Power investment modeling system

Abstract

The invention discloses a power investment modeling system, and relates to the technical field of power investment modeling system design, and the system comprises an application layer which is used for processing system-level function and business service coordination; the data layer is responsible for storage, management and efficient access of various data, and the data layer is connected with the application layer; the infrastructure layer is used for providing technical basic support required by system operation, and the infrastructure layer is connected with the data layer; the service layer is used for realizing core logic and a calculation model of the power investment business, and the service layer is connected with the infrastructure layer; through data driving and a professional model, an experience decision is converted into quantitative analysis, scientization and precision of investment decision are achieved, and the investment success rate and the risk resisting capacity are remarkably improved; through automatic process integration, the analysis work of several weeks is shortened to the hour level, automation and intelligentization of the whole process are achieved, the analysis efficiency is improved, and rapid market decision and response are supported.

Description

Technical Field

[0001] This invention relates to the field of power investment modeling system design technology, and in particular to a power investment modeling system. Background Technology

[0002] Against the backdrop of accelerated global energy transition and the construction of new power systems, the scale of investment in the power industry continues to expand, and investment scenarios cover new energy power plants, grid upgrades, energy storage facilities, etc., becoming increasingly complex and diverse, significantly raising the requirements for investment decision-making quality and response efficiency.

[0003] Traditional power investment decision-making models have several shortcomings: First, decision-making relies heavily on human experience and lacks a data-driven quantitative analysis system and professional modeling support, resulting in strong subjectivity and insufficient accuracy in decision-making. Second, the investment analysis process is fragmented, and the various stages are not automated and integrated, resulting in a complete analysis cycle that often lasts for several weeks, leading to low efficiency and an inability to quickly capture market opportunities, thus restricting the timeliness and competitiveness of power investment projects. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a power investment modeling system that solves the problems of insufficient accuracy in investment decisions and low analysis efficiency caused by a fragmented investment analysis process, as mentioned in the background section.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A power investment modeling system includes an application layer, which is used to handle system-level function and business service coordination.

[0007] The data layer is responsible for the storage, management, and efficient access of various types of data, and it is connected to the application layer.

[0008] The infrastructure layer provides the technical foundation required for system operation and is connected to the data layer.

[0009] The service layer is used to implement the core logic and calculation model of power investment business, and the service layer is connected to the infrastructure layer.

[0010] An external system integration layer is used to interface with external data sources and third-party systems, and the external system integration layer is connected to the service layer;

[0011] The presentation layer provides a user interface and data visualization, and is connected to the application layer and the external system integration layer.

[0012] According to the power investment modeling system provided by the present invention, the application layer includes an API gateway, business services, and authentication and authorization. The API gateway serves as the unified entry point of the system, handling traffic management and scheduling of all external requests. The business services are responsible for specific business logic processing, and the authentication and authorization ensures secure access and access control of the system.

[0013] According to the present invention, a power investment modeling system includes a data layer comprising a relational database, a time-series database, and a cached message sequence. The relational database is used to store and manage structured business data, ensuring data consistency and integrity. The time-series database is used to efficiently store and query time-series data, supporting large-scale time-series data analysis. The cached message sequence is used to provide high-speed data access, reduce the pressure on the backend database, and improve system performance.

[0014] According to the present invention, a power investment modeling system includes an infrastructure layer comprising container orchestration, monitoring logs, and a configuration center. The container orchestration is used for automated deployment, management, and expansion of containerized applications. The monitoring logs are used for real-time monitoring of system operation status, collecting and analyzing log data, and ensuring system stability. The configuration center is used for centralized management of distributed system configurations, enabling dynamic configuration updates and version control.

[0015] According to the present invention, a power investment modeling system includes a service layer comprising data processing, model calculation, and an analysis engine. The data processing is used to clean, transform, and integrate raw data to prepare a high-quality dataset for model calculation. The model calculation is used to perform mathematical model calculations and algorithm operations related to power investment. The analysis engine is used to coordinate and manage complex analysis processes and generate comprehensive investment analysis conclusions.

[0016] According to the present invention, a power investment modeling system includes an external system integration layer comprising power market, meteorological data, and policy data. The power market provides power market operation data to support revenue forecasting and market analysis of investment projects. The meteorological data provides meteorological environmental data to support renewable energy power generation forecasting and risk assessment. The policy data provides policy and regulatory information to assess the impact of the policy environment on investment projects.

[0017] According to the present invention, a power investment modeling system includes a presentation layer comprising a web front-end, a mobile app, and data visualization. The web front-end provides rich interactive functions and handles complex data entry and operations. The mobile app provides a portable access method, focusing on the display of key information and instant notifications. The data visualization displays macroeconomic data and key indicators, and supports dynamic refresh and various chart types.

[0018] A power investment modeling system method provided by the present invention includes the following operating steps:

[0019] S1. Access, Authentication and Authorization: Users access the system through presentation layer entry points such as web front-end, mobile app or data dashboard. The front-end application loads and sends a login request to the authentication and authorization service through the API gateway. The authentication and authorization service verifies the user's identity and returns an access token. The front-end application stores the token locally.

[0020] S2. Initiate an investment analysis request: The user configures investment analysis parameters on the front-end interface, and the front-end application sends the parameters to the corresponding business service through the API gateway.

[0021] S3. API Gateway Request Processing: The API gateway receives requests, performs token verification, rate limiting, logging, and other operations, and forwards the requests to the corresponding business services according to routing rules.

[0022] S4. Business Service Processing: Upon receiving the request, the investment analysis service begins executing the investment analysis process; it retrieves basic data from the data layer and uses caching to accelerate access.

[0023] S5. Data Access and Computation: Data is retrieved from relational databases, time-series databases, and other storage through the data access layer, and complex mathematical operations are performed by calling the model computation service.

[0024] S6. External Data Integration: The system obtains real-time or historical data from external systems and connects the data to the system through predefined interfaces to store it in the corresponding database.

[0025] S7. Generate Analysis Results: Summarize the calculation results of each microservice, generate an investment analysis report, save the analysis results to the database, return the analysis results to the API gateway, and then the gateway returns them to the front-end application, which displays them to the user in an appropriate format.

[0026] Compared with existing technologies, the advantages of this invention are:

[0027] 1. By using data-driven approaches and professional models, experience-based decisions are transformed into quantitative analysis, enabling more scientific and precise investment decisions and significantly improving investment success rates and risk resilience.

[0028] 2: By integrating automated processes, the analysis work that used to take weeks can be reduced to hours, achieving full automation and intelligence, improving analysis efficiency, and supporting rapid market decision-making and response.

[0029] In summary, this system transforms investment decision-making from experience-based judgment to vectorized analysis through data-driven approaches and professional models, significantly improving decision accuracy and risk control capabilities. Simultaneously, by leveraging automated processes, the analysis cycle is reduced from weeks to hours, achieving highly efficient and intelligent end-to-end processing. This ensures investors can respond quickly to market changes, enhancing both investment success rates and market competitiveness. Attached Figure Description

[0030] Figure 1 This is a diagram illustrating the overall architecture of a power investment modeling system proposed in this invention.

[0031] Figure 2 This is a flowchart of a power investment modeling system proposed in this invention. Detailed Implementation

[0032] Reference Figure 1-2 A power investment modeling system includes an application layer, which is used to handle system-level functions and business service coordination. The application layer includes an API gateway, business services, and authentication and authorization. The API gateway serves as the unified entry point of the system and handles traffic management and scheduling of all external requests. The business services are responsible for specific business logic processing, and authentication and authorization ensure secure access and access control of the system.

[0033] Specifically, in actual deployment, the application layer can adopt a microservice architecture to improve modularity and maintainability. For example, an API gateway can be used to route and rate limit requests to prevent system overload. Business services may be subdivided into multiple sub-services, such as investment analysis services and user management services, and asynchronous communication can be achieved through an event-driven pattern. The authentication and authorization module can integrate multi-factor authentication and role-based access control to ensure that only authorized users can access sensitive data, while supporting audit logs to record all operations to meet industry compliance requirements.

[0034] The data layer is responsible for the storage, management, and efficient access of various types of data. It connects with the application layer and includes relational databases, time-series databases, and cached message sequences. Relational databases are used to store and manage structured business data, ensuring data consistency and integrity. Time-series databases are used to efficiently store and query time-series data, supporting large-scale time-series data analysis. Cached message sequences are used to provide high-speed data access, reduce the pressure on the backend database, and improve system performance.

[0035] Specifically, to optimize data layer performance, relational databases can adopt a distributed architecture, such as MySQL clusters or PostgreSQL partitioned tables, to handle high-concurrency transactions; time-series databases can combine compression algorithms and index optimization to quickly query historical electricity price trends; and cached message sequences can utilize Redis clusters or Apache Kafka to achieve data persistence and real-time stream processing, support event sourcing mode, ensure that data is not lost in the event of system failure, and ensure business continuity through data backup and disaster recovery strategies.

[0036] The infrastructure layer provides the technical foundation required for system operation. It is connected to the data layer and includes container orchestration, monitoring logs, and a configuration center. Container orchestration is used for the automated deployment, management, and scaling of containerized applications. Monitoring logs are used to monitor the system's operating status in real time, collect and analyze log data, and ensure system stability. The configuration center is used to centrally manage the configuration of the distributed system, enabling dynamic configuration updates and version control.

[0037] Specifically, in the infrastructure layer, container orchestration tools can adopt Kubernetes, which can automatically adjust resource allocation and dynamically scale application instances according to load, reducing manual intervention; the monitoring and logging system can be integrated with Prometheus and Grafana, setting threshold alerts and performance indicator dashboards to quickly respond to potential faults; the configuration center supports environment isolation and A / B testing, and manages configuration change history through version control tools such as Git, ensuring consistency between development, testing and production environments, and improving deployment efficiency and system reliability.

[0038] The service layer is used to implement the core logic and calculation model of power investment business. The service layer is connected to the infrastructure layer. The service layer includes data processing, model calculation and analysis engine. Data processing is used to clean, transform and integrate raw data to prepare high-quality datasets for model calculation. Model calculation is used to execute mathematical model calculations and algorithm operations related to power investment. The analysis engine is used to coordinate and manage complex analysis processes and generate comprehensive investment analysis conclusions.

[0039] Specifically, the service layer design can incorporate domain-driven design principles, encapsulating business logic into reusable microservices. For example, the data processing module uses Apache Spark for distributed ETL to process massive amounts of electricity data; the model computation part integrates machine learning and optimization algorithms, such as Monte Carlo simulation for risk assessment or linear regression for predicting investment returns; the analysis engine orchestrates task dependencies through workflow engines like Apache Airflow, combined with a rule engine to automate decision-making, generate visual reports, and help users quickly identify investment opportunities and risks; in addition, the computational models include financial evaluation models, risk assessment models, and predictive models.

[0040] The calculation method of the financial valuation model is to discount the net cash flows of each year throughout the project's entire life cycle to their present value at the beginning of the year using a certain discount rate. The formula for calculating the net present value (NPV) is as follows:

[0041] NPV=∑(Ct / (1+r)^t)-C0

[0042] In the above formula, Ct represents the net cash flow in year t, C0 represents the total initial investment, r represents the discount rate, and t represents the year;

[0043] The discount rate that makes the project's net present value (NPV) equal to zero is usually determined using an iterative method, as shown in the following formula:

[0044] NPV=∑(Ct / (1+IRR)^t)-C0=0

[0045] In the above formula, IRR represents the internal rate of return, which indicates the project's expected profitability.

[0046] When calculating the Levelized Cost of Electricity (LCOE), which is the ratio of the present value of total costs over the project's lifecycle to the present value of total electricity generation, the formula is as follows:

[0047] LCOE=[∑(It+Mt+Ft) / (1+r)t] / [∑Et / (1+r)^t]

[0048] In the above formula, It represents the investment cost in year t, Mt represents the operation and maintenance cost in year t, Ft represents the fuel cost in year t, Et represents the power generation in year t, and r represents the discount rate;

[0049] Assume a photovoltaic power station project with an initial investment of C0 = 10 million yuan, a project cycle of 3 years, and expected net cash flows of C1 = 4 million yuan, C2 = 5 million yuan, and C3 = 6 million yuan for the next three years, respectively. The company requires a discount rate of r = 8%. The calculation process is as follows:

[0050] NPV=[400 / (1+0.08)^1]+[500 / (1+0.08)^2]+[600 / (1+0.08)^3]-1000

[0051] = (400 / 1.08) + (500 / 1.1664) + (600 / 1.2597) - 1000

[0052] =370.37 + 428.67 + 476.29 - 1000

[0053] =1275.33-1000

[0054] =2.7533 million yuan

[0055] The risk assessment model employs the Monte Carlo simulation method. First, key input variables are identified, and a probability distribution is set for each variable. Then, thousands of random samples are performed using a computer, with the output result (such as NPV) calculated for each sample. Finally, the distribution of the output result is analyzed to obtain the probability distribution, expected value, and Value at Risk (VaR) of NPV.

[0056] Predictive models are typically calculated based on the power curve of the wind turbine and the predicted wind speed. The calculation formula is as follows:

[0057] P = 0.5 * ρ * A * v^3 * Cp

[0058] Assume a project, a 50MW wind farm, with an initial investment of I0 = 300 million yuan, annual operation and maintenance costs M = 6 million yuan, project life n = 25 years, annual equivalent full-load hours H = 220 hours, annual power generation E = 5000kW * 2200h = 110,000,000 kWh, and a discount rate r = 8%. The calculation process is as follows:

[0059] PV_M=6×10^6*[1-(1+0.08)^-25] / 0.08≈6×10^6*10.6748≈6.4×10^7 yuan

[0060] Present value of total cost = 3 × 10^8 + 6.4 × 10^7 = 3.64 × 10^8 yuan

[0061] PV_E=1.1×10^8*[1-(1+0.08)^-25] / 0.08≈1.1×10^8*10.6748≈1.174×10^9

[0062] LCOE = Present Value of Total Costs / Present Value of Total Electricity Generation

[0063] = (3.64 x 10^8) / (1.174 x 10^9)

[0064] = 0.31 yuan / kWh

[0065] The external system integration layer is used to connect with external data sources and third-party systems. The external system integration layer is connected to the service layer and includes electricity market, meteorological data and policy data. The electricity market is used to provide electricity market operation data to support the revenue forecasting and market analysis of investment projects. The meteorological data is used to provide meteorological environmental data to support the forecasting of renewable energy power generation and risk assessment. The policy data is used to provide policy and regulatory information to assess the impact of the policy environment on investment projects.

[0066] Specifically, the external system integration layer needs to address the challenges of data heterogeneity and real-time performance. For example, it needs to achieve stable connections with electricity market data providers through API gateways and message queues to ensure low-latency data synchronization. Meteorological data integration may use satellite data and IoT sensors, combined with data fusion technology to improve forecast accuracy. Policy data will utilize web crawlers and NLP (natural language processing) tools to automatically analyze regulatory changes, build impact assessment models, and dynamically adjust investment strategies to cope with the uncertainties brought about by market fluctuations and policy changes.

[0067] The presentation layer provides user interfaces and data visualization. It connects to the application layer and external system integration layer. The presentation layer includes a web front-end, a mobile app, and data visualization. The web front-end provides rich interactive functions and handles complex data entry and operations. The mobile app provides a portable access method and focuses on displaying key information and providing instant notifications. Data visualization displays macro data and key indicators and supports dynamic refresh and various chart types.

[0068] Specifically, the presentation layer prioritizes user experience and accessibility. The web front-end can use React or Angular frameworks to build responsive interfaces, supporting multiple languages ​​and custom themes. The mobile app integrates push services and offline mode to ensure users can still view cached data even without a network connection. The data visualization part uses D3.js or Tableau to embed interactive charts, such as heatmaps to show regional investment activity or timeline animations to demonstrate trend changes. At the same time, A / B testing is used to optimize the interface layout, improving user engagement and decision-making efficiency.

[0069] An embodiment of the present invention provides a power investment modeling system method, comprising the following operational steps:

[0070] S1. Access, Authentication and Authorization: Users access the system through presentation layer entry points such as web front-end, mobile app or data dashboard. The front-end application loads and sends a login request to the authentication and authorization service through the API gateway. The authentication and authorization service verifies the user's identity and returns an access token. The front-end application stores the token locally.

[0071] S2. Initiate an investment analysis request: The user configures investment analysis parameters on the front-end interface, and the front-end application sends the parameters to the corresponding business service through the API gateway.

[0072] S3. API Gateway Request Processing: The API gateway receives requests, performs token verification, rate limiting, logging, and other operations, and forwards the requests to the corresponding business services according to routing rules.

[0073] S4. Business Service Processing: Upon receiving the request, the investment analysis service begins executing the investment analysis process; it retrieves basic data from the data layer and uses caching to accelerate access.

[0074] S5. Data Access and Computation: Data is retrieved from relational databases, time-series databases, and other storage through the data access layer, and complex mathematical operations are performed by calling the model computation service.

[0075] S6. External Data Integration: The system obtains real-time or historical data from external systems and connects the data to the system through predefined interfaces to store it in the corresponding database.

[0076] S7. Generate Analysis Results: Summarize the calculation results of each microservice, generate an investment analysis report, save the analysis results to the database, return the analysis results to the API gateway, and then the gateway returns them to the front-end application, which displays them to the user in an appropriate format.

[0077] This invention, based on the core principles of data-driven and model-based computation, integrates multi-source data from the electricity market, meteorological environment, and policies and regulations to construct a complete analytical dataset. The system utilizes professional models such as risk assessment, return forecasting, and cash flow analysis to perform quantitative calculations and multi-dimensional evaluations of investment projects, transforming traditional experience-based decision-making into scientifically precise quantitative analysis. With the aid of an automated process engine, it achieves intelligent processing across the entire chain from data acquisition and model computation to report generation, providing investors with comprehensive decision support and significantly improving the efficiency and accuracy of investment analysis.

[0078] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A power investment modeling system, characterized in that, include: The application layer is used to handle the coordination of system-level functions and business services; The data layer is responsible for the storage, management, and efficient access of various types of data, and it is connected to the application layer. The infrastructure layer provides the technical foundation required for system operation and is connected to the data layer. The service layer is used to implement the core logic and calculation model of power investment business, and the service layer is connected to the infrastructure layer. An external system integration layer is used to interface with external data sources and third-party systems, and the external system integration layer is connected to the service layer; The presentation layer provides a user interface and data visualization, and is connected to the application layer and the external system integration layer.

2. The power investment modeling system according to claim 1, characterized in that, The application layer includes an API gateway, business services, and authentication and authorization. The API gateway serves as the unified entry point for the system, handling traffic management and scheduling for all external requests. The business services are responsible for specific business logic processing, and the authentication and authorization ensures secure access and access control for the system.

3. The power investment modeling system according to claim 1, characterized in that, The data layer includes a relational database, a time-series database, and a cached message sequence. The relational database is used to store and manage structured business data, ensuring data consistency and integrity. The time-series database is used to efficiently store and query time-series data, supporting large-scale time-series data analysis. The cached message sequence is used to provide high-speed data access, reduce the pressure on the backend database, and improve system performance.

4. The power investment modeling system according to claim 1, characterized in that, The infrastructure layer includes container orchestration, monitoring logs, and a configuration center. The container orchestration is used for automated deployment, management, and scaling of containerized applications. The monitoring logs are used for real-time monitoring of system operation status, collecting and analyzing log data to ensure system stability. The configuration center is used for centralized management of distributed system configurations, enabling dynamic configuration updates and version control.

5. The power investment modeling system according to claim 1, characterized in that, The service layer includes data processing, model calculation, and analysis engine. The data processing is used to clean, transform, and integrate the raw data to prepare a high-quality dataset for model calculation. The model calculation is used to perform mathematical model calculations and algorithm operations related to power investment. The analysis engine is used to coordinate and manage complex analysis processes and generate comprehensive investment analysis conclusions.

6. The power investment modeling system according to claim 1, characterized in that, The external system integration layer includes electricity market, meteorological data, and policy data. The electricity market is used to provide electricity market operation data to support the revenue forecasting and market analysis of investment projects. The meteorological data is used to provide meteorological environment data to support the forecasting of renewable energy power generation and risk assessment. The policy data is used to provide policy and regulatory information to assess the impact of the policy environment on investment projects.

7. The power investment modeling system according to claim 1, characterized in that, The presentation layer includes a web front-end, a mobile app, and data visualization. The web front-end provides rich interactive functions and handles complex data entry and operations. The mobile app provides a portable access method, focusing on the display of key information and instant notifications. The data visualization displays macro data and key indicators, and supports dynamic refresh and various chart types.

8. The power investment modeling system method according to claim 1, characterized in that, The following steps are included: S1. Access, Authentication and Authorization: Users access the system through presentation layer entry points such as web front-end, mobile app or data dashboard. The front-end application loads and sends a login request to the authentication and authorization service through the API gateway. The authentication and authorization service verifies the user's identity and returns an access token. The front-end application stores the token locally. S2. Initiate an investment analysis request: The user configures investment analysis parameters on the front-end interface, and the front-end application sends the parameters to the corresponding business service through the API gateway. S3. API Gateway Request Processing: The API gateway receives requests, performs token verification, rate limiting, logging, and other operations, and forwards the requests to the corresponding business services according to routing rules. S4. Business Service Processing: Upon receiving the request, the investment analysis service begins executing the investment analysis process; it retrieves basic data from the data layer and uses caching to accelerate access. S5. Data Access and Computation: Data is retrieved from relational databases, time-series databases, and other storage through the data access layer, and complex mathematical operations are performed by calling the model computation service. S6. External Data Integration: The system obtains real-time or historical data from external systems and connects the data to the system through predefined interfaces to store it in the corresponding database. S7. Generate Analysis Results: Summarize the calculation results of each microservice, generate an investment analysis report, save the analysis results to the database, return the analysis results to the API gateway, and then the gateway returns them to the front-end application, which displays them to the user in an appropriate format.

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