Method for constructing carbon reduction transformation optimization model of energy system

Abstract

The invention relates to the technical field of carbon reduction transformation evaluation models, in particular to a method for constructing a carbon reduction transformation optimization model of an energy system, and solves the defect that a traditional bottom-up system optimization model does not sufficiently describe association of all departments and all industries in the prior art on the whole. According to the method, the problem that optimization analysis and carbon reduction transformation of a whole social system transformation scheme from the global perspective are not facilitated is solved, local balance is achieved through energy market clearing, and in other words, set energy service requirements are met through cost minimization under a plurality of limiting conditions; therefore, the method is suitable for evaluating the emission reduction potential of a technology taking cost as an optimization core in a department.

Description

Technical Field

[0001] This invention relates to the field of carbon reduction transition assessment model technology, specifically a method for constructing an optimization model for carbon reduction transition of energy systems. Background Technology

[0002] Faced with the escalating pressure of climate change and the increasingly urgent need for dual-carbon goals, how various industries can achieve low-carbon transformation has become a crucial and pressing practical issue. The task of low-carbon transformation is to find the optimal systemic transformation plan, including sectoral transformation goals and implementation paths. This is a complex systemic problem encompassing multiple fields such as economy, energy, and environment. Therefore, optimizing the selection of multiple transformation plans within a comprehensive assessment framework has become an important research tool for optimizing transformation paths.

[0003] Current mainstream assessment models only study a single country or a portion of a region, which fails to guarantee the reliability of data—especially key parameters—and cannot provide higher resolution. Furthermore, most regional models do not address the shortcomings of traditional bottom-up system optimization models in their insufficient characterization of inter-sectoral and inter-industry relationships. Instead, they still make independent assumptions and optimizations for each industry, and the transformation plans for multiple sectors of society are simply an accumulation of multiple single-sector transformation plans, which is not conducive to optimizing and analyzing the transformation plans of the entire social system from a global perspective. Therefore, research on optimization models oriented towards multi-sectoral technology optimization, based on local actual data and in the context of climate change and dual-carbon goals, focusing on a holistic and comprehensive assessment of low-carbon transformation, remains insufficient.

[0004] Based on the above reasons, this invention designs a method for constructing an optimization model for the carbon reduction and transformation of energy systems. Starting from energy service demand, it predicts the energy service demand of different sectors of society using socio-economic data (such as population, economy, industrial structure, etc.). Then, under the condition of meeting energy service demand and other constraints, it selects technologies based on the principle of minimizing energy costs, thereby calculating the energy consumption of different technologies. Furthermore, it calculates the greenhouse gas and air pollutant emissions of the energy system through the carbon emission / pollutant emission factors of different energy sources. From the demand side, it makes a comprehensive assessment of the emission reduction potential of technologies with cost as the core of optimization in the low-carbon transformation process in various sectors. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method for constructing an optimization model for the carbon reduction and transformation of energy systems. Starting from energy service demand, the method predicts the energy service demand of different sectors in various provinces using socio-economic data (such as population, economy, and industrial structure). Then, under the premise of meeting energy service demand and other constraints, the method selects technologies based on the principle of minimizing energy costs, thereby calculating the energy consumption of different technologies. Furthermore, the method calculates the greenhouse gas and air pollutant emissions of the energy system through the carbon emission / pollutant emission factors of different energy sources. From the demand side, the method makes a comprehensive assessment of the emission reduction potential of technologies with cost as the core of optimization in the low-carbon transformation process in various sectors.

[0006] To achieve the above objectives, this invention provides a method for constructing an optimization model for the carbon reduction and transformation of an energy system, comprising the following:

[0007] S1 initializes various parameters for the basic year of technological evolution in various industries;

[0008] S2, given the target year and policy constraints for the transformation of various industries;

[0009] S3, set target values ​​for different scenarios and transition stages;

[0010] S4 predicts the service demand of each department throughout the entire simulation period;

[0011] S5, determine the specific detailed parameters of the technologies involved in the transformation of different departments during the simulation period, and the technical details such as unit energy service demand and unit energy consumption, as well as various technical costs, are obtained from public literature;

[0012] S6, calculates and calibrates historical year data based on the collected data;

[0013] S7, under constraints, takes minimizing total cost as the objective function, iteratively calculates the technical solutions that meet the requirements under different scenarios, gives the greenhouse gas emissions, and assesses the emission reduction potential of each sector;

[0014] S7-1, the constraints include emission constraints, technology constraints, energy service supply and demand constraints, energy constraints, operational capacity constraints and the dynamic balance of technology capacity;

[0015] S7-2, the total cost includes technology investment costs, operating and maintenance costs, energy and raw material costs, energy taxes and emissions taxes;

[0016] The formula for the objective function is:

[0017] Formula 1:

[0018]

[0019] Emissions originate from production process emissions and fuel combustion, as shown in the following formula:

[0020] Formula 2:

[0021] The emission constraint is: the total emissions from all departments and equipment must be less than or equal to the maximum emission limit, as shown in the formula:

[0022] Formula 3: ∑ sg∈gg VQ sg ≤Q gg .

[0023] The technology constraint is that the proportion of service output from a specific technology in total service output must fall within a maximum and minimum limit, as shown in the formula:

[0024] Formula 4:

[0025] The supply and demand constraints for energy services are:

[0026] The constraint that service supply meets service demand is expressed by the following formula:

[0027] Formula 5:

[0028] For endogenous energy and endogenous services group Ω sd,se Endogenous services satisfy endogenous needs, and their formula is:

[0029] Formula Six:

[0030] The energy constraint is defined as the energy supply falling between a maximum and a minimum limit, and its formula is:

[0031] Formula 7:

[0032] The operational capacity constraint is: the amount of technology in operation shall not exceed the product of the existing technology stock and the operating rate, as shown in the formula:

[0033] Formula 8: VX st ≤(1+η st )·VS st .

[0034] The dynamic balance of technological capacity is defined as follows: the technological stock of the current year is the sum of the remaining technological stock from the previous year, taking into account technological obsolescence, and the technological stock newly invested in during the current year. The formula is as follows:

[0035] Formula Nine:

[0036] Compared with existing technologies, the model of this invention achieves partial equilibrium through energy market clearing, that is, by minimizing costs under certain constraints to meet the given energy service demand. Therefore, it is suitable for evaluating the emission reduction potential of technologies with cost as the core of optimization in a sector.

[0037] This invention starts with energy service demand, predicting the energy service needs of different sectors using socioeconomic data (such as population, economy, and industrial structure). Then, based on the principle of minimizing energy costs while meeting energy service needs and other constraints, it selects technologies to calculate the energy consumption of different technologies. Furthermore, it calculates the greenhouse gas and air pollutant emissions of the energy system using carbon emission / pollutant emission factors of different energy sources. The model allows each province to set parameters such as service demand, policy constraints, and resource endowment, enabling more targeted regional carbon reduction pathway analysis.

[0038] In addition, the model of this invention can be used to assess the impact of different policies on energy service demand and technology choices, and further evaluate the environmental and economic impacts of different policies on the energy system. Compared with existing assessment models, this invention achieves multi-sectoral emission reduction path optimization with higher spatial resolution and has the flexible expansion capability from the national overall level to the provincial level, providing more targeted decision support for policy making. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the IMED|TEC model framework of the present invention.

[0040] Figure 2 This is a schematic diagram of the flow of cement departments in China according to an embodiment of the present invention. Detailed Implementation

[0041] The present invention will now be further described with reference to the accompanying drawings.

[0042] See Figures 1-2 This invention provides a method for constructing an optimization model for the carbon reduction and transformation of energy systems, which is called the IMED|TEC model:

[0043] 1) Future service requirements are derived from external models or scenario analysis.

[0044] 2) Optimize the selection of production technologies based on meeting service requirements.

[0045] 3) Calculate the amount of energy consumed, carbon emissions, and pollutant emissions during the production process.

[0046] Therefore, the model outputs the energy consumption, carbon dioxide, and pollutant emissions for the industry's target year.

[0047] Furthermore, this embodiment describes the specific implementation steps of the IMED|TEC multi-sector technology optimization model, and takes the provincial cement industry as an example to illustrate the paradigm of IMED|TEC in depicting the industry's technology evolution path.

[0048] Step 1: Initialize various parameters for the base year of technological evolution in each industry. Taking the cement sector as an example, the base year is selected as 2020, and the energy flow of the cement sector in each province is simulated, such as... Figure 2 As shown.

[0049] Step 2: Given the target year and policy constraints for industry transformation. In this example, the carbon emission targets for the cement sector in each province are used as policy constraints to assess the synergistic benefits of different targets in carbon reduction and air pollution improvement. The model uses 2060 as the policy target year. In this example, near-zero emissions from the cement sector in each province by 2060 are used as the constraint.

[0050] Step 3: Set target values ​​for different transition phases under different scenarios. The phased transition target under the strictly constrained policy scenario is to peak carbon emissions before 2030 and transition to the emission reduction period as quickly as possible. The relaxed policy scenario aims to peak carbon emissions before 2040. Finally, the baseline scenario does not set phased targets.

[0051] Step 4: Predict the service demand of each sector throughout the entire simulation period. In this example, the future cement production demand data is calculated based on the per capita cement ceiling and dynamic material flow analysis of each province in the literature. China's future cement demand will decrease from 2 billion tons in 2020 to 1.4 billion tons in 2060. The cement demand of each province will be determined based on the historical cement stock, future cement saturation, GDP and population development level of each province.

[0052] Step 5: Determine the specific detailed parameters of the technologies involved in the transformation of different sectors during the simulation period. Technical details such as unit energy service demand and unit energy consumption, as well as various technology costs, are obtained from literature. In this example, the energy consumption and pollutant emission factors of various cement-related technologies are also derived from publicly available data from both domestic and international sources.

[0053] Step 6: Calculate and calibrate the historical data based on the collected data.

[0054] Step 7: Building upon Steps 1-6 and under constraints, since this model simulates technology choices across sectors at the lowest cost to achieve carbon constraints, iteratively calculates the technological solutions that meet the requirements under different scenarios based on the cost minimization objective function, and provides greenhouse gas emissions to assess the emission reduction potential of each sector. The simulation process concludes, and the formulas are shown below:

[0055] Formula 1:

[0056]

[0057] The constraints include emission constraints, technological constraints, energy service supply and demand constraints, energy constraints, operational capacity constraints, and the dynamic balance between technological capacity. These constraints refer to the need for the model to impose constraints on various parameters in order to find the optimal solution. For example, the technological constraint could be that by 2030, the proportion of new dry-process cement kilns in a certain province of the "cement sector" is more than 90%. In this case, a technological constraint of cement kiln proportion ≥ 90% would be imposed on 2030 as a constraint.

[0058] S7-2, the total cost includes technology investment costs, operation and maintenance costs, energy and raw material costs, energy taxes and emissions taxes; the total cost here is based on the fact that the objective function of this model is to minimize costs.

[0059] Emissions originate from production process emissions and fuel combustion, as shown in the following formula:

[0060] Formula 2:

[0061] The emission constraint is: the total emissions from all departments and equipment must be less than or equal to the maximum emission limit, as shown in the formula:

[0062] Formula 3: ∑ sg∈gg VQ sg ≤Q gg .

[0063] The technology constraint is that the proportion of service output from a specific technology in total service output must fall within a maximum and minimum limit, as shown in the formula:

[0064] Formula 4:

[0065] The supply and demand constraints for energy services are:

[0066] The constraint that service supply meets service demand is expressed by the following formula:

[0067] Formula 5:

[0068] For endogenous energy and endogenous services group Ω sd,se Endogenous services must meet endogenous needs, and the formula is:

[0069] Formula Six:

[0070] Endogenous here refers to the energy and services inherent in each production process itself.

[0071] The energy constraint is defined as the energy supply falling between a maximum and a minimum limit, and its formula is:

[0072] Formula 7:

[0073] The operational capacity constraint is: the amount of technology in operation shall not exceed the product of the existing technology stock and the operating rate, as shown in the formula:

[0074] Formula 8: VX st ≤(1+η st )·VS st .

[0075] The dynamic balance of technological capacity is defined as follows: the technological stock of the current year is the sum of the remaining technological stock from the previous year, taking into account technological obsolescence, and the technological stock newly invested in during the current year. The formula is as follows:

[0076] Formula Nine:

[0077] Where i represents the department, t represents the year, C represents the total cost, st represents the technology, and β represents the cost. st Let α be the subsidy rate for technology st, and L be the discount rate. st For the lifespan of technology, IC st For the initial investment cost of technology ST, OMC st The operating and maintenance cost of technology ST, SE is the energy type, SM is the raw material, and Mpri is the cost of operation and maintenance. sm It's the price of raw materials, E sm·st It is the consumption of raw material sm for operating unit technology st, VX st It refers to the operating volume of technology (ST), gas type (SG), and TAX. sg It's an emissions tax on gaseous substances (SG) and VQ. sg It refers to the emissions of gas sg, TAXE. se It is the energy tax of energy se, VE se It is energy consumption. It is the emission of ST gas SG by the operating unit technology, γ sg It refers to the removal rate of pollutant SG by end-of-pipe treatment technology, EMF se,sg It is the emission factor of energy se sg, EX st It is the energy efficiency improvement rate of technology ST, NE st,se Q is the proportion of energy (se) in technology (st) that is not used for combustion. gg It is the maximum emission limit for gas group GG. It represents the upper limit of the proportion of technical service output (ST) in total service output (SD). A is the lower limit of the proportion of service output of technology ST in total service output SD. st,sd It is the output of the operating unit technology st, T sd It is the total service output of all technologies, SDV sd It's a service demand, Ω sd,seIt is the endogenous energy and endogenous services group (se, sd). It is the maximum energy supply, se. It is the minimum energy supply se, VS st It is the stock of technology st, η st It is the running rate of technology ST, SS st It is the stock of technology ST from the previous year, VR st This represents the current investment amount in technology ST.

[0078] The above are merely preferred embodiments of the present invention, intended only to aid in understanding the method and core ideas of this application. The scope of protection of the present invention is not limited to the above embodiments; all technical solutions falling within the scope of the present invention's concept are within its protection. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

[0079] This invention addresses the shortcomings of existing technologies in failing to adequately characterize the interconnectedness of all sectors and industries in traditional bottom-up system optimization models. This hinders the optimization and analysis of carbon reduction transformation plans for the entire social system from a global perspective. It achieves partial equilibrium through energy market clearing, that is, by minimizing costs under certain constraints to meet predetermined energy service demands. Therefore, it is suitable for evaluating the emission reduction potential of cost-based optimization technologies within a sector.

Claims

1. A method for constructing an optimization model for the carbon reduction and transformation of an energy system, characterized in that, Includes the following: S1 initializes various parameters for the basic year of technological evolution in various industries; S2, given the target year and policy constraints for the transformation of various industries; S3, set target values ​​for different scenarios and transition stages; S4 predicts the service demand of each department throughout the entire simulation period; S5, determine the specific detailed parameters of the technologies involved in the transformation of different departments during the simulation period, and the technical details of unit energy service demand and unit energy consumption, as well as the costs of various technologies, are obtained from public literature; S6, calculates and calibrates historical year data based on the collected data; S7, under constraints, takes minimizing total cost as the objective function, iteratively calculates the technical solutions that meet the requirements under different scenarios, gives the greenhouse gas emissions, and assesses the emission reduction potential of each sector; S7-1, the limiting conditions include emission constraints, technology constraints, energy service supply and demand constraints, energy constraints, operational capacity constraints, and the dynamic balance of technological capacity; S7-2, the total cost includes technology investment costs, operating and maintenance costs, energy and raw material costs, energy taxes and emissions taxes; The formula for the objective function is: Formula 1:

2. The method for constructing an optimization model for the carbon reduction and transformation of an energy system according to claim 1, characterized in that, The emissions originate from emissions during the production process and fuel combustion, as shown in the following formula: Formula 2:

3. The method for constructing an optimization model for the carbon reduction and transformation of an energy system according to claim 1, characterized in that, The emission constraint is that the total emissions from all departments and equipment must be less than or equal to the maximum emission limit, and the formula is as follows: Formula 3: ∑ sg∈gg VQ sg ≤Q gg .

4. The method for constructing an optimization model for carbon reduction and transformation of energy systems according to claim 1, characterized in that, The technical constraint is that the proportion of service output from a specific technology in the total service output is within a maximum and minimum limit, and the formula is as follows: Formula 4:

5. The method for constructing an optimization model for carbon reduction and transformation of energy systems according to claim 1, characterized in that, The energy service supply and demand constraints are as follows: The constraint that service supply meets service demand is expressed by the following formula: Formula 5: For endogenous energy and endogenous services group Ω sd,se Endogenous services satisfy endogenous needs, and their formula is: Formula Six:

6. The method for constructing an optimization model for carbon reduction and transformation of energy systems according to claim 1, characterized in that, The energy constraint is defined as follows: the energy supply is between a maximum and a minimum limit, and the formula is as follows: Formula 7:

7. The method for constructing an optimization model for carbon reduction and transformation of energy systems according to claim 1, characterized in that, The operational capacity constraint is: the amount of technical operation shall not exceed the product of the technical inventory and the operating rate, and the formula is: Formula 8: VX st ≤(1+η st )·VS st .

8. The method for constructing an optimization model for carbon reduction and transformation of energy systems according to claim 1, characterized in that, The dynamic balance of technological capacity is defined as follows: the current year's technological stock is the sum of the remaining technological stock from the previous year, taking into account technological obsolescence, and the technological stock newly invested in during the current year. The formula is as follows: Formula Nine:

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