Carbon emission assessment method, system and device for collaborative planning of multi-type adjusting power supplies and new energy, and storage medium

Abstract

The invention relates to the technical field of electric power system planning, in particular to a carbon emission evaluation method, system and device for multi-type adjusting power supply and new energy collaborative planning and a storage medium. The method comprises the following steps: acquiring power system equipment parameters, constructing a hierarchical planning platform architecture, and establishing a parameterized model library through data collection and governance; based on the parameterized model library, various adjusting power supply operation characteristic parameters are extracted, a collaborative optimization objective function is constructed in combination with new energy output characteristics, and a preliminary collaborative planning scheme is generated; formulating an active support strategy according to a power supply configuration result, determining a scheduling plan and generating an energy management and control instruction sequence, constructing typical and extreme weather scenes for collaborative operation simulation, and obtaining power supply output and power flow distribution; the node carbon emission intensity and the total system carbon emission amount are calculated through tidal current tracking in combination with the carbon emission factors, and carbon emission space-time distribution is generated; and comprehensively evaluating the collaborative planning scheme through a multi-dimensional evaluation index, and generating a visual decision support report.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning, and in particular to a carbon emission evaluation method, system, device and storage medium for coordinated planning of multi-type regulating power sources and new energy. BACKGROUND

[0002] With the advancement of new-type power system construction, large-scale new energy grid connection has brought new challenges to power system planning. Traditional power system planning methods are mainly based on deterministic load forecasting and power source output characteristics, and use single-objective optimization for power source configuration, which is difficult to adapt to the randomness and volatility of new energy output. Existing planning tools mostly use independent function modules, lack of coordinated optimization mechanism for multi-type regulating power sources and new energy, and cannot effectively evaluate the system operation characteristics under different power source combinations.

[0003] In terms of carbon emission evaluation, current methods mainly focus on direct emissions on the power source side, and lack of tracking analysis of the full-process carbon emissions of the power system. Traditional evaluation models fail to fully consider the differences in carbon emission characteristics of various power sources under different operating conditions, and also fail to establish a correlation between carbon emissions and power flow, resulting in inaccurate spatial distribution evaluation of carbon emissions. At the same time, existing planning platforms have deficiencies in extreme weather scenario simulation, active support capacity evaluation, and lack of unified data management and visualization display mechanism.

[0004] In view of the planning needs of new-type power systems, it is urgent to build an integrated planning evaluation platform to realize coordinated optimization and configuration of multi-type regulating power sources and new energy. The platform needs to have a complete data management system, multi-scenario operation simulation capability, accurate carbon emission evaluation function and comprehensive scheme evaluation mechanism to provide scientific decision support tools for low-carbon transformation of the power system. SUMMARY

[0005] In view of the problems existing in the prior art, the present application is proposed.

[0006] Therefore, the present application mainly solves the problems of existing power system planning methods, such as inability to adapt to large-scale new energy access, lack of coordinated optimization mechanism for multi-type regulating power sources, inaccurate and incomplete carbon emission evaluation, insufficient consideration of extreme scenarios, and scattered planning evaluation functions, by building a hierarchical planning platform architecture, establishing a coordinated optimization model, developing operation simulation and carbon emission evaluation function modules, and realizing coordinated planning of multi-type regulating power sources and new energy and accurate evaluation of full-process carbon emissions.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] In a first aspect, the embodiments of the present application provide a carbon emission evaluation method for multi-type regulating power supply and new energy coordinated planning, which comprises: obtaining power system device parameters and constructing a hierarchical planning platform architecture, establishing a parameterized model library through data collection and management, and building a parameterized model library based on the operation characteristic parameters of thermal power, hydropower, pumped storage and energy storage regulating power supply, combining with new energy output characteristic data, constructing a coordinated optimization objective function of multi-type regulating power supply and new energy, and generating a preliminary coordinated planning scheme;

[0009] Based on the parameterized model library, the operation characteristic parameters of thermal power, hydropower, pumped storage and energy storage regulating power supply are extracted, combined with new energy output characteristic data, a coordinated optimization objective function of multi-type regulating power supply and new energy is constructed, and a preliminary coordinated planning scheme is generated;

[0010] According to the power supply configuration result in the preliminary coordinated planning scheme, the active support strategy of regulating power supply and energy storage is formulated, the dispatching plan of various types of power supply in different time periods is determined, the energy control instruction sequence is generated, the typical operation scene and extreme weather scene are constructed based on the energy control instruction sequence, the system operation state under each scene is calculated through the coordinated operation simulation algorithm, the power supply output data and the power flow distribution result are obtained;

[0011] According to the power supply output data obtained by the operation simulation, combined with the carbon emission factor of each type of power supply, the node carbon emission intensity and the system carbon emission total amount are calculated through the power flow tracking method, the carbon emission space-time distribution is generated, based on the carbon emission distribution result and the operation state data, the coordinated planning scheme is comprehensively evaluated through multi-dimensional evaluation indexes, and a visual decision support report is generated.

[0012] As a preferred scheme of the carbon emission evaluation method for multi-type regulating power supply and new energy coordinated planning, wherein: the power system device parameters include device parameters, topological structure, operation scene, resource endowment data and coordinated planning parameters, the hierarchical planning platform architecture includes data layer, business layer and auxiliary layer, the device parameters, topological structure, operation scene, resource endowment data and coordinated planning parameters are collected in the data layer, and a unified control database is established to realize data interaction;

[0013] The functions of basic model management, active support energy control, system operation simulation, carbon emission evaluation, planning evaluation analysis and scheme comprehensive display are integrated in the business layer;

[0014] The human-computer interaction interface is designed based on the componentized front-end development framework in the auxiliary layer, and the high-visualization rendering engine is developed to realize data display.

[0015] As a preferred scheme of the carbon emission evaluation method for the multi-type regulating power source and the new energy coordinated planning, in the scheme, the operation characteristic parameters of the thermal power, the hydropower, the pumped storage power and the energy storage regulating power source are extracted based on a parameterized model library, the minimum technical output, the climbing rate and the start-stop time constraint of the thermal power unit are identified, the water level storage constraint, the discharge flow limit and the regulating capacity of the hydropower station are determined, the charging and discharging efficiency, the storage capacity limit and the response time of the pumped storage power station are analyzed, and the charging and discharging power, the capacity configuration and the cycle life characteristic of the energy storage system are evaluated.

[0016] As a preferred scheme of the carbon emission evaluation method for the multi-type regulating power source and the new energy coordinated planning, in the scheme, the active support strategy of the regulating power source and the energy storage is formulated, the support demand level in different time periods is calculated based on the new energy predicted output and the load demand curve, the primary frequency modulation and the secondary frequency modulation parameters of the regulating power source are determined according to the system inertia level and the frequency regulation demand, the voltage support strategy of the energy storage system is configured through dynamic reactive power compensation demand analysis, and the 24-hour energy scheduling plan is generated by comprehensively considering the power balance constraint and the standby capacity requirement.

[0017] As a preferred scheme of the carbon emission evaluation method for the multi-type regulating power source and the new energy coordinated planning, in the scheme, the system operation state in each scene is calculated through a coordinated operation simulation algorithm, the spring, summer, autumn and winter typical day operation scenes are constructed based on historical meteorological data and new energy output characteristics, the extreme scene boundary of new energy output sudden drop and sudden rise is established for typhoon, freezing and drought extreme weather conditions, and the system power and energy balance and the new energy consumption rate are calculated through 8760-hour time sequence production simulation to obtain the line load rate and the node voltage distribution.

[0018] As a preferred scheme of the carbon emission evaluation method for the multi-type regulating power source and the new energy coordinated planning, in the scheme, the node carbon emission intensity and the system carbon emission total amount are calculated through a power flow tracking method, the carbon emission factor database of the thermal power unit under different coal types and load rates is established, the proportional allocation method is adopted to calculate the carbon emission intensity of each node based on the power flow calculation result and the power transmission path, the daily, monthly and annual carbon emission total amount is calculated through period accumulation, and the regional carbon emission distribution heat map is generated according to geographical information and administrative division.

[0019] As a preferred scheme of the carbon emission evaluation method of the multi-type regulation power supply and the new energy collaborative planning of the application, wherein: the comprehensive evaluation of the collaborative planning scheme through the multi-dimensional evaluation index includes: calculating the investment cost, the operation cost, the carbon trading income and the degree of electricity cost from the economic dimension; evaluating the power supply reliability, the new energy utilization rate, the load loss expectation and the system flexibility index from the reliability dimension; calculating the total carbon emission, the carbon emission intensity, the sulfur dioxide and nitrogen oxide emission reduction amount from the environmental protection dimension; determining the index weight by using the analytic hierarchy process, calculating the comprehensive score and generating the radar chart display.

[0020] In the second aspect, the embodiments of the application provide a carbon emission evaluation system for multi-type regulation power supply and new energy collaborative planning, which comprises a data acquisition module, which acquires power system equipment parameters and constructs a hierarchical planning platform architecture, and establishes a parameterized model library through data collection and management;

[0021] A construction module extracts the operation characteristic parameters of thermal power, hydropower, pumped storage and energy storage regulation power supply based on the parameterized model library, combines the new energy output characteristic data, constructs a collaborative optimization objective function of multi-type regulation power supply and new energy, and generates a preliminary collaborative planning scheme;

[0022] A data generation module formulates an active support strategy of regulation power supply and energy storage according to the power supply configuration result in the preliminary collaborative planning scheme, determines the dispatching plan of various types of power supply in different time periods, generates an energy management and control instruction sequence, constructs typical operation scenarios and extreme weather scenarios based on the energy management and control instruction sequence, calculates the system operation state in each scenario through a collaborative operation simulation algorithm, and acquires power supply output data and power flow distribution results;

[0023] A result output module calculates the node carbon emission intensity and the total system carbon emission by using a power flow tracking method according to the power supply output data obtained through operation simulation, combines the carbon emission factors of various types of power supply, generates a carbon emission space-time distribution, and performs comprehensive evaluation of the collaborative planning scheme through multi-dimensional evaluation indexes based on the carbon emission distribution results and the operation state data, and generates a visual decision support report.

[0024] In the third aspect, the embodiments of the application provide a computer device, which comprises a memory and a processor, and the memory stores a computer program, wherein: the computer program instructions are executed by the processor to realize the steps of the carbon emission evaluation method of the multi-type regulation power supply and the new energy collaborative planning of the first aspect of the application.

[0025] In the fourth aspect, the embodiments of the application provide a computer readable storage medium, which stores a computer program, wherein: the computer program instructions are executed by the processor to realize the steps of the carbon emission evaluation method of the multi-type regulation power supply and the new energy collaborative planning of the first aspect of the application.

[0026] The application has the beneficial effects that: the application realizes unified management of multi-source heterogeneous data by constructing a hierarchical planning platform architecture and a parameterized model library, provides a reliable data foundation for subsequent optimization analysis, and solves the technical problems of data dispersion and non-uniform format in traditional planning. By extracting the operation characteristics of multiple types of regulating power and constructing a collaborative optimization objective function, the optimal configuration and complementary operation between different power sources are realized to improve the overall regulation ability and new energy consumption level of the system. The initiative support for strategy formulation and multi-scenario operation simulation verifies the adaptability of the planning scheme under various working conditions, and the consideration of extreme weather scenarios improves the risk resistance of the system. The carbon emission calculation method based on power flow tracking establishes the coupling relationship between carbon emission and power flow, realizes accurate positioning and quantitative analysis of carbon emission, and solves the limitation of traditional methods which only consider direct emission on the power supply side. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0028] Figure 1 The flowchart of the carbon emission evaluation method for the coordinated planning of multiple types of regulating power and new energy;

[0029] Figure 2 The computer device diagram of the carbon emission evaluation method for the coordinated planning of multiple types of regulating power and new energy;

[0030] Figure 3 The overall unified flowchart of the carbon emission evaluation method for the coordinated planning of multiple types of regulating power and new energy. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the drawings of the specification.

[0032] In the following description, many specific details are set forth in order to provide a thorough understanding of the application, but the application can also be implemented in other ways different from the description, and those skilled in the art can make similar generalizations without departing from the scope of the application, therefore the application is not limited to the specific embodiments disclosed below.

[0033] Second, the "one embodiment" or "an embodiment" referred to herein means a specific feature, structure, characteristic, or combination of features and characteristics described herein that can be included in at least one implementation of the present application. The various appearances of "in one embodiment" or "in an embodiment" in the specification do not all refer to the same embodiment, although they can.

[0034] Embodiment 1

[0035] With reference to Figure 1 - Figure 2 For the first embodiment of the present application, the embodiment provides a carbon emission evaluation method for multi-type regulating power supply and new energy collaborative planning, comprising,

[0036] S100: Obtain power system equipment parameters and construct a hierarchical planning platform architecture, and establish a parameterized model library through data collection and management;

[0037] S200: Extract the operating characteristic parameters of thermal power, hydropower, pumped storage and energy storage regulating power supply based on the parameterized model library, combine the new energy output characteristic data, construct a collaborative optimization objective function of multi-type regulating power supply and new energy, and generate a preliminary collaborative planning scheme;

[0038] S300: According to the power supply configuration result in the preliminary collaborative planning scheme, formulate an active support strategy of regulating power supply and energy storage, determine the dispatching plan of various types of power supply in different time periods, generate an energy control instruction sequence, based on the energy control instruction sequence, construct a typical operation scenario and an extreme weather scenario, calculate the system operation state under each scenario through a collaborative operation simulation algorithm, obtain the power supply output data and the power flow distribution result;

[0039] S400: According to the power supply output data obtained by the operation simulation, combining the carbon emission factors of various types of power supply, calculating the node carbon emission intensity and the total system carbon emission through the power flow tracking method, generating the carbon emission space-time distribution, based on the carbon emission distribution result and the operation state data, through multi-dimensional evaluation index, the collaborative planning scheme is comprehensively evaluated, and a visual decision support report is generated.

[0040] It should be noted that after the large-scale new energy is connected to the new power system, the randomness and volatility of wind and light new energy output bring great challenges to the stable operation of the power grid. The traditional planning method independently configures various types of power supply, without considering the collaborative complementary characteristics between different regulating power supplies, resulting in insufficient system regulation capacity and difficulty in new energy consumption. At the same time, the existing carbon emission evaluation only focuses on the direct emission of the power supply side, ignoring the carbon emission distribution in the process of power flow transmission, and cannot accurately locate the high carbon emission node. The frequent occurrence of extreme weather also puts new requirements on the planning of the power system, which needs to consider the system adaptability under extreme scenarios in the planning stage.

[0041] Therefore, in view of the existing problems of collaborative planning and accurate carbon emission evaluation, through the steps of S100-S400, a hierarchical planning platform is constructed to realize unified management of data and establishment of a model library, providing a data basis for subsequent analysis; a planning scheme considering the complementary characteristics of multiple types of power sources is generated through a collaborative optimization objective function, improving the overall regulation capacity of the system; active support strategies are developed based on the planning scheme and multi-scenario operation simulation is performed to verify the adaptability of the scheme under various operating conditions; finally, through power flow tracking, accurate positioning and evaluation of carbon emissions are realized, comprehensive support is provided for decision-making by combining multi-dimensional indicators, and collaborative optimization configuration of multiple types of regulating power sources and new energy and accurate evaluation of carbon emissions are realized.

[0042] Embodiment 2 With reference to Figure 1 Figure 3 This is a second embodiment of the present application.

[0044] In this embodiment, in step S100, power system equipment parameters are acquired and a hierarchical planning platform architecture is constructed, and a parameterized model library is established through data collection and management, including the following steps A1-A3:

[0045] A1: The power system equipment parameters include equipment parameters, topological structure, operating scenarios, resource endowment data, and collaborative planning parameters. The hierarchical planning platform architecture includes a data layer, a business layer, and an auxiliary layer. The equipment parameters, topological structure, operating scenarios, resource endowment data, and collaborative planning parameters are collected in the data layer, and a unified control database is established to realize data interaction;

[0046] Specifically, the process of acquiring power system equipment parameters first needs to export the basic parameters of equipment from the energy management system (EMS) of the power grid dispatching center, including the nameplate parameters of generator sets, transformer parameters, and transmission line parameters. For thermal power units, the unit number, rated capacity, minimum technical output, maximum technical output, ramp rate, start-up and shutdown time, and coal consumption curve coefficient need to be acquired. These parameters are obtained through real-time data interfaces with each power plant and stored in an Oracle database. The parameters of hydropower stations include installed capacity, water turbine model, rated water head, design flow, reservoir capacity curve, and inflow historical data, which are extracted from the water regulation automation system. Pumped storage power stations need to record the upper and lower reservoir capacities, reversible unit parameters, charge and discharge efficiency curves, and operating condition conversion time. The parameters of energy storage systems include battery type, rated power, rated capacity, charge and discharge efficiency, self-discharge rate, and cycle life. The parameters of new energy power stations include wind turbine model and quantity, photovoltaic module specifications, inverter parameters, and historical output data.

[0047] ​Topology data is imported through CIM model, including the connection relationship of busbar, circuit breaker, disconnector, line, transformer and other equipment, forming a complete power grid topology. The operation scenario data is derived from the historical SCADA system, including at least 3 years of load data, new energy output data, unit operation data, etc., with a data sampling interval of 15 minutes. Resource endowment data is obtained from meteorological departments and resource assessment agencies, including wind tower data, irradiance data, hydrological data, etc. Collaborative planning parameters are provided by planning departments, including planning horizon years (usually 5 years, 10 years, 15 years), investment cost forecasts of various types of power sources, fuel price forecasts, carbon emission constraint targets, etc.

[0048] In the business layer, the model management, active support for energy control, system operation simulation, carbon emission evaluation, planning evaluation analysis and scheme comprehensive display functions are integrated;

[0049] In the auxiliary layer, the human-computer interaction interface is designed based on the componentized front-end development framework, and the high-visualization rendering engine is developed to realize data display.

[0050] A2: The construction of the hierarchical planning platform architecture adopts micro-service architecture design. The construction of the data layer first establishes a data warehouse, adopts a star model design, and takes time, equipment, and geographic location as dimensions, and takes running data, economic data, and environmental data as real-time tables. Apache Kafka is used as a message middleware for data collection to realize the access of real-time data streams. Through ETL tools such as Kettle, data extraction, conversion, and loading are realized, and data cleaning rules include: deleting duplicate records, filling missing values (using linear interpolation or mean filling), identifying and processing abnormal values (data exceeding 3 times the standard deviation). A data quality monitoring system is established, setting quality indicators in four dimensions of data integrity, accuracy, consistency, and timeliness, and automatically alarming when the data quality is below the threshold.

[0051] The data storage adopts a hybrid architecture, structured data is stored in a PostgreSQL relational database, time series data is stored in an InfluxDB time series database, and unstructured data such as reports and drawings is stored in a MinIO object storage. A unified metadata management system is established to record the source, update time, quality level, usage rights, and other information of the data. The data access layer uses GraphQL to build a unified API gateway, supporting flexible data query and subscription mechanisms.

[0052] A3: The basic model management module of the business layer is responsible for maintaining mathematical models of various power sources, including the cost function, efficiency curve, and constraint conditions of the unit. Each model has version control, recording the creation time, modification history, and verification results of the model. The active support energy management module implements AGC (Automatic Generation Control) and AVC (Automatic Voltage Control) functions, calculates the regional control deviation ACE according to the system frequency deviation and tie-line power deviation, and allocates the adjustment task according to the adjustment performance of the unit. The system operation simulation module integrates power flow calculation, optimal power flow, transient stability, small signal stability analysis functions, and uses parallel computing technology to improve calculation efficiency.

[0053] The carbon emission evaluation module establishes a complete carbon emission factor database, including carbon emission factors of different types of units under various operating conditions, realizing real-time calculation and statistical analysis of carbon emissions. The planning evaluation analysis module provides calculation of economic indicators such as net present value (NPV), internal rate of return (IRR), and investment payback period, as well as evaluation of technical indicators such as power supply reliability and new energy consumption rate. The scheme comprehensive display module realizes the display of power grid geographic connection diagram based on WebGIS technology, supporting functions such as hierarchical display, thematic map production, and spatial analysis.

[0054] The auxiliary layer adopts a front-end and back-end separation architecture, with the front-end using the Vue.js framework to develop a single-page application and the user interface built with the Element UI component library. Data visualization uses ECharts and D3.js to implement various chart drawing, including line charts, bar charts, pie charts, scatter plots, and heat maps. Three-dimensional display functions are developed based on Three.js, enabling three-dimensional modeling and scene rendering of power grid equipment. User permission management uses the RBAC (Role-Based Access Control) model, defining roles such as system administrator, planning engineer, and operation analyst, each with different function permissions and data permissions.

[0055] In an alternative embodiment, the establishment of the parameterized model library in step S100 can also use knowledge graph technology to build knowledge graphs of power grid equipment, operating rules, and constraint conditions. First, define entity types, including power plants, units, substations, and lines; then define relationship types, such as "belongs to", "connects", and "supplies power"; finally, store knowledge in the form of triples, such as (unit A, belongs to, power plant B), (line C, connects, substation D). The knowledge graph is stored in the Neo4j graph database and searched using the Cypher query language. This approach can more intuitively express the topological relationship and constraint conditions of the power grid, supporting graph-based path search and reasoning analysis.

[0056] In another alternative embodiment, the data governance in step S100 can also introduce data lineage analysis to track the complete flow process of data from the source to the final application. Through data governance tools such as Apache Atlas, the dependency between data is automatically discovered, and a data lineage graph is constructed. When a data source changes, the affected downstream applications can be quickly identified, and the change impact range can be evaluated. At the same time, a data dictionary is established to define the business meaning, value range, update frequency, and other attributes of each data field, ensuring the consistency and accuracy of data use.

[0057] It should be noted that the design of the hierarchical planning platform architecture fully considers the scalability, maintainability and high availability of the system. Through the micro-service architecture, the decoupling of functional modules is realized, and each service can be independently deployed and upgraded. The hybrid storage architecture of the data layer can meet the storage needs of different types of data and improve data access efficiency. The modular design of the business layer facilitates the extension and customization of functions. The component-based development of the auxiliary layer improves code reuse and shortens the development cycle.

[0058] In the present embodiment, the extraction of operating characteristic parameters based on the parameterized model library in step S200, the construction of the collaborative optimization objective function, and the generation of the preliminary collaborative planning scheme include the following steps B1-B3:

[0059] B1: Based on the parameterized model library, the operating characteristic parameters of thermal power, hydropower, pumped storage and energy storage regulating power sources are extracted, including identifying the minimum technical output, climbing rate and start-stop time constraints of thermal power units; determining the water level and storage capacity constraints, discharge flow limits and regulation capacity of hydropower stations; analyzing the charge and discharge efficiency, storage capacity limits and response time of pumped storage power stations; evaluating the charge and discharge power, capacity configuration and cycle life characteristics of energy storage systems.

[0060] Specifically, when extracting the operating characteristic parameters of various types of regulating power sources from the parameterized model library, first, the device parameter table is read from the database through an SQL query statement. For thermal power units, the minimum technical output constraint is queried, which is related to the boiler type and turbine characteristics of the unit. Due to the stability requirements of boiler combustion, the minimum technical output of subcritical units is generally not less than 45% of the rated capacity, and the minimum technical output of supercritical and ultra-supercritical units can be reduced to 30%-35% due to the use of straight-flow boilers. The determination of the climbing rate needs to consider the thermal inertia of the boiler and the stress limit of the turbine, and the climbing rate is controlled at 1.5% of the rated capacity per minute under normal operation, and can be increased to 3% in emergency situations. The start-stop time is classified according to the temperature state of the unit, and the cold start needs to go through the processes of boiler ignition, warming, warming, grid connection and load carrying, with a total time of about 10 hours; the warm start can skip part of the warming process, with a time of 5 hours; the hot start only takes 2-3 hours.

[0061] The extraction of operational characteristic parameters for hydropower stations includes reading the reservoir's characteristic curve data. The water level-storage capacity curve is fitted using a piecewise linear function, and the storage capacity corresponding to each water level is calculated through interpolation. Downstream discharge limits must meet downstream ecological needs, navigation needs, and flood control requirements. Ecological discharge is generally taken as 10%–15% of the multi-year average discharge, while navigation discharge is determined based on the waterway grade. Flood control storage capacity must be reserved during flood season. The regulation capacity of hydropower stations is classified based on the ratio of the reservoir's regulation capacity to the annual runoff: a ratio less than 2% is daily regulation, 2%–8% is weekly regulation, 8%–30% is quarterly regulation, and greater than 30% is annual or multi-year regulation.

[0062] The key focus of parameter extraction for pumped storage power stations is on operating condition transition characteristics. It takes 90 seconds to transition from shutdown to full-load power generation, and 5 minutes to switch from power generation to pumping operation. This rapid response capability makes it an important means of peak shaving and frequency regulation for the power grid. Parameter extraction for energy storage systems requires differentiation between different battery technologies. Lithium iron phosphate batteries have a cycle life of over 6000 cycles and an energy density of approximately 140-180 Wh / kg; ternary lithium batteries have a higher energy density, reaching 200-260 Wh / kg, but a relatively shorter cycle life of approximately 3000-4000 cycles. The power and capacity configuration of the energy storage system needs to be determined based on the application scenario. Frequency regulation applications require high-power, short-term energy storage, typically configured for 0.5-1 hour; peak shaving applications require large-capacity, long-term energy storage, typically configured for 2-4 hours.

[0063] B2: When constructing the synergistic optimization objective function for multiple types of regulating power sources and new energy sources, a weighted summation method is used to transform the multi-objective problem into a single-objective optimization problem. The total system cost includes two parts: investment cost and operating cost. The investment cost is calculated using the equivalent annual value method.

[0064]

[0065] Among them, P i Let c be the installed capacity (MW) of the i-th type of power source. i The unit investment cost is 10,000 yuan / MW, r is the discount rate (taken as 8%), and n is the economic life (25 years for thermal power, 50 years for hydropower, and 20 years for new energy).

[0066] Operating costs mainly include fuel costs, operation and maintenance costs, and start-up and shutdown costs. Annual operating costs are calculated through an 8760-hour production simulation. The carbon emission target function considers both direct and indirect emissions. Direct emissions come from fossil fuel combustion, while indirect emissions come from electricity purchases and equipment manufacturing. The renewable energy integration rate, as an important component of the target function, reflects the system's ability to integrate renewable energy.

[0067] The constraint conditions of the optimization model include: power balance constraint, which ensures the balance between supply and demand in each period; reserve capacity constraint, which ensures that the positive reserve is not less than the sum of the maximum single machine capacity and the load forecast error, and the negative reserve is not less than the new energy forecast error; power transmission section constraint, which ensures that the transmission power of each power transmission section does not exceed the stable limit; unit operation constraint, including output upper and lower limits, climbing rate, minimum operation and shutdown time, etc.; hydropower constraint, including water balance, reservoir capacity limit, output-head relationship, etc.; new energy penetration rate constraint, which ensures that the instantaneous penetration rate is not more than 70% to avoid system instability.

[0068] B3: The optimization model is solved by using a mixed integer linear programming (MILP) method, and a commercial solver such as CPLEX or Gurobi is used. The decision variables include the installed capacity of various types of power sources (continuous variables) and the start-stop state of the unit (0-1 variable). The preprocessing of the model includes variable bounding, constraint simplification, and symmetry breaking techniques to improve the solving efficiency. For large-scale problems, Benders decomposition or column generation algorithm is used for solving. The optimization results give the optimal installed capacity, annual power generation, capacity factor and other indicators of various types of power sources.

[0069] The influence of key parameters on the optimization results is evaluated through sensitivity analysis. For example, a 10% change in fuel price has an impact of about 3%-5% on the total cost, and a 50 yuan / ton change in carbon price has a significant impact on the power structure, which may lead to a 5%-10% reduction in coal-fired power installed capacity. Through scenario analysis, the influence of uncertain factors is evaluated. Three load growth scenarios, high, medium and low, and high and low new energy penetration rate scenarios are set to perform optimization calculation respectively.

[0070] In an alternative embodiment, the optimization objective function in step S200 can also consider the system flexibility requirement, and define a flexibility index as the ability of the system to respond to net load changes. The net load climbing demand is obtained by analyzing historical data, and the maximum 1-hour climbing demand is about 15%-20% of the peak load, and the maximum 3-hour climbing demand is about 30%-40% of the peak load. The flexibility resources provided by the system include the adjustment range of thermal power units, the rapid response capability of hydropower and pumped storage, the bidirectional adjustment capability of energy storage, demand response resources, etc. Flexibility balance constraints are added to the optimization model to ensure that the system has sufficient flexibility resources at each time scale.

[0071] In another alternative embodiment, the generation of the preliminary coordinated planning scheme in step S200 can also employ a double-layer optimization model, with the upper layer being investment decision optimization and the decision variable being the installed capacity of various power sources; and the lower layer being operation simulation optimization and the decision variable being unit output and start-stop state. The upper layer optimization passes the installed capacity scheme to the lower layer, and the lower layer calculates the operation cost and technical index through production simulation and feeds back to the upper layer, and the optimal scheme is obtained through iterative solution. This method can more accurately evaluate the operation effect of the investment scheme, but has higher computational complexity and needs to employ heuristic algorithms such as genetic algorithm, particle swarm algorithm, etc. for solution.

[0072] In the present embodiment, the active support strategy is formulated according to the preliminary coordinated planning scheme in step S300, the energy management instruction sequence is generated, and multi-scenario operation simulation is performed, including the following steps C1-C4:

[0073] C1: Formulating the active support strategy of regulating power and energy storage includes calculating the support demand level in different time periods based on new energy predicted output and load demand curve; determining the primary frequency modulation and secondary frequency modulation parameters of the regulating power according to the system inertia level and frequency regulation demand; configuring the voltage support strategy of the energy storage system through dynamic reactive power compensation demand analysis; and generating a 24-hour energy dispatching plan in combination with power balance constraints and reserve capacity requirements.

[0074] Specifically, formulating the active support strategy first needs to analyze the system support demand. Through statistical analysis of 8760 hours of historical net load data, the climbing characteristics of the net load are identified. The net load is defined as the system load minus the new energy output, and its change rate reflects the demand of the system on regulating resources. The statistical results show that in spring and autumn, due to large fluctuations in new energy output, the maximum 1-hour net load climbing rate can reach 8% to 10% of the system capacity; in the summer evening peak period, due to the rapid decline of photovoltaic output and the continuous rise of load, the net load climbing demand is the largest. According to the climbing rate, the support demand is divided into three levels: the first level demand (climbing rate > 5% / hour) mainly occurs in the morning and evening peak transition period and needs to be responded by fast-response resources such as pumped storage and energy storage; the second level demand (3% to 5% / hour) can be met by gas turbine units and part of the flexible and transformed coal-fired units; and the third level demand (<3% / hour) can be met by conventional units.

[0075] The formulation of the support strategy follows the principle of "layering and grading, coordination and cooperation". The first layer is the fast-response layer, which provides second to minute-level power support through energy storage and pumped storage, responding to system frequency and tie-line power deviation; the second layer is the fast-regulation layer, which provides minute to hour-level regulation service through hydroelectric and gas turbine units; and the third layer is the basic regulation layer, which provides hour-level and above energy support through coal-fired units. The layers are smoothly switched through coordinated control to avoid excessive action of the regulating resources.

[0076] C2: Calculate the system operating state under each scenario by co-simulation algorithm, including constructing typical daily operation scenarios in spring, summer, autumn and winter based on historical meteorological data and new energy output characteristics; establishing extreme scenario boundaries for new energy output sudden drop and sudden rise under typhoon, freezing and drought extreme weather conditions; calculating system power and energy balance and new energy consumption rate through 8760-hour time sequence production simulation to obtain line load rate and node voltage distribution.

[0077] Specifically, the generation of energy control instruction sequence is based on day-ahead scheduling plan and intra-day rolling correction. The day-ahead scheduling takes 96 points (15 minutes per point) as the time resolution, and determines the unit start-stop plan and output curve of the next day through security constrained unit commitment (SCUC) and security constrained economic dispatch (SCED) according to load forecast and new energy power forecast. The intra-day rolling correction takes 4 hours as the cycle, and corrects the unit output plan according to the ultra-short-term forecast. The real-time scheduling takes 5 minutes as the cycle, and allocates the adjustment task of the unit through the AGC system.

[0078] The configuration of frequency modulation service considers the frequency response characteristics of the system. The theoretical demand of primary frequency modulation is determined according to the maximum power shortage of the system, generally taking 1.5 times the maximum single machine capacity. When actually configuring, the frequency modulation performance of the unit is considered, the primary frequency modulation capacity of thermal power unit is configured as 3% to 5% of the rated capacity, the water power unit is configured as 5% to 10%, and the energy storage system can provide 100% of the rated power to participate in frequency modulation. The secondary frequency modulation capacity is determined according to the statistical characteristics of ACE, generally 2% to 3% of the maximum load. The selection of frequency modulation resources follows the principle of "high quality first, economic and reasonable", and the resources with fast response speed and high adjustment accuracy are preferred.

[0079] C3: The construction of typical operation scenarios is based on the clustering analysis of historical data. The operation data in the past 3 years are collected, including load, wind power output, photovoltaic power output, temperature and other variables, and the data time resolution is 1 hour. The K-means clustering algorithm is used to cluster the 8760 hours of the year into typical scenarios, and the number of clusters is determined by the silhouette coefficient, generally 30 to 50 typical scenarios are selected. Each typical scenario contains 24 hours of load and new energy output sequence, as well as the probability weight of the scenario. Typical scenarios cover different seasons, different weather types, weekdays and holidays, etc.

[0080] Extreme weather scenarios are built based on historical extreme events. Typhoon scenario: according to historical typhoon path and intensity, simulate the sharp change of wind power output, wind speed exceeding 25 m / s, wind turbine cutout, wind power output drops to zero, impact time 6-24 hours. Extremely cold scenario: temperature drops sharply, leading to 20%-30% increase in load, at the same time, snow accumulation on photovoltaic components leads to 50%-70% reduction in output, gas pipeline pressure deficiency leads to limited output of gas turbine units. Drought scenario: water inflow is biased and dry, leading to 20%-30% of installed capacity of hydropower output, while high temperature leads to 5%-10% reduction in output of thermal power units due to cooling constraints. Through these extreme scenarios, the emergency response capability and power supply reliability of the system are tested.

[0081] C4: Coordinated operation simulation adopts time sequence production simulation method, simulates system operation for 8760 hours in a year with 1 hour as time step. The simulation process considers various constraints of units: output constraint, ramping constraint, minimum operation / shutdown time constraint, etc. The start-stop decision of units adopts priority order method, and the start-stop priority is determined according to the start-stop cost and operation cost of units. In the operation simulation, random events such as line N-1 fault and unit unplanned outage are considered, and the reliability index of the system is evaluated through Monte Carlo simulation.

[0082] The main indicators obtained by simulation calculation include: annual power generation and capacity factor of various types of power sources, capacity factor of thermal power units about 50%-60%, hydropower 30%-40%, wind power 20%-25%, photovoltaic 15%-18%; new energy consumption rate, up to more than 95% under current regulation capacity; curtailed wind and light power and its spatio-temporal distribution characteristics, mainly occurring in low load period of spring and autumn seasons; system operation cost, including fuel cost, start-stop cost, environmental cost, etc.; transmission section utilization rate, identifying transmission bottlenecks and network reinforcement needs.

[0083] In an alternative embodiment, the operation simulation in step S300 can also adopt random production simulation method, and uncertain factors such as load, new energy output, unit failure, etc. are described by probability distribution. Load prediction error obeys normal distribution, and standard deviation is about 3%-5% of predicted value; wind power prediction error increases with increasing prediction length, day-ahead prediction error is about 15%-20%, 4-hour prediction error is about 8%-10%; unit forced outage rate is determined according to historical statistics, large thermal power units about 2%-3%, gas turbine units about 1%-2%. 1000 random scenarios are generated by Latin hypercube sampling, and production simulation is carried out for each scenario to obtain the probability distribution of each index, such as P50, P90 value of power supply cost, to provide support for risk decision-making.

[0084] In another alternative embodiment, the active support strategy in step S300 can also consider the participation of a virtual power plant (VPP), which aggregates distributed photovoltaic, energy storage, controllable load and other resources to participate in system regulation. Through advanced communication and control technology, the virtual power plant realizes the centralized optimization and scheduling of dispersed resources. Controllable loads include industrial users' production load, commercial building air conditioning load, electric vehicle charging load, etc., and the total regulation capacity can reach 5% to 10% of the peak load. The virtual power plant can provide peak regulation, frequency regulation, backup and other auxiliary services, and has fast response speed and high regulation accuracy, which is an important regulation resource for future power systems.

[0085] In the present embodiment, the carbon emissions are calculated according to the operation simulation results in step S400, and a comprehensive evaluation is performed, including the following steps D1-D2:

[0086] D1: The node carbon emission intensity and system carbon emission total are calculated by a power flow tracing method, including establishing a carbon emission factor database of thermal power units under different coal types and load rates; based on the power flow calculation results and power transmission paths, the proportional allocation method is used to calculate the carbon emission intensity of each node; the daily, monthly and annual carbon emission totals are calculated by period accumulation; and a regional carbon emission distribution heat map is generated according to geographic information and administrative division.

[0087] Specifically, the carbon emission calculation first needs to establish a complete carbon emission factor database. The carbon emission factor of a thermal power unit is closely related to the coal type and boiler efficiency, which is determined by measuring the flue gas composition and coal quality analysis. The carbon content of different coal types differs greatly: the carbon content of anthracite is 77%-87%, the carbon content of bituminous coal is 65%-77%, and the carbon content of lignite is 60%-65%. The unit efficiency changes with the load rate, and the efficiency is highest at full load, which can reach 45% (ultra-supercritical unit), and the efficiency decreases to 35%-38% at low load. The carbon emission intensity calculation formula is:

[0088]

[0089] wherein EI is the carbon emission intensity (kgCO2 / kWh), CC is the carbon content of coal (%), OF is the oxidation factor (0.99 is taken), 44 / 12 is the molecular weight ratio of CO2 to C, η is the unit efficiency (%), and LHV is the low calorific value of coal (MJ / kg).

[0090] The carbon emission flow calculation based on power flow tracing adopts the proportional allocation principle to allocate the carbon emission of the power generation side along the power flow path to each load node. For node i, its carbon emission intensity is equal to the weighted average of all incoming power: the incoming power includes local unit power generation and power input from other nodes, and the weight is the proportion of each power in the total incoming power. The carbon emission intensity of each node is calculated by iteration until it converges, and the convergence criterion is that the maximum deviation of adjacent two iterations is less than 0.001 kgCO2 / kWh.

[0091] The spatial and temporal distribution characteristics of carbon emissions are obtained through statistical analysis. In the time dimension, the carbon emissions within a day show a "double peak" feature, with the peaks at 10-12 am and 19-21 pm, and the trough at 2-5 am. In the seasonal dimension, the carbon emissions are higher in summer and winter due to air conditioning and heating loads. In the spatial dimension, the carbon emission intensity is higher in industrial areas and business centers, reaching 0.8-0.9 kgCO2 / kWh, and relatively lower in residential areas, about 0.6-0.7 kgCO2 / kWh. Through GIS technology, the carbon emission intensity is mapped to the geographical space to generate a carbon emission heat map, which intuitively shows the high carbon emission areas.

[0092] D2: Comprehensive evaluation of collaborative planning schemes through multi-dimensional evaluation indicators includes, from the economic dimension, calculating investment cost, operation cost, carbon trading income and degree of electricity cost; from the reliability dimension, evaluating power supply reliability, new energy utilization rate, load loss expectation and system flexibility indicators; from the environmental protection dimension, statistics carbon emission total, carbon emission intensity, sulfur dioxide and nitrogen oxides emission reduction; using analytic hierarchy process to determine the weight of each indicator, calculating the comprehensive score and generating radar chart.

[0093] Specifically, the multi-dimensional evaluation index system includes economic, reliability and environmental protection. Economic indicators include: total investment cost, life cycle investment calculated in present value; levelized cost of electricity (LCOE), considering the whole cost of investment, operation, fuel, etc.; internal rate of return (IRR), reflecting the profitability of the project; investment recovery period, divided into static and dynamic. Reliability indicators include: power adequacy, meeting the power supply capacity under N-1 and N-2 failure; new energy consumption rate, the actual consumption capacity accounts for the proportion of the available capacity; power supply reliability, the proportion of normal power supply time in a year, the target value is more than 99.9%; system regulation ability, the ability to respond to net load fluctuation. Environmental protection indicators include: carbon emission total and intensity, carbon emission per unit of GDP or unit of electricity; pollutant emission, SO2, NOx, dust and other conventional pollutants; land occupation, land area per unit of installed capacity; water resource consumption, cooling water demand of thermal power units.

[0094] The weight of the evaluation index is determined by the analytic hierarchy process (AHP), and the judgment matrix is constructed for pairwise comparison. The relative importance of each indicator is determined by expert scoring, and the weights of economic, reliability and environmental protection are 0.4, 0.35 and 0.25 respectively. The maximum eigenvalue and eigenvector of the judgment matrix are calculated, and the consistency test is conducted to ensure that CR<0.1. The comprehensive score adopts linear weighting method, which standardizes each indicator to 0-1 interval, multiplies the corresponding weight and sums up.

[0095] Further, the generation of the decision support report includes both a text report and a visual display. The text report uses a standard template, including chapters such as planning scheme overview, technical and economic analysis, environmental impact assessment, risk assessment, and conclusion and recommendations. The data tables and charts in the report are automatically generated using Python's matplotlib and seaborn libraries, ensuring data consistency and standardization. The visual display uses an interactive dashboard, allowing users to customize the viewing of indicators in different dimensions, compare schemes, and conduct sensitivity analysis.

[0096] The key achievements include: a power supply structure pie chart showing the installed capacity proportion of different types of power; a power generation time series curve showing the output changes of various types of power throughout the year; a carbon emission distribution heat map identifying high-carbon emission areas; an economic indicator radar chart showing the performance of the scheme in multiple dimensions; and a sensitivity analysis chart showing the impact of changes in key parameters on the results. Through these visualization methods, decision-makers can intuitively understand the characteristics and advantages and disadvantages of the planning scheme, providing a scientific basis for the final decision.

[0097] In an alternative embodiment, the carbon emission assessment in step S400 can also introduce the impact analysis of carbon capture and storage (CCS) technology. For a thermal power unit equipped with CCS, the carbon capture rate can reach 85% to 90%, but it will cause a decrease in unit efficiency of 8 to 12 percentage points and an increase in investment cost of 60% to 80%. In the optimization model, CCS is considered as a carbon reduction option to evaluate its technical and economic feasibility. When the carbon price exceeds 300 yuan / tCO2, the thermal power unit equipped with CCS has economic competitiveness. The application boundary conditions of CCS technology are determined through sensitivity analysis.

[0098] In another alternative embodiment, the comprehensive assessment in step S400 can also use multi-criteria decision-making methods (MCDM) such as the TOPSIS method or the ELECTRE method. These methods can handle compensatory and non-compensatory relationships between indicators, resulting in a more reasonable ranking of schemes. For example, the TOPSIS method calculates the relative closeness to the ideal solution and negative ideal solution, and the greater the closeness, the better the scheme. This method avoids the information loss that may be caused by simple linear weighting.

[0099] It should be noted that the above carbon emission assessment method realizes the tracking of carbon flow from the source to the load, accurately quantifying the carbon emission characteristics of the power system. The multi-dimensional evaluation system comprehensively reflects the comprehensive performance of the planning scheme, providing a scientific basis for decision-making. Visualization techniques improve the readability and understandability of the results, facilitating communication and exchange among all parties.

[0100] In summary, at the data level, by establishing a hybrid storage architecture, structured data is stored in a relational database, time series data is stored in a time series database, and unstructured data is stored in an object storage, the advantages of different storage technologies are leveraged, and data access efficiency and system response speed are improved.

[0101] At the business logic level, the decoupling of functional modules is achieved through a microservice architecture, each service can be independently deployed and upgraded, and the maintainability and scalability of the system are improved. The accurate extraction of various regulating power operating characteristic parameters, considering the efficiency changes and start-stop time differences of the unit under different load rates, makes the optimization model closer to the actual operation. The multi-objective optimization function can adapt to different planning requirements and policy orientations through flexible configuration of weight coefficients.

[0102] In terms of operation simulation, by constructing a scenario set combining typical scenarios and extreme scenarios, the running characteristics of the system under various operating conditions are tested. The time series production simulation considers various constraint conditions of the unit to reflect the operation process of the system. The hierarchical and graded design of the active support strategy leverages the technical characteristics of different types of regulating resources.

[0103] In terms of carbon emission assessment, the established carbon emission factor database considers the differences in coal types and load rates, improving the accuracy of carbon emission calculation. The carbon emission flow calculation method based on power flow tracing realizes the spatial distribution of carbon emissions, identifies high-carbon emission nodes, and provides a basis for developing emission reduction measures. The multi-dimensional evaluation index system and visualization display technology make the evaluation results more comprehensive and intuitive, facilitating decision-makers to understand and use them.

[0104] Embodiment 3

[0105] The above is a schematic scheme of a carbon emission evaluation method for multi-type regulating power and new energy collaborative planning. It should be noted that the technical scheme of the carbon emission evaluation system for multi-type regulating power and new energy collaborative planning is the same as the technical scheme of the carbon emission evaluation method for multi-type regulating power and new energy collaborative planning described above. The technical scheme of the carbon emission evaluation system for multi-type regulating power and new energy collaborative planning in this embodiment is not described in detail, and can be referred to the description of the technical scheme of the carbon emission evaluation method for multi-type regulating power and new energy collaborative planning.

[0106] The embodiment also provides a carbon emission evaluation system for multi-type regulating power and new energy collaborative planning, comprising:

[0107] A data acquisition module acquires power system equipment parameters and constructs a hierarchical planning platform architecture, and establishes a parameterized model library through data collection and management;

[0108] The construction module extracts operation characteristic parameters of thermal power, hydropower, pumped storage and energy storage regulating power sources based on a parameterized model library, constructs a coordinated optimization objective function of multiple types of regulating power sources and new energy in combination with new energy output characteristic data, and generates a preliminary coordinated planning scheme;

[0109] The data generation module formulates an active support strategy of regulating power sources and energy storage according to power configuration results in the preliminary coordinated planning scheme, determines dispatching plans of various power sources in different time periods, generates an energy management and control instruction sequence, constructs typical operation scenarios and extreme weather scenarios based on the energy management and control instruction sequence, calculates system operation states in each scenario through a coordinated operation simulation algorithm, and obtains power output data and power flow distribution results;

[0110] The result output module calculates node carbon emission intensity and system carbon emission total amount through a power flow tracking method according to power output data obtained through operation simulation, in combination with carbon emission factors of various types of power sources, generates carbon emission spatial and temporal distribution, and generates a visual decision support report through multi-dimensional evaluation indexes for comprehensive evaluation of the coordinated planning scheme based on carbon emission distribution results and operation state data.

[0111] The embodiment also provides an electronic device suitable for carbon emission evaluation of coordinated planning of multiple types of regulating power sources and new energy, which comprises a memory and a processor.

[0112] The embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to implement the carbon emission evaluation method of coordinated planning of multiple types of regulating power sources and new energy.

[0113] The storage medium proposed in the embodiment and the carbon emission evaluation method of coordinated planning of multiple types of regulating power sources and new energy proposed in the above embodiment belong to the same inventive concept, and technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0114] Those skilled in the art can clearly understand the present application by the above description of the embodiments, and the present application can be realized by software and necessary general hardware, and of course, can also be realized by hardware. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A carbon emission assessment method for coordinated planning of multiple types of regulating power sources and new energy sources, characterized in that: This includes acquiring power system equipment parameters and constructing a hierarchical planning platform architecture, and establishing a parameterized model library through data collection and governance; Based on the parametric model library, the operating characteristic parameters of thermal power, hydropower, pumped storage and energy storage regulating power sources are extracted. Combined with the output characteristic data of new energy sources, a collaborative optimization objective function of multiple types of regulating power sources and new energy sources is constructed to generate a preliminary collaborative planning scheme. Based on the power configuration results in the preliminary collaborative planning scheme, an active support strategy for regulating power supply and energy storage is formulated, the scheduling plan for various power sources at different times is determined, an energy management command sequence is generated, and based on the energy management command sequence, typical operating scenarios and extreme weather scenarios are constructed. The system operating status under each scenario is calculated through a collaborative operation simulation algorithm, and power output data and power flow distribution results are obtained. Based on the power output data obtained from the operation simulation, and combined with the carbon emission factors of various types of power sources, the carbon emission intensity of nodes and the total carbon emission of the system are calculated using the power flow tracing method. The spatiotemporal distribution of carbon emissions is generated. Based on the carbon emission distribution results and operation status data, the collaborative planning scheme is comprehensively evaluated through multi-dimensional evaluation indicators, and a visualized decision support report is generated.

2. The carbon emission assessment method for the coordinated planning of multiple types of regulating power sources and new energy sources as described in claim 1, characterized in that: Power system equipment parameters include equipment parameters, topology, operating scenarios, resource endowment data, and collaborative planning parameters. The hierarchical planning platform architecture includes a data layer, a business layer, and an auxiliary layer. The data layer collects equipment parameters, topology, operating scenarios, resource endowment data, and collaborative planning parameters, and establishes a unified control database to achieve data interaction. The business layer integrates basic model management, proactive support for energy control, system operation simulation, carbon emission assessment, planning assessment and analysis, and comprehensive solution display functions. In the auxiliary layer, a human-computer interaction interface is designed based on a component-based front-end development framework, and a highly visual rendering engine is developed to display data.

3. The carbon emission assessment method for the coordinated planning of multiple types of regulating power sources and new energy sources as described in claim 2, characterized in that: Based on a parametric model library, operational characteristic parameters of thermal power, hydropower, pumped storage, and energy storage regulating power sources are extracted. These parameters include: identifying the minimum technical output, ramp rate, and start-up / shutdown time constraints of thermal power units; determining the water level and reservoir capacity constraints, outflow limits, and regulation capabilities of hydropower stations; analyzing the charging and discharging efficiency, reservoir capacity limits, and response time of pumped storage power stations; and evaluating the charging and discharging power, capacity configuration, and cycle life characteristics of energy storage systems.

4. The carbon emission assessment method for the coordinated planning of multiple types of regulating power sources and new energy sources as described in claim 3, characterized in that: Developing proactive support strategies for regulating power sources and energy storage includes calculating the support demand levels for different time periods based on the predicted output and load demand curves of new energy sources. Based on the system inertia level and frequency regulation requirements, the primary and secondary frequency regulation parameters of the regulating power supply are determined; through dynamic reactive power compensation demand analysis, the voltage support strategy of the energy storage system is configured; and by considering power balance constraints and reserve capacity requirements, a 24-hour energy dispatch plan is generated.

5. The carbon emission assessment method for the coordinated planning of multiple types of regulating power sources and new energy sources as described in claim 4, characterized in that: The system operation status under various scenarios is calculated through collaborative operation simulation algorithms. This includes constructing typical daily operation scenarios for spring, summer, autumn and winter based on historical meteorological data and the characteristics of new energy output; establishing extreme scenario boundaries for sudden drops and increases in new energy output under extreme weather conditions such as typhoons, freezing and drought; and calculating the system power balance and new energy absorption rate through 8760 hours of time-series production simulation, as well as obtaining line load rate and node voltage distribution.

6. The carbon emission assessment method for the coordinated planning of multiple types of regulating power sources and new energy sources as described in claim 5, characterized in that: The calculation of node carbon emission intensity and total system carbon emissions using the power flow tracing method includes: establishing a database of carbon emission factors for thermal power units under different coal types and load rates; and calculating the carbon emission intensity of each node based on power flow calculation results and transmission paths using the proportional allocation method. The total daily, monthly, and annual carbon emissions are calculated cumulatively over time periods. A heat map of regional carbon emission distribution is generated based on geographic information and administrative divisions.

7. The carbon emission assessment method for the coordinated planning of multiple types of regulating power sources and new energy sources as described in claim 6, characterized in that: The collaborative planning scheme is comprehensively evaluated through multi-dimensional assessment indicators, including: calculating investment costs, operating costs, carbon trading revenue, and cost per kilowatt-hour from an economic perspective; assessing power supply reliability, renewable energy utilization rate, expected load loss, and system flexibility from a reliability perspective; and calculating total carbon emissions, carbon emission intensity, and reductions in sulfur dioxide and nitrogen oxides from an environmental perspective. The weight of each indicator is determined using the analytic hierarchy process (AHP), a comprehensive score is calculated, and a radar chart is generated for display.

8. A carbon emission assessment system for the coordinated planning of multiple types of regulating power sources and new energy sources, based on the carbon emission assessment method for the coordinated planning of multiple types of regulating power sources and new energy sources according to any one of claims 1 to 7, characterized in that: It also includes a data acquisition module, which acquires power system equipment parameters and builds a hierarchical planning platform architecture, and establishes a parameterized model library through data collection and governance; The module is constructed by extracting the operating characteristic parameters of thermal power, hydropower, pumped storage and energy storage regulating power sources based on the parametric model library, and combining the output characteristic data of new energy sources to construct the synergistic optimization objective function of multiple types of regulating power sources and new energy sources, and generate a preliminary synergistic planning scheme. The data generation module formulates active support strategies for regulating power supply and energy storage based on the power configuration results in the preliminary collaborative planning scheme, determines the scheduling plans for various power sources at different times, generates energy management command sequences, constructs typical operating scenarios and extreme weather scenarios based on the energy management command sequences, calculates the system operating status under each scenario through collaborative operation simulation algorithms, and obtains power output data and power flow distribution results. The results output module calculates the node carbon emission intensity and the total system carbon emission using the power output data obtained from the simulation, combined with the carbon emission factors of various types of power sources, and generates the spatiotemporal distribution of carbon emissions. Based on the carbon emission distribution results and operating status data, it comprehensively evaluates the collaborative planning scheme through multi-dimensional evaluation indicators and generates a visualized decision support report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the carbon emission assessment method of the multi-type regulating power source and new energy synergistic planning according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the carbon emission assessment method for the coordinated planning of multiple types of regulating power sources and new energy sources as claimed in any one of claims 1 to 7.

Images