An evaluation method, device and equipment for a coal electricity clean low-carbon transformation technical path

Abstract

This invention relates to the field of energy and power system planning and evaluation technology, specifically to an evaluation method, apparatus, and equipment for clean and low-carbon transformation technology paths of coal-fired power. In this invention, by integrating the full life-cycle carbon emission data of multiple technology paths and simulating the dynamics of actual power supply and demand through operational simulation, both the carbon emission per kilowatt-hour and the levelized cost per kilowatt-hour of the transformation path are simultaneously used as optimization objectives, with load fulfillment rate as a constraint. Using a multi-objective optimization algorithm, a series of Pareto optimal paths can be automatically identified under a given scenario. These paths clearly reveal the trade-off between cost and emission reduction, thereby helping decision-makers select transformation strategies that achieve the optimal balance between carbon emissions and power generation costs while ensuring a safe and reliable power supply. This provides accurate and reliable quantitative basis for the coal-fired power industry to formulate a low-carbon development path that is both environmentally friendly and economically feasible.

Description

Technical Field

[0001] This invention relates to the field of energy and power system planning and evaluation technology, specifically to an evaluation method, apparatus and equipment for a clean and low-carbon transformation technology path of coal-fired power. Background Technology

[0002] In the field of technology assessment, commonly used assessment methods include constructing multi-level assessment models, such as a four-level assessment model that includes a target layer, a criterion layer, an element layer, and an indicator layer. Then, the assessment results of each technology are calculated using a weighted method across multiple dimensions to select the superior technology.

[0003] However, these general comprehensive evaluation methods focus on calculating and weighting scores based on parameters under static or typical operating conditions. The performance of coal-fired power transition paths, however, is highly dependent on the coordinated operation of coal-fired power units with renewable energy and energy storage devices on an hourly basis throughout the year. The general evaluation methods fail to deeply integrate with the actual dynamic operation of the power system, resulting in evaluation results that cannot accurately reflect the actual performance of the technology path, and insufficient precision and reliability of the evaluation conclusions. Summary of the Invention

[0004] This invention provides an evaluation method, apparatus, and equipment for the clean and low-carbon transformation technology path of coal-fired power, in order to solve the problem that the reliability of evaluation results is insufficient when using a general comprehensive evaluation method in the evaluation of coal-fired power transformation path in the prior art.

[0005] In a first aspect, the present invention provides an evaluation method for a clean and low-carbon transition technology path for coal-fired power, the method comprising:

[0006] Based on the transformation goals of the target coal-fired power system, a set of candidate pathways with multiple technology combinations was identified, and carbon emission data for the candidate pathways were determined. Construct a multi-objective optimization model with levelized cost per kilowatt-hour (LCOE) and carbon emissions per LCOE as objectives and load fulfillment rate as constraints. Based on the simulation results of the target coal-fired power system under the preset scenario and candidate path set, and combined with the carbon emission data, a multi-objective optimization algorithm is used to solve the multi-objective optimization model to obtain the path evaluation results.

[0007] This invention integrates lifecycle carbon emission data from multiple technology pathways and simulates actual power supply and demand dynamics through operational simulation. It simultaneously optimizes both levelized cost per kilowatt-hour (LCOE) and load fulfillment rate as constraints. A multi-objective optimization algorithm is used to automatically identify a series of Pareto-optimal paths under a given scenario. These paths clearly reveal the trade-off between cost and emission reduction, helping decision-makers select the optimal transformation strategy that balances carbon emissions and generation costs while ensuring a safe and reliable power supply. This provides precise and reliable quantitative data for the coal-fired power industry to formulate a low-carbon development path that is both environmentally friendly and economically feasible.

[0008] In one optional implementation, a multi-objective optimization algorithm is used to solve the multi-objective optimization model to obtain path evaluation results, including: A multi-objective optimization algorithm is used to solve the multi-objective optimization model to generate a Pareto optimal path set. The paths in the Pareto optimal path set are evaluated from multiple dimensions, and technical path suggestions are generated based on the evaluation results.

[0009] This invention employs a multi-objective optimization algorithm to effectively balance and integrate multiple key and often conflicting objectives, such as economic costs, carbon emissions, and system reliability, generating a set of Pareto optimal paths. This set provides diverse equilibrium solutions, rather than a single outcome, enabling decision-makers to clearly understand the trade-offs between different objectives. Furthermore, each path within the set undergoes a quantitative evaluation covering technical, economic, and environmental dimensions, ensuring that the scientific validity and feasibility of each path are fully validated. Finally, the technical path recommendations generated based on the systematic evaluation provide data-driven decision support for decision-makers, helping them select the best-performing transformation strategy under multiple constraints, thereby significantly improving the foresight, systematic nature, and overall effectiveness of planning.

[0010] In one optional implementation, the Pareto optimal path set includes the optimal installed capacity sequence of various technologies each year within a preset planning period; the multi-dimensional assessment includes carbon emission reduction ratio assessment, power generation composition assessment, installed capacity composition assessment, power supply coal consumption assessment, investment cost assessment, fuel cost assessment, and carbon dioxide emission reduction cost assessment.

[0011] In this invention, the output Pareto optimal path set is not a single result, but rather provides a sequence of optimal installed capacity for each year and technology over a multi-year planning period. This provides decision-makers with a clear and actionable roadmap for technology development and an investment timetable. Based on this, the scheme conducts a multi-dimensional quantitative assessment of each path, covering environmental (carbon emission reduction ratio), technological (power generation and installed structure, coal consumption for power supply), and economic (investment, fuel, and CO2 emission reduction costs). This comprehensively reveals the specific performance and inherent trade-offs of different development paths in terms of emission reduction effects, system structure, and economic costs. Ultimately, this enables decision-makers to scientifically select the transformation strategy that achieves the best balance between long-term costs and emission reduction targets, based on clear quantitative indicators and while ensuring the reliability of the power system. This significantly improves the foresight, scientific rigor, and precision of planning and decision-making.

[0012] In one alternative implementation, the time-level carbon emissions are calculated as follows: Based on the simulation results, annual power generation data, annual power consumption data, and carbon emission data are obtained; The carbon emission data per kilowatt-hour in the target is calculated based on the annual power generation data, annual power consumption data, and carbon emission data.

[0013] In this invention, the reliability and dynamism of the calculation basis are ensured by relying on the accurate annual power generation, annual power consumption and carbon emission data obtained by operation simulation, so as to accurately reflect the actual carbon intensity of a specific technology path under real operating conditions.

[0014] In one optional implementation, the levelized cost of electricity (LCOE) is expressed by the following formula:

[0015] In the formula, n represents the number of technology types included in the technology path; i represents the i-th technology; Ni represents the initial investment cost of the i-th technology; Ni represents the economic life of the i-th technology. This represents the annual operating and maintenance cost of the i-th technology; Represents the annual fuel cost of the i-th technology; Indicate the other annual variable costs of the i-th technology; This represents the annual net power supply of the technology path.

[0016] In this invention, an integrated mathematical formula is used to allocate the initial investment cost over its economic life cycle, summarizing it with annual operating and maintenance costs, fuel costs, and other variable costs, and then dividing by the system's net annual power supply. This calculation method ensures the comparability of life-cycle costs between different energy technologies or technological pathways, enabling decision-makers to scientifically assess the long-term economic viability of each option and providing a unified and transparent quantitative basis for investment optimization and energy project decision-making.

[0017] In one optional implementation, the load fulfillment rate is expressed by the following formula:

[0018] In the formula, This indicates unmet electricity demand; Indicates the power demand; This indicates the time resolution used in the calculation.

[0019] In this invention, based on time integration, the proportion of the total power demand that the system fails to meet during the operating cycle to the total demand is accurately measured, thereby transforming the complex system reliability performance into an intuitive numerical indicator.

[0020] In one optional implementation, the carbon emission reduction ratio is expressed by the following formula:

[0021] In the formula, Indicates the carbon emissions per kilowatt-hour of the candidate pathway; Indicates carbon emissions per kilowatt-hour along the baseline path; The coal consumption for power generation is expressed by the following formula:

[0022] In the formula, , These are the output and input energy subsets, respectively. , These are the energy value and coal conversion factor of the j-th type of input energy, respectively; , These are the energy value and the conversion factor of the i-th type of output energy, respectively; The cost of carbon dioxide emission reduction is expressed by the following formula:

[0023] In the formula, This represents the levelized cost of electricity (LCOE) of the candidate path; This represents the levelized cost of electricity (LCOE) for the baseline path.

[0024] This invention provides a precise decision-making benchmark for the low-carbon transformation of coal-fired power by clearly defining and quantifying core evaluation indicators. Specifically, the carbon emission reduction ratio formula intuitively reveals the emission reduction magnitude of candidate paths compared to the baseline path, quantifying the improvement in environmental benefits; the power generation coal consumption formula scientifically measures the overall energy utilization efficiency of the system through a unified conversion standard; and the carbon dioxide emission reduction cost formula calculates the increased economic cost of reducing one unit of carbon emissions, clearly revealing the economic cost of emission reduction measures. These formulas together constitute a rigorous quantitative analysis framework that can objectively and transparently compare the environmental friendliness and economic efficiency of different technological paths, thereby supporting decision-makers in making scientific trade-offs and decisions between emission reduction effects and implementation costs.

[0025] Secondly, the present invention provides an evaluation device for the technological path of clean and low-carbon transformation of coal-fired power, the device comprising: The path determination module is used to determine a set of candidate paths for various technology combinations based on the transformation goals of the target coal-fired power system, and to determine the carbon emission data of the candidate paths; The model building module is used to construct a multi-objective optimization model with levelized cost per kilowatt-hour and carbon emissions per kilowatt-hour as objectives and load fulfillment rate as a constraint. The optimization module is used to solve the multi-objective optimization model based on the simulation results of the target coal-fired power system under a preset scenario and a set of candidate paths, combined with the carbon emission data, and to obtain the path evaluation results.

[0026] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the evaluation method for the clean and low-carbon transformation technology path of coal-fired power as described in the first aspect or any corresponding embodiment.

[0027] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the evaluation method for the clean and low-carbon transformation technology path of coal-fired power as described in the first aspect or any corresponding embodiment.

[0028] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause the computer to execute the evaluation method for the clean and low-carbon transformation technology path of coal-fired power in the first aspect or any corresponding embodiment described above. Attached Figure Description

[0029] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of the first process of the evaluation method for the clean and low-carbon transformation technology path of coal-fired power according to an embodiment of the present invention; Figure 2 This is a structural block diagram of an evaluation device for the clean and low-carbon transformation technology path of coal-fired power according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the working process of an evaluation device for the clean and low-carbon transformation technology path of coal-fired power according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the Pareto front of the photovoltaic-coal power-energy storage coupling transformation path according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0033] 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 one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0034] According to an embodiment of the present invention, an evaluation method for a clean and low-carbon transformation technology path of coal-fired power is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] This embodiment provides an evaluation method for the technological path of clean and low-carbon transformation of coal-fired power. Figure 1 This is a flowchart of an evaluation method for a clean and low-carbon transition technology path for coal-fired power according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Based on the transformation goals of the target coal-fired power system, determine a set of candidate paths for multiple technology combinations and determine the carbon emission data of the candidate paths.

[0036] Specifically, through research on the technological pathways for the clean and low-carbon transformation of coal-fired power, the current main categories include energy efficiency improvement technologies, fuel substitution technologies, multi-energy coupling technologies, and CCUS technologies (Carbon Capture, Utilization, and Storage). Energy efficiency improvement technologies include improving boiler efficiency and steam parameters; fuel substitution technologies include co-firing biomass and ammonia in coal-fired power units; multi-energy coupling technologies include coal-fired power and photovoltaic coupling, coal-fired power and energy storage coupling, etc.; CCUS includes carbon capture, utilization, and storage. Each category also includes several specific technological options. Therefore, when determining the technological pathway, multiple technologies can be combined to form candidate technological pathways. It should be noted that this embodiment only identified four major categories of technologies through research; other embodiments may include other types of technologies for the clean and low-carbon transformation of coal-fired power, and this embodiment does not specifically limit this.

[0037] The evaluation of technological pathways for the clean and low-carbon transformation of coal-fired power plants should not be conducted in isolation from the specific circumstances of the coal-fired power system. Instead, it should be evaluated in conjunction with the specific coal-fired power system and the chosen transformation technological pathways. Therefore, when determining candidate pathways, it is necessary to first identify which coal-fired power system will undergo the clean and low-carbon transformation, i.e., to identify the target coal-fired power system. Then, based on the transformation goals of this system, various technologies should be selected to form a set of candidate pathways.

[0038] To facilitate technology selection, this embodiment first constructs a basic database containing technical characteristic parameters and full life-cycle carbon emission data for various technologies. Specifically, technical characteristic parameters include efficiency, investment cost, operation and maintenance cost, and technology readiness level, etc. Carbon emission data includes direct carbon emissions, indirect carbon emissions, and total carbon emissions consisting of the sum of the two. Direct CO2 emissions mainly include emissions from the combustion of fossil fuels during coal combustion; indirect CO2 emissions come from upstream fuel chain emissions, electricity consumed in the power generation process, and the carbon emissions implicit in the materials consumed. Upstream fuel chain emissions cover the entire process from coal mining, washing, transportation to the plant boundary.

[0039] The transformation goals can include parameters such as the capacity of the target generating units, key parameters, local resource endowment, and grid demand. These parameters are matched with a basic database to select multiple technology paths to form a candidate path set. Each technology path can include a combination of multiple technologies; for example, path A includes photovoltaic + coal power + energy storage system; path B includes photovoltaic + coal power + thermal storage system; and path C includes photovoltaic + coal power + ammonia storage system. When determining each technology path in the candidate path set, the carbon emission intensity per unit of power generation for each technology path must also be calculated to provide basic data for subsequent path evaluation.

[0040] Step S102: Construct a multi-objective optimization model with levelized cost per kilowatt-hour (LCOE) and carbon emissions per LCOE as objectives and load fulfillment rate as constraints.

[0041] Specifically, the levelized cost of electricity (LCOE) is a core comprehensive indicator for evaluating the techno-economic viability of power generation. It is the cost of power generation calculated after leveling off the costs and power generation over the project's lifecycle. This indicator reflects the average cost of power generation over the entire lifecycle, facilitating cost comparisons between different power generation projects. In this embodiment, all costs over the entire lifecycle, including investment costs, fuel costs, and operation and maintenance costs, are determined through discounted calculations. Carbon emissions per unit of electricity (kWh) are the average amount of carbon dioxide emitted by the system for every kilowatt-hour of electricity provided, reflecting the absolute level of environmental performance. Using kWh as a target allows for an environmental assessment of the system.

[0042] Load fulfillment rate reflects the system's ability to stably supply user load under complex operating conditions. Using load fulfillment rate as a constraint ensures the system's technical performance meets relevant requirements. Besides load fulfillment rate, this multi-objective optimization model also includes constraints on power balance and unit operating characteristics. Power balance constraints require power generation to be greater than or equal to power demand, while operating characteristic constraints include generator ramp rate constraints and upper and lower output limits. Other constraints may include power demand constraints, carbon emission constraints, and investment budget constraints. Power demand refers to the predicted, time-series power load curve (e.g., 8760 hours of annual load), which serves as a boundary condition in the model and directly affects power generation decisions. Carbon emission constraints refer to the set annual carbon emission cap (carbon quota), which acts as an inequality constraint in the model, limiting total carbon emissions. Investment budget refers to the annual upper limit of funds available for new project investment, which acts as an inequality constraint in the model, limiting new installed capacity.

[0043] Step S103: Based on the simulation results of the target coal-fired power system under the preset scenario and candidate path set, and combined with the carbon emission data, a multi-objective optimization algorithm is used to solve the multi-objective optimization model to obtain the path evaluation results.

[0044] Specifically, the preset scenario includes pre-defined scenario parameters, which define a set of assumptions about various possible external conditions and internal constraints in the future. These scenario parameters provide the basic data for system operation simulation. Specifically, the scenario parameters include user load curves, resource endowments, carbon emission reduction targets, cost parameters, and market prices. The user load curves describe the changes in electricity demand of the power grid throughout the year, and resource endowments refer to the natural resource conditions of the system's location, such as solar irradiance and wind energy density. The carbon emission reduction target is similar to the carbon emission target mentioned above and can be set selectively. Cost parameters and market prices include equipment investment costs, fuel costs, operation and maintenance costs, and carbon trading prices.

[0045] Meanwhile, to achieve operational simulation of the target coal-fired power system, this embodiment constructs an hourly simulation model of the coal-fired power system's operation throughout the year. This model is a high-time-resolution, multi-device coupled time-series operation simulator. By simulating the detailed hourly operation of the coal-fired power transition system over 8760 hours a year, its technical and economic performance can be accurately quantified. When using this model for simulation, the system's structure and technical parameters must first be input, such as specifying the equipment included in the system and its characteristic parameters. This data can be determined from the candidate path set for the technology path currently being evaluated. For example, if evaluating technology path A, which includes a photovoltaic + coal-fired power + energy storage system, the equipment includes coal-fired units, photovoltaic power plants, and energy storage systems. Equipment characteristic parameters include capacity, power, power generation efficiency, and charge / discharge efficiency, etc. Furthermore, if it is necessary to evaluate and determine the installed capacity of each technology path, the installed capacity of each technology path can also be used as a variable to be optimized. In addition to the system structure and technical parameters, parameters from a preset scenario also need to be input to drive the model's operation.

[0046] During the simulation process, the power generation is adjusted based on the power demand, simulating the system's operating state hourly. This adjustment of power generation can be based on power balance constraints and coordination between generating units, combined with pre-defined control strategies such as peak shaving and valley filling. The model then outputs the corresponding simulation results in chronological order, including data on equipment power generation, consumption, carbon emissions, and costs.

[0047] Based on the simulation results of the model, the multi-objective optimization model is solved to evaluate the merits of the technical path. Furthermore, based on the evaluation results, the multi-objective optimization algorithm generates a new batch of candidate solutions (such as adjusting the installed capacity of equipment in the path), and re-simulates and evaluates these candidate solutions. This process is repeated continuously, and after multiple iterations, the optimized path is obtained. It should be noted that this multi-objective optimization algorithm can employ optimization algorithms from related technologies, such as non-dominated sorting genetic algorithms, multi-objective particle swarm optimization algorithms, etc. This embodiment does not specifically limit the specific algorithms used.

[0048] This embodiment provides an evaluation method for the technological path of clean and low-carbon transformation of coal-fired power, which includes the following steps: Step S201: Based on the transformation goals of the target coal-fired power system, determine a set of candidate pathways for various technology combinations, and determine the carbon emission data of the candidate pathways; for details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0049] Step S202: Construct a multi-objective optimization model with levelized cost per kilowatt-hour (LCOE) and carbon emissions per LCOE as objectives and load fulfillment rate as constraints.

[0050] Specifically, the levelized cost of electricity (LCOE) is expressed by the following formula:

[0051] In the formula, n represents the number of technology types included in the technology path; i represents the i-th technology; Ni represents the initial investment cost (yuan) of the i-th technology; Ni represents the economic life (years) of the i-th technology. This represents the annual operating and maintenance cost (yuan / year) for the i-th technology. Represents the annual fuel cost of the i-th technology; Indicates the other annual variable costs (yuan / year) for the i-th technology; This represents the annual net power generation (kWh / year) of the technology path.

[0052] The above parameters are expressed by the following formulas:

[0053]

[0054]

[0055]

[0056] In the formula, This represents the rated installed capacity of the i-th technology; This represents the unit capacity investment cost of the i-th technology; This represents the percentage of annual fixed maintenance costs for the i-th technology (%). This represents the fuel price of the i-th technology (yuan / kg or yuan / m³). This represents the fuel consumption rate per unit of electricity generated by the i-th technology (kg / kWh or m³ / kWh). Let represent the annual power generation (kWh) of the i-th technology. represents the annual power consumption (kWh) of the k-th energy-consuming device; m represents the number of different types of energy-consuming devices in the technology path.

[0057] Carbon emissions per kilowatt-hour are expressed using the following formula:

[0058]

[0059] In the formula, Carbon emissions per kilowatt-hour (g CO2 / kWh); The net carbon emissions of the system (t); Carbon emissions from the power plant (t); The amount of carbon captured is t.

[0060] The load fulfillment rate is expressed by the following formula:

[0061] In the formula, Indicates unmet electricity demand (i.e., power shortage), kW; This indicates the power demand, expressed in kW. This indicates the time resolution used in the calculation.

[0062] Step S203: Based on the simulation results of the target coal-fired power system under the preset scenario and candidate path set, and combined with the carbon emission data, a multi-objective optimization algorithm is used to solve the multi-objective optimization model to obtain the path evaluation results. Specifically, when solving the multi-objective optimization model, the simulation results are substituted into the model to calculate the levelized cost per kilowatt-hour (LCOE), carbon emissions per kilowatt-hour, and load fulfillment rate. When calculating the LCOE and carbon emissions per kilowatt-hour, data such as power generation and power consumption can be directly extracted from the simulation results. For example, carbon emissions per kilowatt-hour are calculated as follows: annual power generation data, annual power consumption data, and carbon emission data are obtained based on the simulation results; the target carbon emissions per kilowatt-hour are then calculated based on the annual power generation data, annual power consumption data, and carbon emission data. The load fulfillment rate is also obtained from the simulation data, such as parameters like power shortage and power demand obtained through simulation, and calculated using the above formula.

[0063] Specifically, the multi-objective optimization model is solved using a multi-objective optimization algorithm to obtain the path evaluation results, including the following steps: Step S2031: A multi-objective optimization algorithm is used to solve the multi-objective optimization model, generating a Pareto optimal path set. Specifically, in this embodiment, a non-dominated sorting genetic algorithm is used as the multi-objective optimization algorithm to iteratively solve the multi-objective optimization model and obtain the final Pareto optimal path set. Since the two objectives of "lowest total cost" and "lowest carbon emissions per kilowatt-hour" are usually conflicting (emission reduction often requires increased investment), there is no single optimal solution. The Pareto optimal solution set is a set of compromise solutions where "neither objective can be improved without harming the other."

[0064] In one optional implementation, the Pareto optimal path set includes the optimal installed capacity sequence of various technologies each year within a preset planning period. In this case, each optimal path in the set includes the optimal installed capacity sequence of various technologies each year within the preset planning period. To achieve multi-year optimization within the planning period, when constructing a year-round hourly operation simulation model of the coal-fired power system, the model can be changed to a multi-year hourly operation simulation model of the coal-fired power system. Then, annual data, such as annual load curves, annual technology costs, annual fuel prices, and carbon prices, are input into this model. Simultaneously, dynamic coupling constraints are established, including a capacity balance equation, meaning that the installed capacity for the current year is determined by the state of the previous year and the current decision. Therefore, the total cost and carbon emissions obtained by solving the model using simulation data also aim to minimize the total cost and carbon emissions per kilowatt-hour within the planning period. Thus, the Pareto optimal path set obtained through the multi-objective optimization algorithm includes the optimal installed capacity sequence of various technologies each year within the preset planning period.

[0065] Step S2032 involves performing a multi-dimensional evaluation of each path in the Pareto optimal path set, and generating technical path recommendations based on the evaluation results. Specifically, for the path set, multi-dimensional indicators can be used for evaluation to guide the generation of the final technical path recommendations. This embodiment's multi-dimensional evaluation assesses environmental benefits, technical performance, and economic benefits. Environmental benefits include carbon emission reduction ratios; technical performance includes power generation composition, equipment composition, and coal consumption for power supply; and economic benefits include investment costs, fuel costs, and carbon dioxide emission reduction costs.

[0066] Specifically, the carbon emission reduction ratio is expressed by the following formula:

[0067] In the formula, Indicates the carbon emissions per kilowatt-hour of the candidate pathway; Indicates carbon emissions per kilowatt-hour along the baseline path; The coal consumption for power generation is expressed by the following formula:

[0068] In the formula, , These are the output and input energy subsets, respectively. , These are the energy value and coal conversion factor of the j-th type of input energy, respectively; , These are the energy value and the conversion factor of the i-th type of output energy, respectively; The cost of carbon dioxide emission reduction is expressed by the following formula:

[0069] In the formula, This represents the levelized cost of electricity (LCOE) of the candidate path; This represents the levelized cost of electricity (LCOE) for the baseline path.

[0070] When conducting multi-dimensional evaluations using the aforementioned indicators, quantitative calculations can be performed using methods such as weighted calculations, and a final evaluation report can be generated and visualized to guide the generation of final technical path recommendations.

[0071] As one or more specific application embodiments of the present invention, the evaluation method for the clean and low-carbon transformation technology path of coal-fired power includes: S1. Analyze the carbon emissions of coal-fired power technology throughout its entire life cycle, and calculate the direct and indirect CO2 emissions per unit of electricity generated by different coal-fired power technology routes, as well as the total CO2 emission intensity. Direct CO2 emissions mainly include emissions from the combustion of fossil fuels during coal combustion. Indirect CO2 emissions come from emissions from the upstream fuel chain, the electricity consumed during power generation, and the carbon emissions implied by the materials consumed. Upstream fuel chain emissions cover the entire process from coal mining, washing, transportation to the plant boundary.

[0072] S2. Establish a multi-dimensional evaluation index system that includes environmental benefits, technical performance and economic benefits, and evaluate coal-fired power clean and low-carbon transformation technologies from multiple standards; environmental benefits are quantified by carbon emissions per kilowatt-hour and carbon emission reduction ratio; technical performance is quantified by power generation composition, equipment composition, coal consumption for power supply and load fulfillment rate; economic benefits are quantified by levelized cost per kilowatt-hour, investment cost, fuel cost and carbon dioxide emission reduction cost.

[0073] S3. Combining the carbon emission results calculated in step S1 and the evaluation results of each transition technology in step S2, the optimal technology development path is determined based on multi-objective dynamic optimization, and the impact of different technology combinations on the carbon emissions and system economics of the coal-fired power industry is evaluated. Specifically, the method for determining the optimal technology development path is as follows: Based on the carbon emission intensity data obtained in step S1 and the evaluation results of each transition assessment indicator in step S2, and considering electricity demand, carbon emission constraints, investment budget, technology learning rate, and energy and carbon price scenarios, under the constraints of power balance and unit operating characteristics, a multi-objective optimization model is constructed with the goal of minimizing carbon emissions per kilowatt-hour and total cost. The Pareto optimal path set is obtained by solving the optimization algorithm, and the optimal dynamic evolution path of technology penetration rate is determined accordingly.

[0074] Carbon emission data can be compared with "carbon emission constraints" (such as annual carbon quotas) to form constraints; it is also used to calculate "carbon emissions per kilowatt-hour," one of the objective functions. The technology learning rate refers to the rate at which technology costs decrease as market size expands. It is not directly used as a constraint in the model, but rather its impact on the total cost calculation, one of the objective functions, is influenced by the varying costs of different technologies under different scenarios. The energy and carbon price scenario assumes fuel prices and carbon trading prices. Energy prices directly affect the costs in the objective function, while carbon trading prices can directly affect the total cost and indirectly affect the carbon emission cap constraint. The optimization model directly outputs the optimal installed capacity sequence for various technologies each year within the planning period (e.g., 35 or 50 years), representing the optimal dynamic evolution path of technology penetration rate.

[0075] This invention establishes a simulation model that operates hourly throughout the year, which can accurately simulate the dynamic operating status of various equipment under different technical paths and output refined data such as power generation, carbon emissions, and costs, providing a high-time-resolution quantitative basis for evaluation. Therefore, this method has the technical effect of significantly improving the accuracy and reliability of the evaluation.

[0076] This invention provides decision-makers with intuitive and quantitative scientific evidence by generating Pareto optimal solution sets and visualizing cost-benefit analysis, clearly demonstrating the trade-offs between multiple objectives and supporting multi-scenario comparisons.

[0077] This embodiment also provides an evaluation device for the technological path of clean and low-carbon transformation of coal-fired power. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0078] This embodiment provides an evaluation device for the technological path of clean and low-carbon transformation of coal-fired power, such as... Figure 2 As shown, it includes: The path determination module 21 is used to determine a set of candidate paths for multiple technology combinations based on the transformation goals of the target coal-fired power system, and to determine the carbon emission data of the candidate paths; Model building module 22 is used to build a multi-objective optimization model with levelized cost per kilowatt-hour and carbon emissions per kilowatt-hour as objectives and load fulfillment rate as a constraint. The optimization module 23 is used to solve the multi-objective optimization model based on the simulation results of the target coal-fired power system under the preset scenario and candidate path set, combined with the carbon emission data, and to obtain the path evaluation results.

[0079] As one or more specific application embodiments of the present invention, the evaluation device for the clean and low-carbon transformation technology path of coal-fired power plants works collaboratively according to the following process modules: Technology Research and Database Module: Research on clean and low-carbon transformation technologies for coal-fired power, covering four major categories of technologies: energy efficiency improvement, fuel substitution, multi-energy coupling and CCUS, and build a basic database containing technical characteristic parameters and carbon emission data throughout the entire life cycle; Comprehensive evaluation system module: Built-in multi-dimensional technical evaluation indicator system (environmental, technical, and economic indicators) and its calculation model for the above steps S2; Multi-factor scenario setting module: used to configure and manage various input scenario parameters required for evaluation and optimization, including user load curves, resource endowment, carbon emission reduction targets, candidate technology routes, technology characteristics, cost parameters and market prices; The simulation and path optimization module is as follows: First, based on the scenario input, a simulation model of the coal-fired power system is established for hourly operation throughout the year to simulate the operating status of equipment and system performance under various technical paths. Then, the objective function of the multi-objective optimization model is constructed using the core indicators in the comprehensive evaluation system (such as levelized cost of electricity, total investment cost, cost per ton of carbon dioxide emission reduction, and total carbon dioxide emission reduction). The optimization solution is performed under scenario constraints to select the optimal technical path and output the operation results of the optimal path. The comprehensive evaluation and results output module receives the results from the simulation and path optimization modules, calls the comprehensive evaluation model for quantitative calculations, and generates an evaluation report containing core indicators. It stores the evaluation results in a cost-benefit database and outputs visual analysis charts; simultaneously, it supports importing engineering case data for model calibration and validation.

[0080] As one or more specific application embodiments of the present invention, a 1000MW supercritical coal-fired power plant in a certain region of China is taken as a potential retrofit target, such as... Figure 3 As shown, the evaluation device for this clean and low-carbon transformation technology path of coal-fired power adopts the following process: 1. Technical research and database module.

[0081] This module begins with the preparation of basic data. It systematically surveys and organizes the main clean and low-carbon transformation technologies in the coal-fired power industry, forming four major technology lists: energy efficiency improvement technologies, fuel substitution technologies, multi-energy coupling technologies, and CCUS technologies. A profile is created for each technology, recording its key performance parameters (efficiency, cost, technology readiness level, etc.), and a full life-cycle carbon emission accounting sub-model is integrated to calculate the unit carbon emission intensity per unit of power generation for various technology pathways, forming a structured basic database to provide data support for subsequent evaluation and optimization.

[0082] Based on the target unit's capacity, key parameters, local resource endowment, and grid demand, three technically feasible paths were selected from the technology database for evaluation: Path A: Photovoltaic power + coal-fired power + energy storage system; Path B: Photovoltaic power + coal-fired power + thermal storage system; Path C: Photovoltaic power + coal-fired power + ammonia storage system; 2. Comprehensive evaluation system module.

[0083] This module incorporates a comprehensive evaluation index system and calculation model covering three dimensions: environment, technology, and economy. For the optimization objectives of this embodiment, the module establishes a dual-objective evaluation system centered on carbon emissions per kilowatt-hour and levelized cost per kilowatt-hour.

[0084] 3. Multi-element scenario setting module.

[0085] This module provides users with an interactive interface for flexibly defining future evaluation scenarios. Specific scenarios for the examples are defined in this module: Demand and Targets: The baseline carbon emission per kilowatt-hour is 738 g / kWh, and the LCOE is 0.353 yuan / kWh. Different load fulfillment rate requirements (80%, 90%, 95%, and 99%) are set as comparison scenarios.

[0086] Resources and Markets: Input local photovoltaic resource data and unified coal and electricity market prices.

[0087] Technology and Cost: Three candidate technology routes were selected: "photovoltaic-coal power-thermal storage", "photovoltaic-coal power-electricity storage", and "photovoltaic-coal power-ammonia storage", and cost parameters for each technology were set.

[0088] 4. Run the simulation and path optimization module.

[0089] This module is the core algorithm engine of the system. It receives input from the database and the scenario module and performs the following key operations: Simulation Model Construction and Operation: The system automatically constructs a time-series simulation model for each candidate path, covering 8760 hours throughout the year. This model accurately simulates how coal-fired power units, photovoltaic and energy storage devices work together to meet the hourly load under different photovoltaic installed capacities and energy storage configurations, while simultaneously calculating fuel consumption, carbon emissions, and operating costs.

[0090] Multi-objective optimization is performed: Since this embodiment needs to optimize numerous configuration combinations of multiple paths, the system initiates an optimization process. It uses minimizing carbon emissions per unit of electricity and LCOE as the objective function, while satisfying the load factor constraint, and uses a multi-objective evolutionary algorithm (such as NSGA-II) for the search.

[0091] Output Pareto optimal solution set: After optimization, the module outputs the Pareto fronts of the three technical paths under different load satisfaction rate constraints.

[0092] 5. Comprehensive evaluation and result output module.

[0093] This module performs final processing and displays the optimization results: Comprehensive evaluation: The system automatically calculates the evaluation index of each representative solution on the Pareto front.

[0094] Data management and visualization: All solution data is stored in a cost-benefit database. The system automatically generates key charts.

[0095] Decision support: Based on comparative analysis of visual charts and databases, the system clearly supports the following decisions: given emission reduction targets and reliability requirements, which technology path and its optimal capacity configuration scheme should be selected, realizing the transformation from complex simulation optimization to clear decision-making basis.

[0096] The evaluation and optimization results of the three "photovoltaic-coal power-energy storage" coupled transformation paths applied using the system described in this invention are analyzed as follows: The Pareto frontiers along the three routes, such as Figure 4 As shown in the figure, each point on the curve represents a feasible technology development path (i.e., the installation plan for each technology in each year), plotting the Pareto fronts of different technology combinations on the same graph. It is clear which combination has a lower cost (Y-axis) under a certain emission reduction target (X-axis); or, under a certain cost budget, which combination has greater emission reduction potential. Analysis shows that there is a clear critical threshold for system performance (e.g., when the load fulfillment rate is ≥99%, the carbon emission per kilowatt-hour is approximately 720 g / kWh). Above this critical threshold is the "emission reduction and cost reduction synergy zone," where cost reduction can be achieved simultaneously while reducing carbon emissions; once the carbon emission target per kilowatt-hour falls below this critical value, it enters the "emission reduction and cost reduction trade-off zone," where any further carbon emission reduction will lead to an increase in the levelized cost per kilowatt-hour. This is because after entering the emission reduction and cost reduction trade-off zone, further emission reduction requires the installation of more photovoltaic equipment, and the increased investment cost from this installation exceeds the fuel cost savings from reduced coal consumption.

[0097] More importantly, the system quantitatively compared the dynamic advantages of different technological approaches. Thermal energy storage demonstrates a cost advantage in the medium-term emission reduction phase due to the reuse of existing thermal systems, but its technological characteristics create bottlenecks in deep emission reduction scenarios. In contrast, while electric energy storage has a higher initial investment, its operational independence makes it a more sustainable choice for pursuing deep decarbonization. Ammonia storage, however, remains uncompetitive under current techno-economic parameters. Furthermore, the analysis clearly indicates that as load fulfillment constraints increase, thermal and electric energy storage will demonstrate their advantages earlier (the carbon emissions per kilowatt-hour when energy storage is installed begin earlier). This suggests that under the constraint of high load fulfillment rates, the demand for energy storage systems will increase.

[0098] This invention constructs a modular evaluation framework covering the entire process of "technology research - indicator system construction - scenario simulation - path optimization - cost-benefit analysis". It integrates full life cycle carbon accounting, multi-dimensional indicator system, scenario simulation and intelligent optimization algorithm, forming a standardized analysis platform that can efficiently and systematically evaluate and compare various single or composite transformation paths, thereby improving the scientific nature and efficiency of decision-making.

[0099] The evaluation device for clean and low-carbon transformation technology paths of coal-fired power provided in this embodiment of the invention can execute the evaluation method for clean and low-carbon transformation technology paths of coal-fired power provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0100] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0101] The following is a detailed reference. Figure 5 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 11, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 12 or a program loaded from memory 18 into random access memory (RAM) 13. The RAM 13 also stores various programs and data required for the operation of the electronic device. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0102] Typically, the following devices can be connected to I / O interface 15: input devices 16 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 17 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 18 including, for example, magnetic tapes, hard disks, etc.; and communication devices 19. Communication device 19 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0103] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 19, or installed from a memory 18, or installed from a ROM 12. When the computer program is executed by the processor 11, it performs the functions defined in the evaluation method for the clean and low-carbon transformation technology path of coal-fired power in embodiments of the present invention.

[0104] Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0105] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium after being downloaded via a network. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the evaluation method for the clean and low-carbon transformation technology path of coal-fired power shown in the above embodiments is implemented.

[0106] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0107] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An evaluation method of a coal electricity clean low-carbon transformation technology path, characterized in that, The method includes: Based on the transformation goals of the target coal-fired power system, a set of candidate pathways with multiple technology combinations was identified, and carbon emission data for the candidate pathways were determined. Construct a multi-objective optimization model with levelized cost per kilowatt-hour (LCOE) and carbon emissions per LCOE as objectives and load fulfillment rate as constraints. Based on the simulation results of the target coal-fired power system under the preset scenario and candidate path set, and combined with the carbon emission data, a multi-objective optimization algorithm is used to solve the multi-objective optimization model to obtain the path evaluation results.

2. The method of claim 1, wherein, The multi-objective optimization model is solved using a multi-objective optimization algorithm to obtain path evaluation results, including: A multi-objective optimization algorithm is used to solve the multi-objective optimization model to generate a Pareto optimal path set. The paths in the Pareto optimal path set are evaluated from multiple dimensions, and technical path suggestions are generated based on the evaluation results.

3. The method of claim 2, wherein, The Pareto optimal path set includes the optimal installed capacity sequence of various technologies each year within the preset planning period; the multi-dimensional assessment includes carbon emission reduction ratio assessment, power generation composition assessment, installed capacity composition assessment, power supply coal consumption assessment, investment cost assessment, fuel cost assessment, and carbon dioxide emission reduction cost assessment.

4. The method of claim 1, wherein, The carbon emissions per unit of electricity are calculated using the following method: Based on the simulation results, annual power generation data, annual power consumption data, and carbon emission data are obtained; The carbon emission data per kilowatt-hour in the target is calculated based on the annual power generation data, annual power consumption data, and carbon emission data.

5. The method of claim 1, wherein, The levelized cost of electricity (LCOE) is expressed by the following formula: In the formula, n represents the number of technical categories contained in the technical path; i represents the i-th technical category; represents the initial investment cost of the i-th technical category; Ni represents the economic life of the i-th technical category; represents the annual operation and maintenance cost of the i-th technical category; represents the annual fuel cost of the i-th technical category; represents the other annual variable cost of the i-th technical category; represents the annual net power supply of the technical path.

6. The method of claim 1, wherein, The load fulfillment rate is expressed by the following formula: wherein represents the unmet power demand; represents the power demand power; represents the time resolution employed for the calculation.

7. The method of claim 3, wherein, The carbon emission reduction ratio is expressed by the following formula: In the formula, Indicates the carbon emissions per kilowatt-hour of the candidate pathway; Indicates carbon emissions per kilowatt-hour along the baseline path; The coal consumption for power generation is expressed by the following formula: In the formula, , These are the output and input energy subsets, respectively. , These are the energy value and coal conversion factor of the j-th type of input energy, respectively; , These are the energy value and the conversion factor of the i-th type of output energy, respectively; The cost of carbon dioxide emission reduction is expressed by the following formula: In the formula, This represents the levelized cost of electricity (LCOE) of the candidate path; This represents the levelized cost of electricity (LCOE) for the baseline path.

8. An evaluation device for a clean and low-carbon transition technology path for coal-fired power, characterized in that, The device includes: The path determination module is used to determine a set of candidate paths for various technology combinations based on the transformation goals of the target coal-fired power system, and to determine the carbon emission data of the candidate paths; The model building module is used to construct a multi-objective optimization model with levelized cost per kilowatt-hour and carbon emissions per kilowatt-hour as objectives and load fulfillment rate as a constraint. The optimization module is used to solve the multi-objective optimization model based on the simulation results of the target coal-fired power system under a preset scenario and a set of candidate paths, combined with the carbon emission data, and to obtain the path evaluation results.

9. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the evaluation method for the clean and low-carbon transformation technology path of coal power as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the evaluation method for the clean and low-carbon transformation technology path of coal-fired power as described in any one of claims 1 to 7.

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