Homer Pro-based wind and light storage system yield calculation method and system

Abstract

The invention discloses a Homer Pro-based wind and light storage system yield calculation method and system, and the method comprises the steps: constructing a wind and light storage system model in Homer Pro software, and configuring corresponding parameters; establishing one or more configuration schemes for the wind and light storage system model, and setting corresponding simulation boundary conditions for the configuration schemes to form different simulation scenes; performing automatic simulation on each simulation scene, and automatically extracting an internal return rate from a simulation result; carrying out structured processing on the extracted internal return rates to generate internal return rates containing different configuration schemes, and carrying out comparative analysis on the internal return rates; and obtaining a sensitivity analysis report and an optimal configuration scheme based on a comparative analysis result. By means of the scheme, it can be ensured that the simulation scene can flexibly fit the real boundary conditions of various projects. And a clear sensitivity analysis report and an optimal configuration scheme can be generated, so that a tedious technical measurement and calculation process is converted into an efficient evaluation tool.

Description

Technical Field

[0001] This application generally relates to the field of renewable energy and power system planning technology. More specifically, this application relates to a method and system for calculating the yield to return of a wind-solar-storage system based on Homer Pro. Background Technology

[0002] As the global energy structure accelerates its transformation towards cleaner and lower-carbon energy, integrated wind-solar-storage systems, which combine wind power, photovoltaic power generation, and energy storage technologies, have become an important development direction in the new power system and energy sector due to their ability to efficiently utilize renewable energy and achieve flexible regulation through energy storage. In promoting the implementation and investment of wind-solar-storage projects, scientific planning and accurate rate-of-return calculations are crucial.

[0003] However, current technologies and methods for project planning and rate-of-return (IRR) calculation of wind, solar, and energy storage systems face numerous challenges and limitations, primarily in the following aspects: First, traditional RRR calculation methods largely rely on static parameter assumptions, such as fixed feed-in tariffs, constant power loads, and simplified resource data. These models cannot accurately reflect the natural volatility of wind and solar resources, the dynamic changes in time-of-use pricing policies, and the profound impact of complex energy storage charging and discharging strategies on the overall economic performance of the system. The calculated results may deviate significantly from the optimal configuration in actual operating scenarios, leading to insufficient accuracy and reliability in the evaluation results. Second, general-purpose software lacks targeted and standardized analytical models for accurate calculation of the IRR of wind, solar, and energy storage systems. Especially when facing multi-energy coupled systems such as wind-solar-hydrogen production or complex scenarios requiring hybrid scheduling strategies such as load following and cyclic charging, general-purpose software struggles to provide direct support, typically requiring cumbersome manual settings and secondary development, failing to achieve efficient and automated evaluation. Third, energy storage is the core regulating unit of a wind-solar-storage system, and the balance between its costs and benefits directly determines the investment feasibility of the entire project. However, existing electricity pricing policies and market mechanisms often lack independent and effective support for the value of energy storage, resulting in long investment recovery cycles and difficulty in guaranteeing economic viability. Furthermore, existing technologies generally lack convenient tools for sensitivity analysis of different energy storage technologies (such as lithium iron phosphate batteries and flow batteries) during project evaluation, making it difficult to quantify the specific impact of technology selection on the project's internal rate of return (IRR).

[0004] In view of this, there is an urgent need to provide a yield calculation scheme for wind, solar and energy storage systems based on Homer Pro, which can overcome the above-mentioned shortcomings and achieve efficient, accurate and automated yield calculation of wind, solar and energy storage systems, so as to provide a scientific and reliable basis for project investment, design and policy formulation. Summary of the Invention

[0005] In order to at least address one or more of the technical problems mentioned above, this application proposes a yield calculation scheme for wind-solar-storage systems based on Homer Pro in several aspects.

[0006] In a first aspect, this application provides a method for calculating the rate of return of a wind-solar-storage system based on Homer Pro, comprising: constructing a wind-solar-storage system model in Homer Pro software, including a wind power generation module, a photovoltaic power generation module, an energy storage module, an electrical load module, and a grid module; configuring corresponding parameters for the wind power generation module, photovoltaic power generation module, energy storage module, and electrical load module by calling a technical and economic parameter library or user-defined parameters received through a dynamic strategy interface; establishing one or more configuration schemes for the wind-solar-storage system model based on the technical and economic parameter library or user-defined parameters received through the dynamic strategy interface, and setting corresponding simulation boundary conditions for the configuration schemes to form different simulation scenarios; calling the simulation calculation engine of Homer Pro software to perform automated simulation for each simulation scenario, and automatically extracting the internal rate of return calculated by the Homer Pro built-in economic model from the simulation results; structuring the extracted internal rate of return to generate a comparative analysis of the internal rate of return under different configuration schemes; and obtaining a sensitivity analysis report and the optimal configuration scheme based on the comparative analysis results.

[0007] In some embodiments, the technical and economic parameter library stores initial investment costs, operation and maintenance costs, lifespan, power generation efficiency, energy storage module charging and discharging efficiency, electricity pricing policies, load data, and meteorological data.

[0008] In some embodiments, the configuration scheme is formed by adjusting at least one parameter among capacity, percentage change in initial investment cost, percentage change in operation and maintenance cost, charge and discharge efficiency of energy storage modules, electricity pricing policy, load data, and meteorological data.

[0009] In some embodiments, the simulation boundary conditions include electricity pricing policies and meteorological data, wherein the electricity pricing policies include at least one or more of time-of-use pricing, feed-in tariffs, purchase tariffs, and subsidy standards.

[0010] In some embodiments, the capacity includes the capacity of the wind power generation module, the capacity of the photovoltaic power generation module, and the capacity of the energy storage module; during the capacity adjustment process, a search space containing zero capacity and at least one non-zero predetermined capacity is defined for at least one of the wind power generation module, photovoltaic power generation module, or energy storage module.

[0011] In some embodiments, the simulation scenario includes a baseline scenario that only includes grid power supply and zero electrical load; in the process of establishing the baseline scenario, a search space with no capacity is selected in the configuration of wind power generation module, photovoltaic power generation module and energy storage module, and the electrical load of the electrical load module is set to zero, so as to form a simulation scenario that only includes grid module and electrical load module.

[0012] In some embodiments, the user-defined parameters include energy storage charging and discharging strategy parameters and policy sensitivity parameters.

[0013] In some embodiments, multi-threaded parallel processing is configured to automatically simulate each simulation scenario.

[0014] In some embodiments, during the process of obtaining a sensitivity analysis report based on the comparative analysis results, the following steps are performed: generating and displaying a numerical relationship curve showing the impact on the internal rate of return when any variable in the key parameters changes, wherein the key parameters include at least the capacity of the energy storage module, the initial investment cost of the photovoltaic power generation module, and the feed-in tariff; calculating and displaying the decrease or increase in the internal rate of return caused by changes in the electricity price policy, wherein the electricity price policy includes at least one or more of the time-of-use tariff, feed-in tariff, electricity purchase price, and subsidy standards.

[0015] In a second aspect, this application provides a wind-solar-storage system yield calculation system based on Homer Pro. The system employs the yield calculation method for wind-solar-storage systems based on Homer Pro as described in any embodiment of the first aspect. The system includes: a model building and configuration module, used to build a wind-solar-storage system model in Homer Pro software, comprising a wind power generation module, a photovoltaic power generation module, an energy storage module, an electrical load module, and a grid module, and to configure corresponding parameters for the wind power generation module, photovoltaic power generation module, energy storage module, and electrical load module by calling a technical and economic parameter library or user-defined parameters received through a dynamic strategy interface; a simulation scenario formation module, used to establish one or more configuration schemes for the wind-solar-storage system model based on the technical and economic parameter library or user-defined parameters received through the dynamic strategy interface, through the application programming interface or scripts of Homer Pro software, and to set corresponding simulation boundary conditions for the configuration schemes to constitute different simulation scenarios; and an internal rate of return (IRR) calculation module, used to call the simulation calculation engine of Homer Pro software to perform automated simulation for each simulation scenario, and to automatically extract the IRR calculated from the simulation results. The Pro module includes a built-in economic model that calculates the internal rate of return (IRR); a comparative analysis module that structures the extracted IRR and generates a comparative analysis of IRR under different configuration schemes; and a results acquisition module that generates a sensitivity analysis report and the optimal configuration scheme based on the comparative analysis results.

[0016] Through the Homer Pro-based wind-solar-storage system yield calculation scheme provided above, this application embodiment transforms a complex manual evaluation process into a fully automated decision support system by constructing a system model encompassing wind, solar, storage, and grid, and utilizing application programming interfaces (APIs) or scripts. First, by calling upon a technical and economic parameter library and an automated simulation engine, it can quickly simulate one or more configuration schemes, efficiently obtaining accurate energy flow and cash flow data, thus overcoming the inefficiency and potential errors of manual operation. Second, by receiving user-defined parameters through a dynamic strategy interface, it ensures that the simulation scenario flexibly conforms to the real boundary conditions of various projects. Third, it can directly structure complex simulation data into a comparative analysis of the internal rate of return, generating clear sensitivity analysis reports and optimal configuration schemes, thereby transforming the tedious technical calculation process into a highly efficient evaluation tool capable of outputting clear and reliable business decision-making basis.

[0017] Furthermore, in some embodiments, a comprehensive technical and economic parameter library storing data ranging from initial investment costs and operation and maintenance costs to electricity pricing policies and meteorological data provides a complete and standardized realistic basis for simulation calculations. Based on this, by allowing systematic adjustments to key parameters such as capacity, percentage changes in cost, and charge / discharge efficiency, the method gains significant flexibility in generating diverse configuration schemes, enabling comprehensive sensitivity analysis. By setting a search space for modules such as wind, solar, and storage, including zero-capacity and non-zero predetermined capacity, the system can autonomously decide whether to include a module in the configuration during the optimization process, thereby avoiding unnecessary equipment investment and ensuring that the final optimal solution is a truly economical, streamlined, and efficient configuration.

[0018] Furthermore, in some embodiments, by establishing a baseline scenario that includes only grid power supply and zero load, an absolute and objective reference point is provided for the economic evaluation of all wind, solar, and energy storage configuration schemes. By actively setting wind, solar, and energy storage modules as a search space with no capacity, it constructs a theoretical reference system of zero investment and zero return in the model. This allows the rate of return calculated for any other configuration scheme to be clearly interpreted as the pure incremental value brought by investing in wind, solar, and energy storage assets, thereby eliminating the relativity and ambiguity of the evaluation, ensuring that all comparative analyses are based on a unified and unambiguous scientific foundation, and greatly enhancing the reliability of the final decision.

[0019] Furthermore, in some embodiments, by generating and displaying numerical relationship curves related to the internal rate of return (IRR), it can intuitively reveal the sensitivity of a project's profitability to fluctuations in key parameters such as energy storage capacity, photovoltaic investment costs, or feed-in tariffs, helping decision-makers accurately identify core risk points. By further calculating and displaying the specific decay or growth values ​​of the IRR when complex electricity pricing policies such as time-of-use pricing and subsidy standards change, it provides clear quantitative indicators for assessing the financial impact of policy changes, enabling sensitivity analysis to move beyond the macro level and reach a level of sophistication that can directly guide business strategies. Attached Figure Description

[0020] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of this application are illustrated by way of example and not limitation, and the same or corresponding reference numerals denote the same or corresponding parts, wherein:

[0021] Figure 1 An exemplary flowchart of the yield calculation method for a wind-solar-storage system based on Homer Pro, according to an embodiment of this application, is shown;

[0022] Figure 2 An exemplary structural block diagram of a wind-solar-storage system yield calculation system based on Homer Pro, according to an embodiment of this application, is shown. Detailed Implementation

[0023] 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, not all, of the embodiments of this application. 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.

[0024] It should be understood that the terms "comprising" and "including" used in the specification and claims of this application indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0025] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0026] Figure 1 An exemplary flowchart of a wind-solar-storage system yield calculation method 100 based on Homer Pro, according to an embodiment of this application, is shown.

[0027] like Figure 1 As shown, in step S110, a wind-solar-storage system model is constructed in the Homer Pro software, which includes a wind power generation module, a photovoltaic power generation module, an energy storage module, an electrical load module, and a grid module. The corresponding parameters are configured for the wind power generation module, photovoltaic power generation module, energy storage module, and electrical load module by calling the technical and economic parameter library or by receiving user-defined parameters through the dynamic strategy interface.

[0028] In the embodiments of this application, the wind-solar-storage system model also includes a converter module, and the corresponding parameters of the converter module are configured by calling the technical and economic parameter library.

[0029] In the embodiments of this application, the technical and economic parameter library stores initial investment costs, operation and maintenance costs, lifespan, power generation efficiency, charging and discharging efficiency of energy storage modules, electricity pricing policies, load data, meteorological data, predefined templates, etc.

[0030] Specifically, the predefined templates include off-grid system templates and wind-solar-hydrogen production scenario templates. By using these predefined templates, the corresponding modules can be configured with a single click to create the desired scenario. For common application scenarios (such as off-grid systems and wind-solar-hydrogen production), users do not need to start from scratch; they can generate a basic model with a single click by calling the "predefined templates." This significantly reduces modeling time.

[0031] By automatically filling in the technical and economic parameter library, the tedious data collection and entry work is avoided, and the parameter configuration is automated.

[0032] In the embodiments of this application, user-defined parameters include energy storage charging and discharging strategy parameters and policy sensitivity parameters. Specifically, energy storage charging and discharging strategy parameters include peak-valley arbitrage models, and policy sensitivity parameters include subsidy reduction gradients. All custom settings made by the user on the front-end interface (such as peak-valley periods, prices, subsidy gradients, etc.) are automatically and accurately translated and written into the corresponding parameter positions in the .homer.input file through the dynamic strategy interface.

[0033] User-defined strategies (such as peak-valley arbitrage and subsidy reduction) can be automatically translated and written into Homer Pro's input file through a dynamic strategy interface. Users do not need to learn Homer Pro's complex parameter file syntax and underlying logic; they can simply perform intuitive operations on the front-end graphical interface.

[0034] Compared to manually searching for data and filling out complex .homer.input files, this method, by calling parameter libraries and automatically translating user strategies, reduces the workload from hours or even days to minutes. Simultaneously, the programmed automatic writing avoids spelling and formatting errors common in manual input, ensuring the accuracy of simulation input data. Furthermore, users can perform quick and standard parameter configuration using parameter libraries and templates, or fine-grained and targeted parameter configuration through custom interfaces. For example, it allows for rapid evaluation of the impact of different energy storage charging and discharging strategies on returns, or simulation of the impact of different policy changes on the long-term stability of projects, which is crucial for project investment decisions and risk control.

[0035] In the embodiments of this application, the parameters of the wind power generation module include the capacity of the wind power generation module (custom capacity or system automatic optimization configuration), wind turbine cost, replacement cost, annual maintenance cost, service life, hub height, power curve, power generation reduction factor, etc.

[0036] In the embodiments of this application, the parameters of the photovoltaic power generation module include the capacity of the photovoltaic power generation module (custom capacity or system automatic optimization configuration), the cost of the photovoltaic array, replacement cost, annual maintenance cost, service life, system efficiency, bracket type, ground reflectivity, etc.

[0037] In the embodiments of this application, the parameters of the energy storage module include system cost, replacement cost, annual maintenance cost, charging and discharging power, service life, capacity of the energy storage module (custom capacity or system automatic optimization configuration), maximum and minimum power, etc.

[0038] In the embodiments of this application, the parameters of the converter module include converter capacity (custom capacity or system-optimized configuration), efficiency, service life, cost, replacement cost, annual maintenance cost, etc.

[0039] In the embodiments of this application, the parameters of the electrical load module include load type (including ordinary electrical load, deferred load, etc.), daily electricity consumption, load fluctuation rate, etc.

[0040] In the embodiments of this application, the parameters of the power grid module include grid-connected power limits, feed-in tariffs, and electricity purchase tariffs.

[0041] After completing step S110, in step S120, based on the technical and economic parameter library or user-defined parameters received through the dynamic strategy interface, one or more configuration schemes are established for the wind-solar-storage system model through the application programming interface or script of the Homer Pro software, and corresponding simulation boundary conditions are set for the configuration schemes to form different simulation scenarios.

[0042] In the embodiments of this application, the configuration scheme is formed by adjusting at least one parameter among capacity, percentage change in initial investment cost, percentage change in operation and maintenance cost, charging and discharging efficiency of energy storage module, electricity price policy, load data, and meteorological data.

[0043] In the embodiments of this application, the simulation boundary conditions include electricity pricing policies and meteorological data. The electricity pricing policies include at least one or more of the following: time-of-use pricing, feed-in tariff, purchase tariff, and subsidy standards.

[0044] By separating and combining system design (configuration schemes) with varying external environments (boundary conditions) for testing, sensitivity analysis can be systematically conducted. Decision-makers can clearly quantify the specific impact of risk factors such as electricity price fluctuations, subsidy reductions, and weather resource uncertainties on the project's financial performance, thereby assessing the financial robustness of different schemes under future uncertain environments and providing a basis for developing risk hedging strategies. Simultaneously, during the configuration scheme development process, users can flexibly explore issues of interest. For example, fixing all other conditions and only adjusting the "percentage change in initial investment cost" can quickly reveal the impact of costs on returns. Fixing the system configuration and only adjusting electricity price policies or subsidy standards as boundary conditions allows for the assessment of the project's policy dependence risk.

[0045] In the embodiments of this application, the capacity includes the capacity of the wind power generation module, the capacity of the photovoltaic power generation module, and the capacity of the energy storage module. During the process of adjusting the capacity, a search space containing zero capacity and at least one non-zero predetermined capacity is defined for at least one of the wind power generation module, photovoltaic power generation module, or energy storage module.

[0046] By introducing a search space that includes zero-capacity options, the simulation engine autonomously seeks the equipment capacity combination that minimizes the system's total lifecycle cost or optimizes its net present value within a given set of options. This process transforms the crucial decision of "whether to invest and the scale of investment" from relying on subjective judgment to data-driven, prescriptive optimization. This ensures that the final recommended system configuration is the optimal solution rigorously derived from economic calculations, effectively avoiding subjective biases and unnecessary investments. This guarantees the efficiency of capital allocation, ensuring that every investment decision is supported by solid data.

[0047] In the embodiments of this application, the simulation scenario includes a baseline scenario that only includes grid power supply and zero electrical load. In the process of establishing the baseline scenario, a search space with no capacity is selected in the configuration of wind power generation module, photovoltaic power generation module and energy storage module, and the electrical load of the electrical load module is set to zero, so as to form a simulation scenario that only includes grid module and electrical load module.

[0048] By establishing a baseline scenario that includes only the power grid and zero load, an absolute and scientific comparative reference system is created. This baseline isolates and quantifies the system's fixed costs from its background costs, representing zero operational costs without any investment in renewable energy or meeting any load demand. The economics of all other configuration options are compared to this baseline, thereby accurately calculating the net incremental economic value of any portfolio.

[0049] After step S120 is completed, in step S130, the simulation calculation engine of Homer Pro software is invoked to perform automated simulation for each simulation scenario, and the internal rate of return calculated by the built-in economic model of Homer Pro is automatically extracted from the simulation results.

[0050] In the embodiments of this application, the simulation calculation engine first performs a physical-level energy balance simulation. Based on the input simulation boundary conditions, the simulation calculation engine performs supply and demand calculations and determines the corresponding energy flow path according to a built-in scheduling strategy aimed at optimal economic efficiency. For example, when photovoltaic power generation exceeds local load, it decides whether to prioritize charging energy storage or selling electricity to the grid. When supply falls short of demand, it decides whether to prioritize discharging energy storage or purchasing electricity from the grid. This process generates detailed basic physical data, including but not limited to: hourly output power of each power generation module, charging and discharging status of energy storage, electricity purchased and sold by the grid, the proportion of served load, and key equipment utilization data, such as the annual throughput of energy storage modules, the capacity factor of wind power generation modules / photovoltaic power generation modules, and the curtailment rate of solar / wind power.

[0051] Based on energy balance data, the simulation engine simultaneously performs economic calculations. It transforms every physical action into a financial transaction. For example, the hourly electricity purchased from the grid is multiplied by the electricity purchase price for that period to calculate operating costs, while the hourly electricity sold to the grid is multiplied by the grid connection price for that period to calculate operating revenue. The simulation engine integrates initial investment costs, annual maintenance costs, future equipment replacement costs, and residual value to ultimately construct a net cash flow statement.

[0052] After generating a complete net cash flow statement, Homer Pro's built-in economic model immediately applies standard financial calculation methods to automatically calculate the internal rate of return (IRR) corresponding to that cash flow statement. As a core economic indicator, IRR intuitively reflects the potential profitability in this simulation scenario.

[0053] The simulation engine performs physical and economic calculations simultaneously, meaning that every technical decision (such as prioritizing charging energy storage) is directly linked to an economic consequence (such as avoiding the cost of purchasing electricity at high prices). This deep coupling ensures that the simulation results can realistically reflect the actual economic behavior of an energy system under a specific electricity price environment, rather than analyzing technical performance and economic benefits in isolation.

[0054] In the embodiments of this application, multi-threaded parallel simulation of each simulation scenario is performed automatically. This avoids repetitive manual configuration and significantly improves computational efficiency.

[0055] After step S130 is completed, in step S140, the extracted internal rate of return is structured to generate a comparative analysis of the internal rate of return under different configuration schemes.

[0056] In the embodiments of this application, a data pivoting operation is performed during the structuring process of the extracted internal rate of return (IRR). Specifically, the configuration scheme is set as the rows of a new table, all different simulation boundary conditions are set as columns, and then the corresponding IRR values ​​are precisely filled into the corresponding cells of the table. This transformation process reshapes a long and loosely structured data list into an intuitive and well-organized two-dimensional decision matrix. This matrix clearly shows the economic performance of different system design schemes under various future possibilities, condensing complex multidimensional data relationships into a single table, making in-depth comparative analysis possible.

[0057] Based on the generated decision matrix, the system can perform multi-dimensional comparative analysis and generate business insights. By examining each row of the matrix horizontally, the stability and risk sensitivity of any configuration scheme under different external environments can be evaluated. By examining each column vertically, the economically optimal design scheme under specific market or policy conditions can be quickly identified. To make these comparative results clearer and easier to understand, the system also automatically generates visual charts such as heatmaps and grouped bar charts, presenting the quantitative analysis conclusions to decision-makers in the most intuitive way, thereby providing strong, data-driven decision support for the final strategic selection.

[0058] After completing step S140, in step S150, a sensitivity analysis report and the optimal configuration scheme are obtained based on the comparative analysis results.

[0059] In the embodiments of this application, during the process of obtaining a sensitivity analysis report based on the comparative analysis results, a numerical relationship curve is generated and displayed showing the impact of any change in any of the key parameters on the internal rate of return. Simultaneously, the decrease or increase in the internal rate of return caused by changes in electricity pricing policy is calculated and displayed.

[0060] Specifically, key parameters include at least the capacity of the energy storage module, the initial investment cost of the photovoltaic power generation module, and the feed-in tariff.

[0061] In the embodiments of this application, the scheme that maximizes the internal rate of return is taken as the optimal configuration scheme.

[0062] Sensitivity analysis reports transform future uncertainties (such as cost fluctuations and policy changes) from vague concepts into measurable impacts. By generating numerical relationship curves, decision-makers can visually see how a project's IRR changes when key variables such as energy storage capacity, photovoltaic costs, or feed-in tariffs fluctuate within a certain range.

[0063] Sensitivity analysis is essentially a process of identifying key factors. By calculating and demonstrating the impact of changes in different parameters on the internal rate of return, it can automatically identify which parameters are highly sensitive to the project's profitability.

[0064] In summary, based on the Homer Pro-based wind-solar-storage system yield calculation scheme provided above, this application provides a Homer Pro-based wind-solar-storage system yield calculation system. It employs the Homer Pro-based wind-solar-storage system yield calculation method as described in any embodiment of the first aspect to perform the Homer Pro-based wind-solar-storage system yield calculation. The system includes: a model building and configuration module, used to build a wind-solar-storage system model in Homer Pro software, comprising a wind power generation module, a photovoltaic power generation module, an energy storage module, an electrical load module, and a grid module, and to configure corresponding parameters for the wind power generation module, photovoltaic power generation module, energy storage module, and electrical load module by calling the technical and economic parameter library or user-defined parameters received through the dynamic strategy interface; a simulation scenario formation module, used to establish one or more configuration schemes for the wind-solar-storage system model based on the technical and economic parameter library or user-defined parameters received through the dynamic strategy interface, through the Homer Pro software's application programming interface or scripts, and to set corresponding simulation boundary conditions for the configuration schemes to constitute different simulation scenarios; and an internal rate of return calculation module, used to call Homer Pro... The simulation engine of the Pro software automatically simulates each simulation scenario and automatically extracts the internal rate of return (IRR) calculated by the built-in economic model of Homer Pro from the simulation results; the comparative analysis module is used to structure the extracted IRR and generate comparative analysis of IRR under different configuration schemes; the result acquisition module is used to obtain a sensitivity analysis report and the optimal configuration scheme based on the comparative analysis results.

[0065] Through the Homer Pro-based wind-solar-storage system yield calculation scheme provided above, this application embodiment transforms a complex manual evaluation process into a fully automated decision support system by constructing a system model encompassing wind, solar, storage, and grid, and utilizing application programming interfaces (APIs) or scripts. First, by calling upon a technical and economic parameter library and an automated simulation engine, it can quickly simulate one or more configuration schemes, efficiently obtaining accurate energy flow and cash flow data, thus overcoming the inefficiency and potential errors of manual operation. Second, by receiving user-defined parameters through a dynamic strategy interface, it ensures that the simulation scenario flexibly conforms to the real boundary conditions of various projects. Third, it can directly structure complex simulation data into a comparative analysis of the internal rate of return, generating clear sensitivity analysis reports and optimal configuration schemes, thereby transforming the tedious technical calculation process into a highly efficient evaluation tool capable of outputting clear and reliable business decision-making basis.

[0066] Furthermore, in some embodiments, a comprehensive technical and economic parameter library storing data ranging from initial investment costs and operation and maintenance costs to electricity pricing policies and meteorological data provides a complete and standardized realistic basis for simulation calculations. Based on this, by allowing systematic adjustments to key parameters such as capacity, percentage changes in cost, and charge / discharge efficiency, the method gains significant flexibility in generating diverse configuration schemes, enabling comprehensive sensitivity analysis. By setting a search space for modules such as wind, solar, and storage, including zero-capacity and non-zero predetermined capacity, the system can autonomously decide whether to include a module in the configuration during the optimization process, thereby avoiding unnecessary equipment investment and ensuring that the final optimal solution is a truly economical, streamlined, and efficient configuration.

[0067] Furthermore, in some embodiments, by establishing a baseline scenario that includes only grid power supply and zero load, an absolute and objective reference point is provided for the economic evaluation of all wind, solar, and energy storage configuration schemes. By actively setting wind, solar, and energy storage modules as a search space with no capacity, it constructs a theoretical reference system of zero investment and zero return in the model. This allows the rate of return calculated for any other configuration scheme to be clearly interpreted as the pure incremental value brought by investing in wind, solar, and energy storage assets, thereby eliminating the relativity and ambiguity of the evaluation, ensuring that all comparative analyses are based on a unified and unambiguous scientific foundation, and greatly enhancing the reliability of the final decision.

[0068] Furthermore, in some embodiments, by generating and displaying numerical relationship curves related to the internal rate of return (IRR), it can intuitively reveal the sensitivity of a project's profitability to fluctuations in key parameters such as energy storage capacity, photovoltaic investment costs, or feed-in tariffs, helping decision-makers accurately identify core risk points. By further calculating and displaying the specific decay or growth values ​​of the IRR when complex electricity pricing policies such as time-of-use pricing and subsidy standards change, it provides clear quantitative indicators for assessing the financial impact of policy changes, enabling sensitivity analysis to move beyond the macro level and reach a level of sophistication that can directly guide business strategies.

[0069] This application also provides a system for calculating the yield of a wind-solar-storage system based on Homer Pro. It can use the aforementioned method 100 for calculating the yield of a wind-solar-storage system based on Homer Pro, or other methods can be used for calculating the yield of a wind-solar-storage system based on Homer Pro. This application does not impose any restrictions on this method.

[0070] Figure 2 An exemplary structural block diagram of a wind-solar-storage system yield calculation system based on Homer Pro, according to an embodiment of this application, is shown.

[0071] like Figure 2 As shown, the system 200 includes a model building and configuration module 210, a simulation scene formation module 220, an internal rate of return (IRR) calculation module 230, a comparative analysis module 240, and a result acquisition module 250. In the embodiments of this application, the model building and configuration module 210, the simulation scene formation module 220, the IRR calculation module 230, the comparative analysis module 240, and the result acquisition module 250 may be separate units or integrated into the same controller; this application does not impose any restrictions here.

[0072] Specifically, the model building and configuration module 210 is used to build a wind-solar-storage system model in the Homer Pro software, which includes a wind power generation module, a photovoltaic power generation module, an energy storage module, an electrical load module, and a grid module, and to configure the corresponding parameters for the wind power generation module, photovoltaic power generation module, energy storage module, and electrical load module by calling the technical and economic parameter library or by receiving user-defined parameters through the dynamic strategy interface.

[0073] Specifically, the simulation scenario formation module 220 is used to establish one or more configuration schemes for the wind-solar-storage system model based on the technical and economic parameter library or user-defined parameters received through the dynamic strategy interface, through the application programming interface or script of the Homer Pro software, and to set corresponding simulation boundary conditions for the configuration schemes to form different simulation scenarios.

[0074] Specifically, the internal rate of return calculation module 230 calls the simulation calculation engine of the Homer Pro software to perform automated simulation for each simulation scenario and automatically extracts the internal rate of return calculated by the Homer Pro built-in economic model from the simulation results.

[0075] Specifically, the comparative analysis module 240 is used to structure the extracted internal rate of return and generate a comparative analysis of the internal rates of return under different configuration schemes.

[0076] Specifically, the results acquisition module 250 is used to obtain a sensitivity analysis report and the optimal configuration scheme based on the comparative analysis results.

[0077] When system 200 uses the aforementioned Homer Pro-based wind-solar-storage system yield calculation method 100 to calculate the yield of the Homer Pro-based wind-solar-storage system, the aforementioned steps S110 are executed through the model building and configuration module 210, S120 through the simulation scenario formation module 220, S130 through the internal rate of return calculation module 230, S140 through the comparative analysis module 240, and S150 through the result acquisition module 250. The specific execution process can be found above and will not be repeated here.

[0078] While numerous embodiments of this application have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will arise for those skilled in the art without departing from the spirit and intent of this application. It should be understood that various alternatives to the embodiments of this application described herein may be employed in the practice of this application. The appended claims are intended to define the scope of protection of this application and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A method for calculating the yield of a wind-solar-storage system based on Homer Pro, characterized in that, include: In Homer Pro software, a wind-solar-storage system model is built, which includes a wind power generation module, a photovoltaic power generation module, an energy storage module, an electrical load module, and a grid module. The corresponding parameters are configured for the wind power generation module, photovoltaic power generation module, energy storage module, and electrical load module by calling the technical and economic parameter library or by receiving user-defined parameters through the dynamic strategy interface. Based on the aforementioned technical and economic parameter library or user-defined parameters received through the dynamic strategy interface, one or more configuration schemes are established for the wind-solar-storage system model through the application programming interface or script of HomerPro software, and corresponding simulation boundary conditions are set for the configuration schemes to form different simulation scenarios. The simulation calculation engine of Homer Pro software is invoked to perform automated simulation for each simulation scenario, and the internal rate of return calculated by the built-in economic model of Homer Pro is automatically extracted from the simulation results. The extracted internal rate of return is structured to generate a comparative analysis of internal rates of return under different configuration schemes; Based on the comparative analysis results, a sensitivity analysis report and the optimal configuration scheme are obtained.

2. The method for calculating the yield of a wind-solar-storage system based on Homer Pro according to claim 1, characterized in that, The technical and economic parameter database stores initial investment costs, operation and maintenance costs, lifespan, power generation efficiency, energy storage module charging and discharging efficiency, electricity pricing policies, load data, and meteorological data.

3. The method for calculating the yield of a wind-solar-storage system based on Homer Pro according to claim 2, characterized in that, The configuration scheme is formed by adjusting at least one parameter among capacity, percentage change in initial investment cost, percentage change in operation and maintenance cost, charging and discharging efficiency of energy storage modules, electricity price policy, load data, and meteorological data.

4. The method for calculating the yield of a wind-solar-storage system based on Homer Pro according to claim 1 or 3, characterized in that, The simulation boundary conditions include electricity pricing policies and meteorological data. The electricity pricing policies include at least one or more of the following: time-of-use pricing, grid connection pricing, electricity purchase pricing, and subsidy standards.

5. The method for calculating the yield of a wind-solar-storage system based on Homer Pro according to claim 3, characterized in that, The capacity includes the capacity of the wind power generation module, the capacity of the photovoltaic power generation module, and the capacity of the energy storage module; During the capacity adjustment process, a search space is defined for at least one of the wind power generation module, photovoltaic power generation module, or energy storage module, which includes zero capacity and at least one non-zero predetermined capacity.

6. The method for calculating the yield of a wind-solar-storage system based on Homer Pro according to claim 5, characterized in that, The simulation scenarios include a baseline scenario that only includes grid power supply and zero electrical load; In establishing the baseline scenario, a search space with no capacity is selected in the configuration of the wind power generation module, photovoltaic power generation module and energy storage module, and the electrical load of the electrical load module is set to zero, so as to form a simulation scenario that only includes the grid module and the electrical load module.

7. The method for calculating the yield of a wind-solar-storage system based on Homer Pro according to claim 1, characterized in that, The user-defined parameters include energy storage charging and discharging strategy parameters and policy sensitivity parameters.

8. The method for calculating the yield of a wind-solar-storage system based on Homer Pro according to claim 1, characterized in that, Configure multi-threaded parallel processing to automate the simulation of each scenario.

9. The method for calculating the yield of a wind-solar-storage system based on Homer Pro according to claim 3, characterized in that, In the process of obtaining a sensitivity analysis report based on the comparative analysis results, the following steps are performed: Generate and display a numerical relationship curve showing the impact of any change in any of the key parameters on the internal rate of return, wherein the key parameters include at least the capacity of the energy storage module, the initial investment cost of the photovoltaic power generation module, and the feed-in tariff. Calculate and display the decrease or increase in the internal rate of return caused by changes in electricity pricing policies, wherein the electricity pricing policies include at least one or more of time-of-use pricing, feed-in tariffs, purchase tariffs, and subsidy standards.

10. A yield calculation system for wind-solar-storage systems based on Homer Pro, characterized in that, The yield calculation of a wind-solar-storage system based on Homer Pro is performed using the yield calculation method for a wind-solar-storage system based on any one of claims 1-9, wherein the system comprises: The model building and configuration module is used to build a wind-solar-storage system model in Homer Pro software, which includes a wind power generation module, a photovoltaic power generation module, an energy storage module, an electrical load module, and a grid module. It configures the corresponding parameters for the wind power generation module, photovoltaic power generation module, energy storage module, and electrical load module by calling the technical and economic parameter library or by receiving user-defined parameters through the dynamic strategy interface. The simulation scenario generation module is used to establish one or more configuration schemes for the wind-solar-storage system model based on the technical and economic parameter library or user-defined parameters received through the dynamic strategy interface, through the application programming interface or script of Homer Pro software, and to set corresponding simulation boundary conditions for the configuration schemes to form different simulation scenarios. The internal rate of return (IRR) calculation module is used to call the simulation calculation engine of Homer Pro software to perform automated simulation for each simulation scenario and automatically extract the IRR calculated by the built-in economic model of Homer Pro from the simulation results. The comparative analysis module is used to structure the extracted internal rate of return (IRR) and generate a comparative analysis of the IRR under different configuration schemes. The results acquisition module is used to generate a sensitivity analysis report and the optimal configuration scheme based on the comparative analysis results.

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