Power system carbon emission prediction and optimization method based on electricity-carbon coupling simulation

Abstract

The invention relates to a power system carbon emission prediction and optimization method based on electricity-carbon coupling simulation, and the method comprises the steps: constructing a power system basic model based on a preset reference load flow calculation case file; configuring a multi-dimensional attribute parameter set for the generator set; presetting a plurality of simulation scenes; adjusting the output strategy of each generator set based on the simulation scene, and obtaining the distribution result of the system power flow through the optimal power flow calculation; predicting the average carbon emission intensity, the carbon emission of each unit and the total carbon emission of the system by adopting a carbon emission flow tracking algorithm; and generating a comparative analysis chart under multiple scenes based on a calculation result, evaluating carbon reduction effects and economy of different scenes based on the comparative analysis chart, obtaining a low-carbon scheduling strategy, and optimizing carbon emission of the power system. Compared with the prior art, the method can quantitatively evaluate the carbon reduction effect and economy of different scheduling scenes, and provides a quantitative basis for obtaining a low-carbon scheduling strategy for decision making and optimizing the carbon emission of a power system.

Description

Technical Field

[0001] This invention relates to the field of power system operation, analysis and planning technology, and in particular to a method for predicting and optimizing carbon emissions from power systems based on electric-carbon coupling simulation. Background Technology

[0002] Addressing climate change and promoting a clean and low-carbon energy transition has become a global consensus. As the largest single carbon emitter, the power industry's emission reduction efforts are directly related to achieving the "dual carbon" targets. Currently, carbon emissions from the power system are mostly measured using macro-statistical methods based on the production side, estimating emissions by multiplying power generation by an average emission factor. This method cannot reflect the impact of complex operational characteristics such as power flow distribution, inter-provincial transmission, and unit scheduling sequences on the spatial distribution of carbon emissions, making it difficult to meet the refined management requirements for "precise carbon reduction."

[0003] Especially in regions rich in renewable energy, the high proportion of new energy integration has drastically changed the traditional operation mode and carbon flow pattern of the power grid. How to quantitatively assess the carbon emission effects under different dispatch strategies and energy policies, and predict the carbon emission reduction potential of various development scenarios, is a core challenge facing power grid operators and policymakers.

[0004] Chinese patent application CN119765309A discloses a method and system for determining carbon emissions from a distribution network system. The method involves inputting collected power flow distribution data, distributed generation data, and energy storage operation data of the target distribution network into a carbon emission simulation model to obtain a first carbon emission calculation result for the target distribution network. Based on the unit parameters of the target distribution network, the method determines the corresponding carbon emission scenario type. According to the carbon emission calculation strategy corresponding to the carbon emission scenario type, a second carbon emission calculation result for the target distribution network is obtained. The first and second carbon emission calculation results are compared and analyzed to determine the final carbon emission result for the target distribution network. While this patent addresses the inaccuracy of carbon emission calculations by performing carbon emission calculations, it does not reflect the impact of complex operational characteristics such as power flow distribution, inter-provincial transmission, and unit scheduling sequences on the spatial distribution of carbon emissions. It only provides a total carbon emission statistical result for the distribution network and cannot support decision-making.

[0005] In summary, existing technologies lack an integrated analysis tool that can couple electric current and carbon flow, support multi-scenario comparative simulation, and provide intuitive decision support. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for predicting and optimizing carbon emissions from power systems based on electro-carbon coupling simulation.

[0007] The objective of this invention can be achieved through the following technical solutions: A method for predicting and optimizing carbon emissions from power systems based on electro-carbon coupling simulation, the method comprising: Based on the preset benchmark power flow calculation case files, a basic power system model is constructed. Configure a multi-dimensional attribute parameter set for each generator unit in the power system basic model. The multi-dimensional attribute parameter set includes at least a fuel type identifier, a carbon emission intensity coefficient per unit of power generation, and a priority value representing the scheduling priority. Based on different energy policies and dispatch strategies, multiple simulation scenarios are preset. The scenario variables of the simulation scenarios include at least the penetration rate of renewable energy and the peak-shaving start-up and shutdown strategy of thermal power units. The output strategy of each generator unit is adjusted based on the simulation scenario, and the optimal power flow calculation is performed to obtain the distribution result of the system power flow. Based on the distribution result of the system power flow and the multi-dimensional attribute parameter set, the carbon emission flow tracking algorithm is used to predict the average carbon emission intensity, the carbon emission of each unit and the total carbon emission of the system. Based on the calculation results, comparative analysis charts for multiple scenarios are generated. Based on these charts, the carbon reduction effect and economic efficiency of different scenarios are evaluated, low-carbon dispatch strategies are obtained, and carbon emissions of the power system are optimized.

[0008] Furthermore, the reference power flow calculation case file includes the power grid topology, branch parameters, node load data, and steady-state operating parameters of generator units.

[0009] Furthermore, the calculation of the carbon emission intensity coefficient per unit of power generation takes into account the additional carbon emissions during the start-up and shutdown process of the unit. The carbon emission intensity coefficient per unit of power generation is calculated based on the power generation of the unit, the carbon emission coefficient per unit calorific value of the fuel, the operating efficiency factor considering the load rate, the baseline carbon emissions of the unit during a single start-up and shutdown, and the start-up and shutdown frequency factor related to the operating status of the unit.

[0010] Furthermore, the simulation scenarios include a baseline scenario, a high renewable energy penetration scenario, and an optimized peak-shaving strategy scenario.

[0011] Furthermore, the scheduling rule for the baseline scenario is: maintain the existing unit combination and output strategy; The scheduling rules for the high renewable energy penetration scenario are as follows: increase the predicted output of wind turbines and photovoltaic units, and reduce the output of traditional thermal power units according to the scheduling priority. The scheduling rules for the optimized peak-shaving strategy scenario are as follows: prioritize increasing the output of low-carbon and zero-carbon units, and prioritize reducing the output of high-carbon units according to the order of carbon emission intensity from high to low.

[0012] Furthermore, the average carbon emission intensity is obtained by solving for the carbon potential of all nodes in the power grid, and the formula for calculating the carbon potential is: in, Let be the carbon potential at node j. Let J be the set of generators flowing towards node j. Let j be the set of the starting nodes of the path leading to node j. Let be the carbon emission intensity coefficient per unit of electricity generated by unit i. Let k be the carbon potential at node k. The injected power of unit i, Let be the line power flowing from node k to node j.

[0013] Furthermore, the carbon emissions of each unit include operating carbon emissions and start-up / shutdown carbon emissions. The operating carbon emissions are calculated based on the unit's power generation, the carbon emission coefficient per unit calorific value of the fuel, and the operating efficiency factor considering the unit's current load rate level. The start-up / shutdown carbon emissions are calculated based on the unit's single start-up / shutdown baseline carbon emissions and the start-up / shutdown frequency factor related to the unit's operating status.

[0014] Furthermore, the lower the current load rate of the unit, the worse the efficiency, and the smaller the operating efficiency factor.

[0015] Furthermore, the comparative analysis charts include a total carbon emission comparison chart, a carbon emission intensity comparison chart, a power generation composition stacking chart, a unit output change chart, a carbon emission reduction effect comparison chart, and a cost-benefit analysis chart.

[0016] Furthermore, the cost-benefit analysis diagram compares and analyzes the total economic cost under different scenarios. The total economic cost consists of fuel costs and carbon trading costs, and its calculation formula is as follows: in, The total economic cost, For fuel costs, For carbon trading costs, Let i be the power generation capacity of unit i. This represents the total carbon emissions of the system. The unit price of fuel used by unit i. This refers to the carbon trading market or carbon tax price.

[0017] Compared with the prior art, the beneficial effects of the present invention include: 1. This invention can quantitatively assess the carbon reduction effect and economic efficiency of different dispatching scenarios, providing a quantitative basis for decision-making to obtain low-carbon dispatching strategies and optimize power system carbon emissions. The multi-dimensional attribute parameter set of this invention goes beyond the traditional scope of only including economic parameters, introducing carbon attributes and dispatching attributes, focusing on low-carbon needs. The carbon emission intensity coefficient is directly related to the unit's fuel characteristics and carbon emissions, and the dispatching priority value clarifies the order of unit output adjustment, aligning with low-carbon dispatching logic. This invention pre-sets multiple representative simulation scenarios to simulate and quantitatively assess the impact of various energy policies, market mechanisms, and technological advancements on future power system carbon emissions, providing forward-looking and quantitative data support for strategic decision-making, and the analysis results are more practically instructive.

[0018] 2. In calculating carbon emissions, this invention not only considers the carbon emissions from generator unit operation, but also innovatively introduces efficiency factors and start-stop carbon emissions related to load factor and start-stop frequency, distinguishing between operation and start-stop carbon emissions. This addresses the one-sidedness of traditional models that only calculate operation carbon emissions. The nodal carbon potential calculation can reflect the impact of power flow distribution and inter-provincial power transmission on the spatial pattern of carbon emissions, filling the gap in traditional macro-statistics that cannot locate the source of carbon emissions, and meeting the needs of precise carbon reduction and refined management. The carbon emission calculation results of this invention are closer to the actual operating conditions of the power system, far exceeding the accuracy of the traditional average factor method.

[0019] 3. The multi-dimensional comparison charts automatically generated by this invention transform complex power flow and carbon emission data into intuitive and easy-to-understand graphical language, greatly reducing the threshold for data analysis, improving decision-making efficiency, and directly serving multiple business processes such as grid dispatching, power planning, and carbon asset management.

[0020] 4. The chart system of this invention covers all dimensions of total carbon emissions, carbon emission intensity, power structure, unit output and cost-effectiveness. Decision-makers can simultaneously grasp the carbon reduction effect and economic cost, avoiding irrational decisions that only reduce carbon emissions without considering costs. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a comparison chart of carbon emission intensity in various scenarios in the embodiments of the present invention. Detailed Implementation

[0022] 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, not all, of the embodiments of the present invention. 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.

[0023] Example 1 This embodiment discloses a method for predicting and optimizing carbon emissions from power systems based on electro-carbon coupling simulation. The method is as follows: Figure 1 As shown, steps S1-S5 are included: Step S1: Construct a basic power system model based on a preset benchmark power flow calculation case file.

[0024] The baseline power flow calculation case file fully defines the topology of the power grid, including the topology, branch parameters, node load data, and steady-state operating parameters of generator units, such as maximum / minimum output range and cost coefficient, forming the mathematical model foundation that can be used for optimal power flow calculation.

[0025] The basic power system model is built on the open-source power system analysis software MATPOWER.

[0026] Step S2: Configure a multi-dimensional attribute parameter set for each generator unit in the power system basic model. The multi-dimensional attribute parameter set includes at least the fuel type identifier, the carbon emission intensity coefficient per unit of power generation, and the priority value representing the scheduling priority.

[0027] Carbon emission intensity coefficient per unit of electricity generation This coefficient represents the amount of carbon dioxide emissions directly generated by unit i for every kilowatt-hour of electricity generated. This coefficient is directly related to the calorific value and carbon content of the fuel used by the unit.

[0028] Scheduling priority value : A quantitative indicator used to guide the order of unit output adjustment in scenario simulation. The lower the value, the higher the priority. It is activated first when it is necessary to increase output and de-escalated first when it is necessary to reduce output.

[0029] Start-up and shutdown carbon emission parameters: used to build more accurate carbon emission models, taking into account the additional fuel consumption and carbon emissions during unit start-up and shutdown.

[0030] The carbon emission intensity factor per unit of electricity generated takes into account the additional carbon emissions during unit start-up and shutdown. The calculations are based on the unit's power generation, the carbon emission coefficient per unit calorific value of the fuel, the operating efficiency factor considering the load factor, the unit's baseline carbon emissions for a single start-up and shutdown, and the start-up and shutdown frequency factor related to the unit's operating status.

[0031] The specific formula for calculating the additional carbon emissions during the start-up and shutdown of the unit is as follows: in, The carbon emissions of unit i during operation. This refers to the carbon emissions during the start-up and shutdown of unit i. The total carbon emissions of unit i. Let i be the power generation capacity of unit i. Let be the carbon emission coefficient per unit calorific value of unit i. For unit i, considering the load factor, the lower the current load factor, the worse the efficiency and the smaller the operating efficiency factor. The baseline carbon emissions for a single start-up and shutdown of unit i. This is the start-stop frequency factor related to the operating state of unit i.

[0032] Start-up and shutdown additional carbon emissions are carbon emissions generated during the start-up / shutdown process of the unit due to additional fuel consumption (such as preheating, purging, etc.). These are implicit carbon emissions ignored by traditional models. Operational carbon emissions are the basic carbon emissions generated during the normal power generation process of the unit due to fuel combustion. Total carbon emissions are the sum of operational carbon emissions and start-up and shutdown additional carbon emissions, reflecting the complete carbon emissions of the unit throughout its entire operating cycle.

[0033] The carbon intensity coefficient per unit of electricity generation is the ratio of total carbon emissions to total electricity generation, thereby distributing the additional carbon emissions from start-up and shutdown to a unit of electricity.

[0034] Step S3: Based on different energy policies and dispatch strategies, multiple simulation scenarios are preset. The scenario variables of the simulation scenarios include at least the penetration rate of renewable energy and the peak-shaving start-up and shutdown strategy of thermal power units.

[0035] Specifically, the simulation scenarios include a baseline scenario, a scenario with high renewable energy penetration, and a scenario with optimized peak-shaving strategies.

[0036] The scheduling rule for the baseline scenario is: maintain the existing unit combination and output strategy; The dispatching rules for scenarios with high renewable energy penetration are: increase the predicted output of wind turbines and photovoltaic units, and reduce the output of traditional thermal power units according to the dispatching priority order in order to maintain real-time power balance; The scheduling rules for optimizing peak-shaving strategies are as follows: simulate the effects of implementing advanced scheduling strategies, such as prioritizing the increase of output of low-carbon and zero-carbon units, and prioritizing the reduction of output of coal-fired units with the highest carbon emission intensity according to the order of carbon emission intensity from high to low, thereby maximizing carbon emission reduction benefits.

[0037] Step S4: Adjust the output strategy of each generator unit based on the simulation scenario, perform optimal power flow calculation, and obtain the distribution result of the system power flow; based on the power flow distribution result and the multi-dimensional attribute parameter set, use the carbon emission flow tracking algorithm to calculate the average carbon emission intensity, the carbon emission of each unit, and the total carbon emission of the system.

[0038] For each simulation scenario designed in step 3, the output setpoints of the corresponding generator units in the baseline scenario are modified, and then optimal power flow calculation is performed. After the power flow calculation converges, the voltage, phase angle of all buses in the entire power grid, and the active / reactive power distribution of all branches can be obtained. Based on this, carbon emission flow tracing calculation based on power distribution is used to calculate the average carbon emission intensity, the carbon emissions of each unit, and the total carbon emissions of the system. This step includes two core calculation models: Refined Calculation Model for Unit Carbon Emissions: Unit i Total carbon emissions It consists of two parts: carbon emissions during operation and carbon emissions during start-up and shutdown.

[0039] Operational carbon emissions are calculated based on the unit's power generation, the carbon emission coefficient per unit calorific value of the fuel, and the operating efficiency factor considering the unit's current load rate level. Start-up and shutdown carbon emissions are calculated based on the unit's single start-up and shutdown baseline carbon emissions and the start-up and shutdown frequency factor related to the unit's operating status.

[0040] The specific calculation formula is as follows: in, For the unit i Power generation capacity, The carbon emission coefficient per unit calorific value of its fuel. The operating efficiency factor is determined by taking into account the current load rate level of the unit. The lower the current load rate level of the unit, the worse the efficiency, and the smaller the operating efficiency factor. For the unit i The baseline carbon emissions per start-stop cycle. This is a start-stop frequency factor related to the unit's operating status. For renewable energy units, its... and All are zero.

[0041] System carbon flow tracing model: To calculate the carbon emission intensity on the electricity consumption side, it is necessary to calculate the carbon intensity at each node. Node j carbon potential The carbon potential is calculated by weighting the carbon emissions carried by all power flows flowing into the node. The specific formula for calculating the carbon potential is as follows: in, Let be the carbon potential at node j. Let J be the set of generators flowing towards node j. Let j be the set of the starting nodes of the path leading to node j. Let be the carbon emission intensity coefficient per unit of electricity generated by unit i. Let k be the carbon potential at node k. The injected power of unit i, Let be the line power flowing from node k to node j.

[0042] By solving the carbon flow tracing model of the system sequentially starting from the source node, the carbon potential of all nodes can be obtained, and thus the average carbon emission intensity of the system can be obtained. Step S5: Generate comparative analysis charts for multiple scenarios based on the calculation results. Based on the comparative analysis charts, evaluate the carbon reduction effect and economic efficiency of different scenarios, obtain low-carbon dispatch strategies, and optimize the carbon emissions of the power system.

[0043] The comparative analysis charts include a total carbon emissions comparison chart, a carbon emissions intensity comparison chart, a stacked chart of power generation composition, a chart of changes in unit output, a chart of carbon emission reduction effects, and a cost-benefit analysis chart.

[0044] Total Carbon Emissions Comparison Chart: Visually displays the total carbon emissions of the system under various scenarios. The differences.

[0045] Carbon emission intensity comparison chart: showing the system's average carbon emission intensity under various scenarios. The differences.

[0046] Power generation composition stacked chart: This chart displays the changes in the proportion of power generation from different power sources, such as coal-fired power, gas-fired power, and renewable energy, in various scenarios using a stacked bar chart format.

[0047] Unit output variation chart: The output variation of each unit under different scenarios is displayed in the form of grouped bar charts, which clearly reflects the impact of dispatching strategies on specific units.

[0048] Carbon emission reduction effect comparison chart: Calculate and display the percentage reduction in carbon emissions in other scenarios compared to the baseline scenario.

[0049] Cost-benefit analysis chart: This chart comprehensively calculates fuel costs and carbon trading (or carbon tax) costs under various scenarios, comparing and analyzing the total economic costs of different scenarios. The total cost consists of fuel costs and carbon trading costs, and its calculation formula is as follows: in, The total economic cost, For fuel costs, For carbon trading costs, Let i be the power generation capacity of unit i. This represents the total carbon emissions of the system. The unit price of fuel used by unit i. This refers to the carbon trading market or carbon tax price.

[0050] This set of visualization charts constitutes a powerful decision support dashboard, enabling decision-makers to quickly and comprehensively grasp the carbon reduction effects, techno-economic feasibility, and underlying reasons under different development paths.

[0051] Example 2 This embodiment, based on Embodiment 1 above, discloses a practical simulation example of a power system carbon emission prediction and optimization method based on electric-carbon coupling simulation, the specific content of which is as follows: First, the basic model and configuration parameters of the power grid system are constructed (corresponding to steps 1 and 2). In the MATLAB environment, the `loadcase` function of `MATPOWER` is called to load and read the collected basic power grid parameters. Then, multi-dimensional attribute parameters are configured for the five generating units in the system. Unit 1 is a coal-fired unit with a carbon emission intensity coefficient of 0.85 tons of CO2 per megawatt-hour and a scheduling priority of 3. Unit 2 is also a coal-fired unit with a slightly lower carbon emission intensity coefficient of 0.82 and a higher scheduling priority of 2. Unit 3 is a high-carbon coal-fired unit with a carbon emission intensity coefficient of 0.88 and the lowest scheduling priority of 4. Unit 4 is a gas-fired unit, a low-carbon unit with a carbon emission intensity coefficient of 0.45 and a high scheduling priority of 1. Unit 5 is a wind turbine unit, a zero-carbon unit with a carbon emission intensity coefficient of 0 and the highest scheduling priority of 0. The baseline start-stop carbon emissions for each unit are also set according to its capacity and fuel type.

[0052] Secondly, simulation scenarios were designed and executed (corresponding to steps 3 and 4). This embodiment designed three typical scenarios for comparative analysis. The first scenario is the baseline scenario, maintaining the initial output of each unit. The second scenario is a high renewable energy penetration scenario, in which the output of the wind turbine (Unit 5) is planned to be increased by 80 MW. To balance system power, the output of coal-fired units such as Units 3 and 1 is reduced accordingly, according to the unit scheduling priority from low to high (i.e., prioritizing the reduction of units with higher priority values). The third scenario is an optimized peak-shaving strategy scenario, in which the output of the wind turbine is increased by 60 MW and the output of the gas turbine (Unit 4) is increased by 30 MW. To balance power, the output of high-carbon units such as Units 3 and 1 is reduced accordingly, according to the unit carbon emission intensity from high to low (i.e., prioritizing the reduction of units with higher carbon emission intensity). For each scenario, the `runpf` function of `MATPOWER` is called to perform optimal power flow calculations to ensure the system operates in a reasonable state.

[0053] Then, refined carbon emission calculations are performed (corresponding to step 4). After the power flow calculation converges, for each scenario, the custom function `calculate_carbon_emissions` is called to calculate the unit and system carbon emissions. Based on the refined unit carbon emission calculation model and system carbon flow tracing model in Example 1, this function calculates not only the operating carbon emissions based on power generation and carbon intensity coefficient, but also the efficiency factor based on the ratio of the unit's current output to its maximum output. Based on this, the carbon emissions during operation are corrected, and the carbon emission contributions during start-up and shutdown are also taken into account, ultimately yielding the total carbon emissions and average carbon emission intensity of each unit and system.

[0054] Finally, generate a visualization analysis report (corresponding to step 5). Call the custom function `plot_detailed_results` to read the calculation results for all scenarios. Decision-makers can draw clear conclusions by observing these charts: for example, from... Figure 2 It is evident that both scenarios two and three achieved carbon emission reduction, with scenario three (optimized peak shaving) showing the most significant emission reduction effect.

[0055] Example 3 Based on Embodiment 1, this embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the aforementioned method for predicting and optimizing carbon emissions from a power system based on electro-carbon coupling simulation.

[0056] At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the aforementioned method for predicting and optimizing carbon emissions from a power system based on electro-carbon coupling simulation. Of course, besides software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0057] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0058] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0059] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting and optimizing carbon emissions from power systems based on electro-carbon coupling simulation, characterized in that, The method includes: Based on the preset benchmark power flow calculation case files, a basic power system model is constructed. Configure a multi-dimensional attribute parameter set for each generator unit in the power system basic model. The multi-dimensional attribute parameter set includes at least a fuel type identifier, a carbon emission intensity coefficient per unit of power generation, and a priority value representing the scheduling priority. Based on different energy policies and dispatch strategies, multiple simulation scenarios are preset. The scenario variables of the simulation scenarios include at least the penetration rate of renewable energy and the peak-shaving start-up and shutdown strategy of thermal power units. The output strategy of each generator unit is adjusted based on the simulation scenario, and the optimal power flow calculation is performed to obtain the distribution result of the system power flow. Based on the distribution result of the system power flow and the multi-dimensional attribute parameter set, the carbon emission flow tracking algorithm is used to predict the average carbon emission intensity, the carbon emission of each unit and the total carbon emission of the system. Based on the calculation results, comparative analysis charts for multiple scenarios are generated. Based on these charts, the carbon reduction effect and economic efficiency of different scenarios are evaluated, low-carbon dispatch strategies are obtained, and carbon emissions of the power system are optimized.

2. The method for predicting and optimizing carbon emissions from power systems based on electro-carbon coupling simulation according to claim 1, characterized in that, The reference power flow calculation case file includes the power grid topology, branch parameters, node load data, and steady-state operating parameters of generator units.

3. The method for predicting and optimizing carbon emissions from power systems based on electro-carbon coupling simulation according to claim 1, characterized in that, The carbon emission intensity coefficient per unit of power generation is calculated taking into account the additional carbon emissions during the start-up and shutdown process of the unit. The carbon emission intensity coefficient per unit of power generation is calculated based on the power generation of the unit, the carbon emission coefficient per unit calorific value of the fuel, the operating efficiency factor considering the load factor, the baseline carbon emission of the unit during a single start-up and shutdown, and the start-up and shutdown frequency factor related to the operating status of the unit.

4. The method for predicting and optimizing carbon emissions from power systems based on electro-carbon coupling simulation according to claim 1, characterized in that, The simulation scenarios include a baseline scenario, a high renewable energy penetration scenario, and an optimized peak-shaving strategy scenario.

5. The method for predicting and optimizing carbon emissions from a power system based on electro-carbon coupling simulation according to claim 4, characterized in that, The scheduling rule for the baseline scenario is: maintain the existing unit combination and output strategy; The scheduling rules for the high renewable energy penetration scenario are as follows: increase the predicted output of wind turbines and photovoltaic units, and reduce the output of traditional thermal power units according to the scheduling priority. The scheduling rules for the optimized peak-shaving strategy scenario are as follows: prioritize increasing the output of low-carbon and zero-carbon units, and prioritize reducing the output of high-carbon units according to the order of carbon emission intensity from high to low.

6. The method for predicting and optimizing carbon emissions from power systems based on electro-carbon coupling simulation according to claim 1, characterized in that, The average carbon emission intensity is obtained by solving for the carbon potential of all nodes in the power grid. The formula for calculating the carbon potential is as follows: in, Let be the carbon potential at node j. Let J be the set of generators flowing towards node j. Let j be the set of the starting nodes of the path leading to node j. Let be the carbon emission intensity coefficient per unit of electricity generated by unit i. Let k be the carbon potential at node k. The injected power of unit i, Let be the line power flowing from node k to node j.

7. The method for predicting and optimizing carbon emissions from power systems based on electro-carbon coupling simulation according to claim 1, characterized in that, The carbon emissions of each unit include operating carbon emissions and start-up / shutdown carbon emissions. The operating carbon emissions are calculated based on the unit's power generation, the carbon emission coefficient per unit calorific value of the fuel, and the operating efficiency factor considering the unit's current load rate level. The start-up / shutdown carbon emissions are calculated based on the unit's single start-up / shutdown baseline carbon emissions and the start-up / shutdown frequency factor related to the unit's operating status.

8. The method for predicting and optimizing carbon emissions from a power system based on electro-carbon coupling simulation according to claim 7, characterized in that, The lower the current load rate of the unit, the worse the efficiency, and the smaller the operating efficiency factor.

9. The method for predicting and optimizing carbon emissions from a power system based on electro-carbon coupling simulation according to claim 1, characterized in that, The comparative analysis charts include a total carbon emissions comparison chart, a carbon emissions intensity comparison chart, a stacked chart of power generation composition, a chart of changes in unit output, a chart of carbon emission reduction effects, and a cost-benefit analysis chart.

10. A method for predicting and optimizing carbon emissions from a power system based on electro-carbon coupling simulation as described in claim 9, characterized in that, The cost-benefit analysis chart compares and analyzes the total economic cost under different scenarios. The total economic cost consists of fuel cost and carbon trading cost, and its calculation formula is as follows: in, The total economic cost, For fuel costs, For carbon trading costs, Let i be the power generation capacity of unit i. This represents the total carbon emissions of the system. The unit price of fuel used by unit i. This refers to the carbon trading market or carbon tax price.

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