3 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0051] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0052] Embodiment 1:

[0053] The present invention provides a carbon peak path planning method that takes energy saving and carbon reduction costs into consideration, as shown in the schematic diagram Figure 1 As shown, including:

[0054] Step S110: for a carbon peak planning period, considering the carbon emission impact of energy activities and industrial production in the planning area, a carbon emission model of the planning area is constructed; considering the impact of energy conservation and carbon reduction on the economy of the planning area, a carbon emission economic disturbance model of the planning area is constructed;

[0055] Step S120, with the goal of minimizing the carbon emissions in the planning area and minimizing the economic disturbance of the carbon emissions in the planning area, based on the carbon emission model of the planning area, the carbon emission economic disturbance model of the planning area, and the coupling relationship between the carbon emissions and economic indicators of the planning area, a carbon peak multi-objective optimization model for the planning area is constructed;

[0056] Step S130, optimizing and solving the carbon peak multi-objective optimization model of the planning area based on the improved multi-objective firefly algorithm, and obtaining the optimized results of energy consumption of each energy category at each planning time point in the planning area during the carbon peak planning period, the optimized results of the output of each industrial product, and the optimized results of economic indicators;

[0057] Step S140, plans the carbon peak path of the planning area during the carbon peak planning period based on the optimization results of energy consumption of each energy category at each planning time point, the optimization results of output of each industrial product and the optimization results of economic indicators.

[0058] In this example implementation, the carbon peak planning period may be a carbon peak time period specified by a country or region, such as 2020-2035. The planning area may be one or several administrative regions, a country, a regional community, etc. that set carbon peak targets. The planning time point refers to each time node of the plan. For example, it can be planned on an annual basis, with each year as a planning time point. Energy activities refer to the consumption behavior of different energy varieties, and energy varieties may include raw coal, crude oil, natural gas, thermal power, etc. Industrial production may include the production of various industrial products, and the types of industrial products may include industrial products such as sulfuric acid, agricultural nitrogen, phosphorus, and potassium chemical fertilizers. The economic indicators include at least one of the annual total investment, annual total consumption, and annual total discounted utility of consumption in the planning area. Accordingly, the economic indicator optimization results include the optimization results corresponding to each economic indicator. Based on the coupled MARKAL-MACRO model, a carbon peak multi-objective optimization model considering energy saving and carbon reduction costs is constructed. The carbon peak multi-objective optimization model is a multi-objective optimization problem. For multi-objective optimization problems, the various objectives are generally conflicting and mutually constrained. Optimizing one of the objectives may lead to the degradation of the optimization effect of some other objectives. Therefore, the direction of the solution is not to find a unique decision that allows all objectives to achieve the best optimization effect at the same time, but to seek to obtain a set of optimal solutions by weighing all objective functions. The improved multi-objective firefly algorithm can be an algorithm that improves the multi-objective firefly algorithm (MOFA) algorithm using step-size perturbation and / or genetic mutation algorithms to alleviate the problem that the algorithm is prone to fall into local optimality. This example analyzes the synergy of industry, energy and economy, takes into account the implementation cost of carbon emission reduction, optimizes the industrial structure and energy configuration, finds the best carbon peak path, takes the minimum disturbance of energy supply changes to economic development as the optimization goal, and establishes a multi-objective optimization model of annual carbon peak cost-energy saving-carbon reduction. The improved MOFA algorithm is used to solve the multi-objective optimization model, enhance the convergence of the algorithm, solve the problem of easily falling into local optimality, and improve the calculation accuracy. Through the carbon peak path predicted by the model, we analyze the annual evolution boundaries of energy supply and industrial energy consumption, and formulate evolution paths for various energy and industrial paths in my country under the dual carbon goals and carbon reduction costs.

[0059] In some embodiments, the carbon emission model of the planning area is constructed by considering the carbon emission impact of energy activities and industrial production in the planning area in step S110, including:

[0060] Determine the energy carbon emission model within the planning area based on the energy categories within the planning area and the carbon emission impact factors of each energy type;

[0061] Determine the industrial production carbon emission model within the planning area based on the types of industrial products within the planning area and the carbon emission impact factor of each industrial product;

[0062] Based on the energy carbon emission model of the planning area and the industrial production carbon emission model of the planning area, a carbon emission model of the planning area is constructed.

[0063] In this example implementation, the carbon emission impact factor of each energy type may include the standard conversion coefficient and the standard coal carbon emission factor of each energy type. The energy carbon emission model in the planning area is expressed as follows:

[0064]

[0065] In the formula, λ t is the standard coal carbon emission factor in year t, μ dt is the discount coefficient of the energy variety in the dth year, X dt is the energy consumption of the dth energy type in year t.

[0066] The carbon emission impact factor of each industrial product can be the carbon emission factor of each industrial product. The industrial production carbon emission model is expressed as follows:

[0067]

[0068] The final carbon emission model includes an industrial production carbon emission model and an energy carbon emission model, which determines the regional carbon emissions situation from the two aspects of industrial production and energy consumption, and improves the accuracy of the model's carbon emission assessment.

[0069] In an example implementation, the step S110 of considering the impact of energy conservation and carbon reduction on the economy of the planning area and constructing a carbon emission economic disturbance model for the planning area includes:

[0070] For the planning area, determining the relationship between the corresponding carbon emissions and economic growth rate based on historical carbon emissions, historical labor, historical production factors and historical economic data;

[0071] Determine the consumption utility discount factor at each planning time node during the carbon peak planning period based on the relationship between the carbon emissions and the economic growth rate and the economic data at each planning time node during the carbon peak planning period;

[0072] For each planned time node within the carbon peak planning period, based on the consumption utility discount factor and the total consumption in the planning area, determine the total discounted utility of consumption at each planned time node;

[0073] The carbon emission economic disturbance at each planning time node is reversely represented by the total discounted utility of consumption at each planning time node, and the carbon emission economic disturbance model of the planning area is obtained.

[0074] In this example implementation, the main factors affecting the prediction of economic growth rate, that is, the potential economic growth rate, include labor, capital, production factors or total factor productivity, and carbon emissions. Changes in the number of labor and improvements in quality, the formation and utilization efficiency of capital, technological progress, and institutional arrangements will all affect the potential economic growth rate. This example specifically incorporates the changing trend of carbon emissions into the factors affecting the prediction of the potential economic growth rate, taking into account the disturbance of energy conservation and emission reduction on economic growth. Specifically, the correlation between the historical economic growth rate and the historical carbon emission growth rate can be fitted based on the historical economic growth rate and the historical carbon emission growth rate, and the weight of the carbon emission growth rate on the economic growth rate can be estimated. This example constructs an economic disturbance model of carbon emissions based on the coupled MARKAL-MACRO model. The MARKAL model is a dynamic linear energy system optimization model based on a reference energy system, which can provide a detailed description of various energy mining, processing, conversion, transportation and distribution links and terminal energy use links in the energy system. The optimization goal of the model is to minimize the total energy supply cost during the planning period. The MACRO model is a macroeconomic model. The production function adopts a relatively simple nested constant substitution elasticity form. The basic input factors are capital, labor and energy services in various sectors. The output is used for investment, consumption and payment of energy costs. Investment is used to establish capital stock. The MARKAL model is often used alone. The energy service demand of each sector is exogenously given, but the fixed energy service demand cannot reflect the impact of energy prices on it. Therefore, the MARKAL model is coupled with the MACRO model through the energy service demand. The coupled MARKAL-MACRO model is a dynamic nonlinear programming model. It reversely represents the economic disturbance of carbon emissions through the total discounted utility of consumption. The carbon emission economic disturbance model is as follows:

[0075]

[0076] In the formula, depr represents the annual depreciation rate of fixed assets in the planning area, which is generally 6%; grow(t) is the potential economic growth rate of the planning area in year t, which is estimated based on historical carbon emission data and economic data. This example starts from the fact that energy-saving and carbon-reduction measures cannot reduce people's consumption level and willingness to consume, and uses the disturbance of low carbon on GDP growth rate to represent the economic impact. By maximizing the total discounted utility of consumption in the planning area, the economic disturbance of carbon reduction is minimized, and a multi-objective optimization model is constructed based on this.

[0077] In an example implementation, in step S120, with the goal of minimizing carbon emissions in the planning area and minimizing the economic disturbance of carbon emissions in the planning area, a carbon peak multi-objective optimization model for the planning area is constructed based on the carbon emission model of the planning area, the carbon emission economic disturbance model of the planning area, and the coupling relationship between carbon emissions and economic indicators in the planning area, including:

[0078] The objective function of the model is to minimize the carbon emissions in the planning area and maximize the total discounted utility of consumption in the planning area;

[0079] The model constraints are the coupling relationship constraints between carbon emissions and economic indicators in the planning area, the structural change constraints of various energy types in the planning area, the constraints of various industrial products in the planning area, and the economic indicator constraints in the planning area;

[0080] Based on the model objective function and the model constraints, a multi-objective optimization model for carbon peak in the planning area is obtained.

[0081] In this example implementation, minimizing carbon emissions within the planning area includes minimizing energy carbon emissions within the planning area and minimizing industrial production carbon emissions within the planning area. Combining the total discounted utility of maximizing consumption forms three objective functions as the objective function of the model. Exemplarily, the expression of the model objective function is:

[0082]

[0083] Based on relevant data such as carbon emissions and energy consumption of industrial sectors, combined with the requirements of national carbon emission reduction policies, rational constraints such as carbon emission intensity indicators, energy structure evolution trends, and industry development plans for the planning period (such as 2020-2035) are sorted out, combined with the coupling relationship constraints of carbon emissions and economic indicators as model constraints. Rationality constraints specifically include structural change constraints of various energy types in the planning area, constraints on various industrial products in the planning area, and economic indicator constraints in the planning area; the constraints on various industrial products in the planning area include the output change range constraints of various industrial products and the total energy consumption constraints of the industrial production process; the economic indicator constraints in the planning area include the relationship constraints between the total consumption and the total output and total investment of the planning area at each planning time node during the carbon peak planning period, the change range constraints of the economic potential growth rate of the planning area, the change range constraints of the total output of the planning area, and the change range constraints of the total investment of the planning area. Exemplarily, the coupling relationship constraint between carbon emissions and economic indicators in the planning area is the correlation between the carbon emissions of the planning area and the consumption and investment of the planning area. The model constraints are as follows:

[0084] The structural change constraints of various energy types in the planning area are:

[0085]

[0086] In the formula, d = 1, 2, 3, 4, respectively representing the structural change range of the four energy types of raw coal, crude oil, natural gas, and thermal power. They respectively represent the lower limit and upper limit of the structural change of the dth energy type in year t.

[0087] The output variation range of each industrial product is constrained as follows:

[0088]

[0089] In the formula, They are respectively the lower and upper limits of the output of the e-th industrial product in year t.

[0090] The total energy consumption of the industrial production process is constrained as follows:

[0091]

[0092] In the formula, ω et represents the unit energy consumption of the e-th industrial product in year t, W t Represents the total energy consumption of industrial production in year t.

[0093] The relationship between total consumption, total output and total investment in the planning area at each planning time node during the carbon peak planning period is constrained as follows:

[0094] C(t)≤Y(t)-I(t)

[0095] Where C(t) is the total consumption of the planning area in year t, Y(t) is the total output of the planning area in year t, and I(t) is the total investment of the planning area in year t.

[0096] The range of changes in the potential economic growth rate of the planning area is constrained as follows:

[0097] grow(t) min ≤grow(t)≤grow(t) max

[0098] In the formula, grow(t) min 、grow(t) max They are respectively the lower and upper limits of the potential economic growth rate in the planning area in year t.

[0099] The variation range constraint of the total output of the planning area

[0100] Y(t) min ≤Y(t)≤Y(t) max

[0101] Where, Y(t) min 、Y(t) max are the lower and upper limits of the total output of the planning area in year t respectively.

[0102] The variation range of the total investment in the planning area is constrained as follows:

[0103] I(t) min ≤I(t)≤I(t) max

[0104] Among them, I(t) min 、I(t) max They are respectively the lower and upper limits of the total investment in the planning area in year t.

[0105] The expression for the coupling relationship constraint between carbon emissions and economic indicators in the planning area is:

[0106]

[0107] Among them, t is the year within the carbon peak planning period, t∈T, T is the carbon peak planning period, d is the index of energy type, μ dt is the discount coefficient of the dth energy source in year t, X dt is the consumption of the d-th energy in year t, λ t is the carbon emission factor of standard coal in year t; e is the index of industrial product types, is the carbon emission factor of the e-th industrial product in year t; P et is the output of the e-th industrial product in year t; C(t) is the total consumption in the planning area in year t, I(t) is the total investment in the planning area in year t, A, α, β are all correlation parameters between carbon emissions and economic indicators, which can be obtained by fitting historical carbon emission data and historical economic data.

[0108] In the above embodiments, an annual carbon peak and path evolution model is constructed based on the coordinated emission reduction mechanism of industry, energy, and economy, a multi-objective optimization algorithm suitable for the model feature requirements is constructed, key carbon emission reduction paths are proposed, and differentiated carbon peak path evolution is achieved for various types of energy and industries in different regions, clarifying the peak development direction of various types of energy and industries.

[0109] The MOFA algorithm for solving the traditional multi-objective optimization model is prone to oscillation and aggregation during the solution process, and has problems such as poor optimization efficiency, slow convergence speed in the later stage, and easy formation of local optimal solutions, resulting in poor solution accuracy. In some example implementations, the improved multi-objective firefly algorithm in step S130 optimizes and solves the carbon peak multi-objective optimization model of the planning area, and obtains the energy consumption optimization results of each energy category at each planning time point in the planning period of the carbon peak, the output optimization results of each industrial product, and the economic indicator optimization results, including the following processes:

[0110] The energy consumption of each energy category, the output of each industrial product, the total consumption and the total discounted utility of consumption are respectively used as decision variables, a decision vector consisting of a group of decision variables in the feasible solution region of the decision variables is used as a firefly, the value of the objective function corresponding to each decision variable in the decision vector is used as the brightness of the firefly, and the position of the firefly is searched in the feasible solution region based on the brightness of the firefly;

[0111] Determine the current moving step length of the firefly based on the Levy flight disturbance, and update the position and brightness of the firefly based on the current moving step length and the position search result;

[0112] Performing individual mutation of the fireflies based on a mutation operation of the decision variables in the decision vector to obtain the mutated positions of the fireflies;

[0113] The firefly position search, the updating of the position and brightness of the fireflies, and the individual variation of the fireflies are repeated until the iteration termination condition is reached to obtain an optimized decision vector, i.e., the optimized results of energy consumption of each energy category at each planning time point, the optimized results of the output of each industrial product, the optimized results of total consumption, and the optimized results of the total discounted utility of consumption.

[0114] In this example implementation, the solution of the carbon peak multi-objective optimization model belongs to a type of multi-objective optimization problem (MOP). The various objectives in the MOP compete and influence each other, and the optimization of one objective solution means that two or more objective solutions need to be sacrificed. Therefore, after the MOP model is solved, it is not a single solution, but a compromise comprehensive optimal solution set, that is, the Pareto solution set. This example uses an improved multi-objective firefly algorithm to solve the multi-objective optimization problem. The basic introduction of the MOFA algorithm is as follows:

[0115] For multi-objective optimization problems:

[0116]

[0117] Among them, F(x) is the objective function of the multi-objective optimization problem, that is, the carbon peak multi-objective optimization model, f m (x) is the mth objective function, x represents the decision vector of the multi-objective optimization problem, that is, the vector composed of all decision variables in the optimization problem. For example, x is the consumption of each energy type in the planning period X dt , the output of each industrial product P et , the vector consisting of the annual total consumption C(t) and the total discounted utility of consumption U(t), x n is the nth decision variable, n represents the dimension of the decision variable, y m is the value of the mth objective function, m is the number of objective functions, for example, m = 3; X represents the feasible solution region of the decision variables, that is, the feasible range of all decision variables, y represents the target vector, that is, the vector composed of the objective function values ​​corresponding to each decision variable in the decision vector, g p (X) represents the pth inequality constraint in the model constraint condition, P is the index of the inequality constraint, and h q (X) represents the qth equality constraint in the model constraint, and Q is the index of the equality constraint. i ,x j ∈X, and satisfies the following formula:

[0118]

[0119] In the formula, l and l′ are the indexes of the objective function, respectively, and f l is the lth objective function, f l’ is the l′th objective function. If the above formula is satisfied, x i Pareto dominance x j , this definition can be used to compare the advantages and disadvantages of two feasible solutions. In MOP, the Pareto optimal solution is defined as a solution in the solution set that is not dominated by any other feasible solution; the Pareto optimal solution set is the set of all Pareto optimal solutions in the feasible solution region.

[0120] The MOFA algorithm regards the decision vector in the feasible solution area as a firefly and the target vector in the target space as the brightness of the firefly. It defines that when firefly i Pareto dominates firefly j, the brightness of firefly i is greater than the brightness of firefly j. The search process is simulated as the process of a firefly with small brightness moving towards a firefly with large brightness.

[0121] Define the attraction between fireflies i and j as:

[0122]

[0123] Among them, β ijis the attraction between fireflies i and j, β0 is the maximum attraction, that is, the attraction at the light source r = 0, usually taken as 1. γ is the light absorption coefficient, γ∈[0.01,100], r ij is the spatial distance from firefly i to firefly j, which is generally expressed as Euclidean distance, but is not limited to Euclidean distance.

[0124] Update the firefly location based on the search results:

[0125] The first update method is that firefly i finds the dominated firefly j during the search process. At this time, the position update formula of firefly i is:

[0126]

[0127] Among them, t′ is the current iteration number, x i (t′) and x j (t′) represents the current position of firefly i and firefly j, respectively, and x i (t′+1) is the new position of firefly i after iteration, α t′ is the step size factor, usually α t′ ∈[0,1]. is a vector of random numbers obtained from a Gaussian, uniform, or other distribution.

[0128] The second update method is that firefly i does not find the dominated firefly during the search process. In this case, the brightness of the two fireflies cannot be directly compared. Therefore, the position update formula of firefly i in this scenario is:

[0129]

[0130] in, The multi-objective function is converted into a single objective function through a conversion formula. The method is to combine each objective value into a single objective function through random weighted summation. The conversion formula is:

[0131]

[0132] Among them, Ψ(x) is the combined function, that is, the converted single objective function, m represents the number of objective functions, that is, the dimension of the decision variable, and w k ∈(0,1) is the weight of the kth objective function, f k is the kth objective function, weight w k In each iteration, the non-dominated solutions are randomly selected to sample in different directions along the Pareto front.

[0133] The above MOFA algorithm is prone to fall into local optimality, resulting in poor convergence and distribution. For example, when a target value f of an individual firefly i m When it is much better than other individuals, even if the weight assigned to this goal is small, it still accounts for a large proportion in the combination function, which will make the other goal values ​​of this individual much smaller than those of other individuals, resulting in the overall combination function of this individual being smaller than that of other individuals. Although the non-dominated solution selected each time may be different, as the number of iterations increases, the elite individuals will get closer and closer to this individual, causing the entire population to fall into a local optimum and unable to obtain better distribution and convergence. To address the above problems, this example introduces individual mutation operations and Levy flight perturbations, specifically:

[0134] Individual mutation operation: Let individual x i =[x i1 ,…,x ik ,…,x in ]’s k-th decision variable x ik The feasible solution range is The mutation rate mu is:

[0135]

[0136] in, The decision variables x are ik The lower and upper bounds of feasible solutions, x in For individual x i is the n-th dimension decision variable, l1 is the number of dimensions randomly selected for mutation. It can be seen that mu determines the number of dimensions of the individual in the mutation stage, so mu should not be too large. Too large a size will easily cause the population after mutation to deviate from the optimal evolutionary direction, while too small a size will make the mutation ineffective. After many experiments, mu is set to 0.1. When m is less than 10 dimensions, one dimension is randomly selected for mutation; when the calculated l1 is not an integer, the number of dimensions of mutation is determined by rounding. The mutation formula of the individual is as follows:

[0137]

[0138] Among them, x ik ′ is the variation result of the k-th dimension decision variable, σ∈(0,1) is the step size factor; is a real number that obeys a normal distribution. The above process is used to determine the variation dimension of individual fireflies and perform a variation operation on the corresponding dimension to obtain the mutated individual.

[0139] Levy flight perturbation: The update of the position of the firefly individual after being attracted is not only related to the original position of the individual, but also affected by the random step size. However, the random step size of the individual movement is only related to the maximum number of iterations, which causes the difference in the individual step size to become smaller and smaller when the number of iterations increases, causing the iterative solution to fall into the local optimum. This example can solve the problem that conventional multi-objective optimization algorithms are prone to fall into the local optimum when solving by perturbing the step size factor term. Levy flight is a non-Gaussian random process proposed by mathematician Levy in the late 1930s. Its flight trajectory belongs to a Markov random process. The walking step size satisfies a heavy-tailed distribution. It has a large jump probability at the local position and can jump out of the local optimum, thereby expanding the population search area. The random step size calculation format of Levy flight is as follows:

[0140]

[0141] Where s is the n-dimensional Levy random step, δ is a random number, 0<δ≤2, u=(u1,…u k ,…,u n ) is the n-dimensional step parameter vector, u k 、u n are the kth and nth elements of u respectively, v is the step size parameter, u and v both obey the normal distribution, for all k=1,2,…n, And σ u and σ v u k , v corresponds to the variance parameter of the normal distribution, σ u and σ v Satisfy the following formulas respectively:

[0142]

[0143] Here, Γ is the standard Gamma function.

[0144] Therefore, the formula of fireflies after the introduction of Levy flight perturbation is updated as follows:

[0145] x i (t′+1)=x i (t′)+β ij (r ij )·(x j (t′)-x i (t′))+α t′ ·s.

[0146] like Figure 2As shown in the figure, the execution process of the improved multi-objective firefly algorithm is as follows: first initialize the algorithm parameters, firefly position and brightness, then search for the position of the firefly, determine whether the brightness of the searched individual j is greater than that of the individual i, if so, use the first update method mentioned above (the first method, that is, the dominated firefly is found) to calculate the firefly position, otherwise, calculate the firefly position according to the second update method mentioned above (the second method, that is, no dominated firefly is found); calculate the Levy flight random step length, update the position and brightness of the firefly, perform mutation operation on the current individual, determine whether the iteration termination condition is met, if so, output the optimal solution set, otherwise go to the position search step to continue the next iteration process. Finally, the optimization results of energy consumption of each energy type in each year during the planning period, the optimized output structure of each industrial product, the optimization results of total consumption in the planning area, and the optimization results of total discounted utility of consumption are obtained.

[0147] The known quantities required in the process of solving the above model include the carbon emission factors of various energy types and various industrial products, the energy consumption of various energy types and the upper and lower limits of the output of various industrial products in each year of the planning period predicted based on policies, market trends, etc., as well as the upper and lower limits of relevant economic data.

[0148] This example improves the MOFA algorithm, draws on the mutation operation in the genetic algorithm, fine-tunes the decision vector, and introduces Levy flight perturbations to improve the algorithm's solution capability. Combined with mutation, the algorithm's local development capability is improved, and the algorithm's ability to find dominant solutions is enhanced, thereby improving the superiority of the final solution.

[0149] In some embodiments, the evolution paths of energy, industry, cost, etc. under the optimal carbon peak path can be formulated based on the model solution results (optimization problem solution results); the carbon peak path within the planning period is planned based on the model solution results, and the energy consumption, carbon emissions, energy-saving and carbon reduction costs and other data for each year of the planning period are obtained. Combined with my country's existing energy-saving and carbon reduction policies and implementation plans, the evolution path of energy demand and industrial structure under the optimal carbon peak situation and the corresponding energy-saving and emission reduction costs are depicted, and annual development plans for various types of energy and industries are formulated based on the evolution path.

[0150] Regarding how to achieve steady and accelerated economic development under the "dual carbon" goal, achieving green transformation is a major practice of practicing a community with a shared future for mankind, and it helps promote the green and low-carbon transformation of the world. The process of ecological and environmental protection is the process of constantly adjusting and correctly handling the relationship between ecological and environmental protection and economic and social development. Strengthening the construction of ecological civilization, resolutely fighting the tough battle against pollution, and making overall, historical, and turning changes in the quality of the ecological environment, so that the relationship between the environment and the economy is in a relatively balanced state, is the key to achieving low-carbon development. At present, the "dual carbon" goal has put forward new issues for correctly handling the relationship between the environment and the economy under the new situation. Promoting the comprehensive green transformation of economic and social development, deeply adjusting the industrial structure, accelerating the construction of a clean, low-carbon, safe and efficient energy system, accelerating the construction of a low-carbon transportation system, improving the quality of green and low-carbon development in urban and rural construction, strengthening the research and promotion of major green and low-carbon science and technology, continuously consolidating and improving carbon sink capacity, and improving the level of green and low-carbon development in opening up to the outside world are all related to the implementation of the "dual carbon" goal tasks. It can be seen that low carbon involves multiple areas of the national economy, and it is necessary to make major adjustments to the long-term high-carbon energy system and the corresponding lifestyle, promote the comprehensive green transformation of economic and social development, and build a higher form of environmental and economic relationship. Therefore, how to implement carbon targets with minimal impact on industrial and economic development becomes the key to the implementation of low-carbon measures, which has important theoretical value and practical significance.

[0151] This invention is triggered by multiple energy sources and multiple industries, and uses the "industry-energy-economy" approach to conduct research on the annual carbon peak path of the industry, energy, and economic coordinated emission reduction optimization model under the dual carbon goals, providing a theoretical basis for low-carbon path optimization and providing decision-making support and mechanism guarantee for the effective promotion of the country's dual carbon goals.

[0152] The present invention is based on the MARKAL-MACRO model and the improved MOFA multi-objective optimization algorithm, and studies the annual carbon peak path for industry, energy, and economy. It can be applied to the construction of the carbon trading market and regional carbon emission reduction planning, and provide a reference for energy planning departments to optimize and transform their energy structure, and provide support for enterprises to manage carbon emission reduction, which is helpful to promote the realization of carbon emission reduction goals. The carbon peak multi-objective optimization model and multi-objective optimization algorithm constructed by the present invention can help power companies to lay out long-term energy transformation and emission reduction project implementation plans in advance, so as to seize the initiative in low-carbon transformation and development, achieve risk avoidance, and improve economic benefits.

[0153] Embodiment 2:

[0154] Based on the same inventive concept, the present invention also provides a carbon peak path planning system that takes energy saving and carbon reduction costs into consideration, the system comprising:

[0155] The carbon emission economic model construction module is used to construct a carbon emission model for a planning area during a carbon peak planning period, taking into account the carbon emission impact of energy activities and industrial production in the planning area; and to construct a carbon emission economic disturbance model for the planning area by taking into account the impact of energy conservation and carbon reduction on the economy of the planning area;

[0156] The multi-objective optimization model building module is used to build a multi-objective optimization model for carbon peak in the planning area based on the carbon emission model of the planning area, the carbon emission economic disturbance model of the planning area, and the coupling relationship between carbon emissions and economic indicators in the planning area, with the goal of minimizing carbon emissions in the planning area and minimizing the economic disturbance of carbon emissions in the planning area.

[0157] An optimization solution module is used to optimize and solve the carbon peak multi-objective optimization model of the planning area based on the improved multi-objective firefly algorithm, and obtain the optimization results of energy consumption of each energy category in the planning area at each planning time point during the carbon peak planning period, the optimization results of the output of each industrial product, and the optimization results of economic indicators;

[0158] The path planning module is used to plan the carbon peak path for the planning area during the carbon peak planning period based on the optimization results of energy consumption of each energy category at each planning time point, the optimization results of output of each industrial product and the optimization results of economic indicators.

[0159] In a possible implementation, the carbon emission economic model construction module includes a carbon emission model construction submodule, and the carbon emission model construction submodule is used to:

[0160] Determine the energy carbon emission model within the planning area based on the energy categories within the planning area and the carbon emission impact factors of each energy type;

[0161] Determine the industrial production carbon emission model within the planning area based on the types of industrial products within the planning area and the carbon emission impact factor of each industrial product;

[0162] Based on the energy carbon emission model of the planning area and the industrial production carbon emission model of the planning area, a carbon emission model of the planning area is constructed.

[0163] In a possible implementation, the carbon emission economic model construction module includes an economic model construction submodule, and the economic model construction submodule is used to:

[0164] For the planning area, determining the relationship between the corresponding carbon emissions and economic growth rate based on historical carbon emissions, historical labor, historical production factors and historical economic data;

[0165] Determine the consumption utility discount factor at each planning time node during the carbon peak planning period based on the relationship between the carbon emissions and the economic growth rate and the economic data at each planning time node during the carbon peak planning period;

[0166] For each planned time node within the carbon peak planning period, based on the consumption utility discount factor and the total consumption in the planning area, determine the total discounted utility of consumption at each planned time node;

[0167] The carbon emission economic disturbance at each planning time node is reversely represented by the total discounted utility of consumption at each planning time node, and the carbon emission economic disturbance model of the planning area is obtained.

[0168] In a possible implementation, the multi-objective optimization model building module includes:

[0169] An objective function construction submodule, used to take minimizing carbon emissions within the planning area and maximizing the total discounted utility of consumption within the planning area as the model objective function;

[0170] A constraint construction submodule is used to use the coupling relationship constraint between carbon emissions and economic indicators in the planning area, the structural change constraint of various energy types in the planning area, the constraint of various industrial products in the planning area and the economic indicator constraint in the planning area as model constraint conditions;

[0171] The model building submodule is used to obtain a multi-objective optimization model for carbon peak in the planning area based on the model objective function and the model constraints.

[0172] In a possible implementation, the expression of the model objective function is:

[0173]

[0174] Among them, τ and t are the years in the carbon peak planning period, t∈t, T is the carbon peak planning period, d is the index of energy type, μ dt is the discount coefficient of the dth energy source in year t, X dt is the consumption of the d-th energy in year t, λ t is the carbon emission factor of standard coal in year t; e is the index of industrial product types, is the carbon emission factor of the e-th industrial product in year t; P etis the output of the e-th industrial product in year t; U(t) is the total discounted utility of consumption in the planning area in year t, udf(t) is the utility discount factor in the planning area in year t; C(t) is the total consumption in the planning area in year t, udr(τ) is the utility discount rate in the planning area in year τ; kpvs(t) is the proportion of the added value of capital in the planning area in year t to the regional GDP, kgdp is the ratio of the capital stock in the base year of the planning area to the regional GDP; depr is the annual depreciation rate of fixed assets in the planning area; grow(t) is the potential economic growth rate of the planning area in year t.

[0175] In a possible implementation, the constraints on various industrial products in the planning area include constraints on the output variation range of each industrial product and constraints on the total energy consumption of the industrial production process;

[0176] The economic indicator constraints within the planning area include the relationship constraints between the total consumption, total output and total investment in the planning area at each planning time node during the carbon peak planning period, the change range constraints of the economic potential growth rate of the planning area, the change range constraints of the total output of the planning area and the change range constraints of the total investment in the planning area.

[0177] In a possible implementation manner, the coupling relationship constraint between the carbon emissions of the planning area and the economic indicators is the correlation relationship between the carbon emissions of the planning area and the consumption and investment of the planning area.

[0178] In a possible implementation, the expression of the coupling relationship constraint between the carbon emissions and economic indicators of the planning area is:

[0179]

[0180] Among them, t is the year within the carbon peak planning period, t∈T, T is the carbon peak planning period, d is the index of energy type, μ dt is the discount coefficient of the dth energy source in year t, X dt is the consumption of the d-th energy in year t, λ t is the carbon emission factor of standard coal in year t; e is the index of industrial product types, is the carbon emission factor of the e-th industrial product in year t; P et is the output of the e-th industrial product in year t; C(t) is the total consumption in the planning area in year t, I(t) is the total investment in the planning area in year t, and A, α, and β are all correlation parameters between carbon emissions and economic indicators.

[0181] In a possible implementation, the optimization solution module is specifically used to:

[0182] The energy consumption of each energy category, the output of each industrial product, the total consumption and the total discounted utility of consumption are respectively used as decision variables, a decision vector consisting of a group of decision variables in the feasible solution region of the decision variables is used as a firefly, the value of the objective function corresponding to each decision variable in the decision vector is used as the brightness of the firefly, and the position of the firefly is searched in the feasible solution region based on the brightness of the firefly;

[0183] Determine the current moving step length of the firefly based on the Levy flight disturbance, and update the position and brightness of the firefly based on the current moving step length and the position search result;

[0184] Performing individual mutation of the fireflies based on a mutation operation of the decision variables in the decision vector to obtain the mutated positions of the fireflies;

[0185] The firefly position search, the updating of the position and brightness of the fireflies, and the individual variation of the fireflies are repeated until the iteration termination condition is reached to obtain an optimized decision vector, i.e., the optimized results of energy consumption of each energy category at each planning time point, the optimized results of the output of each industrial product, the optimized results of total consumption, and the optimized results of the total discounted utility of consumption.

[0186] Embodiment 3:

[0187] like Figure 3 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0188] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to implement the steps of a carbon peak path planning method that considers energy saving and carbon reduction costs in the above-mentioned embodiment.

[0189] Example 4

[0190] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a carbon peak path planning method that considers energy saving and carbon reduction costs in the above embodiment.

[0191] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0192] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0193] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0194] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0195] 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 its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A carbon peak path planning method considering energy saving and carbon reduction costs, characterized in that: include: For a carbon peak planning period, the carbon emission model of the planning area is constructed by considering the carbon emission impact of energy activities and industrial production in the planning area; the carbon emission economic disturbance model of the planning area is constructed by considering the impact of energy conservation and carbon reduction on the economy of the planning area; With the goal of minimizing carbon emissions in the planning area and minimizing the economic disturbance of carbon emissions in the planning area, a multi-objective optimization model for carbon peak in the planning area is constructed based on the carbon emission model of the planning area, the carbon emission economic disturbance model of the planning area, and the coupling relationship between carbon emissions and economic indicators in the planning area. Based on the improved multi-objective firefly algorithm, the carbon peak multi-objective optimization model of the planning area is optimized and solved, and the optimization results of energy consumption of each energy category, the output optimization results of each industrial product and the optimization results of economic indicators of the planning area at each planning time point during the carbon peak planning period are obtained; Based on the optimization results of energy consumption of each energy category at each planning time point, the optimization results of output of each industrial product and the optimization results of economic indicators, the carbon peak path of the planning area during the carbon peak planning period is planned.

2. The method according to claim 1, characterized in that The carbon emission model of the planning area is constructed by considering the carbon emission impact of energy activities and industrial production in the planning area, including: Determine the energy carbon emission model within the planning area based on the energy categories within the planning area and the carbon emission impact factors of each energy type; Determine the industrial production carbon emission model within the planning area based on the types of industrial products within the planning area and the carbon emission impact factor of each industrial product; Based on the energy carbon emission model of the planning area and the industrial production carbon emission model of the planning area, a carbon emission model of the planning area is constructed.

3. The method according to claim 2, characterized in that The carbon emission economic disturbance model of the planning area is constructed by considering the impact of energy conservation and carbon reduction on the planning area economy, including: For the planning area, determining the relationship between the corresponding carbon emissions and economic growth rate based on historical carbon emissions, historical labor, historical production factors and historical economic data; Determine the consumption utility discount factor at each planning time node during the carbon peak planning period based on the relationship between the carbon emissions and the economic growth rate and the economic data at each planning time node during the carbon peak planning period; For each planned time node within the carbon peak planning period, based on the consumption utility discount factor and the total consumption in the planning area, determine the total discounted utility of consumption at each planned time node; The carbon emission economic disturbance at each planning time node is reversely represented by the total discounted utility of consumption at each planning time node, and the carbon emission economic disturbance model of the planning area is obtained.

4. The method according to claim 3, characterized in that The carbon peak multi-objective optimization model for the planning area is constructed with the goal of minimizing the carbon emissions in the planning area and minimizing the economic disturbance of the carbon emissions in the planning area, based on the carbon emission model of the planning area, the carbon emission economic disturbance model of the planning area, and the coupling relationship between the carbon emissions and economic indicators of the planning area, including: The objective function of the model is to minimize the carbon emissions in the planning area and maximize the total discounted utility of consumption in the planning area; The model constraints are the coupling relationship constraints between carbon emissions and economic indicators in the planning area, the structural change constraints of various energy types in the planning area, the constraints of various industrial products in the planning area, and the economic indicator constraints in the planning area; Based on the model objective function and the model constraints, a multi-objective optimization model for carbon peak in the planning area is obtained.

5. The method according to claim 4, characterized in that The expression of the objective function of the model is: Among them, τ and t are the years in the carbon peak planning period, t∈T, T is the carbon peak planning period, d is the index of energy type, μ dt is the discount coefficient of the dth energy source in year t, X dt is the consumption of the d-th energy in year t, λ t is the carbon emission factor of standard coal in year t; e is the index of industrial product types, is the carbon emission factor of the e-th industrial product in year t; P et is the output of the e-th industrial product in year t; U(t) is the total discounted utility of consumption in the planning area in year t, udf(t) is the utility discount factor in the planning area in year t; C(t) is the total consumption in the planning area in year t, udr(τ) is the utility discount rate in the planning area in year τ, udr(t) is the utility discount rate in the planning area in year t; kpvs(t) is the proportion of the added value of capital in the planning area in year t to the regional GDP, kgdp is the ratio of the capital stock in the base year to the regional GDP in the planning area; depr is the annual depreciation rate of fixed assets in the planning area; grow(t) is the potential economic growth rate of the planning area in year t.

6. The method according to claim 4, characterized in that The constraints on various industrial products in the planning area include the output variation range constraints of various industrial products and the total energy consumption constraints of the industrial production process; The economic indicator constraints within the planning area include the relationship constraints between the total consumption, total output and total investment in the planning area at each planning time node during the carbon peak planning period, the change range constraints of the economic potential growth rate of the planning area, the change range constraints of the total output of the planning area and the change range constraints of the total investment in the planning area.

7. The method according to claim 4, characterized in that The coupling relationship constraint between the carbon emission of the planning area and the economic index is the correlation relationship between the carbon emission of the planning area and the consumption and investment of the planning area.

8. The method according to claim 7, characterized in that The expression for the coupling relationship constraint between carbon emissions and economic indicators in the planning area is: Among them, t is the year within the carbon peak planning period, t∈T, T is the carbon peak planning period, d is the index of energy type, μ dt is the discount coefficient of the dth energy source in year t, X dt is the consumption of the d-th energy in year t, λ t is the carbon emission factor of standard coal in year t; e is the index of industrial product types, is the carbon emission factor of the e-th industrial product in year t; P et is the output of the e-th industrial product in year t; C(t) is the total consumption in the planning area in year t, I(t) is the total investment in the planning area in year t, and A, α, and β are all correlation parameters between carbon emissions and economic indicators.

9. The method according to any one of claims 1 to 8, characterized in that: The improved multi-objective firefly algorithm is used to optimize and solve the carbon peak multi-objective optimization model of the planning area, and the optimization results of energy consumption of each energy category, the output optimization results of each industrial product and the economic indicator optimization results of the planning area at each planning time point during the carbon peak planning period are obtained, including: The energy consumption of each energy category, the output of each industrial product, the total consumption and the total discounted utility of consumption are respectively used as decision variables, a decision vector consisting of a group of decision variables in the feasible solution region of the decision variables is used as a firefly, the value of the objective function corresponding to each decision variable in the decision vector is used as the brightness of the firefly, and the position of the firefly is searched in the feasible solution region based on the brightness of the firefly; Determine the current moving step length of the firefly based on the Levy flight disturbance, and update the position and brightness of the firefly based on the current moving step length and the position search result; Performing individual mutation of the fireflies based on a mutation operation of the decision variables in the decision vector to obtain the mutated positions of the fireflies; The firefly position search, the updating of the position and brightness of the fireflies, and the individual variation of the fireflies are repeated until the iteration termination condition is reached to obtain an optimized decision vector, i.e., the optimized results of energy consumption of each energy category at each planning time point, the optimized results of the output of each industrial product, the optimized results of total consumption, and the optimized results of the total discounted utility of consumption.

10. A carbon peak path planning system considering energy conservation and carbon reduction costs, characterized in that: include: The carbon emission economic model construction module is used to construct a carbon emission model for a planning area during a carbon peak planning period, taking into account the carbon emission impact of energy activities and industrial production in the planning area; and to construct a carbon emission economic disturbance model for the planning area by taking into account the impact of energy conservation and carbon reduction on the economy of the planning area; The multi-objective optimization model building module is used to build a multi-objective optimization model for carbon peak in the planning area based on the carbon emission model of the planning area, the carbon emission economic disturbance model of the planning area, and the coupling relationship between carbon emissions and economic indicators in the planning area, with the goal of minimizing carbon emissions in the planning area and minimizing the economic disturbance of carbon emissions in the planning area. An optimization solution module is used to optimize and solve the carbon peak multi-objective optimization model of the planning area based on the improved multi-objective firefly algorithm, and obtain the optimization results of energy consumption of each energy category in the planning area at each planning time point during the carbon peak planning period, the optimization results of the output of each industrial product, and the optimization results of economic indicators; The path planning module is used to plan the carbon peak path for the planning area during the carbon peak planning period based on the optimization results of energy consumption of each energy category at each planning time point, the optimization results of output of each industrial product and the optimization results of economic indicators.

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