An energy investment project economy and boundary condition linkage intelligent simulation prediction method

Abstract

This invention discloses an intelligent simulation prediction method linking economic performance and boundary conditions for energy investment projects. It relates to the field of economic evaluation technology for energy investment projects and aims to address the problems of existing static financial models being unable to respond to dynamic changes in boundary conditions, insufficient coverage of multiple scenarios, and weak decision support capabilities. This method collects and classifies multi-dimensional dynamic boundary conditions to establish a dynamically updated boundary condition library; constructs a nonlinear linkage model between economic indicators and boundary conditions, embedding a dynamic iterative calculation algorithm; performs simulation tests on multiple preset scenarios, generates sensitivity analysis curves, identifies key influencing factors, and finally outputs the optimal investment range and risk warnings. This invention achieves real-time linkage simulation of boundary conditions and economic indicators, comprehensively covers different market scenarios, significantly improves the accuracy of economic prediction and decision support capabilities, and can provide reliable technical support for the economic evaluation of energy investment projects throughout their entire life cycle.

Description

Technical Field

[0001] This invention relates to the field of economic evaluation technology for energy investment projects, specifically to an intelligent simulation prediction method linking the economic viability and boundary conditions of energy investment projects. Background Technology

[0002] Driven by the "dual-carbon" strategy, my country's investment in new energy sources such as wind power, photovoltaics, and energy storage continues to expand, and the economic evaluation of energy investment projects has become a core basis for investment decisions. Energy investment projects are characterized by long investment cycles, large capital scale, and complex market environments, and their economic performance is significantly affected by various dynamic boundary conditions such as electricity price fluctuations, utilization hours, subsidy policies, financing interest rates, and curtailment rates.

[0003] In existing technologies, the economic evaluation of energy investment projects mainly adopts static financial models, which can only perform a single calculation based on the boundary conditions at a fixed point in time, and has the following obvious drawbacks: Unable to respond to dynamic changes in boundary conditions: Static models cannot track fluctuations in boundary conditions such as electricity prices and policies in real time. When the market environment changes, parameters need to be manually re-entered for calculation, which is inefficient and has poor timeliness. Insufficient coverage of multiple scenarios: Traditional evaluations often only calculate economic indicators under benchmark scenarios, making it difficult to fully simulate project performance under optimistic, pessimistic, and extreme conditions, resulting in insufficient investment risk assessment; Weak decision support capabilities: It cannot automatically identify key factors affecting economic performance, it is difficult to quantify the impact of different boundary condition changes on project returns, and it cannot provide investors with a reference for the optimal investment range. Lack of full lifecycle tracking: The project lacks a dynamic tracking and early warning mechanism after it is put into operation, making it impossible to detect the risk of deviation in returns caused by changes in boundary conditions in a timely manner.

[0004] Therefore, there is an urgent need to develop an intelligent simulation prediction technology that can achieve real-time linkage between boundary conditions and economic indicators, support multi-scenario simulation, and provide full life-cycle decision support in order to solve the above-mentioned problems of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an intelligent simulation prediction method that links the economic performance and boundary conditions of energy investment projects. This method constructs a nonlinear linkage model between boundary conditions and economic indicators, realizes dynamic simulation prediction in multiple scenarios, automatically identifies key influencing factors and outputs the optimal investment range, and provides comprehensive, accurate and real-time technical support for energy investment decisions.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A smart simulation prediction method linking the economics and boundary conditions of energy investment projects includes the following steps: S1: Boundary Condition Collection and Classification. Data on electricity price fluctuations, project utilization hours, policy subsidies, financing interest rates, curtailment rates, equipment operation and maintenance costs, and raw material prices for energy investment projects are collected through multiple interfaces. These boundary conditions are categorized into controllable boundary conditions (financing interest rate, construction period, operation and maintenance costs) and uncontrollable boundary conditions (electricity price fluctuations, utilization hours, policy subsidies, curtailment rates). A dynamic update library for boundary conditions is established, supporting automatic daily / weekly / monthly data collection and updates, as well as manual correction.

[0007] S2: Construction of the Linked Simulation Model. Based on historical energy investment project data, a nonlinear linkage model is generated using a multivariate nonlinear regression algorithm to establish the relationship between core economic indicators such as internal rate of return (IRR), net present value (NPV), static payback period, dynamic payback period, and cost per kilowatt-hour and various boundary conditions. A dynamic iterative calculation algorithm is embedded, and a threshold for triggering changes in boundary conditions is set. When the change in any boundary condition exceeds the preset threshold, the model is automatically triggered to recalculate all economic indicators.

[0008] S3: Multi-scenario simulation testing. Four typical simulation scenarios are preset: a baseline scenario, an optimistic scenario, a pessimistic scenario, and an extreme operating condition scenario. The parameters for each scenario are configured according to the following rules: the baseline scenario uses the historical average of boundary conditions over the past three years; the optimistic scenario uses boundary conditions for profit-related factors increased by 10%-15% and risk-related factors decreased by 5%-10%; the pessimistic scenario uses boundary conditions for profit-related factors decreased by 10%-15% and risk-related factors increased by 5%-10%; and the extreme operating condition scenario uses boundary conditions for profit-related factors decreased by 20%-30% and risk-related factors increased by 10%-20%. The parameters for each scenario are input into the linked simulation model, and multiple rounds of iterative calculations are performed until the results converge, yielding the predicted economic indicators for each scenario.

[0009] S4: Results Analysis and Output. The univariate control method is used to generate economic sensitivity analysis curves for each boundary condition, quantifying the impact of changes in different boundary conditions on project returns; the top 3-5 key boundary conditions affecting project economics are identified through ranking; based on a preset internal rate of return threshold (e.g., ≥8%), the optimal range of controllable boundary conditions is calculated; and a standardized simulation prediction report containing economic data for each scenario, sensitivity analysis curves, key influencing factors, and risk warnings is automatically generated.

[0010] S5: Dynamic Tracking and Early Warning. Throughout the entire project lifecycle, it tracks changes in boundary conditions in real time; when any boundary condition deviates from the preset safety range by more than a threshold, it automatically issues a risk warning to investors; simultaneously, it triggers the linkage simulation model to recalculate the latest economic indicators of the project and generate dynamically updated forecast reports.

[0011] An intelligent simulation and prediction system linking the economics and boundary conditions of energy investment projects to implement the above method includes: The boundary condition acquisition and update module is responsible for collecting various dynamic boundary condition data through API interfaces, web crawlers, and manual import, and then classifying, cleaning, and storing the data in the boundary condition dynamic update library. The linkage simulation model module has built-in model training unit, parameter configuration unit and iterative calculation unit, which is used to build and update nonlinear linkage relationship model and perform dynamic iterative calculation. The scene setting module provides preset scene templates and custom scene functions, and supports users to configure different combinations of boundary condition parameters for different scenes; The results analysis and output module includes a sensitivity analysis unit, a key factor identification unit, an investment range calculation unit, and a report generation unit, which are used to complete the results analysis and output a visual report. The dynamic early warning module monitors changes in boundary conditions in real time. When the threshold is exceeded, a risk warning is triggered and a recalculation process is initiated.

[0012] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages compared with the prior art: 1. Achieve dynamic linkage simulation and improve forecast accuracy: Construct a nonlinear linkage model between boundary conditions and economic indicators, support real-time updates and automatic recalculation of boundary conditions, solve the problem that traditional static models cannot respond to market changes, and significantly improve the timeliness and accuracy of economic forecasts. 2. Comprehensive coverage of multiple scenarios to reduce investment risks: It presets four typical scenarios and supports custom scenarios, which can comprehensively simulate the economic performance of projects under different market conditions and help investors fully identify potential risks; 3. Enhance decision support capabilities: Automatically generate sensitivity analysis curves and identify key influencing factors, quantify the impact of each factor on returns, and output the optimal investment range, providing intuitive and quantitative reference for investment decisions; 4. Enable full lifecycle management: Provides dynamic tracking and risk warning functions after project commissioning, which can promptly detect risks of returns deviating from expectations and help investors take countermeasures in advance; 5. Improve evaluation efficiency: Achieve full automation from data collection to report generation, reducing the time for a single simulation prediction from several hours to several minutes, and significantly reducing labor costs.

[0013] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0014] Figure 1 The flowchart shows the intelligent simulation prediction method for linking the economics and boundary conditions of energy investment projects as described in this invention. Figure 2 This is an architecture diagram of the intelligent simulation and prediction system for energy investment projects that links economic efficiency and boundary conditions, as described in this invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] It should be noted that the terms "vertical," "horizontal," "up," "down," "left," "right," and similar expressions used in this article are for illustrative purposes only and do not represent the only possible implementation.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0018] The following is in conjunction with the appendix Figure 1-2 The present invention will be further described in detail with reference to specific embodiments.

[0019] Example: Simulation and Prediction of Wind Power Investment Projects This embodiment takes a 50MW onshore wind power investment project as an example to illustrate the specific implementation process of the present invention.

[0020] Step 1: Boundary Condition Acquisition and Classification The following data is collected through the boundary condition acquisition and update module: Electricity price data: Local monthly benchmark electricity price for desulfurized coal over the past 5 years, market-based electricity price, and electricity price forecasts for the next 3 years; Utilization hours: Historical data of wind power utilization hours in the project location over the past 15 years and predicted values ​​from wind resource assessment reports; Policy data: National and local wind power subsidy policies, electricity price subsidy periods and phase-out mechanisms; Financing data: Current long-term bank loan interest rates, project loan ratios, and loan terms; Curtailment rate data: The local power grid's average wind power curtailment rate over the past three years and its future absorption capacity forecast; Cost data: wind turbine equipment procurement cost, construction and installation cost, and annual operation and maintenance cost.

[0021] The above data is categorized as follows: controllable boundary conditions include financing interest rate (4.35%), construction period (18 months), and annual operation and maintenance cost (800,000 yuan / MW); uncontrollable boundary conditions include electricity price (0.38 yuan / kWh), utilization hours (2200 hours), subsidy standard (0.05 yuan / kWh), and curtailment rate (6%). A dynamic update library for boundary conditions is established and set to automatically update the electricity price and curtailment rate data once a month.

[0022] Step 2: Construction of the linkage simulation model The linkage simulation model module calls data from more than 800 wind power projects in the historical case library and uses a multivariate nonlinear regression algorithm to train and generate a nonlinear linkage model of IRR, NPV and various boundary conditions; the iteration calculation trigger threshold is set to 5%, that is, when any boundary condition changes by more than 5%, a recalculation is automatically triggered; the iteration convergence condition is set to the deviation of two consecutive calculation results being less than 0.1%.

[0023] Step 3: Multi-scenario simulation testing Configure four types of scene parameters through the scene settings module: Baseline scenario: Electricity price 0.38 yuan / kWh, utilization hours 2200 hours, curtailment rate 6%, financing interest rate 4.35%; Optimistic scenario: Electricity price increases by 10% to 0.418 yuan / kWh, utilization hours increase by 5% to 2310 hours, and curtailment rate decreases by 5% to 5.7%; Pessimistic scenario: Electricity price drops by 10% to 0.342 yuan / kWh, utilization hours decrease by 5% to 2090 hours, and curtailment rate increases by 5% to 6.3%; Extreme operating conditions: Electricity price drops by 20% to 0.304 yuan / kWh, utilization hours drop by 10% to 1980 hours, and curtailment rate increases by 10% to 6.6%.

[0024] The parameters for each scenario are input into the model for iterative calculation to obtain the economic indicators for each scenario: Baseline scenario: IRR = 8.5%, NPV = 32 million yuan, static payback period = 11.2 years; Optimistic scenario: IRR = 10.2%, NPV = 58 million yuan, static payback period = 9.5 years; Pessimistic scenario: IRR = 6.8%, NPV = 6.5 million yuan, static payback period = 13.7 years; Extreme operating conditions: IRR=5.1%, NPV=-18 million yuan, static payback period=16.3 years.

[0025] Step 4: Results Analysis and Output The results analysis and output module generates sensitivity analysis curves for each boundary condition. The calculation shows that the influence of each factor on the IRR is ranked as follows: electricity price fluctuation (impact coefficient 0.72) > utilization hours (impact coefficient 0.65) > curtailment rate (impact coefficient 0.28) > financing interest rate (impact coefficient 0.21). Electricity price fluctuation and utilization hours are identified as the key factors affecting the economics of the project.

[0026] Based on a preset investment threshold of IRR ≥ 8%, the optimal financing interest rate range is calculated to be ≤ 4.8%, and the optimal utilization hours range is ≥ 2100 hours. A simulation prediction report containing data for each scenario, sensitivity analysis curves, key influencing factors, and risk warnings is generated, and it supports exporting to PDF and Excel formats.

[0027] Step 5: Dynamic Tracking and Early Warning After the project is put into operation, the system will automatically update the local electricity price and curtailment rate data every month. If the electricity price drops to 0.35 yuan / kWh in a certain month (a change of 7.89%, exceeding the 5% trigger threshold), the system will automatically trigger a recalculation, resulting in a new IRR of 7.6%, which is lower than the preset threshold. The system will then issue a risk warning to investors that "the decline in electricity prices has led to lower-than-expected returns" and generate an updated forecast report.

[0028] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Anyone skilled in the art can make various modifications and alterations without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the claims.

Claims

1. A smart simulation prediction method linking the economics and boundary conditions of energy investment projects, characterized in that, Includes the following steps: S1: Boundary condition acquisition and classification. Collect multi-dimensional dynamic boundary condition data of energy investment projects, classify them into controllable boundary conditions and uncontrollable boundary conditions, and establish a dynamic update library of boundary conditions that supports real-time updates. S2: Construction of a linkage simulation model, establishing a nonlinear linkage model between core economic indicators such as internal rate of return, net present value, investment payback period, and cost per kilowatt-hour and various boundary conditions, embedding a dynamic iterative calculation algorithm to achieve real-time linkage between changes in boundary conditions and the calculation of economic indicators; S3: Multi-scenario simulation test, presets benchmark scenario, optimistic scenario, pessimistic scenario and extreme working condition scenario, configures the boundary condition parameter combination corresponding to each scenario, inputs into the linkage simulation model to perform multiple rounds of iterative calculation, and obtains the economic index prediction results under each scenario. S4: Results Analysis and Output. Automatically generates economic sensitivity analysis curves for various scenarios, identifies key boundary conditions affecting project economics, calculates and outputs the optimal investment range and corresponding risk warnings, and generates standardized simulation prediction reports.

2. The intelligent simulation prediction method for energy investment projects linking economic efficiency and boundary conditions according to claim 1, characterized in that, The dynamic boundary condition data mentioned in step S1 includes electricity price fluctuation data, project utilization hours, policy subsidy data, financing interest rate data, curtailment rate data, equipment operation and maintenance cost data, and raw material price data; the controllable boundary conditions include financing interest rate, construction period, and operation and maintenance cost; and the uncontrollable boundary conditions include electricity price fluctuation, utilization hours, policy subsidies, and curtailment rate.

3. The intelligent simulation prediction method for energy investment projects linking economic efficiency and boundary conditions according to claim 1, characterized in that, The boundary condition dynamic update library mentioned in step S1 supports automatic collection and updating of boundary condition data at preset time intervals, and also supports manual import and correction of data. The updated data is automatically synchronized to the linkage simulation model.

4. The intelligent simulation prediction method for energy investment projects linking economic efficiency and boundary conditions according to claim 1, characterized in that, The dynamic iterative calculation algorithm described in step S2 is set with a trigger threshold. When the change in any boundary condition exceeds the preset threshold, the model is automatically triggered to recalculate all economic indicators. The nonlinear linkage model is generated by training a multivariate nonlinear regression algorithm based on historical project data.

5. The intelligent simulation prediction method for energy investment projects linking economic efficiency and boundary conditions according to claim 1, characterized in that, The preset scenario parameter configuration rules mentioned in step S3 are as follows: the baseline scenario uses the historical average of the boundary conditions over the past 3 years; the optimistic scenario uses a 10%-15% increase in the benefit-related boundary conditions and a 5%-10% decrease in the risk-related boundary conditions; the pessimistic scenario uses a 10%-15% decrease in the benefit-related boundary conditions and a 5%-10% increase in the risk-related boundary conditions; and the extreme operating condition scenario uses a 20%-30% decrease in the benefit-related boundary conditions and a 10%-20% increase in the risk-related boundary conditions.

6. The intelligent simulation prediction method for energy investment projects linking economic efficiency and boundary conditions according to claim 1, characterized in that, The sensitivity analysis curve mentioned in step S4 is generated by the univariate control method. The value of each single boundary condition is changed one by one, and the change range of the corresponding economic indicators is calculated. The optimal investment range is the range of controllable boundary conditions that meet the preset internal rate of return threshold.

7. The intelligent simulation prediction method for energy investment projects linking economic efficiency and boundary conditions according to claim 1, characterized in that, It also includes step S5: dynamic tracking and early warning, which tracks changes in boundary conditions in real time throughout the entire project lifecycle. When the boundary conditions deviate from the preset range and exceed the threshold, a risk warning is automatically issued and the project's economic indicators are recalculated.

8. A smart simulation and prediction system for energy investment projects that links economic efficiency and boundary conditions, implementing the method of any one of claims 1-7, characterized in that, include: The boundary condition acquisition and update module is responsible for collecting various dynamic boundary condition data, classifying and organizing them, and establishing a dynamic update library. The linkage simulation model module communicates with the boundary condition acquisition and update module, and has a built-in nonlinear linkage relationship model and dynamic iterative calculation algorithm to realize the linkage calculation of boundary conditions and economic indicators. The scene setting module communicates with the linkage simulation model module and is used to preset multiple simulation scenes and configure corresponding boundary condition parameters. The results analysis and output module communicates with the linkage simulation model module to generate sensitivity analysis curves, identify key influencing factors, calculate the optimal investment range, and output simulation reports. The dynamic early warning module is connected to the boundary condition acquisition and update module and the linkage simulation model module to track changes in boundary conditions in real time and trigger risk warnings.

9. The intelligent simulation and prediction system for energy investment projects linking economic efficiency and boundary conditions as described in claim 8, characterized in that, The linkage simulation model module includes a model training unit, a parameter configuration unit, and an iterative calculation unit; the model training unit is used to train and update the nonlinear linkage relationship model based on historical project data, and the parameter configuration unit is used to set the iterative calculation trigger threshold and convergence conditions.

10. The intelligent simulation and prediction system for linking the economics and boundary conditions of energy investment projects according to claim 8, characterized in that, The results analysis and output module includes a sensitivity analysis unit, a key factor identification unit, an investment range calculation unit, and a report generation unit; the report generation unit supports exporting simulation prediction reports and visualization charts in PDF and Excel formats.

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