A life cycle assessment-based power grid equipment cost quantification modeling method

Abstract

The application discloses a kind of life cycle assessment-based power grid equipment cost quantification modeling method, it is related to power grid equipment related technical field, the method includes: obtaining the input data and output data of target power grid equipment in whole life cycle, structured modeling is carried out, and the whole life cycle inventory database is obtained;The whole life cycle cost function of target power grid equipment is constructed;The entry in the whole life cycle inventory database is corresponded with evaluation factor, and the factor corresponding result is obtained, and each item in it is weighted calculation according to corresponding evaluation factor, and the weighted sum of environmental impact index is obtained;The whole life cycle cost function and the weighted sum of environmental impact index are used as comprehensive objective function, and the whole life cycle cost quantification model of target power grid equipment is constructed;Pareto optimal solution set is obtained after comprehensive solution.The application realizes the systematization quantification of power grid equipment whole life cycle cost, and economic and environmental factors are simultaneously included in decision-making process.

Description

Technical Field

[0001] This application relates to the technical field of power grid equipment, and in particular to a method for quantitative modeling of power grid equipment costs based on life cycle assessment. Background Technology

[0002] With economic development and population growth, global electricity demand continues to rise. Simultaneously, under the policy trend of "phasing out coal and reducing carbon emissions," the power grid must continuously expand and accelerate the integration of renewable energy. Traditional power planning often aims to minimize investment and operating costs or minimize unsupplied electricity, failing to fully reflect environmental costs. In fact, any power generation technology can adversely affect air quality, water resources, material consumption, and human health throughout its entire life cycle; therefore, incorporating sustainability assessments into power grid expansion planning is of great significance.

[0003] However, in the context of power grid expansion and low-carbon transformation, traditional methods that focus solely on economic efficiency or carbon emissions are no longer sufficient to meet the demands. Summary of the Invention

[0004] The purpose of this application is to provide a method for quantitative modeling of power grid equipment costs based on life cycle assessment, which can simultaneously consider multiple factors such as environmental impact, resource consumption and health effects.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for quantitative modeling of power grid equipment costs based on life cycle assessment, including: Acquire input and output data of the target power grid equipment throughout its entire life cycle; the input data includes resource and energy input data at each stage of the life cycle; the output data includes environmental and economic output data at each stage of the life cycle. After classifying the input and output data according to their life cycle stages, structured modeling is performed to obtain a full life cycle inventory database; the full life cycle inventory database includes: resource inventory, energy inventory, emissions inventory, and economic inventory; Based on the full lifecycle inventory database, construct the full lifecycle cost function of the target power grid equipment; The entries in the full life cycle inventory database are matched with evaluation factors to obtain factor matching results; the evaluation factors include: environmental impact assessment factors and resource consumption factors. Each item in the results corresponding to the aforementioned factors is weighted according to the corresponding evaluation factors to obtain the weighted sum of environmental impact indicators; The weighted sum of the life-cycle cost function and the environmental impact indicators is used as the comprehensive objective function to construct a quantitative model of the life-cycle cost of the target power grid equipment. The Pareto optimal solution set is obtained by comprehensively solving the life cycle cost quantification model using a multi-objective weighted summation method, an ε-constraint method, or a multi-objective evolutionary algorithm. The Pareto optimal solution set includes the trade-off between total life cycle cost and environmental impact indicators.

[0006] Optionally, the input data includes: raw material costs, equipment manufacturing costs, equipment transportation and installation costs, operation and maintenance costs, and scrapping and recycling costs; The raw material cost includes: the type, quantity, and procurement cost of the raw materials; the raw materials include: steel, copper, aluminum, silicon steel sheets, insulating materials, rubber, plastics, and composite coatings; the raw materials are used to manufacture transformer cores, transformer shells, conductors, windings, cables, busbars, insulating components, seals, anti-corrosion coatings, and protective layers; The cost of the equipment manufacturing stage includes: processing technology, energy consumption, and manufacturing costs; The costs of the equipment transportation and installation phase include: transportation distance, transportation method, transportation energy consumption, and the labor and auxiliary material costs required for on-site installation; The costs of the operation and maintenance phase include: energy consumption during operation, regular maintenance, and replacement component costs; The costs of the end-of-life recycling phase include the costs of dismantling, recycling, and waste disposal when the equipment reaches the end of its lifespan.

[0007] Optionally, after classifying the input data and output data according to their lifecycle stages, structured modeling is performed to obtain a full lifecycle inventory database, specifically including: The input and output data are categorized according to their lifecycle stages, resulting in a unified list of items. The lifecycle stages include, in sequence: raw material acquisition, equipment manufacturing, transportation and installation, operation and maintenance, and end-of-life recycling. Quantitative modeling is performed on the resource and energy input data at each stage of the life cycle of the unified list project to obtain input modeling results; Quantitative modeling is performed on the environmental and economic output data of each stage of the life cycle in the unified list of projects to obtain output modeling results; Based on the input modeling results and the output modeling results, a full life cycle inventory database is obtained; the full life cycle inventory database includes: resource inventory, energy inventory, emission inventory and economic inventory for each stage.

[0008] Optionally, the objective function of the life-cycle cost quantification model for the target power grid equipment is: ; Where F represents the objective function;C total The total life-cycle cost function; E env This is the weighted sum of environmental impact indicators; α and β are the weighting coefficients. The constraints of the life-cycle cost quantification model for the target power grid equipment include: technical constraints, cost constraints, and environmental constraints.

[0009] Optionally, the life-cycle cost function of the target power grid equipment is: ; in, C total Total cost; C raw Cost of acquiring raw materials; C manu For manufacturing costs; C trans For transportation and installation costs; C op Energy consumption cost during operation; C maint To cover maintenance, repair, and parts replacement costs; C end The disposal costs during the end-of-life and recycling phases minus residual value gains.

[0010] Optionally, the evaluation factors include: global warming potential factor, acidification potential factor, eutrophication potential factor, particulate matter emission factor, human health damage factor, ecosystem damage factor, and resource depletion factor.

[0011] Optionally, the Pareto optimal solution set includes: Pareto optimal solutions under several weight combinations; After obtaining the Pareto optimal solution set, the following steps are also included: The cost distribution, key influencing factors, and optimization results at each stage are presented in the form of data tables. The changing trends of life cycle cost and environmental impact under various schemes are presented using comparative curves. Generate visualization charts, including: a pie chart of stage cost distribution, a bar chart of stage comparison, and a Pareto front dissipation point plot; the Pareto front solution is used to show the distribution of the optimal solution set in the target space.

[0012] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method for quantitative modeling of power grid equipment costs based on life cycle assessment. The method includes: acquiring input and output data of the target power grid equipment throughout its entire life cycle; the input data comprises resource and energy input data at each stage of the life cycle; the output data includes environmental and economic output data at each stage of the life cycle; classifying the input and output data according to life cycle stages and performing structured modeling to obtain a full life cycle inventory database; the full life cycle inventory database includes resource inventory, energy inventory, emission inventory, and economic inventory; constructing a full life cycle cost function for the target power grid equipment based on the full life cycle inventory database; and quantifying the full life cycle inventory data... The entries in a single database are matched with evaluation factors to obtain factor correspondence results. These evaluation factors include environmental impact assessment factors and resource consumption factors. Each item in the factor correspondence results is weighted according to its corresponding evaluation factor to obtain a weighted sum of environmental impact indicators. The weighted sum of the life-cycle cost function and the environmental impact indicators is used as a comprehensive objective function to construct a quantitative model of the life-cycle cost of the target power grid equipment. A multi-objective weighted summation method, ε-constraint method, or multi-objective evolutionary algorithm is used to comprehensively solve the quantitative model of the life-cycle cost, obtaining a Pareto optimal solution set. The Pareto optimal solution set includes the trade-off between total life-cycle cost and environmental impact indicators. This application obtains input and output data of the target power grid equipment throughout its entire life cycle, and after structural modeling, introduces environmental impact assessment factors and resource consumption factors for weighted calculation. Finally, a quantitative model of comprehensive cost and environmental impact is constructed, which realizes the systematic quantification of the life-cycle cost of power grid equipment and incorporates both economic and environmental factors into the decision-making process, providing power grid companies with a quantitative and operable basis for planning and cost management. By deeply coupling the results of multiple indicators of LCA with the power grid expansion plan, the expansion scheme can simultaneously consider multiple factors such as environmental impact, resource consumption and health effects during the design and selection stages, thereby achieving coordination and unity between sustainability goals and engineering feasibility. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is an application environment diagram of a power grid equipment cost quantification modeling method based on life cycle assessment in one embodiment of this application.

[0015] Figure 2This is a flowchart illustrating a method for quantitative modeling of power grid equipment costs based on life cycle assessment, provided as an embodiment of this application.

[0016] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Life cycle assessment (LCA) is currently the most widely used sustainability analysis method. Through a standardized process, it evaluates the entire process of a system, from raw material acquisition, manufacturing, operation to final disposal, quantifying the environmental impact at different stages. Introducing LCA into power grid expansion studies allows for the simultaneous consideration of multiple indicators, including greenhouse gas emissions, acidification, eutrophication, particulate matter emissions, toxic effects, and resource consumption, thus avoiding a one-sided focus on carbon emissions while ignoring other environmental factors.

[0019] However, existing research and practice still have some shortcomings: (1) Most studies only focus on greenhouse gases, and do not adequately cover other key impact categories; (2) Transmission and distribution links often only consider line losses, while ignoring the upstream environmental burden caused by line construction, substation equipment, etc.; (3) Environmental impacts have significant temporal and spatial differences. If only the annual average power structure is used, it may deviate significantly from the actual marginal power situation, especially in high-proportion renewable energy power systems; (4) Differences in system boundary setting and data quality can bring uncertainty. These problems lead to a systematic underestimation of the environmental costs of capacity expansion schemes, thereby affecting the scientific nature and reliability of decision-making.

[0020] At the same time, it's important to recognize that renewable energy is not "zero-impact." Wind and solar power require significant amounts of materials and energy during construction, and because their resources are often located far from load centers, they also drive the construction of additional transmission infrastructure. The metal consumption and eutrophication of water bodies caused by high-voltage lines, submarine cables, and transformer equipment cannot be ignored. Energy losses in distribution networks are typically higher than in transmission networks, and in some cases, their emissions contribution is greater. Furthermore, electrochemical energy storage systems configured to balance wind and solar power fluctuations also create environmental pressures in terms of toxicity and resource depletion.

[0021] In conclusion, against the backdrop of power grid expansion and low-carbon transformation, traditional methods that solely focus on economic efficiency or carbon emissions are no longer sufficient. There is an urgent need for a method that deeply couples LCA (Limited Capacity Analysis) results with power grid expansion planning, enabling expansion schemes to simultaneously consider multi-dimensional factors such as environmental impact, resource consumption, and health effects during the design and selection phases, thereby achieving a harmonious balance between sustainability goals and engineering feasibility.

[0022] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] The power grid equipment cost quantification modeling method based on life cycle assessment provided in this application can be applied to, for example... Figure 1 The application environment shown is illustrated. Terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server.

[0024] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0025] In one exemplary embodiment, such as Figure 2 As shown, a method for quantitative modeling of power grid equipment costs based on life cycle assessment is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps S1 to S7. Wherein: S1. Obtain input and output data of the target power grid equipment throughout its entire life cycle; the input data includes resource and energy input data at each stage of the life cycle; the output data includes environmental and economic output data at each stage of the life cycle.

[0026] In this embodiment, lifecycle data of power grid equipment is collected: The computer's data acquisition module acquires input and output data of the target power grid equipment throughout its entire life cycle, including but not limited to: (1) Data on raw material acquisition: including the types, quantities, and procurement costs of steel, copper, aluminum, silicon steel sheets, insulating materials, rubber, plastics, composite coatings, etc. These raw materials are used to manufacture transformer cores and shells (steel, silicon steel sheets), conductors and windings (copper, aluminum), cables and busbars (copper, aluminum), insulating components and seals (insulating materials, rubber, plastics), and anti-corrosion coatings and protective layers (composite coatings).

[0027] (2) Data on the equipment manufacturing stage: including processing technology, energy consumption and manufacturing costs. For example, energy use and labor costs in the process of transformer winding processing, core assembly, circuit breaker and switch cabinet machining and insulation filling, wire drawing and stranding of transmission conductors, welding and anti-corrosion treatment of tower components.

[0028] (3) Data for equipment transportation and installation: including transportation distance, transportation mode, transportation energy consumption and cost, and labor and auxiliary material costs required for on-site installation. Typical equipment such as large transformers and switchgear require dedicated transportation and hoisting, while poles and cables need to be transported in sections and assembled on-site.

[0029] (4) Data during operation and maintenance: including energy consumption, regular maintenance, replacement of parts and related costs during operation. For example, transformers need to have their insulating oil replaced and windings inspected regularly, switchgear needs to have its arc-extinguishing chamber or contacts inspected and replaced, conductors need to be inspected regularly to prevent corrosion or mechanical damage, and towers need to be protected against rust and have their foundations reinforced.

[0030] (5) Data on the end-of-life recycling stage: This includes dismantling, recycling, and waste disposal at the end of the equipment's lifespan. Metal components such as steel, copper, and aluminum can be dismantled, recycled, and reused; insulating oil, rubber, and plastics require harmless treatment due to aging or performance degradation; some coatings and composite materials cannot be recycled and require safe disposal. The reasons for equipment scrapping include performance degradation (such as reduced transformer insulation levels), safety hazards (such as decreased switching capacity of switchgear), and structural aging (such as conductor corrosion and tower rust). Relevant data includes dismantling costs, residual value, and waste disposal costs.

[0031] Output value: Input dataset for the entire life cycle of power grid equipment.

[0032] S2. After classifying the input data and output data according to the life cycle stage, perform structured modeling to obtain a full life cycle inventory database; the full life cycle inventory database includes: resource inventory, energy inventory, emission inventory and economic inventory.

[0033] In this embodiment, the collected full lifecycle data of power grid equipment is input into the Life Cycle Inventory (LCI) module to perform structured modeling of the inputs and outputs at each stage. The specific process includes: (1) Data classification and aggregation: Input data is categorized according to lifecycle stages (raw material acquisition, equipment manufacturing, transportation and installation, operation and maintenance, and end-of-life recycling), and each type of data is transformed into a unified list of items. For example, raw materials such as steel, copper, and aluminum are summarized by quality and unit cost; electricity and fuel consumption during the manufacturing stage are calculated by quantity and energy value; and the number of replacement parts and corresponding energy costs during the operation and maintenance stage are recorded uniformly.

[0034] (2) Input modeling: Quantitative modeling of resource and energy inputs at each stage of the life cycle is performed. Specifically, this includes: ① Raw material input (quality and cost of steel, copper, aluminum, insulation materials, etc.); ② Energy input (electricity, fuel consumption) and man-hours during the manufacturing stage; ③ Transportation energy consumption (fuel, electricity) and auxiliary material consumption during the transportation and installation phase; ④ Energy consumption, maintenance consumables, and replacement parts input during the operation phase.

[0035] (3) Output modeling: Quantitative modeling of environmental and economic outputs at each stage of the life cycle is conducted. Specifically, this includes: ① Greenhouse gas emissions, wastewater discharge, and solid waste during manufacturing and operation; ② Quantification of the power transmission / distribution efficiency provided by the equipment during the operation phase (such as the total amount of transmitted power and power supply reliability indicators); ③ The amount of metal recovered, the residual value of the recovered metal, and the cost of disposing of non-recyclable waste during the end-of-life recycling stage.

[0036] (4) Generate list results: Through the above modeling, a complete lifecycle inventory database is obtained. Database entries include: ① Resource list for each stage (x tons of steel, y tons of copper, z tons of aluminum, etc.); ② Energy inventory for each stage (electricity consumption MWh, fuel consumption GJ); ③ Emission inventory for each stage (CO2 emissions (t), SO2 emissions (kg), wastewater volume (m³)) 3 Solid waste volume (t). ④ Economic list for each stage (procurement cost, manufacturing cost, transportation and installation cost, operation and maintenance cost, residual value, and waste disposal cost).

[0037] Output: A full life-cycle inventory database (containing structured results of four major categories of data: resources, energy, emissions, and economy), providing direct input for subsequent life cycle assessment (LCA) and life cycle cost analysis (LCC).

[0038] S3. Based on the full life cycle inventory database, construct the full life cycle cost function of the target power grid equipment.

[0039] In this embodiment, the computer's model calculation module calls the full lifecycle inventory database to construct the full lifecycle cost function of the power grid equipment.

[0040] Let the total cost function be: ; in, C total Total cost; C raw Cost of acquiring raw materials; C manu For manufacturing costs; C trans For transportation and installation costs; C op Energy consumption cost during operation; C maint To cover maintenance, repair, and parts replacement costs; C end The disposal costs during the end-of-life and recycling phases minus residual value gains.

[0041] Output value: Lifecycle cost function model S4. Match the entries in the full life cycle inventory database with the evaluation factors to obtain the factor matching results; the evaluation factors include: environmental impact assessment factors and resource consumption factors.

[0042] S5. For each item in the results corresponding to the factors, perform weighted calculation according to the corresponding evaluation factors to obtain the weighted sum of environmental impact indicators.

[0043] Specifically, this embodiment quantifies multi-dimensional impact indicators: Based on the established full life cycle inventory database, and combined with the standard methodology of Life Cycle Assessment (LCA), environmental impact assessment factors and resource consumption factors are introduced to quantify the environmental and economic impacts of target power grid equipment throughout its entire life cycle from multiple dimensions. (1) Factor introduction and matching: The input and emission items in the life cycle inventory are mapped to the corresponding evaluation factors, mainly including: ①Global Warming Potential (GWP): This converts emissions of carbon dioxide, methane, nitrous oxide, etc., into carbon dioxide equivalents.

[0044] ②Acidification Potential (AP): Converting emissions of sulfur dioxide, nitrogen oxides, ammonia, etc., into sulfur dioxide equivalents.

[0045] ③Eutrophication Potential (EP): Converts nitrogen and phosphorus emissions into phosphate equivalents.

[0046] ④ Particulate matter emission factor: Convert the emissions of PM10 and PM2.5 into particulate matter equivalents.

[0047] ⑤ Factors that damage human health: These are converted into disability-adjusted life years based on the emissions of toxic substances and particulate matter.

[0048] ⑥ Ecosystem damage factors: These are calculated as potential species loss rates based on environmental pressures such as land use, acidification, and eutrophication.

[0049] ⑦ Resource depletion factor: Converts the consumption of metals and fossil energy into a scarcity index or economic value loss.

[0050] (2) Calculation of indicators: For each input or emission in the inventory database, a weighted calculation is performed according to corresponding factors to obtain the contribution value of each environmental load. The calculation methods include: ①Global warming potential = Σ (greenhouse gas emissions × global warming potential factor).

[0051] ② Acidification potential = Σ (acidification substance emissions × acidification potential factor).

[0052] ③Eutrophication potential = Σ (nutrient emissions × eutrophication potential).

[0053] (3) Stage decomposition and aggregation: The evaluation results are calculated and summarized according to the life cycle stages (raw material acquisition, manufacturing, transportation and installation, operation and maintenance, and end-of-life recycling) to obtain the phased environmental impact values ​​and the total life cycle results, while identifying the main sources of environmental pressure.

[0054] (4) Result format and output: The final multi-indicator impact assessment results generated throughout the entire life cycle include: ①Indicator Data Table: Rows represent lifecycle stages, columns represent different factor indicators, and cells contain corresponding values.

[0055] ②Graphical results: bar charts, radar charts, etc., are used to compare the distribution of environmental impact at each stage or for each device.

[0056] ③ Structured database: The results are stored in the form of matrices or data tables, which facilitates subsequent coupling with economic analysis (LCC).

[0057] S6. The weighted sum of the full life cycle cost function and the environmental impact index is used as the comprehensive objective function to construct a quantitative model of the full life cycle cost of the target power grid equipment.

[0058] In this embodiment, a cost-impact coupled optimization model is established: The optimization calculation module calls the results of steps S3 and S5, using life cycle cost and environmental impact results as a comprehensive objective function or constraint to construct a quantitative model of the full life cycle cost of power grid equipment. Its optimization objective is expressed as: ; Where F represents the objective function; C total The total life-cycle cost function; E env This is a weighted sum of environmental impact indicators; α and β are weighting coefficients, set according to planning requirements.

[0059] The specific planning method is as follows: (1) Target setting: Determine the optimization focus based on application requirements. When focusing on economic benefits, increase the weight of coefficient α; when focusing on environmental benefits, increase the weight of coefficient β.

[0060] (2) Establishment of constraints: ① Technical constraints: The reliability, safety and power supply capacity of the equipment must meet the power grid operation standards.

[0061] ② Cost constraints: Investment costs and operation and maintenance expenses must not exceed the planned budget.

[0062] ③ Environmental constraints: Carbon emissions and pollutant emissions must meet relevant national and industry standards.

[0063] (3) Optimization solution method: The life cycle cost and environmental impact are comprehensively solved by using the multi-objective weighted summation method, ε-constraint method and multi-objective evolutionary algorithm to obtain the Pareto optimal solution set that satisfies the constraints; (4) Output format: Output the Pareto optimal solution set under different weight combinations, including the trade-off between total life cycle cost and environmental impact indicators. The results are presented in the form of data tables and comparison curves to support power grid equipment planning and investment decisions.

[0064] Output values: The power grid equipment lifecycle optimization model and its optimal solution set can provide quantitative decision support for equipment selection, investment planning, and operation and maintenance strategies under different cost and environmental impact trade-offs. S7. Using a multi-objective weighted summation method, ε-constraint method, or multi-objective evolutionary algorithm, the life-cycle cost quantification model is solved in a comprehensive manner to obtain the Pareto optimal solution set; the Pareto optimal solution set includes the trade-off relationship between total life-cycle cost and environmental impact indicators.

[0065] Specifically, in this embodiment, the optimization results are calculated and output as follows: The coupled optimization model in step S6 is run by the computer optimization module to obtain the cost quantification results for the entire life cycle of power grid equipment. The specific process includes: (1) Cost distribution calculation at each stage: Based on the life cycle inventory database, the costs of each stage are decomposed and summarized according to the stages of raw material acquisition, equipment manufacturing, transportation and installation, operation and maintenance, and scrapping and recycling, and the proportion and absolute value of each stage in the total cost are output.

[0066] (2) Identification of key influencing factors: Through sensitivity analysis and regression analysis, factors with a significant impact on life-cycle costs and environmental indicators are identified. The output includes data items such as factor name, sensitivity coefficient, and contribution rate, which are used to quantify the influence of each factor.

[0067] (3) Generation of overall optimization scheme: Under the set technical, cost and environmental constraints, the comprehensive solution set of the optimization model is calculated. The results present the Pareto optimal solution under different weight combinations, reflecting the quantitative relationship between life cycle cost and environmental impact, and forming an overall optimization scheme.

[0068] (4) Output format: ① Data Table: Displays cost distribution, key influencing factor data, and optimization results at each stage.

[0069] ② Comparison curves: show the changing trends of life cycle costs and environmental impacts under different schemes.

[0070] ③ Visual charts: including pie charts (cost distribution at each stage), bar charts (comparison between stages), and scatter plots (Pareto front solutions). The Pareto front solutions are used to show the distribution of the optimal solution set in the target space, intuitively reflecting the trade-off between life cycle costs and environmental impacts, and supporting equipment selection, planning, and investment decisions.

[0071] Compared with the prior art, this embodiment has the following effects: (1) Through full life cycle data collection and inventory modeling, the entire process of power grid equipment from raw material acquisition, manufacturing, transportation and installation, operation and maintenance to scrapping and recycling is covered, avoiding the limitations of traditional methods that only focus on initial investment or operation stage, and realizing complete and accurate cost assessment.

[0072] (2) By introducing life cycle assessment indicators into the model, this method not only quantifies economic costs, but also considers environmental impact factors such as global warming potential, acidification potential, eutrophication potential, particulate matter emission factor, and resource depletion factor, thus unifying economic efficiency and environmental sustainability in the same analytical framework.

[0073] (3) By establishing a coupled optimization model of cost and environmental impact, a comprehensive evaluation can be achieved under multi-objective constraints, so that the results can be balanced between economic rationality and environmental friendliness. The focus (economic benefits and environmental benefits) can be flexibly adjusted according to the setting of weight factors α and β to meet different application needs.

[0074] (4) The use of computer modular modeling and calculation is more efficient than manual estimation, and the results are more consistent and repeatable, making it easier to promote and apply in power grid equipment planning and decision-making.

[0075] In summary, this embodiment achieves the systematic quantification of the full life cycle cost of power grid equipment and incorporates both economic and environmental factors into the decision-making process, providing power grid companies with a quantitative and operable basis for planning and cost management.

[0076] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 3 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for quantitative modeling of power grid equipment costs based on life cycle assessment.

[0077] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0078] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0079] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0080] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0081] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0082] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0083] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0084] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for quantitative modeling of power grid equipment costs based on life cycle assessment, characterized in that, include: Acquire input and output data of the target power grid equipment throughout its entire lifecycle; The input data refers to resource and energy input data at each stage of the life cycle; the output data includes environmental and economic output data at each stage of the life cycle. After classifying the input and output data according to their life cycle stages, structured modeling is performed to obtain a full life cycle inventory database; the full life cycle inventory database includes: resource inventory, energy inventory, emissions inventory, and economic inventory; Based on the full lifecycle inventory database, construct the full lifecycle cost function of the target power grid equipment; The entries in the full life cycle inventory database are matched with evaluation factors to obtain factor matching results; the evaluation factors include: environmental impact assessment factors and resource consumption factors. Each item in the results corresponding to the aforementioned factors is weighted according to the corresponding evaluation factors to obtain the weighted sum of environmental impact indicators; The weighted sum of the life-cycle cost function and the environmental impact indicators is used as the comprehensive objective function to construct a quantitative model of the life-cycle cost of the target power grid equipment. The Pareto optimal solution set is obtained by comprehensively solving the life cycle cost quantification model using a multi-objective weighted summation method, an ε-constraint method, or a multi-objective evolutionary algorithm. The Pareto optimal solution set includes the trade-off between total life cycle cost and environmental impact indicators.

2. The method for quantitative modeling of power grid equipment costs based on life cycle assessment according to claim 1, characterized in that, The input data includes: raw material costs, equipment manufacturing costs, equipment transportation and installation costs, operation and maintenance costs, and scrapping and recycling costs. The raw material cost includes: the type, quantity, and procurement cost of the raw materials; the raw materials include: steel, copper, aluminum, silicon steel sheets, insulating materials, rubber, plastics, and composite coatings; the raw materials are used to manufacture transformer cores, transformer shells, conductors, windings, cables, busbars, insulating components, seals, anti-corrosion coatings, and protective layers; The cost of the equipment manufacturing stage includes: processing technology, energy consumption, and manufacturing costs; The costs of the equipment transportation and installation phase include: transportation distance, transportation method, transportation energy consumption, and the labor and auxiliary material costs required for on-site installation; The costs of the operation and maintenance phase include: energy consumption during operation, regular maintenance, and replacement component costs; The costs of the end-of-life recycling phase include the costs of dismantling, recycling, and waste disposal when the equipment reaches the end of its lifespan.

3. The method for quantitative modeling of power grid equipment costs based on life cycle assessment according to claim 1, characterized in that, After classifying the input and output data according to their lifecycle stages, structured modeling is performed to obtain a full lifecycle inventory database, which specifically includes: The input and output data are categorized according to their lifecycle stages, resulting in a unified list of items. The lifecycle stages include, in sequence: raw material acquisition, equipment manufacturing, transportation and installation, operation and maintenance, and end-of-life recycling. Quantitative modeling is performed on the resource and energy input data at each stage of the life cycle of the unified list project to obtain input modeling results; Quantitative modeling is performed on the environmental and economic output data of each stage of the life cycle in the unified list of projects to obtain output modeling results; Based on the input modeling results and the output modeling results, a full life cycle inventory database is obtained; the full life cycle inventory database includes: resource inventory, energy inventory, emission inventory and economic inventory for each stage.

4. The method for quantitative modeling of power grid equipment costs based on life cycle assessment according to claim 1, characterized in that, The objective function of the life-cycle cost quantification model for the target power grid equipment is: ； Where F represents the objective function; C total The total life-cycle cost function; E env This is the weighted sum of environmental impact indicators; α and β are the weighting coefficients. The constraints of the life-cycle cost quantification model for the target power grid equipment include: technical constraints, cost constraints, and environmental constraints.

5. The method for quantitative modeling of power grid equipment costs based on life cycle assessment according to claim 1, characterized in that, The life-cycle cost function of the target power grid equipment is: ； in, C total Total cost; C raw Cost of acquiring raw materials; C manu For manufacturing costs; C trans For transportation and installation costs; C op Energy consumption cost during operation; C maint To cover maintenance, repair, and parts replacement costs; C end The disposal costs during the end-of-life and recycling phases minus residual value gains.

6. The method for quantitative modeling of power grid equipment costs based on life cycle assessment according to claim 1, characterized in that, The evaluation factors include: global warming potential factor, acidification potential factor, eutrophication potential factor, particulate matter emission factor, human health damage factor, ecosystem damage factor, and resource depletion factor.

7. The method for quantitative modeling of power grid equipment costs based on life cycle assessment according to claim 1, characterized in that, The Pareto optimal solution set includes: Pareto optimal solutions under several weight combinations; After obtaining the Pareto optimal solution set, the following steps are also included: The cost distribution, key influencing factors, and optimization results at each stage are presented in the form of data tables. The changing trends of life cycle cost and environmental impact under various schemes are presented using comparative curves. Generate visualization charts, including: a pie chart of stage cost distribution, a bar chart of stage comparison, and a Pareto front dissipation point plot; the Pareto front solution is used to show the distribution of the optimal solution set in the target space.

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