Electricity-carbon collaborative analysis and evaluation method and system fusing actual measurement track and simulation track

Abstract

The invention discloses an electricity-carbon collaborative analysis and evaluation method and system fusing an actual measurement track and a simulation track, and aims at an interconnected electric power system covering all links of a source network load to construct a multi-service collaborative and multi-level through electricity-carbon collaborative system, and the method comprises the following steps: determining a to-be-evaluated electric power system and a related information physical social system; actual measurement type data and prediction type data are collected; constructing a candidate object model and designing a candidate evaluation model; carrying out on-line monitoring on actually-measured data to obtain an electricity-carbon collaborative analysis and evaluation actually-measured track of the electric power system; carrying out power system power-carbon collaborative analysis, evaluation and simulation deduction to obtain a power system power-carbon collaborative analysis, evaluation and simulation track; carrying out knowledge extraction by fusing causal knowledge and statistical analysis according to the actually measured trajectory and the simulated trajectory; and optimizing the power-carbon collaborative decision according to the knowledge extraction result, carrying out power-carbon collaborative benefit post-evaluation, and identifying the responsibility contribution of the active support of each main body in each link of source network load storage to the influence of the overall green low-carbon benefit of the to-be-evaluated power system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of electric power system electric-carbon collaborative analysis and evaluation, and more particularly relates to a method and system for electric-carbon collaborative analysis and evaluation of an interconnected power system by fusing measured trajectories and simulated trajectories. BACKGROUND

[0002] To achieve the "double carbon" goal, energy is the main battlefield, electricity is the main force, and the new power system is the key carrier. Carbon management of interconnected power systems is a key mesoscopic measure that connects national macro and individual micro. It is to manage the power system as the object and reflect the green and low-carbon properties of different types of power resources. "Electric-carbon collaboration" is the main feature of carbon management of interconnected power grids. The essence of management is to make decisions, which consists of five links: data acquisition, information collection, knowledge extraction, decision support, and real combat decision-making. Therefore, the electric-carbon collaborative management of interconnected power systems includes five links: data acquisition, information collection, knowledge extraction, decision support, and real combat decision-making.

[0003] Prior art document 1 (CN119515628A) discloses a method and system for evaluating the pollution reduction and carbon reduction collaboration degree of the coal-fired power generation industry based on online monitoring data, but does not disclose the calculation method of the power generation carbon emission of the source-side thermal power unit, but directly takes the power supply carbon emission factor for standardization, and the universality needs to be improved.

[0004] Prior art document 2 (CN118172076B) discloses a pollution reduction and carbon reduction collaborative analysis method and device for coal-fired power generation, but its technical solution can only show the trend relationship of pollutants and carbon emissions over time. The thermal power unit reduces pollutant emissions through a series of devices such as desulfurization and denitrification, and reduces carbon emissions through carbon capture devices. There is no physical collaboration between pollution reduction and carbon reduction.

[0005] Prior art document 3 (CN119809684A) discloses a carbon trading power market simulation prediction system based on big data analysis, but does not disclose the objects, data, simulation models, and transaction processes of carbon trading and power market transaction simulation, and the carbon market and the power market are two independent markets, and the collaboration between the two is not described. SUMMARY

[0006] In order to solve the deficiencies of the existing electric-carbon collaborative analysis and evaluation method of interconnected power systems, the present application provides an electric-carbon collaborative analysis and evaluation method and system by fusing measured trajectories and simulated trajectories. Specifically, an electric power system electric-carbon collaborative analysis and evaluation framework is provided, which covers data acquisition, information collection, knowledge extraction, decision support, and real combat decision-making of electric-carbon collaboration.

[0007] The application adopts the technical solutions as follows.

[0008] The first aspect of the application provides a method for analyzing and evaluating electric-carbon synergy by fusing measured trajectories and simulation trajectories, comprising the following steps: determining an electric power system to be evaluated and a related information-physical-social system, collecting measured data and predicted data of the electric power system to be evaluated and the related information-physical-social system; constructing candidate object models and designing candidate evaluation models, performing data preprocessing on the measured data, matching an evaluation object model from the candidate object models, and selecting an evaluation model from the candidate evaluation models according to the evaluation object, carrying out online monitoring on the measured data, and substituting the evaluation model to obtain a measured trajectory of electric-carbon synergy analysis and evaluation of the electric power system; determining a simulation boundary of electric-carbon synergy analysis and evaluation of the electric power system, determining electric-carbon synergy simulation input parameters, constructing an electric-energy-carbon fusion model of the electric power system to be evaluated, carrying out simulation deduction of electric-carbon synergy analysis and evaluation of the electric power system, and obtaining a simulation trajectory of electric-carbon synergy analysis and evaluation of the electric power system; fusing causal knowledge and statistical analysis to extract knowledge according to the measured trajectory and the simulation trajectory; optimizing electric-carbon synergy decision-making according to the knowledge extraction result, carrying out post-evaluation of electric-carbon synergy benefits, and identifying the responsibility and contribution of each link and each subject of source, network, load and storage to the overall green and low-carbon benefits of the electric power system to be evaluated.

[0009] Preferably, the determination of the electric power system to be evaluated and the related information-physical-social system comprises: determining the electric power system to be evaluated and physical facilities and social behaviors in related fields; The physical facilities of the electric power system to be evaluated include source-side physical facilities, network-side physical facilities and load-side physical facilities.

[0010] Preferably, the collected measured data of the electric power system to be evaluated and the related information-physical-social system is subjected to openness analysis to determine the types of obtainable data.

[0011] Preferably, the construction of the candidate object models comprises: respectively constructing electric-energy-carbon evaluation models of candidate objects on the source side, the network side and the load side; The candidate objects on the source side include one or more of a coal-fired unit, a gas-fired unit, a wind turbine, a photovoltaic unit, a hydroelectric unit, a nuclear power unit, a biomass unit, a pumped storage unit or an energy storage unit; The candidate objects on the network side include one or more of a power transmission network, a power distribution network or a substation; The candidate objects on the load side include various types of users.

[0012] Preferably, the electricity-energy-carbon evaluation model comprises an evaluation type model and a diagnosis type model. The evaluation type model comprises one or more of an electricity proportion model, an energy consumption model, a carbon emission model, and a carbon emission intensity model; and the diagnosis type model comprises one or more of a unit load rate-unit average carbon emission intensity, a unit heat supply rate-unit average carbon emission intensity, a unit up-regulation sensitivity, a unit down-regulation sensitivity, a grid average load rate-grid average carbon emission intensity, and a measure carbon reduction contribution.

[0013] Preferably, the selection of the evaluation model according to the evaluation object comprises: For source-side thermal power units, a thermal power unit carbon emission evaluation model is established by taking into account a fuel consumption and quality parameter method, a flue gas flow and concentration method, and an electricity-carbon factor method; and / or For source-side energy storage units, the carbon emission evaluation model is designed to calculate indirect carbon emissions when charging according to load processing, and to be a zero-carbon unit when discharging to the grid, and to process power generation carbon emissions as zero; and / or For grid-side transmission grids, distribution grids, and substation areas, carbon emission factor evaluation models for different levels of grid objects are designed by dividing the grid coverage and / or by dividing the carbon emission factors by use; For load-side users, a user electricity carbon emission factor evaluation model is designed by taking into account power sources.

[0014] Preferably, the data preprocessing of the measured data comprises: An original data preprocessing model is established to realize data preprocessing of multiple data sources by defining a flow-based preprocessing rule to eliminate dirty data. The data preprocessing rule mainly comprises a reasonableness checking rule and data integration alignment.

[0015] Preferably, the determination of the simulation boundary of the electricity-carbon collaborative analysis and evaluation of the power system and the determination of the electricity-carbon collaborative simulation input parameters comprise: The determination of the simulation boundary of the electricity-carbon collaborative analysis and evaluation of the power system and the determination of the electricity-carbon collaborative simulation input parameters comprise: Based on the prediction data, a part of the electricity-carbon collaborative simulation input parameters are determined, and another part of the input parameters for simulation are extracted based on the prediction data and combined with large model analysis.

[0016] Preferably, the construction of the electricity-energy-carbon fusion model of the power system to be evaluated, the development of the electricity-carbon collaborative analysis and evaluation simulation deduction of the power system, and the obtaining of the electricity-carbon collaborative analysis and evaluation simulation trajectory of the power system comprise: The source network load of the to-be-evaluated power system is evaluated by combining the online monitoring of electricity-carbon coordination, and the electricity-energy-carbon evaluation model of each link of the to-be-evaluated power system is constructed, and the electricity-energy-carbon fusion model of each link object is constructed, which is suitable for the granularity of the electricity-carbon coordination hybrid simulation of the to-be-evaluated power system and takes into account the physical facilities of the to-be-evaluated power system; The simulation model of the electricity-energy-carbon fusion model of each link object is built. The simulation scene is designed, the electricity-carbon coordination analysis and evaluation simulation deduction of the power system is carried out, and the electricity-carbon coordination analysis and evaluation simulation trajectory of the power system is obtained.

[0017] Preferably, the design simulation scene comprises: The electricity-carbon coordination hybrid simulation scene of the to-be-evaluated power system with the lowest carbon emission intensity, the highest green electricity proportion, and the change of one or more key factors is designed.

[0018] Preferably, the knowledge extraction for the measured trajectory and the simulation trajectory comprises: The statistical analysis is carried out for the measured trajectory, and the statistical analysis and the causal analysis are carried out for the simulation analysis, the phase plane set is extracted from the high-dimensional measured trajectory or the simulation trajectory, the quantitative index is extracted based on the phase plane set, and thus the influence of the key factors on the green and low-carbon characteristics of the interconnected power grid is quantitatively evaluated; Through the knowledge extraction for the trajectory, the influence of the key factors of the information factors, the physical factors and the social factors on the green and low-carbon level of the to-be-evaluated power system is evaluated, and the spatio-temporal evolution law of the green and low-carbon characteristics of the interconnected power grid under the comprehensive influence of the multi-dimensional factors is analyzed.

[0019] Preferably, according to the results of the knowledge extraction for the measured trajectory and the simulation trajectory, the candidate object model construction and the candidate evaluation model design are fed back and guided.

[0020] The second aspect of the present application provides an electricity-carbon coordination analysis and evaluation system fusing a measured trajectory and a simulation trajectory, which runs the electricity-carbon coordination analysis and evaluation method according to the first aspect, and is characterized in that it comprises: A data acquisition module is used to determine the to-be-evaluated power system and the related information physical social system, and collect the measured data and the predicted data of the to-be-evaluated power system and the related information physical social system; An information collection module is used to carry out online monitoring for the measured data, obtain the measured trajectory of the electricity-carbon coordination analysis and evaluation of the power system, and carry out the electricity-carbon coordination analysis and evaluation simulation deduction of the power system to obtain the simulation trajectory of the electricity-carbon coordination analysis and evaluation of the power system; A knowledge extraction module is used to extract knowledge by fusing causal knowledge and statistical analysis for the measured trajectory and the simulation trajectory; A decision support module is configured to optimize the electricity-carbon collaborative decision-making based on the knowledge extraction result, carry out the electricity-carbon collaborative benefit post-evaluation, and identify the responsibility contribution of each link and each subject of the source, network, load and storage to the overall green and low-carbon benefit of the power system to be evaluated.

[0021] The third aspect of the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program, when loaded into the processor, implements the electricity-carbon collaborative analysis and evaluation method of fusing the measured trajectory and the simulation trajectory according to the first aspect.

[0022] The fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, characterized in that the computer program, when executed by a processor, implements the electricity-carbon collaborative analysis and evaluation method of fusing the measured trajectory and the simulation trajectory according to the first aspect.

[0023] Compared with the prior art, the present application has at least the following beneficial effects: a standardized electricity-carbon collaborative evaluation and management process for each link of the interconnected power system source, network, load, etc. is proposed, including five links of data acquisition, information collection, knowledge extraction, decision support and actual combat decision-making. Through electricity-carbon collaborative data acquisition, electricity-carbon collaborative information collection, electricity-carbon collaborative knowledge extraction, electricity-carbon collaborative decision support and electricity-carbon collaborative actual combat decision-making, the target power system (provincial power system, regional power system, national power system, etc.) can complete the fine space-time scale green and low-carbon index evaluation, meet the accounting, monitoring and reporting requirements of domestic and foreign green and low-carbon related policies and regulations, guide green and low-carbon power consumption, and support the construction of the double carbon target and carbon emission double control. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The flowchart of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. The described embodiments are only a part of the embodiments of the present application, not all the embodiments. All other embodiments obtained by those skilled in the art without creative labor based on the spirit of the present application are within the protection scope of the present application.

[0026] As shown in Figure 1 A method for analyzing and evaluating the electricity-carbon collaboration of a power system by fusing measured trajectories and simulation trajectories, mainly including five links, namely electricity-carbon collaborative data acquisition, electricity-carbon collaborative information collection, electricity-carbon collaborative knowledge extraction, electricity-carbon collaborative decision support and electricity-carbon collaborative actual combat decision-making, more specifically, comprising the following steps: Step 1: Determine the power system to be evaluated and the related information-physical-social system, collect the measured and predicted data of the power system to be evaluated and the related information-physical-social system, and perform availability analysis on the measured data.

[0027] Preferably but not limitedly, step 1 specifically includes: Step 1.1: Determine the power system to be evaluated and the related information-physical-social system.

[0028] Specifically, the power system generation, transmission, transformation, distribution and use is a complete whole, but it can be divided into different levels of power grids such as national, regional, provincial and municipal levels according to physical topology, management scope, etc. When carrying out the electric-carbon collaborative analysis and evaluation of the power system, the power system to be evaluated needs to be determined first.

[0029] The power system to be evaluated includes source-side physical facilities, network-side physical facilities and load-side physical facilities; wherein the source-side physical facilities include but are not limited to coal-fired units, gas-fired units, wind turbine units, photovoltaic units, hydroelectric units, nuclear units, biomass units, pumped storage units, energy storage, etc.; the network-side physical facilities include but are not limited to lines, transformers, etc.; the load-side physical facilities include but are not limited to various types of load users, etc.

[0030] The operation of the power system to be evaluated covers various social behaviors such as dispatching control, planning arrangement, power trading, carbon market trading, green certificate trading and development planning. The electric-carbon collaborative analysis and evaluation of the power system to be evaluated needs to consider its physical facilities and social behaviors.

[0031] The electric-carbon collaborative analysis and evaluation of the power system also needs to carry out monitoring and evaluation of carbon emissions, and needs physical parameters such as coal consumption and quality parameters, flue gas parameters in the primary energy field. The electric-carbon collaborative evaluation result is affected by factors such as weather and natural disasters. The electric-carbon collaborative analysis and evaluation of the power system to be evaluated also needs to consider the physical facilities and social elements in related fields.

[0032] Step 1.2: Collect the measured and predicted data of the power system to be evaluated and the related information-physical-social system.

[0033] Specifically, after determining the power system to be evaluated, the measured and predicted data of the power system to be evaluated and the related information-physical-social system are collected. The measured data includes but is not limited to electrical quantities, heat supply, fuel, flue gas, carbon sink, market transactions, etc.; the predicted data includes but is not limited to load forecasting, weather forecasting, new energy output forecasting, hydrological forecasting, etc.

[0034] Further, the electrical quantity measured data includes but is not limited to: power generation, unit load rate, received power, power supply, power consumption, etc.; the heat supply measured data includes but is not limited to: heat supply, heat supply ratio, heat supply coal consumption rate, etc.; the fuel measured data includes but is not limited to: fuel consumption, received base moisture, air-dry base element carbon content, air-dry base moisture, dry base element carbon content, low heat value, etc.; the flue gas market data includes but is not limited to: flue gas flow, oxygen concentration, carbon dioxide concentration, etc.; the carbon sink measured data includes flue gas volume, capture rate, etc.; the market transaction measured data includes but is not limited to: power market, carbon market, green power market, green certificate market, etc. The transaction volume, buyers and sellers are measured.

[0035] Step 1.3: Perform availability analysis on the measured data.

[0036] The source-side power plant of the power system belongs to multiple management subjects, the network side belongs to multiple different level management subjects of country-province-city, and the load side faces more extensive power users in the whole society. Generally, the dominant development of power system electric-carbon collaborative analysis and evaluation is only one or part of the many subjects of the power system. Different management subjects manage the objects within their jurisdiction in terms of data, operation, transaction and other aspects. The implementers of electric-carbon collaborative analysis and evaluation often have difficulty in obtaining the measured data of the whole link of the power system source network load.

[0037] The implementers of source-side electric-carbon collaborative analysis and evaluation can only obtain detailed electrical quantity, heat supply, fuel, flue gas, carbon sink, market transaction data of their own management subjects, and cannot obtain the measured data of other source-side subjects and network load side subjects. The implementers of network-side electric-carbon collaborative analysis and evaluation can only obtain the electrical quantity and power market transaction measured data of the source-side subjects, and lack of heat supply, fuel, flue gas, carbon sink, carbon market transaction and other measured data.

[0038] Therefore, when developing electric-carbon collaborative analysis and evaluation, it is necessary to perform availability analysis on the measured data according to the management range of the subject and the existing data basis.

[0039] Step 2: build a candidate object model and design a candidate evaluation model; perform data preprocessing on the measured data, match the evaluation object model from the candidate object model, and select the evaluation model from the candidate evaluation model, develop online monitoring for the measured data, and substitute the evaluation model to obtain the measured trajectory of the power system electric-carbon collaborative analysis and evaluation; Preferably but not limitedly, step 2 specifically includes: Step 2.1: build a candidate object model, wherein the power system electric-carbon collaborative analysis and evaluation involves multiple link and multiple type objects of source network load. After determining the power system to be evaluated, the objects of source network load are determined.

[0040] Further preferably but not limitedly, the source side needs to build an electricity-energy-carbon evaluation model for candidate objects such as coal-fired generating units, gas-fired generating units, wind power generating units, photovoltaic generating units, hydropower generating units, nuclear power generating units, biomass generating units, pumped storage units, and energy storage; the grid side needs to build an electricity-energy-carbon evaluation model for candidate objects such as power transmission grids, power distribution grids, and transformer substations; and the load side needs to build an electricity-energy-carbon evaluation model for candidate objects such as various types of users.

[0041] Further preferably but not limitedly, the electricity-energy-carbon evaluation model includes evaluation type models and diagnosis type models; the evaluation type models include but are not limited to: an electricity quantity proportion model, an energy consumption quantity model, a carbon emission quantity model, and a carbon emission intensity model; and the diagnosis type models include but are not limited to: a unit load rate-unit average carbon emission intensity model, a unit heat supply ratio-unit average carbon emission intensity model, a unit up-regulation sensitivity, a unit down-regulation sensitivity, a grid average load rate-grid average carbon emission intensity model, and a measure carbon reduction contribution quantity.

[0042] Step 2.2: Designing candidate evaluation models, wherein there are multiple possibilities for the electricity-energy-carbon evaluation model of a single object at each link of the source-grid-load, and the candidate evaluation model of the object needs to be designed according to the actual conditions of the object, relevant domestic and foreign standards and policies and regulations, and other external factors.

[0043] Further preferably but not limitedly, the carbon emission of a thermal power generating unit at the source side is the main direct emission source of carbon emission of the power system, and the carbon emission evaluation method of the thermal power generating unit includes but is not limited to: a fuel consumption and quality parameter-based method (hereinafter referred to as the fuel method), a flue gas flow and concentration-based method (hereinafter referred to as the flue gas method), and an electricity-carbon factor-based method (hereinafter referred to as the factor method); the input parameters of the three thermal power generating unit carbon emission evaluation models are different, and the design of the thermal power generating unit carbon emission evaluation model needs to consider the three evaluation models at the same time. Further, in the factor method, the unit power generation carbon emission intensity assignment method can also be assigned according to different official data, and the universality of the assignment model needs to be considered when designing the factor method carbon emission evaluation model of the thermal power generating unit.

[0044] Further preferably but not limitedly, the source side energy storage type units, such as but not limited to, battery energy storage, pumped storage, and mechanical energy storage, are charged at valley load and discharged at peak load, and have no direct carbon emission. For such units, the carbon emission evaluation model is designed to calculate the indirect carbon emission during charging according to the load, and the carbon emission during discharging is zero for the zero-carbon unit.

[0045] Further preferably but not limitedly, the grid-side power transmission grid, distribution grid, and substation carbon emission factor evaluation model can be divided into multiple dimensions. From the perspective of grid coverage, it can be divided into national, regional, provincial, county, and substation levels. From the perspective of carbon emission factor use, it can be divided into average carbon emission factor, residual average carbon emission factor, marginal carbon emission factor, etc. The carbon emission factor evaluation model for different uses is different, and the design of the carbon emission factor evaluation model for different levels of power grid objects needs to consider the use of carbon emission factors.

[0046] Further preferably but not limitedly, the load-side user uses power sources such as grid power supply and direct power supply, for example but not limited to, self-owned thermal power plant, self-owned new energy generator set, etc. The design of the user electricity carbon emission factor evaluation model needs to consider various sources of power of the user.

[0047] Step 2.3: Data preprocessing of measured data.

[0048] Further preferably but not limitedly, combined with the collection of measured data such as electrical quantity, heating, fuel, flue gas, carbon sink, and market transaction, an original data preprocessing model is established. Through defining a flow-based preprocessing rule, data preprocessing of multiple data sources is realized, dirty data is eliminated, data preprocessing is realized, and the accuracy of the basic data used by the upper layer business is ensured.

[0049] Further preferably but not limitedly, the data preprocessing rule mainly includes rationality checking rule and data integration alignment rule. The rationality checking rule mainly checks the rationality of data value and time validity, etc. According to the need, it includes but is not limited to warning for abnormal data such as deviation data; the data integration alignment rule mainly integrates and unifies the time dimension of data according to the belonging object and the belonging accounting type, etc.

[0050] Step 2.4: Matching evaluation object model, including: according to the actual situation of the to-be-evaluated object and the relevant standards and policies and regulations that need to be followed, matching the electricity-energy-carbon evaluation model of the to-be-evaluated object in each link of the source grid load.

[0051] For example but not limited to, the carbon emission of coal-fired units in China and the European Union is evaluated by material method, and the carbon emission of coal-fired units in the United States is evaluated by flue gas method. The evaluation model needs to be matched according to the region where the to-be-evaluated coal-fired unit belongs.

[0052] Step 2.5: Selecting the evaluation model based on the availability analysis result of the measured data.

[0053] Specifically, in the actual operation of the electricity-carbon collaborative analysis and evaluation, only part of the data required for the electricity-carbon collaborative analysis and evaluation of the to-be-evaluated object can be obtained. The selection of the evaluation model for a to-be-evaluated object needs to consider the actual data.

[0054] Further preferably but not limitedly, in the source-side thermal power plant, if the fuel consumption and quality parameters can be collected, the material method can be selected to evaluate the carbon emissions of the thermal power unit, if only the fuel consumption can be collected, combined with the default parameters of the published fuel quality, the material method can also be selected to evaluate the carbon emissions of the thermal power unit; if the flue gas flow rate and carbon dioxide concentration can be collected, the flue gas method can be selected to evaluate the carbon emissions of the thermal power unit, if the flue gas flow rate and oxygen concentration can be collected, the flue gas method can also be selected to approximately evaluate the carbon emissions of the thermal power unit by oxygen concentration; if the fuel and flue gas data cannot be collected, only the electrical quantity data can be collected, then the factor method can be selected to evaluate the carbon emissions of the thermal power unit, and the default power generation carbon emission intensity of the unit has multiple choices according to different published data sources, which can be valued according to the balance value of different types of units in the national carbon market quota allocation scheme, wherein the balance axis is the value corresponding to the balance between the carbon emission quota and the carbon emission amount, or the average carbon emission intensity of the thermal power unit in each province can also be selected, or the typical load rate-power generation carbon emission intensity function relationship of different types of units can also be selected.

[0055] Further preferably but not limitedly, on the basis of evaluating the average carbon emission intensity of the grid power supply on the grid side, if the data of green electricity trading and green certificate trading can be collected, the remaining average carbon emission intensity of the grid power supply can be further evaluated; if the collected historical data reaches a certain amount, the marginal carbon emission intensity of the grid power supply can be further evaluated by machine learning method.

[0056] Step 2.6: online monitoring of the measured data is carried out to obtain the measured trajectory of the electric-carbon collaborative analysis and evaluation of the power system.

[0057] Specifically, after matching the object model and selecting the evaluation model according to the actual collected data, the electric-carbon collaborative online monitoring of the to-be-evaluated power system can be realized to obtain the measured trajectory of the electric-carbon collaborative analysis and evaluation of the power system.

[0058] Step 3: determine the electric power planning, operation, market simulation boundary related to the electric-carbon collaborative analysis and evaluation of the power system, determine the electric-carbon collaborative simulation input parameters combined with the large model analysis method; then build the electric-energy-carbon integrated model of the to-be-evaluated power system, build the simulation environment, design the simulation scene, carry out the electric-carbon collaborative analysis and evaluation simulation deduction of the power system, and obtain the simulation trajectory of the electric-carbon collaborative analysis and evaluation of the power system.

[0059] Step 3.1: determine the electric power planning, operation, market simulation boundary related to the electric-carbon collaborative analysis and evaluation of the power system.

[0060] It can be understood that the measured trajectory is the result of the electric-carbon collaborative analysis and evaluation of the to-be-evaluated power system that has occurred, and the green and low-carbon level evaluation of the to-be-evaluated power system in the future needs to be carried out through electric-carbon collaborative hybrid simulation for analysis and evaluation.

[0061] The future power source planning and grid planning of the power system to be evaluated will affect its green low-carbon level; the future output planning arrangement of the power system to be evaluated directly affects the power generation of different types of units, thereby affecting its green low-carbon level; the medium and long-term electricity trading, day-ahead spot market trading, intraday spot market trading, and reserve market trading of the power system to be evaluated will affect the traded electricity of different types of units, and green electricity trading leads to the transfer of green rights and interests, and future market transactions will all affect the green low-carbon level of the power system to be evaluated. Therefore, in carrying out the electric-carbon collaborative hybrid simulation of the power system to be evaluated, it is necessary to first divide the boundary of the power system to be evaluated and related power planning, operation, market, etc.

[0062] Step 3.2: Determine the electric-carbon collaborative simulation input parameters in combination with the large model analysis method.

[0063] Developing the electric-carbon collaborative hybrid simulation of the power system to be evaluated requires determining input parameters, including but not limited to: load forecasting, weather forecasting, new energy output forecasting, hydrological forecasting, and other forecasting data; and some simulation input parameters need to be extracted for simulation use through statistical analysis, machine learning, and other big data analysis based on original forecasting data. For example, the electric-carbon collaborative hybrid simulation needs to input the output forecasting of wind turbine generators and photovoltaic generators, and the output forecasting of new energy units needs to be obtained through big data analysis in combination with the region where the new energy unit is located and weather forecasting data.

[0064] Step 3.3: Build the electric-energy-carbon fusion model of the power system to be evaluated.

[0065] The electric-energy-carbon evaluation model of each link of the power system to be evaluated is built in combination with the electric-carbon collaborative online monitoring, considering the physical actual situation of the power system to be evaluated, and the electric-energy-carbon fusion model of each link object of the power system to be evaluated is built with a granularity suitable for the electric-carbon collaborative hybrid simulation.

[0066] For example, at the source side, thermal power units are direct carbon emission sources, and in the development of hybrid simulation, the unit-level electric-energy-carbon fusion model needs to be built. For other types of generating units, if the power generation carbon emission is zero, the unit-level electric-energy-carbon fusion model does not need to be built, and only the electric-energy-carbon fusion model needs to be built according to the minimum granularity of the power grid to be evaluated (for example, if the minimum granularity of the power grid to be evaluated is a provincial power grid, then wind power, photovoltaic power, hydropower, and nuclear power units with zero carbon emission each only need to build one unit model, and the total capacity of the unit is equal to the total installed capacity of the units of the type in the province).

[0067] Step 3.4: Build the simulation environment.

[0068] After completing the construction of the electricity-energy-carbon fusion model of the power system to be evaluated, the physical models of the source, network, and load, the electricity-energy-carbon fusion model, and other simulation models are built in the programming software, the input and output parameter tables are constructed, and the interaction modes with other simulation sub-modules are constructed. The interaction modes of the internal modules of the electricity-carbon collaborative hybrid simulation are constructed, and the electricity-carbon collaborative hybrid simulation environment of the power system to be evaluated is constructed.

[0069] Step 3.5: Design the simulation scenario.

[0070] The electricity-carbon collaborative analysis and evaluation simulation of the power system is carried out, and the electricity-carbon collaborative analysis and evaluation simulation trajectory of the power system is obtained. For the problem to be studied of the power system to be evaluated, combined with the physical reality and internal and external related information of the power system to be evaluated, the electricity-carbon collaborative hybrid simulation scenarios of the power system to be evaluated with the lowest carbon intensity, the highest green electricity proportion, and the key factor variation are designed, the simulation parameters corresponding to the simulation scenarios are set, and the electricity-carbon collaborative hybrid simulation is started to obtain the multi-scenario hybrid simulation trajectory of the power system to be evaluated.

[0071] Step 4: For the measured trajectory and the simulation trajectory, knowledge extraction is carried out by fusing causal knowledge and statistical analysis, and the candidate object model construction and the candidate evaluation model design are improved according to the knowledge extraction results.

[0072] Preferably but not limitedly, step 4 specifically includes: Step 4.1: Knowledge extraction by fusing causal knowledge and statistical analysis.

[0073] Statistical analysis is carried out for the measured trajectory, and statistical analysis and causal analysis are carried out for the simulation analysis. The phase plane set is extracted from the high-dimensional measured trajectory or simulation trajectory, and the quantitative index is extracted based on the phase plane set, so as to quantify the influence of the key factors on the green and low-carbon characteristics of the interconnected power grid. Through knowledge extraction for the trajectory, the influence of the key factors on the green and low-carbon level of the power system to be evaluated can be evaluated, such as parameter uncertainty, physical factors such as weather, primary energy quality, power generation and consumption structure, cross-regional power exchange, and social factors such as green electricity trading rules and behaviors. The spatio-temporal evolution law of the green and low-carbon characteristics of the interconnected power grid under the comprehensive influence of multi-dimensional factors such as natural environment, energy chain, and electricity-carbon market is analyzed.

[0074] Step 4.2: Improve the candidate object model construction and the candidate evaluation model design according to the knowledge extraction results.

[0075] The candidate object model constructed in the foregoing steps and the candidate evaluation model designed will affect the measured trajectory and the simulation trajectory. According to the results of knowledge extraction for the measured trajectory and the simulation trajectory, the candidate object model construction and the candidate evaluation model design can be fed back and guided.

[0076] Further preferably but not limitedly, more reliable unit carbon emission curve can be evaluated by flue gas method or material method, and the load rate-power generation carbon emission intensity curve of the unit can be obtained by statistical analysis of historical output data and carbon emission data of the unit, and the curve parameters can be fed back to guide the design of the unit factor method carbon emission evaluation model.

[0077] In the electric-carbon collaborative simulation, the simulation trajectories under different models can be obtained by perturbing the candidate object model or the candidate evaluation model, and the candidate object model or the evaluation model can be iteratively modified according to the knowledge extraction result of the simulation trajectories, so that the evaluation model is more reasonable and accurate.

[0078] Step 5: The decision maker optimizes the electric-carbon collaborative decision according to the knowledge extraction result, and carries out actual decision, and the electric-carbon collaborative simulation perfects the simulation scene design and simulation environment construction according to the knowledge extraction result.

[0079] The management subjects of the source, network, load and other links of the to-be-evaluated power system can construct the electric-carbon collaborative management mechanism and policy within their jurisdiction according to the qualitative or quantitative indicators extracted from the measured trajectory or simulation trajectory, guide the operation of dispatching, market transaction, marketing service, development planning and other business operation actual decision, carry out electric-carbon collaborative benefit post-evaluation, and identify the responsibility contribution of the source, network, load and other links of the to-be-evaluated power system to the overall green and low-carbon benefit of the to-be-evaluated power system.

[0080] When evaluating and analyzing the future green and low-carbon level of the to-be-evaluated power system, the management subjects of the source, network, load and other links can design and construct the simulation scene related to the future state of the to-be-evaluated power system according to the needs of actual operation decision, obtain the related key index information through electric-carbon collaborative simulation, and guide the business actual decision related to the future state in the actual operation work, such as plan arrangement, power transaction and development planning.

[0081] Embodiment 2 of the present application provides an electric-carbon collaborative analysis and evaluation system fusing measured trajectory and simulation trajectory, which runs the electric-carbon collaborative analysis and evaluation method fusing measured trajectory and simulation trajectory as described in embodiment 1, comprising: A data acquisition module is configured to determine a to-be-evaluated power system and a related information-physical-social system, and collect measured data and predicted data of the to-be-evaluated power system and the related information-physical-social system. An information collection module is configured to carry out online monitoring of the measured data, obtain an electric-carbon collaborative analysis and evaluation measured trajectory of the power system, and carry out electric-carbon collaborative analysis and evaluation simulation deduction of the power system to obtain an electric-carbon collaborative analysis and evaluation simulation trajectory of the power system. A knowledge extraction module is configured to fuse causal knowledge and statistical analysis for knowledge extraction of the measured trajectory and the simulation trajectory. A decision support module is configured to optimize the electricity-carbon collaborative decision-making based on the knowledge extraction result, carry out the post-evaluation of the electricity-carbon collaborative benefit, and identify the responsibility contribution of each link and each subject of the source, network, load and storage to the overall green and low-carbon benefit of the evaluated power system.

[0082] Embodiment 3 of the present application provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program, when loaded into the processor, implements the electricity-carbon collaborative analysis and evaluation method of fusing the measured trajectory and the simulation trajectory according to Embodiment 1.

[0083] Embodiment 4 of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program, when executed by a processor, implements the electricity-carbon collaborative analysis and evaluation method of fusing the measured trajectory and the simulation trajectory according to Embodiment 1.

[0084] In order to more clearly introduce the outstanding substantial features of the present application and the significant progress brought to the prior art, an application example of implementing the present application is introduced as follows.

[0085] Taking a provincial power grid in China as an example, the target system is the provincial power system, and the physical and social elements in the related field include energy and power structure, wind and light resource endowment, carbon emission accounting rules, power market mechanism, etc.

[0086] Step 1: Data acquisition For measured data: Measured data collection: ① Physical topology data collection: collect the physical topology data of units, power plants, substations and tie lines of the target power system; ② Electrical quantity data collection: obtain the 15-minute power generation of all units and power plants in the province from the relevant system (such as the dispatching system) of the provincial power company, and the power generation of various types of units in the whole network can be accumulated from the unit power accumulation, and the hourly, daily, monthly and annual time scale power can be accumulated from the 15-minute power; ③ Fossil fuel consumption and quality parameter collection of thermal power plants: collect from the relevant system of the provincial power company or the thermal power plant; ④ Carbon emission flue gas data collection of thermal power plants: collect from the relevant system of the provincial power company or the thermal power plant.

[0087] Data availability analysis: Data availability analysis is carried out for measured data. Since the power grid company and the power generation enterprise do not belong to the same management subject, a thermal power plant in a province generally belongs to multiple power generation groups. When carrying out the target power system electric carbon evaluation, the physical topology data of the power system, the electrical quantity data, the fuel data, the flue gas data are needed, but these data cannot be obtained at the same time, and the data availability analysis needs to be carried out. For the unobtainable data (mainly the fuel data and the flue gas data of the thermal power plant), the default value needs to be found from the data published by the national authoritative organization (such as the power statistics yearbook).

[0088] For simulation data: Prediction data collection: Obtain the output plan of each conventional unit, weather forecast data, output prediction plan of wind and light units, tie-line power transmission and reception plan, load prediction and other data from the relevant system of the provincial power company (such as the dispatching system).

[0089] Step 2: Information collection For the power system of the province to be evaluated, whether it is for the measured data that has occurred to monitor and calculate the electric carbon index, or for the electric carbon index evolution track simulation of the future scenario, the candidate object model needs to be constructed, and the candidate evaluation model needs to be designed.

[0090] Candidate object model construction: After selecting the target power system, the source side contains coal-fired, gas-fired, water, biomass, energy storage and other types of generating units, which need to construct their energy-electricity-carbon evaluation model. The network side contains power transmission network, distribution network, substation, inter-provincial tie line and other links. The carbon emission factor calculation model of multi-region interconnected power grid needs to be constructed due to the transfer of carbon emissions caused by inter-provincial or intra-provincial power transmission. The user on the load side has multiple power supply forms, and the energy-electricity-carbon model needs to be constructed.

[0091] Candidate evaluation model design: Taking the coal-fired power plant power generation carbon emission evaluation model of the source side as an example, there are three kinds of carbon emission calculation methods commonly used in the operation of coal-fired units, which are material method, flue gas method and factor method. The material method is based on the fossil fuel consumption and quality parameters of the coal-fired unit to calculate the carbon emission; the flue gas method is to calculate the carbon emission according to the carbon dioxide concentration or oxygen concentration in the flue gas multiplied by the flue gas flow; the factor method is to calculate the carbon emission according to the power generation capacity multiplied by the default value of the power generation carbon emission factor; the unit in the start-stop process has no power generation capacity, but has fossil fuel consumption, and will have a part of carbon emission, which needs to design the carbon emission model of the unit start-stop process.

[0092] But in the follow-up link of information collection, there are differences between the measured trajectory obtained by online monitoring and the simulation trajectory obtained by simulation deduction. For the measured trajectory, there are processes such as data preprocessing, object model matching, and evaluation model selection. For the simulation trajectory, there are processes such as simulation boundary determination, electric-carbon model fusion, simulation environment building, and simulation scene design.

[0093] For the measured trajectory: Data preprocessing: The data collected by measurement needs to be cleaned. Due to factors such as measurement point abnormalities, network failures, etc., the data obtained by the electric-carbon collaborative evaluation system may have some abnormal or missing data, which needs to be supplemented or removed based on other operating information and data of the unit.

[0094] Object model matching: For the source, network, and load objects of the target provincial power system to be evaluated, their energy-electricity-carbon evaluation models are matched from the candidate object model library.

[0095] Evaluation model selection: Based on the collected data, the evaluation models of carbon emissions and carbon emission factors of the object to be evaluated are selected. For example, if the fossil fuel consumption and quality parameters can be collected, the material method can be used to calculate the carbon emissions of the generating unit; if the flue gas flow and concentration parameters can be collected, the flue gas method can be used to calculate the carbon emissions of the generating unit; if only the power generation capacity of the unit can be collected, the relevant default values published by the state can be used to calculate the carbon emissions of the generating unit. On the grid side, if the received power data can be collected, the carbon emissions of the received power need to be considered when calculating the carbon emission factor of the multi-regional interconnected power grid.

[0096] For the simulation trajectory: Simulation boundary determination: When developing electric-carbon collaborative simulation of the target provincial power system, it is not only for carbon simulation, but also for adding carbon elements based on the original planning, operation, and market simulation. Therefore, before developing the electric-carbon collaborative simulation of the target province, the boundaries of planning, operation, and market need to be determined.

[0097] Electric-carbon model fusion: The original models of the source, network, and load objects of the target provincial power system have physical and economic characteristics. Based on the original models, carbon emission models need to be added to construct electric-carbon fusion models.

[0098] Simulation environment building: Based on the physical reality of the source, network, and load objects of the target province, the physical model of the target provincial power system is constructed. Then, considering the safety constraints such as cross-section constraints, minimum startup, and essential units, as well as the electricity market trading mechanism and reserve market trading mechanism, the simulation environment that meets the physical, market, and operation reality of the province is constructed.

[0099] Simulation scenario design: combined with the target province's electricity-carbon coordination demand, the simulation scenario is designed. For example, to study the low-carbon limit (the minimum value of future carbon emissions) of the target province, the new energy installed capacity and new energy power generation of the province need to be perturbed, and a detailed design of the start-stop carbon emission model of the thermal power unit throughout the operation cycle needs to be considered to find the limit value of the total carbon emission of the province.

[0100] Step 3: knowledge extraction Knowledge extraction: analyze the key mode variables that affect the green low-carbon level of the target province, build a key mode variable plane, and analyze the qualitative or quantitative influence of time variation, load variation, new energy power generation variation, and incoming power variation on the green electricity ratio, carbon emission, carbon emission factor, and other key electricity-carbon indicators of the target province, to provide support for decision-making.

[0101] Model correction: when knowledge extraction is performed on the measured trajectory or simulation trajectory of the target province, if the results are abnormal, or some indicators are missing, or the candidate object evaluation model designed is unreasonable, etc., the candidate object evaluation model can be modified and improved, and through iterative modification, the candidate object evaluation model is continuously improved.

[0102] Step 4: decision support The final purpose of the electricity-carbon coordination evaluation and analysis index is to provide auxiliary decision-making for the planning, operation, transaction, asset management, and other production and operation businesses of the source, network, and load main bodies. Through online monitoring and knowledge extraction of historical data, and simulation deduction and knowledge extraction of future scenarios, a pre-evaluation and post-evaluation index system is constructed, and through qualitative and quantitative evaluation indexes, the source, network, and load management main bodies are supported to make decisions.

[0103] Step 5: real combat decision The decision-making behavior formed by the data acquisition, information collection, knowledge extraction, and decision support of the source, network, and load environmental management main bodies needs to be implemented in the actual business of dispatching operation, market transaction, planning investment, etc., and the implementation results of the electricity-carbon coordination decision will affect the physical entities of the source, network, and load of the target province, and the physical and social results of related fields, and then affect the current operation state and future development state of the target province power system.

[0104] The electricity-carbon coordination analysis and evaluation framework and system proposed in the application fuse the measured trajectory and simulation trajectory, and face the interconnected power system covering the source, network, and load. From the aspects of data acquisition, information collection, knowledge extraction, decision support, and real combat decision, a multi-business coordination and multi-level penetration electricity-carbon coordination system is constructed, the macro and micro carbon management work support is well connected, and active support is provided for the realization of the "double carbon" goal.

[0105] Specifically, the present application adopts the calculation of carbon emissions of source-side thermal power units based on fossil fuel consumption, quality parameters and direct monitoring of CEMS systems, proposes a calculation process and principles of a series of electric carbon evaluation indexes related to the historical state and future state of each link of the power system source network load, and is more general; the present application takes the emissions of thermal power plants as one of the measured sources, and the calculation is not limited to one kind of flue gas monitoring method, proposes electric carbon index evaluation for each link of the target power system source network load, and improves the calculation accuracy; the present application is a green and low-carbon index evaluation method for the historical and future carbon emissions, carbon emission intensity and green electricity proportion of a certain target power system; the results of power market transactions are reflected in the power flow results of each node and tie line after physical delivery, and are included in the input data of the historical state electric carbon evaluation of the present application, while in the future state electric carbon evaluation simulation, the results of the power market are input as the simulation boundary, to effectively support the calculation of the accurate carbon emissions of units and power plants to participate in the evaluation of the winning and losing of carbon market trading quotas.

[0106] It should be noted that in the embodiments of the present application, "step + number" is only a way of expressing the specific implementation of the electric carbon collaborative analysis and evaluation method of fusing measured trajectories and simulated trajectories, and is not an absolute limitation on the order of each step. Under the guidance of the core concept of the present application, the steps can be exchanged in order to obtain the same or similar technical effects, which fall within the scope of the present application.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them, and although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced, without departing from the spirit and scope of the present application, any modification or equivalent replacement thereof should be included in the protection scope of the claims of the present application.

Claims

1. A method for evaluating the synergistic analysis of electric carbon by fusing the actual trajectory and the simulation trajectory, characterized in that, The method comprises the following steps: determining the power system to be evaluated and the related information-physical-social system, collecting measured data and predicted data of the power system to be evaluated and the related information-physical-social system; constructing candidate object models and designing candidate evaluation models; performing data preprocessing on the measured data, matching the evaluation object models from the candidate object models, and selecting the evaluation models from the candidate evaluation models according to the evaluation objects, carrying out online monitoring on the measured data, and substituting the evaluation models to obtain the power system electric-carbon collaborative analysis evaluation measured trajectory; determining the simulation boundary of the power system electric-carbon collaborative analysis evaluation, determining the electric-carbon collaborative simulation input parameters, constructing the electric-energy-carbon fusion model of the power system to be evaluated, and carrying out the power system electric-carbon collaborative analysis evaluation simulation deduction to obtain the power system electric-carbon collaborative analysis evaluation simulation trajectory; for the measured trajectory and the simulation trajectory, knowledge extraction is carried out by fusing causal knowledge and statistical analysis; according to the knowledge extraction result, the electric-carbon collaborative decision is optimized, the electric-carbon collaborative benefit post-evaluation is carried out, and the responsibility contribution of the source, network, load and storage of each link and each subject to the overall green and low-carbon benefit of the power system to be evaluated is identified.

2. The electric-carbon collaborative analysis evaluation method of claim 1, wherein the determination of the power system to be evaluated and the related information-physical-social system comprises: determining the power system to be evaluated and the physical facilities and social behaviors in related fields; the physical facilities of the power system to be evaluated include source-side physical facilities, network-side physical facilities and load-side physical facilities.

3. The electric-carbon collaborative analysis evaluation method of claim 2, wherein the collected measured data of the power system to be evaluated and the related information-physical-social system is subjected to availability analysis to determine the types of available data.

4. The electric-carbon collaborative analysis evaluation method of claim 2 or 3, wherein the construction of the candidate object models comprises: respectively constructing the electric-energy-carbon evaluation models of the candidate objects on the source side, the network side and the load side; the candidate objects on the source side include one or more of coal-fired generating units, gas-fired generating units, wind power generating units, photovoltaic generating units, hydropower generating units, nuclear power generating units, biomass generating units and pumped storage units or energy storage; the candidate objects on the network side include one or more of power transmission networks, power distribution networks or substation transformers; the candidate objects on the load side include various types of users.

5. The electric-carbon collaborative analysis evaluation method of claim 4, wherein the electric-energy-carbon evaluation models include evaluation models and diagnosis models; the evaluation models include one or more of electric quantity proportion models, energy consumption models, carbon emission models and carbon emission intensity models; and the diagnosis models include one or more of unit load rate-average carbon emission intensity of unit, unit heat supply ratio-average carbon emission intensity of unit, unit up-regulation sensitivity, unit down-regulation sensitivity, average load rate of power grid-average carbon emission intensity of power grid and carbon emission reduction contribution of measures. ​ ​ ​ ​ 6. The method according to claim 4, wherein the method further comprises: selecting an evaluation model according to an evaluation object, wherein the selecting an evaluation model according to an evaluation object comprises: establishing a carbon emission evaluation model for a source-side thermal power unit, wherein the carbon emission evaluation model is established based on a fuel consumption and quality parameter method, a flue gas flow and concentration method, and an electricity-carbon factor method; and / or designing a carbon emission evaluation model for a source-side energy storage unit, wherein the carbon emission evaluation model is designed to calculate indirect carbon emissions during charging according to a load, and to be zero carbon during discharging to the power grid, and to calculate power generation carbon emissions according to zero; and / or designing a carbon emission factor evaluation model for a grid-side power transmission network, a power distribution network, and a transformer substation, wherein the carbon emission factor evaluation model is designed according to a power grid coverage range and / or a carbon emission factor use; and / or designing a user electricity carbon emission factor evaluation model for a load-side user, wherein the user electricity carbon emission factor evaluation model is designed according to a power source.

7. The method according to claim 2 or 3, wherein the method further comprises: performing data preprocessing on the measured data, wherein the data preprocessing on the measured data comprises: establishing an original data preprocessing model, and implementing data preprocessing of multiple data sources by defining a flow-based preprocessing rule to eliminate dirty data, wherein the data preprocessing rule mainly comprises a rationality checking rule and a data integration alignment rule.

8. The method according to claim 2 or 3, wherein the method further comprises: determining a simulation boundary of the electricity-carbon collaborative analysis evaluation of the power system, and determining electricity-carbon collaborative simulation input parameters, wherein the determining a simulation boundary of the electricity-carbon collaborative analysis evaluation of the power system comprises: determining a power planning, operation, and market simulation boundary related to the electricity-carbon collaborative analysis evaluation of the power system; and determining part of the electricity-carbon collaborative simulation input parameters based on the prediction data, and extracting another part of the electricity-carbon collaborative simulation input parameters based on a large model analysis of the prediction data.

9. The method according to claim 2 or 3, wherein the method further comprises: constructing an electricity-energy-carbon fusion model of the power system to be evaluated, and performing electricity-carbon collaborative analysis evaluation simulation deduction of the power system to obtain an electricity-carbon collaborative analysis evaluation simulation trajectory of the power system, wherein the constructing an electricity-energy-carbon fusion model of the power system to be evaluated comprises: constructing an electricity-energy-carbon evaluation model of each link of the power system to be evaluated in combination with online monitoring of the electricity-carbon collaboration, and constructing an electricity-energy-carbon fusion model of each link object of the power system to be evaluated in combination with physical facilities of the power system to be evaluated and in combination with a simulation granularity suitable for the electricity-carbon collaborative hybrid simulation; building a simulation model of the electricity-energy-carbon fusion model of each link object; and designing a simulation scenario, and performing electricity-carbon collaborative analysis evaluation simulation deduction of the power system to obtain an electricity-carbon collaborative analysis evaluation simulation trajectory of the power system.

10. The method according to claim 9, wherein the designing a simulation scenario comprises: designing one or more of an electricity-carbon collaborative hybrid simulation scenario in which carbon emission intensity of the power system to be evaluated is the lowest, green electricity proportion is the highest, and a key factor is changed. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 11. The method of claim 2 or 3, wherein the method further comprises: extracting knowledge from the measured trajectory and the simulated trajectory by fusing causal knowledge and statistical analysis; performing statistical analysis on the measured trajectory and the simulated trajectory; extracting phase plane sets from the high-dimensional measured trajectory or the simulated trajectory based on the phase plane sets and quantitative indicators; and quantitatively evaluating the influence of key factors on the green and low-carbon characteristics of the interconnected power grid.

12. The method of claim 11, wherein the method further comprises: feeding back the results of the knowledge extraction from the measured trajectory and the simulated trajectory to guide the construction of candidate object models and the design of candidate evaluation models.

13. The method of claim 11 or 12, wherein the method further comprises: a data acquisition module configured to determine the power system to be evaluated and the related information-physical-social system, and to collect measured data and predicted data of the power system to be evaluated and the related information-physical-social system; an information acquisition module configured to perform online monitoring on the measured data to obtain a measured trajectory of the power system for the electric-carbon collaborative analysis and evaluation, and to perform simulation deduction of the power system for the electric-carbon collaborative analysis and evaluation to obtain a simulated trajectory of the power system for the electric-carbon collaborative analysis and evaluation; a knowledge extraction module configured to extract knowledge from the measured trajectory and the simulated trajectory by fusing causal knowledge and statistical analysis; and a decision support module configured to optimize electric-carbon collaborative decision-making based on the results of the knowledge extraction, to perform post-evaluation of the electric-carbon collaborative benefits, and to identify the responsibility and contribution of each subject in each link of the source, grid, load, and storage to the overall green and low-carbon benefits of the power system to be evaluated.

14. A computer program product comprising a computer program according to claim 13, wherein the computer program is loaded into a processor to implement the method of claim 1 to 12.

15. A computer program product comprising a computer program according to claim 13, wherein the computer program is executed by a processor to implement the method of claim 1 to 12. ​ 13. A system for fusion of measured trajectories and simulated trajectories for co-analysis and evaluation of electricity-carbon synergy, which operates the method for co-analysis and evaluation of electricity-carbon synergy according to any one of claims 1 to 12, characterized in that, ​ ​ ​ ​ ​ 14. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, ​ A computer-readable storage medium storing a computer program, characterized in that, ​

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