A method and system for collaborative planning of energy system pollution reduction and carbon reduction

Abstract

The present application provides a kind of energy system pollution reduction and carbon reduction collaborative planning method and system, it is related to system management technical field, the present application constructs the system structure covering the whole process of energy exploitation, processing, transformation, import and export and terminal consumption, integrates the multi-dimensional database of energy supply and demand, carbon emission and main pollutant emission;Introduce multiple uncertain planning method to represent uncertain factors such as energy price, technological progress and policy constraints, set multiple scenarios;With the goal of maximizing system benefit, set carbon emission, pollutant emission, energy security and capacity constraints in a unified framework, optimize energy structure, conversion path and emission reduction technology combination to achieve the collaborative promotion of pollution reduction and carbon reduction;Use multi-scenario optimization algorithm to solve the model, and identify the robust energy structure and emission reduction path through sensitivity and risk analysis.The present application can provide scientific and robust decision support for carbon peak, carbon neutral and atmospheric environmental improvement.

Description

Technical Field

[0001] This invention relates to the field of system management technology, and in particular to a method and system for collaborative planning of pollution reduction and carbon reduction in energy systems. Background Technology

[0002] Currently, energy system planning models mainly include: (1) energy system optimization models based on linear programming, mixed-integer linear programming, and multi-objective programming; (2) comprehensive evaluation models based on GCAM, TIMES, IMAGE, MESSAGE, etc.; (3) socio-economic models based on LEAP and LMDI; and (4) computable general equilibrium (CGE) models. These models have provided valuable experience for energy structure transformation, carbon emission reduction policy evaluation, and coordinated air pollution control in different countries and regions.

[0003] However, existing planning methods still have several shortcomings: (1) Most planning methods are based on deterministic assumptions, which makes it difficult to fully reflect the inherent uncertainties in future energy supply and demand, emission policies, technological progress and environmental governance, resulting in insufficient reliability of planning results in practical applications; (2) Although some studies have introduced scenario analysis, they mainly stay at the level of setting external parameters and lack systematic modeling of random uncertainties (such as the impact of population and economic development on energy demand) and fuzzy uncertainties (such as policy changes and emission standards); (3) Most models only cover some links of the energy system, such as the supply side or a single sector, and lack a comprehensive characterization of the entire chain of energy extraction, processing, conversion, import and export and end use, making it difficult to support long-term strategic decisions at the macro level; (4) Current studies usually aim at minimizing costs or maximizing benefits, and lack a systematic analysis of the synergistic effect between carbon emission reduction and pollutant emission reduction (such as sulfur dioxide SO2, nitrogen oxides NOx and particulate matter PM), and fail to fully assess the operational robustness and risks of the energy system under the constraints of pollution reduction and carbon reduction. Summary of the Invention

[0004] To address the aforementioned issues, this application proposes a collaborative planning method for energy system pollution reduction and carbon reduction. Under a unified optimization framework, this method constructs an energy system model covering the entire process of energy extraction, processing, conversion, import / export, and end-use, comprehensively considering multi-dimensional factors such as energy supply, demand, policy constraints, carbon emissions, and air pollutant emissions. While ensuring compliance with energy security, installed capacity constraints, energy demand, and emission reduction requirements, it maximizes system benefits and comprehensively depicts the long-term evolution path of the energy system. Simultaneously, by introducing sulfur dioxide (SO2) and nitrogen oxides (NOx)... xBy constraining the reduction of major pollutants such as carbon emissions and particulate matter (PM), and achieving synergistic optimization of carbon emission reduction and pollutant emission reduction, the scientific nature, robustness and operability of energy system planning can be improved, providing effective decision support for achieving carbon peaking, carbon neutrality and improving air quality.

[0005] Another objective of this invention is to propose a collaborative planning system for energy system pollution reduction and carbon reduction.

[0006] To achieve the above objectives, a first aspect of the present invention proposes a collaborative planning method for pollution reduction and carbon reduction in energy systems, comprising:

[0007] S1 constructs a system structure covering the entire process of energy extraction, processing, conversion, import and export, and end-consumer, and establishes a multi-dimensional database including energy supply and demand, carbon emissions, and emissions of major pollutants. System integration is achieved through data cleaning and matching. S2 incorporates uncertainties into the model, employs a multi-uncertainty programming approach for joint characterization, and depicts different future development paths by setting various policy and economic scenarios; wherein, the uncertainties include energy prices, technological progress, policy constraints, and emission factors; S3 aims to maximize system benefits. Under a unified framework, it sets constraints on carbon emissions, pollutant emissions, energy security, and capacity. By optimizing the energy structure, conversion paths, and emission reduction technology combinations, it determines the optimal configuration under different scenarios to achieve synergistic progress in pollution reduction and carbon reduction. S4 employs a multi-scenario optimization algorithm to solve the model, obtaining the optimal energy system scheme under each scenario. Through sensitivity and risk analysis, it identifies robust energy structures and emission reduction paths, realizing the transformation from single optimality to multi-scenario robust decision-making.

[0008] In one embodiment of the present invention, the construction of a system structure covering the entire process of energy extraction, processing, conversion, import and export, and end-use consumption, and the establishment of a multi-dimensional database including energy supply and demand, carbon emissions, and emissions of major pollutants, and the realization of system integration through data cleaning and matching, includes: Based on energy statistics yearbooks and industry reports, we have sorted out the complete material and energy flows of various energy sources from primary extraction to end consumption, forming a system topology covering coal, oil, natural gas, nuclear energy and renewable energy. Based on the system topology, energy supply, consumption, carbon dioxide emission factors, and sulfur dioxide, nitrogen oxide, and particulate matter emission factors for each link are extracted from public databases and industry monitoring platforms to construct a multidimensional database. The system identifies and imputes outliers and missing values ​​in the multidimensional database, and maps field names and units of measurement from different data sources in a unified manner to complete system integration.

[0009] In one embodiment of the present invention, incorporating uncertainties into the model, employing a multiple uncertainty programming method for joint characterization, and characterizing different future development paths by setting multiple policy and economic scenarios includes: Energy prices and technological progress parameters are constructed as random variables that follow a preset probability distribution, and policy constraints and emission factor parameters are constructed as fuzzy interval numbers based on expert judgment. Uncertainty is represented by joint modeling of stochastic programming and fuzzy programming. Based on the aforementioned uncertainty characterization, various policy and economic scenarios are set, including the baseline scenario, the enhanced emission reduction scenario, and the high economic growth scenario, and corresponding parameter value ranges are assigned to each scenario.

[0010] In one embodiment of the present invention, the step of setting constraints such as carbon emissions, pollutant emissions, energy security, and capacity within a unified framework with the goal of maximizing system benefits, and determining the optimal configuration under different scenarios by optimizing the energy structure, conversion paths, and emission reduction technology combinations to achieve the synergistic promotion of pollution reduction and carbon reduction includes: Construct an objective function with the goal of maximizing the net comprehensive benefits of the system. The objective function covers the benefits of end-use energy consumption, energy extraction costs, processing costs, conversion costs, operation and maintenance costs, fixed costs of capacity expansion, and emission reduction costs of sulfur dioxide, nitrogen oxides and particulate matter. Under the aforementioned unified framework, constraints are set for non-electric energy demand, power supply security, pollutant emission reduction, and carbon capture, utilization, and storage emission reduction, forming a constraint system. Based on the objective function and constraint system, the energy structure, conversion path and emission reduction technology combination are synergistically optimized by a linear programming solver to obtain the optimal configuration scheme under each scenario.

[0011] In one embodiment of the present invention, the power supply security constraints include: power supply and demand balance constraints, dynamic balance constraints of installed capacity, upper and lower limits of power generation constraints, and scale constraints of installed capacity construction.

[0012] In one embodiment of the present invention, the step of using a multi-scenario optimization algorithm to solve the model, obtaining the optimal energy system scheme under each scenario, and identifying robust energy structures and emission reduction paths through sensitivity and risk analysis, thereby realizing the transformation from a single optimal solution to multi-scenario robust decision-making, includes: Based on the improved stochastic programming algorithm, the pollution reduction and carbon reduction synergistic optimization model is iteratively solved under each scenario, and the optimal configuration scheme of energy structure, conversion path and emission reduction technology combination corresponding to each scenario is output. Sensitivity analysis is performed on the key parameters of the optimal configuration scheme to identify the core factors affecting system benefits and emission reduction effects. Based on the probability distribution of the core factors, risk quantification assessment is carried out to screen out energy structures and emission reduction paths with high robustness under different scenarios.

[0013] To achieve the above objectives, a second aspect of the present invention proposes an energy system pollution reduction and carbon reduction collaborative planning system, comprising: The multidimensional database construction module is used to build a system structure covering the entire process of energy extraction, processing, conversion, import and export and end consumption, and to establish a multidimensional database containing energy supply and demand, carbon emissions and emissions of major pollutants. System integration is achieved through data cleaning and matching. The joint characterization module is used to incorporate uncertain factors into the model, employ a multiple uncertainty programming method for joint characterization, and characterize different future development paths by setting various policy and economic scenarios; wherein, the uncertain factors include energy prices, technological progress, policy constraints, and emission factors; The optimal configuration module is used to set constraints such as carbon emissions, pollutant emissions, energy security and capacity under a unified framework with the goal of maximizing system benefits. By optimizing the energy structure, conversion path and emission reduction technology combination, it determines the optimal configuration under different scenarios and achieves the synergistic promotion of pollution reduction and carbon reduction. The model solving module is used to solve the model using a multi-scenario optimization algorithm to obtain the optimal energy system scheme under each scenario. Through sensitivity and risk analysis, it identifies robust energy structures and emission reduction paths, realizing the transformation from single optimality to multi-scenario robust decision-making.

[0014] The energy system pollution reduction and carbon reduction coordinated planning method and system of the present invention can effectively solve the problems of excessive deterministic assumptions, incomplete system coverage and insufficient coordination of pollution reduction and carbon reduction in existing energy planning methods, realize the coordinated reduction of carbon emissions and multiple pollutants, and improve the scientificity and robustness of planning results.

[0015] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a collaborative planning method for pollution reduction and carbon reduction in an energy system, provided as an embodiment of the present invention; Figure 2 An architecture diagram of the energy system pollution reduction and carbon reduction collaborative planning method provided in the embodiments of the present invention; Figure 3 This is a schematic diagram illustrating primary energy supply schemes under different scenarios provided in embodiments of the present invention; Figure 4 This is a schematic diagram illustrating future CO2 and pollutant emissions and their synergistic effects under the carbon neutrality target provided by an embodiment of the present invention. Figure 5 This is a structural diagram of the energy system pollution reduction and carbon reduction collaborative planning system provided in an embodiment of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0019] The following describes, with reference to the accompanying drawings, a collaborative planning method and system for energy system pollution reduction and carbon reduction according to an embodiment of the present invention.

[0020] Example 1 This embodiment provides a method for coordinated planning of pollution reduction and carbon reduction in energy systems. For example... Figure 1 As shown, it includes: S1 constructs a system structure covering the entire process of energy extraction, processing, conversion, import and export, and end-consumer, and establishes a multi-dimensional database including energy supply and demand, carbon emissions, and emissions of major pollutants. System integration is achieved through data cleaning and matching.

[0021] Specifically, this method first requires the construction of a framework structure that can comprehensively reflect the overall operation of the energy system. This structure needs to cover the entire chain from the initial acquisition of primary energy, to the processing and conversion in the intermediate stages, to the cross-regional allocation, and finally to the consumption by various end users.

[0022] Based on this structure, a multi-dimensional information set integrating energy supply and demand data, carbon emission data, and major air pollutant emission data is further established. The establishment of this information set is not a simple data accumulation, but rather involves cleaning, verifying, and correlating data from different sources with different formats and dimensions to form a logically consistent and mutually referential integrated data system. This process aims to lay a unified and reliable data foundation for subsequent model building and scenario analysis, ensuring that the entire planning methodology can operate within a realistic and complete system boundary. As one implementation method, this step specifically involves: clearly defining the specific energy types, technology paths, and emission nodes involved in energy extraction, processing, conversion, import / export, and end-use consumption; and establishing a multi-dimensional database containing an energy supply and demand balance sheet, a carbon emission inventory, and emission inventories of major pollutants such as sulfur dioxide, nitrogen oxides, and particulate matter; and then achieving system integration between the various sub-databases through data cleaning and matching algorithms.

[0023] By constructing a system structure covering the entire energy chain and integrating multi-dimensional data, this method can effectively overcome decision-making biases caused by incomplete system boundaries or fragmented data in traditional planning. It provides comprehensive data support and structural foundation for subsequent overall consideration of energy security, economic benefits, and environmental constraints under a unified framework, thereby significantly improving the scientific nature and comprehensiveness of the planning scheme.

[0024] In this embodiment, step S1 involves constructing a system structure covering the entire process of energy extraction, processing, conversion, import / export, and end-use consumption, and establishing a multi-dimensional database containing energy supply and demand, carbon emissions, and emissions of major pollutants. System integration is achieved through data cleaning and matching, specifically including the following implementation methods. First, based on energy statistical yearbooks and industry reports, the complete material and energy flows of various energy sources from primary extraction to end-use consumption are analyzed to form a system topology covering coal, oil, natural gas, nuclear energy, and renewable energy. Specifically, this topology clarifies the extraction volume of various primary energy sources (such as raw coal, crude oil, and natural gas) in the energy extraction stage, the processing volume of oil refining, coal washing, and other processing processes in the processing stage, the conversion volume of power generation, heating, and other conversion processes in the conversion stage, the import / export volume of various energy sources in the import / export stage, and the energy demand types and allocation volumes of different end-use sectors such as agriculture, industry, construction, and transportation in the end-use stage. This topology provides a unified framework and mapping relationship for subsequent data integration. Secondly, based on the system topology, energy supply, consumption, carbon dioxide emission factors, and emission factors for sulfur dioxide, nitrogen oxides, and particulate matter at each stage are extracted from public databases and industry monitoring platforms to construct a multidimensional database. In one possible implementation, energy supply and consumption data are sourced from the National Energy Statistics Yearbook, and emission factor data are sourced from pollution source census data published by the Ministry of Ecology and Environment or the IPCC emission factor database. This multidimensional database stores energy flow and emission coefficients at each stage, indexed by planning period t, energy type m, and end-use sector u. Finally, outliers and missing values ​​in the multidimensional database are identified and imputed, and field names and units of measurement from different data sources are uniformly mapped to complete system integration. Specifically, outliers are identified using the 3σ principle based on statistical distribution, and missing values ​​are imputed using linear interpolation or a moving average method based on historical data. Regarding field mapping, field names representing the same energy type or emission stage from different data sources (such as "raw coal consumption" and "coal terminal consumption") are standardized to standard names, and measurement units (such as "10,000 tons of standard coal" and "GWh") are uniformly converted according to standard conversion factors. Through these processes, a complete, formatted, and logically consistent multidimensional database is formed, providing a reliable data foundation for subsequent model construction and solving.

[0025] This specific implementation method ensures the accuracy and consistency of the multidimensional database through a systematic data collection, cleaning, and integration process, thereby providing high-quality data support for subsequent uncertainty characterization and optimization model construction, and significantly improving the scientificity and reliability of the planning results.

[0026] S2 incorporates uncertainties into the model, employs a multi-uncertainty programming approach for joint characterization, and delineates different future development paths by setting various policy and economic scenarios. Among these uncertainties are energy prices, technological progress, policy constraints, and emission factors.

[0027] Specifically, in the process of energy system planning, key parameters such as future energy prices, the rate of technological progress, policy orientation, and emission factors all have significant uncertainties, which directly affect the reliability and robustness of the planning results.

[0028] Therefore, this step aims to systematically incorporate the aforementioned uncertainties into the optimization model, employing a multiple uncertainty programming approach to jointly represent and quantify these factors. Specifically, this method can simultaneously handle stochastic uncertainties (such as fluctuations in energy demand and changes in resource endowment) and fuzzy uncertainties (such as policy target ranges and technology cost ranges), thereby overcoming the limitations of traditional deterministic programming methods that rely on single parameter assumptions. Based on this, by setting multiple scenarios covering different policy intensities, economic development paths, and technological evolution speeds, a decision space reflecting possible future development paths is constructed. This scenario setting is not a simple parameter adjustment, but rather a comprehensive analysis of the system's key driving factors, forming a set of multiple scenarios with inherent logical consistency, enabling the model to simulate the system's response behavior under high uncertainty conditions in different external environments. As one implementation method, uncertainties such as energy prices, technological progress, policy constraints, and emission factors can be incorporated into the model, jointly represented using a multiple uncertainty programming approach combining stochastic and fuzzy programming, and characterizing different future development paths by setting multiple policy and economic scenarios.

[0029] By introducing a multi-uncertainty programming method and constructing a multi-scenario analysis framework, this step significantly improves the adaptability and decision robustness of the energy system planning model in complex and uncertain environments, avoids planning biases caused by a single deterministic assumption, and provides a more scientific and comprehensive input basis for subsequent optimization solutions, thereby enhancing the ability of the final planning scheme to cope with multiple possibilities in the future.

[0030] Furthermore, in this embodiment, the uncertainty characterization and scenario setting described in step S2 are implemented in the following specific ways. First, for the two types of uncertainties with historical statistical regularities—energy prices and technological progress—they are constructed as random variables that follow a preset probability distribution. Specifically, for example, the future planning periods' energy prices such as coal, oil, and natural gas are set as random variables that follow a log-normal distribution, with their mean and variance determined based on time series analysis of historical price data; the efficiency improvement rate and cost reduction rate of various energy conversion technologies are set as random variables that follow a triangular distribution, with their most likely value, lower limit, and upper limit determined based on the historical trend of the technology learning curve and expert predictions. The probability distribution parameters of these random variables constitute the input source for the stochastic programming part. Second, for the two types of uncertainties that are difficult to describe with precise probability distributions—policy constraints and emission factors—they are constructed as fuzzy interval numbers based on expert judgment. Specifically, for example, policy constraint parameters such as total carbon emission control targets and pollutant emission limits are set as trapezoidal fuzzy numbers, with the four endpoints of their membership functions jointly determined by policy planning departments and industry experts based on the leniency or strictness of policy guidance. Emission factors for sulfur dioxide, nitrogen oxides, and particulate matter from various energy sources in their extraction, processing, conversion, and end-use stages are set as triangular fuzzy numbers, with parameter value ranges reflecting the possible fluctuation ranges under different technological levels and regulatory intensities. The membership functions of these fuzzy interval numbers constitute the input source for the fuzzy programming part. Based on this, by jointly modeling stochastic programming and fuzzy programming—that is, within the same optimization framework, simultaneously incorporating the probability distribution of random variables and the membership functions of fuzzy parameters into the construction of constraints and objective functions—a systematic characterization of multiple uncertainties is achieved. Subsequently, based on the above uncertainty characterization results, various policy and economic scenarios are set to depict different future development paths. Specifically, a baseline scenario is defined, in which all random variables and fuzzy parameters take their median or most probable values, representing the continuation of current policies and the conventional technological development path. An enhanced emission reduction scenario is defined, in which policy constraint parameters take the strict end of the fuzzy interval, emission factors take the lower limit of the fuzzy interval, and technological progress parameters take the high end of the probability distribution, representing a path of strong policy intervention and rapid technological iteration. A high economic growth scenario is defined, in which energy price parameters take the high end of the probability distribution, and policy constraint parameters take the lenient end of the fuzzy interval, representing a path of rapid economic growth and relatively lenient environmental policies. After assigning corresponding parameter value ranges to each scenario, these parameterized scenarios serve as inputs to the optimization model in subsequent step S3, enabling the model to solve for the optimal configuration under different uncertainty conditions.

[0031] This specific implementation significantly improves the model's accuracy in depicting complex and uncertain environments by distinguishing uncertain factors into random variables and fuzzy interval numbers and jointly modeling them. At the same time, it covers different possible future development paths through multiple scenario settings, thereby providing a more scientific and comprehensive input basis for subsequent robust decision-making.

[0032] S3 aims to maximize system benefits. Under a unified framework, it sets constraints on carbon emissions, pollutant emissions, energy security, and capacity. By optimizing the energy structure, conversion paths, and emission reduction technology combinations, it determines the optimal configuration under different scenarios to achieve synergistic progress in pollution reduction and carbon reduction.

[0033] Specifically, in constructing the pollution reduction and carbon reduction synergistic optimization model, the core objective is to maximize the system's net comprehensive benefit. Within a unified mathematical programming framework, carbon emission constraints, air pollutant emission constraints, energy supply security constraints, and various technological capacity constraints are integrated as rigid boundary conditions. This model seeks the optimal solution for the overall system benefit by comprehensively optimizing the energy structure configuration, technology path selection, and emission reduction measure combination throughout the entire energy extraction, processing, conversion, and end-consumer chain, while meeting predetermined energy demand and emission limits.

[0034] Specifically, the model comprehensively considers the benefits of end-use energy consumption, the costs of various energy activities, and the investment in pollution control and carbon reduction technologies in its objective function, thereby achieving a balance between economic and environmental benefits. The constraint system covers multiple dimensions, including energy supply and demand balance, electricity and heat supply security, non-electric energy demand guarantee, carbon emission and major air pollutant emission limits, carbon capture, utilization and storage technology application, and forest carbon sink potential, ensuring the feasibility of the optimization results at the technical, economic, and environmental levels. By solving the model under different policy and economic scenarios, the optimal combination of energy structure and emission reduction technologies can be obtained for each scenario, thereby achieving the synergistic advancement of pollution reduction and carbon reduction goals. As one implementation method, the objective function of this model can be expressed as maximizing the net value after deducting the costs of extraction, processing, conversion, operation and maintenance, capacity expansion investment, and various pollutant and carbon dioxide emission reduction from the total system benefit. Its constraints include specific mathematical expressions for non-electric energy demand constraints, electricity supply security constraints, and pollutant emission reduction constraints.

[0035] Specifically, an objective function is constructed to maximize the net comprehensive benefit of the system. This objective function encompasses the benefits of end-use energy consumption, energy extraction costs, processing costs, conversion costs, operation and maintenance costs, expansion costs, and emission reduction costs for sulfur dioxide, nitrogen oxides, and particulate matter. Under this unified framework, constraints are set for non-electric energy demand, power supply security, pollutant emission reduction, and carbon capture, utilization, and storage (CCUS) emission reduction, forming a constraint system. Based on the objective function and constraint system, a linear programming solver is used to synergistically optimize the energy structure, conversion paths, and emission reduction technology combinations to obtain the optimal configuration scheme under each scenario.

[0036] This step, by constructing a collaborative optimization model that integrates multiple constraints, enables quantitative assessment of the impact of energy structure, conversion pathways, and emission reduction technology combinations on system benefits and environmental objectives within a unified framework. This provides decision-makers with a scientific basis that balances economic efficiency and emission reduction requirements, significantly enhancing the comprehensive decision-making capability of energy system planning under complex constraints.

[0037] S4 employs a multi-scenario optimization algorithm to solve the model, obtaining the optimal energy system scheme under each scenario. Through sensitivity and risk analysis, it identifies robust energy structures and emission reduction paths, realizing the transformation from single optimality to multi-scenario robust decision-making.

[0038] Specifically, after obtaining the completed pollution reduction and carbon reduction synergistic optimization model, it is necessary to solve the model and conduct decision analysis. This method employs a multi-scenario optimization algorithm to solve the model. This algorithm can handle the randomness and fuzzy parameters contained in the model, thereby calculating the optimal energy system configuration scheme for each scenario under various preset policy and economic scenarios. These schemes cover the optimal selection of energy structure, conversion paths, and emission reduction technology combinations.

[0039] Building upon this foundation, sensitivity and risk analyses are further conducted on the optimal solutions obtained for each scenario. By examining the impact of changes in key parameters on the solution outcomes and the volatility of solution performance under different scenarios, highly robust energy structures and emission reduction paths are identified. The core of this process lies in comprehensively considering system benefits, emission reduction constraints, and scenario risks, thereby transforming the traditional single deterministic optimal decision-making model into a multi-scenario robust decision-making model capable of adapting to various uncertain future development paths. As one implementation method, this solution process can be specifically implemented using an improved stochastic programming algorithm. This algorithm can effectively handle the stochastic and fuzzy parameters in the model that represent uncertain factors such as energy prices, technological progress, and policy constraints, thereby obtaining the optimal configuration scheme under each scenario. Subsequently, by conducting sensitivity and risk analyses on different schemes, highly robust energy structures and emission reduction paths are identified, providing multi-scenario, low-risk pollution reduction and carbon reduction decision-making references for policy formulation.

[0040] This method significantly enhances the scientific rigor and robustness of energy system planning decisions by introducing multi-scenario optimization and risk analysis mechanisms. Its beneficial effect lies in its ability to effectively overcome the shortcomings of traditional single-deterministic planning methods in terms of insufficient decision reliability when facing high future uncertainty. By identifying energy structures and emission reduction pathways that demonstrate robustness under different scenarios, it provides policymakers with more adaptable and lower-risk policy options, thereby ensuring the coordinated unity of the energy system's security, economic, and environmental goals during its long-term evolution.

[0041] Furthermore, in this embodiment, the specific implementation method of using a multi-scenario optimization algorithm to solve the model in step S4 and identifying robust energy structures and emission reduction paths through sensitivity and risk analysis is as follows. First, based on an improved stochastic programming algorithm, the pollution reduction and carbon reduction synergistic optimization model is iteratively solved under each scenario. Specifically, the algorithm takes the model constructed in step S3 as input, which includes an objective function aimed at maximizing system benefits and constraints such as carbon emissions, pollutant emissions, energy security, and capacity. Through iterative calculation, the algorithm optimizes decision variables such as energy structure, conversion paths, and emission reduction technology combinations while satisfying all constraints, and outputs the optimal configuration scheme corresponding to each scenario. This scheme specifically includes the extraction, processing, conversion, import and export, and terminal allocation of different energy types within each planning period, as well as key parameters such as the expansion of installed capacity of various energy conversion facilities and the installed capacity rate of carbon capture, utilization, and storage technologies. Subsequently, sensitivity analysis is performed on the key parameters in the optimal configuration scheme. This analysis uses the optimal configuration scheme under various scenarios as input. By successively perturbing uncertain parameters such as energy prices, technological progress rate, emission factors, and policy constraint strength, it observes the degree of their impact on output results such as system benefits, total carbon emissions, and total pollutant emissions, thereby identifying the core factors affecting system benefits and emission reduction effects. After identifying the core factors, a risk quantification assessment is conducted based on the probability distribution of these core factors. Specifically, based on historical data or expert judgment, probability distribution functions are set for each core factor. A large number of random samples are generated using methods such as Monte Carlo simulations, and then substituted into the optimization model for recalculation, thereby obtaining the probability distribution of system benefits and emission reduction effects under different risk levels. Finally, based on the above risk assessment results, energy structures and emission reduction paths with high robustness under different scenarios are selected. This selection process comprehensively compares the expected value, variance, and risk value of each scheme under different scenarios, prioritizing those schemes that can maintain superior performance and smaller fluctuations under multiple uncertain conditions. This provides policy-making with multi-scenario, low-risk pollution reduction and carbon reduction decision-making references, realizing the transformation of the energy system from a single deterministic optimal solution to a multi-scenario robust optimization solution.

[0042] This specific implementation method introduces an improved stochastic programming algorithm for iterative solution, and combines sensitivity analysis and risk quantification assessment. It can effectively identify and screen out energy structures and emission reduction paths that are robust under various uncertain conditions, significantly improving the reliability and risk resistance of the planning results, and overcoming the vulnerability of traditional single deterministic optimal solutions in the face of future uncertainties.

[0043] Example 2 like Figure 2 As shown in the diagram, this invention proposes an architecture diagram for a collaborative planning method for pollution reduction and carbon reduction in energy systems, which may specifically include the following steps: Step A: System Boundaries and Data Integration.

[0044] Construct a system structure covering the entire process of energy extraction, processing, conversion, import and export, and end-use consumption, clearly defining different energy types, technological pathways, and emission points. Establish a multi-dimensional database including energy supply and demand, carbon emissions, and emissions of major pollutants, and achieve system integration through data cleaning and matching.

[0045] Step B: Uncertainty Characterization and Scenario Setting.

[0046] This invention incorporates uncertainties such as energy prices, technological progress, policy constraints, and emission factors into the model, employing a multiple uncertainty programming approach for joint characterization. By setting various policy and economic scenarios, it depicts different future development paths. Compared to traditional deterministic methods, this invention introduces stochastic and fuzzy parameters, making the model more robust under high uncertainty conditions.

[0047] Step C: Construction of a collaborative optimization model for pollution reduction and carbon reduction.

[0048] With the goal of maximizing system benefits, constraints such as carbon emissions, pollutant emissions, energy security, and capacity are set within a unified framework. The model determines the optimal configuration under different scenarios by optimizing the energy structure, conversion pathways, and combinations of emission reduction technologies, achieving synergistic progress in pollution reduction and carbon reduction.

[0049] Step D: Model Solving and Decision Analysis.

[0050] A multi-scenario optimization algorithm is employed to solve the model, obtaining the optimal energy system scheme under each scenario. Through sensitivity and risk analysis, robust energy structures and emission reduction pathways are identified. The innovation lies in combining system benefits, emission reduction constraints, and scenario risks to achieve a shift from single-optimal to multi-scenario robust decision-making.

[0051] The planning method incorporates constraints such as energy extraction and processing capacity, end-user energy demand, energy supply and demand balance, electricity and heat supply, and carbon emissions and air pollutants (SO2, NO). xThe main constraints are emission limits for PM2.5, emission reduction constraints for carbon capture, utilization and storage, and forest carbon sink potential.

[0052] The objective function of the planning model is:

[0053] Where i represents energy extraction type, j represents energy processing type, k represents energy conversion type, n represents energy import type, m represents end-user energy demand type, u represents different end-user sectors, and t represents the planning period.

[0054] It is the benefit generated by the end user consuming a unit of energy type m, and this parameter is set according to the energy consumption per unit of GDP. It is the amount of energy allocated by end user u to energy type m; It is energy i Mining volume; It is the unit extraction cost of energy i; It is an energy processing type j The processing volume; It is an energy processing type j The unit processing cost; It is an energy conversion type k Energy conversion rate; It is the unit fuel cost of energy conversion type k; It is the installed capacity of energy conversion type k; It is an energy conversion type k The expansion of installed capacity; It represents the operation and maintenance costs of different energy conversion types k; It is the fixed cost of expanding energy conversion facilities; It is the energy consumption required to generate one unit of electricity; It is the SO2 emission factor during energy conversion; It is the SO2 emission reduction rate during the power generation process; It is the unit SO2 emission reduction cost during the power generation process; It is the SO2 emission factor when end users use raw coal; It is the average SO2 emission reduction rate of end users using coal-based energy; It is the unit cost of SO2 emission reduction for end users; It is the SO2 emission factor when end users use crude oil; It is the average SO2 emission reduction rate of end users using petroleum products; It is the SO2 emission factor when end users use natural gas; It is the average SO2 emission reduction rate of end users using natural gas; It is the NO used by end users when using raw coal x Emission factors; It is the average NO of end users using coal-related products x Emission reduction rate; Is it end user NO? x The unit cost of emission reduction; It is the NO used by end users when using crude oil x Emission factors; It is the average NO of end users using petroleum products x Emission reduction rate; It is the NO used by end users when using natural gas. x Emission factors; It is the average NO of natural gas used by end users x Emission reduction rate; It is the PM emission factor when end users consume raw coal; It is the average PM emission reduction rate of end users using coal-based products; It is the unit cost of PM emission reduction for end users; It is the PM emission factor when end users use crude oil; It is the average PM emission reduction rate of end users using petroleum products; It is the PM emission factor when end users use natural gas; It is the average PM emission reduction rate of end users using natural gas; It is a CO2 emission factor in the energy conversion process; It refers to the installed capacity of carbon capture, utilization and storage technologies; It is the CO2 capture rate (%) of carbon capture, utilization and storage technology; It is the fixed investment cost of carbon capture, utilization and storage technology.

[0055] The non-electric energy demand constraints of the planning model are:

[0056] in, Let m be the minimum energy type required by end user u.

[0057] The power supply security constraints of the planning model are:

[0058]

[0059]

[0060]

[0061]

[0062] in, The loss rate (%) during power transmission; and These are the maximum import and export limits for electricity (GWh), respectively. New installed capacity (GW) for energy conversion type k; Decommissioning capacity (GW) for energy conversion type k; Annual available power generation hours (hrs); and These are the maximum and minimum operational reliability coefficients (%) for energy conversion type k, respectively. and These are the minimum and maximum construction scales (GW) for energy conversion capacity, respectively.

[0063] The pollutant emission reduction constraints of the planning model are (taking SO2 as an example):

[0064] in, This represents the maximum permissible SO2 emission limit within the planning period t.

[0065] The solution process of the model includes calculating the optimization model under multiple scenarios and obtaining the optimal energy system configuration scheme under each scenario through an improved stochastic programming algorithm. Subsequently, sensitivity and risk analysis are performed on different schemes to identify energy structures and emission reduction paths with high robustness, thereby providing policy-making references for multi-scenario, low-risk pollution reduction and carbon reduction decisions, and realizing the transformation of the energy system from a single deterministic optimal solution to multi-scenario robust optimization.

[0066] Figure 3 This diagram illustrates primary energy supply schemes under different scenarios (maximum benefit scenario, minimum pollutant emission scenario) in this invention embodiment. The diagram reveals the evolution trend of China's energy system under the carbon neutrality target. Initially, energy supply shows an upward trend due to the inertia of economic growth and infrastructure construction, peaking in 2034. With the advancement of low-carbon transformation, optimization of energy demand structure, and improvement of end-use energy efficiency, energy supply gradually declines and stabilizes after 2045. Significant energy structure transformation occurs; coal will still dominate in 2030, but its share will decrease significantly, and clean energy substitution will begin to show results. By 2060, the share of fossil fuels will decrease significantly, and renewable energy will dominate, especially solar and wind power, demonstrating that the energy system is developing towards diversification, cleanliness, and low carbon emissions.

[0067] Figure 4This diagram illustrates the future CO2 and pollutant emissions and their synergistic effects under the carbon neutrality target, as described in this embodiment of the invention. The diagram shows that in the process of achieving carbon neutrality, CO2 emissions from China's energy system will gradually decrease, approaching zero emissions by 2060, primarily relying on carbon capture and negative emission technologies. The transformation of the energy structure not only promotes carbon emission reduction but also significantly improves air quality. As coal is gradually replaced by clean energy, emissions of traditional air pollutants (such as SO2, NOx, and PM) are greatly reduced. This synergistic effect of carbon reduction and pollution reduction demonstrates that through energy structure optimization and technological innovation, not only can the carbon neutrality target be achieved, but environmental quality can also be effectively improved, promoting sustainable development.

[0068] The energy system pollution reduction and carbon reduction planning method in this invention differs from existing technologies. Existing technologies typically employ a single deterministic model for planning, which cannot effectively handle the uncertainties under the rigid constraints of energy demand and carbon neutrality. In contrast, this invention uses stochastic and fuzzy parameters to characterize the system, comprehensively considering multiple uncertainties such as energy prices, technological progress, and policy constraints. In complex and uncertain environments, it provides a more robust and flexible decision-making scheme, significantly improving the accuracy and operability of energy planning.

[0069] The embodiments of this invention, when constructing a planning model for energy system pollution reduction and carbon reduction, take maximizing system benefits as the core objective and set constraints such as carbon emissions, pollutant emissions, energy security, and capacity within a unified framework. Through multiple scenario settings, this invention effectively achieves synergistic optimization of pollution reduction and carbon reduction, ensuring the optimal configuration of energy structure, conversion paths, and emission reduction technologies under different policy and economic scenarios. This comprehensive and synergistic planning method can effectively promote environmental quality improvement and sustainable development while ensuring energy supply security.

[0070] Example 3 To implement the methods of the above embodiments, the present invention also provides an energy system pollution reduction and carbon reduction collaborative planning system 10, such as... Figure 5 As shown, it includes: The multidimensional database construction module 100 is used to build a system structure covering the entire process of energy extraction, processing, conversion, import and export and end consumption, and to establish a multidimensional database containing energy supply and demand, carbon emissions and emissions of major pollutants. System integration is achieved through data cleaning and matching. The joint characterization module 200 is used to incorporate uncertain factors into the model, employ a multiple uncertainty programming method for joint characterization, and characterize different future development paths by setting various policy and economic scenarios; wherein, the uncertain factors include energy prices, technological progress, policy constraints, and emission factors; The optimal configuration module 300 is used to set constraints such as carbon emissions, pollutant emissions, energy security and capacity under a unified framework with the goal of maximizing system benefits. By optimizing the energy structure, conversion path and emission reduction technology combination, it determines the optimal configuration under different scenarios and achieves the synergistic promotion of pollution reduction and carbon reduction. The model solving module 400 is used to solve the model using a multi-scenario optimization algorithm to obtain the optimal energy system scheme under each scenario. Through sensitivity and risk analysis, it identifies robust energy structures and emission reduction paths, realizing the transformation from single optimality to multi-scenario robust decision-making.

[0071] Furthermore, the multidimensional database building module 100 is also used for: Based on energy statistics yearbooks and industry reports, we have sorted out the complete material and energy flows of various energy sources from primary extraction to end consumption, forming a system topology covering coal, oil, natural gas, nuclear energy and renewable energy. Based on the system topology, energy supply, consumption, carbon dioxide emission factors, and sulfur dioxide, nitrogen oxide, and particulate matter emission factors for each link are extracted from public databases and industry monitoring platforms to construct a multidimensional database. The system identifies and imputes outliers and missing values ​​in the multidimensional database, and maps field names and units of measurement from different data sources in a unified manner to complete system integration.

[0072] Furthermore, the joint characterization module 200 is also used for: Energy prices and technological progress parameters are constructed as random variables that follow a preset probability distribution, and policy constraints and emission factor parameters are constructed as fuzzy interval numbers based on expert judgment. Uncertainty is represented by joint modeling of stochastic programming and fuzzy programming. Based on the aforementioned uncertainty characterization, various policy and economic scenarios are set, including the baseline scenario, the enhanced emission reduction scenario, and the high economic growth scenario, and corresponding parameter value ranges are assigned to each scenario.

[0073] Furthermore, the optimal configuration module 300 is also used for: Construct an objective function with the goal of maximizing the net comprehensive benefits of the system. The objective function covers the benefits of end-use energy consumption, energy extraction costs, processing costs, conversion costs, operation and maintenance costs, fixed costs of capacity expansion, and emission reduction costs of sulfur dioxide, nitrogen oxides and particulate matter. Under a unified framework, constraints are set for non-electric energy demand, electricity supply security, pollutant emission reduction, and carbon capture, utilization and storage emission reduction to form a constraint system. Based on the objective function and constraint system, the energy structure, conversion path and emission reduction technology combination are synergistically optimized by a linear programming solver to obtain the optimal configuration scheme under each scenario.

[0074] The energy system pollution reduction and carbon reduction coordinated planning system of this invention can effectively solve the problems of excessive deterministic assumptions, incomplete system coverage and insufficient coordination of pollution reduction and carbon reduction in existing energy planning methods, realize the coordinated reduction of carbon emissions and multiple pollutants, and improve the scientificity and robustness of planning results.

[0075] It should be noted that the foregoing explanation of the embodiment of the energy system pollution reduction and carbon reduction collaborative planning method also applies to the energy system pollution reduction and carbon reduction collaborative planning system of this embodiment, and will not be repeated here.

[0076] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0077] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0078] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A collaborative planning method for pollution reduction and carbon reduction in energy systems, characterized in that, Includes the following steps: S1 constructs a system structure covering the entire process of energy extraction, processing, conversion, import and export, and end-consumer, and establishes a multi-dimensional database including energy supply and demand, carbon emissions, and emissions of major pollutants. System integration is achieved through data cleaning and matching. S2 incorporates uncertainties into the model, employs a multi-uncertainty programming approach for joint characterization, and depicts different future development paths by setting various policy and economic scenarios; wherein, the uncertainties include energy prices, technological progress, policy constraints, and emission factors; S3 aims to maximize system benefits. Under a unified framework, it sets constraints on carbon emissions, pollutant emissions, energy security, and capacity. By optimizing the energy structure, conversion paths, and emission reduction technology combinations, it determines the optimal configuration under different scenarios to achieve synergistic progress in pollution reduction and carbon reduction. S4 employs a multi-scenario optimization algorithm to solve the model, obtaining the optimal energy system scheme under each scenario. Through sensitivity and risk analysis, it identifies robust energy structures and emission reduction paths, realizing the transformation from single optimality to multi-scenario robust decision-making.

2. The method as described in claim 1, characterized in that, The construction of a system structure covering the entire process of energy extraction, processing, conversion, import and export, and end-use consumption, and the establishment of a multi-dimensional database including energy supply and demand, carbon emissions, and emissions of major pollutants, and the achievement of system integration through data cleaning and matching, includes: Based on energy statistics yearbooks and industry reports, we have sorted out the complete material and energy flows of various energy sources from primary extraction to end consumption, forming a system topology covering coal, oil, natural gas, nuclear energy and renewable energy. Based on the system topology, energy supply, consumption, carbon dioxide emission factors, and sulfur dioxide, nitrogen oxide, and particulate matter emission factors for each link are extracted from public databases and industry monitoring platforms to construct a multidimensional database. The system identifies and imputes outliers and missing values ​​in the multidimensional database, and maps field names and units of measurement from different data sources in a unified manner to complete system integration.

3. The method as described in claim 1, characterized in that, The inclusion of uncertainties in the model, the joint representation using a multiple uncertainty programming method, and the characterization of different future development paths by setting various policy and economic scenarios include: Energy prices and technological progress parameters are constructed as random variables that follow a preset probability distribution, and policy constraints and emission factor parameters are constructed as fuzzy interval numbers based on expert judgment. Uncertainty is represented by joint modeling of stochastic programming and fuzzy programming. Based on the aforementioned uncertainty characterization, various policy and economic scenarios are set, including the baseline scenario, the enhanced emission reduction scenario, and the high economic growth scenario, and corresponding parameter value ranges are assigned to each scenario.

4. The method as described in claim 1, characterized in that, The aforementioned approach, aiming to maximize system benefits, sets constraints on carbon emissions, pollutant emissions, energy security, and capacity within a unified framework. It determines the optimal configuration under different scenarios by optimizing energy structure, conversion pathways, and combinations of emission reduction technologies, thereby achieving synergistic progress in pollution reduction and carbon reduction. Construct an objective function with the goal of maximizing the net comprehensive benefits of the system. The objective function covers the benefits of end-use energy consumption, energy extraction costs, processing costs, conversion costs, operation and maintenance costs, fixed costs of capacity expansion, and emission reduction costs of sulfur dioxide, nitrogen oxides and particulate matter. Under the aforementioned unified framework, constraints are set for non-electric energy demand, power supply security, pollutant emission reduction, and carbon capture, utilization, and storage emission reduction, forming a constraint system. Based on the objective function and constraint system, the energy structure, conversion path and emission reduction technology combination are synergistically optimized by a linear programming solver to obtain the optimal configuration scheme under each scenario.

5. The method as described in claim 4, characterized in that, The constraints on power supply security include: power supply and demand balance constraints, dynamic balance constraints on installed capacity, upper and lower limits constraints on power generation, and constraints on the scale of installed capacity construction.

6. The method as described in claim 1, characterized in that, The process involves employing a multi-scenario optimization algorithm to solve the model, obtaining the optimal energy system scheme under each scenario, and identifying robust energy structures and emission reduction paths through sensitivity and risk analysis. This achieves a shift from single-optimal to multi-scenario robust decision-making, including: Based on the improved stochastic programming algorithm, the pollution reduction and carbon reduction synergistic optimization model is iteratively solved under each scenario, and the optimal configuration scheme of energy structure, conversion path and emission reduction technology combination corresponding to each scenario is output. Sensitivity analysis is performed on the key parameters of the optimal configuration scheme to identify the core factors affecting system benefits and emission reduction effects. Based on the probability distribution of the core factors, risk quantification assessment is carried out to screen out energy structures and emission reduction paths with high robustness under different scenarios.

7. A collaborative planning system for pollution reduction and carbon reduction in an energy system, characterized in that, include: The multidimensional database construction module is used to build a system structure covering the entire process of energy extraction, processing, conversion, import and export and end consumption, and to establish a multidimensional database containing energy supply and demand, carbon emissions and emissions of major pollutants. System integration is achieved through data cleaning and matching. The joint characterization module is used to incorporate uncertain factors into the model, employ a multiple uncertainty programming method for joint characterization, and characterize different future development paths by setting various policy and economic scenarios; wherein, the uncertain factors include energy prices, technological progress, policy constraints, and emission factors; The optimal configuration module is used to set constraints such as carbon emissions, pollutant emissions, energy security and capacity under a unified framework with the goal of maximizing system benefits. By optimizing the energy structure, conversion path and emission reduction technology combination, it determines the optimal configuration under different scenarios and achieves the synergistic promotion of pollution reduction and carbon reduction. The model solving module is used to solve the model using a multi-scenario optimization algorithm to obtain the optimal energy system scheme under each scenario. Through sensitivity and risk analysis, it identifies robust energy structures and emission reduction paths, realizing the transformation from single optimality to multi-scenario robust decision-making.

8. The system as described in claim 7, characterized in that, The multidimensional database construction module is also used for: Based on energy statistics yearbooks and industry reports, we have sorted out the complete material and energy flows of various energy sources from primary extraction to end consumption, forming a system topology covering coal, oil, natural gas, nuclear energy and renewable energy. Based on the system topology, energy supply, consumption, carbon dioxide emission factors, and sulfur dioxide, nitrogen oxide, and particulate matter emission factors for each link are extracted from public databases and industry monitoring platforms to construct a multidimensional database. The system identifies and imputes outliers and missing values ​​in the multidimensional database, and maps field names and units of measurement from different data sources in a unified manner to complete system integration.

9. The system as described in claim 7, characterized in that, The joint characterization module is also used for: Energy prices and technological progress parameters are constructed as random variables that follow a preset probability distribution, and policy constraints and emission factor parameters are constructed as fuzzy interval numbers based on expert judgment. Uncertainty is represented by joint modeling of stochastic programming and fuzzy programming. Based on the aforementioned uncertainty characterization, various policy and economic scenarios are set, including the baseline scenario, the enhanced emission reduction scenario, and the high economic growth scenario, and corresponding parameter value ranges are assigned to each scenario.

10. The system as described in claim 7, characterized in that, The optimal configuration module is also used for: Construct an objective function with the goal of maximizing the net comprehensive benefits of the system. The objective function covers the benefits of end-use energy consumption, energy extraction costs, processing costs, conversion costs, operation and maintenance costs, fixed costs of capacity expansion, and emission reduction costs of sulfur dioxide, nitrogen oxides and particulate matter. Under the aforementioned unified framework, constraints are set for non-electric energy demand, power supply security, pollutant emission reduction, and carbon capture, utilization, and storage emission reduction, forming a constraint system. Based on the objective function and constraint system, the energy structure, conversion path and emission reduction technology combination are synergistically optimized by a linear programming solver to obtain the optimal configuration scheme under each scenario.

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