Photovoltaic and energy storage combined configuration aid decision-making method and device, equipment and medium

Abstract

The invention provides an auxiliary decision-making method and device for photovoltaic and energy storage combined configuration, equipment and a medium, and relates to the technical field of photovoltaic installation and maintenance. The method comprises the following steps: establishing hourly power generation power models of photovoltaic power generation devices of various photovoltaic support types, and calculating an electricity price weighted unit capacity efficiency function in combination with a dynamic electricity price sequence generated by a long short-term memory network; building a ternary coupling optimization model and solving a candidate configuration solution set by taking the installed capacity and the energy storage capacity of each photovoltaic support type photovoltaic power generation device as joint optimization variables; and performing weighted sorting and visual display in combination with the multi-dimensional economic indexes, and outputting configuration recommendation schemes of the photovoltaic power generation devices of various photovoltaic support types and energy storage. According to the scheme, efficient matching and scientific decision-making of the photovoltaic support and the energy storage configuration scheme in a dynamic market environment can be realized.

Description

Technical Field

[0001] The present disclosure relates to the field of photovoltaic installation and maintenance technology, and more specifically, to a photovoltaic and energy storage joint configuration auxiliary decision-making method, device, equipment, and medium. Background Art

[0002] Photovoltaic power generation technology is widely used in renewable energy power generation systems due to its renewable and clean nature. With the advancement of electricity marketization, an increasing number of photovoltaic projects are directly participating in spot transactions on the power grid, resulting in significant dynamic fluctuations in electricity prices. The design and configuration of traditional photovoltaic power plants are often based on the annual average revenue of a single power generation curve. This makes it difficult to fully adapt to the differences in electricity prices over time, and is prone to mismatches between peak power generation and low electricity prices, resulting in failure to maximize power generation revenue.

[0003] In related technologies, power generation forecasts for different types of photovoltaic brackets primarily rely on annual average irradiation models, lacking refined modeling of hourly power generation. Although some solutions attempt to combine historical irradiation with component performance parameters to form a power generation curve, there is a lack of a complete calculation method that can intuitively reflect the impact of bracket type on hourly power generation differences. At the same time, electrochemical energy storage has gradually been applied to photovoltaic power stations to balance loads and improve power dispatch flexibility. However, related energy storage configurations are often based on fixed capacity or empirical judgment, making it difficult to consider time-series changes in electricity prices and fluctuations in power generation, resulting in a low return on energy storage investment and unstable overall economic benefits of the system.

[0004] In addition, the economic analysis methods for related photovoltaic projects are relatively limited, mainly based on static profit accounting or single technical parameter comparison. It is difficult to comprehensively demonstrate the differentiated performance of different bracket types and energy storage capacity solutions in a dynamic market environment. Moreover, the project development and investment decision-making process often relies on manual judgment and lacks effective data support and solution comparison. This shows obvious limitations in the environment of dynamic electricity prices and diversified scenarios. Summary of the Invention

[0005] The purpose of the embodiments of the present disclosure is to provide a photovoltaic and energy storage joint configuration auxiliary decision-making method and device, equipment, and medium, thereby enabling efficient matching and scientific decision-making of photovoltaic brackets and energy storage configuration solutions in a dynamic market environment.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0007] According to a first aspect of an embodiment of the present disclosure, a method for assisting decision-making for joint configuration of photovoltaics and energy storage is provided, comprising: Obtain historical meteorological data, grid spot electricity price data, and basic project parameters for the target site, establish hourly power generation models per unit capacity for fixed-tilt mounts, vertically mounted mounts, and horizontal single-axis tracking mounts, and generate annual unit capacity power generation forecast curves for each type of mount. Based on the pre-trained long short-term memory network prediction model, the system conducts time series analysis on historical electricity price data, regional electricity load and meteorological factors to generate dynamic electricity price time series for future periods. The annual unit capacity power generation prediction curve is weightedly coupled with the dynamic electricity price time series hour by hour to determine the electricity price weighted unit capacity efficiency function corresponding to the photovoltaic power generation device of each photovoltaic bracket type; Based on the electricity price weighted unit capacity efficiency function, the installed capacity of the fixed-tilt bracket, vertical mounting bracket, and flat single-axis tracking bracket photovoltaic power generation device and the capacity of the electrochemical energy storage system are used as joint optimization variables, and with maximizing the project internal rate of return as the core goal, a ternary coupled optimization model of photovoltaics, energy storage, and electricity price is constructed, and a solution set of candidate configurations is solved; Calculate and visualize the economic indicators of the candidate configuration solution set, and generate weighted scoring ranking results based on user preference setting parameters; According to the weighted scoring ranking results, a recommended solution for the combined configuration of photovoltaic power generation devices and energy storage for each photovoltaic bracket type is output.

[0008] In some exemplary embodiments of the present disclosure, based on the aforementioned solution, generating a full-year unit capacity power generation prediction curve corresponding to each photovoltaic power generation device of each photovoltaic bracket type includes: Collecting hourly incident radiation and ambient temperature data of the target site; Based on the hourly incident radiation and the ambient temperature, respectively calculating the photoelectric conversion efficiency of the components corresponding to the photovoltaic power generation device of the fixed tilt bracket, the vertical mounting bracket, and the flat single-axis tracking bracket type; Determining hourly generated power based on the photoelectric conversion efficiency of each component, a temperature correction factor, a dust shielding factor, and inverter efficiency; Normalizing the hourly generated power according to unit capacity to form hourly generated power per unit capacity models for the fixed tilt bracket, the vertical mounting bracket, and the flat single-axis tracking bracket type photovoltaic power generation devices; The hourly power generation model per unit capacity is expanded over the entire year time series to obtain the annual unit capacity power generation prediction curve corresponding to each photovoltaic power generation device of each photovoltaic bracket type.

[0009] In some exemplary embodiments of the present disclosure, based on the aforementioned solution, constructing a ternary coupling optimization model of photovoltaics, energy storage, and electricity prices includes: Establishing an energy storage dynamic equation, which is used to describe the charging power, discharging power and state of charge of the electrochemical energy storage system in each time period, and introducing charging and discharging efficiency and charging and discharging mutual exclusion constraints into the energy storage dynamic equation to form an energy storage submodel; The annual unit capacity power generation forecast curve and the electricity price weighted unit capacity efficiency function are combined with the energy storage sub-model to construct a ternary coupling optimization model.

[0010] In some example embodiments of the present disclosure, based on the aforementioned solution, solving the candidate configuration solution set includes: Inputting the installed capacity of the fixed tilt bracket, the vertical mounting bracket, the flat single-axis tracking bracket type photovoltaic power generation device and the capacity of the electrochemical energy storage system into the ternary coupling optimization model; Based on the ternary coupling optimization model, a non-dominated sorting genetic algorithm is used to perform multi-objective evolutionary search; Performing iterative calculation of internal rate of return during the multi-objective evolutionary search process, and calculating the internal rate of return for each set of joint optimization variables; The internal rate of return is fed back as a fitness value to the evolutionary search process of the ternary coupling optimization model to generate a Pareto non-inferior solution set, and the Pareto non-inferior solution set is used as a candidate configuration solution set.

[0011] In some example embodiments of the present disclosure, based on the aforementioned solution, generating a weighted score ranking result by combining user preference setting parameters includes: Obtaining preference weight parameters set by a user, wherein the preference weight parameters include weight coefficients for internal rate of return, net present value, cost per kilowatt-hour, and investment payback period; Performing weighted calculation on the economic performance indicators of the candidate configuration solution set and the preference weight parameter to obtain a comprehensive score value for each solution; The solutions in the candidate configuration solution set are sorted according to the comprehensive score value to obtain a weighted score sorting result.

[0012] In some example embodiments of the present disclosure, based on the aforementioned solution, economic performance index calculation is performed on the candidate configuration solution set, including: Calculating the internal rate of return and net present value of the project life cycle based on the installed capacity and energy storage capacity in the candidate configuration solution set; A comprehensive economic indicator matrix is ​​generated according to the internal rate of return, the net present value, the cost per kilowatt-hour, and the investment payback period, and the comprehensive economic indicator matrix is ​​used as the economic indicator of the candidate configuration solution set.

[0013] In some example embodiments of the present disclosure, based on the aforementioned solution, visually displaying the candidate configuration solution set includes: Generate an economic indicator radar chart, a configuration ratio chart, and an energy storage behavior trajectory chart for the candidate configuration solution set; After receiving the user's adjustment operation on the installed capacity or energy storage capacity of the photovoltaic power generation device of each photovoltaic bracket type, recalculating the economic indicator matrix based on the adjusted installed capacity and energy storage capacity; The updated economic indicator matrix is ​​fed back to the economic indicator radar chart, the configuration ratio chart and the energy storage behavior trajectory chart in real time to achieve dynamic visual update.

[0014] According to a second aspect of an embodiment of the present disclosure, a photovoltaic and energy storage joint configuration decision-making auxiliary device is provided, comprising: The power generation forecast curve generation module is used to obtain historical meteorological data, grid spot electricity price data and basic project parameters of the target site, establish hourly power generation models per unit capacity for fixed-tilt brackets, vertical-mount brackets, and horizontal single-axis tracking brackets, and generate annual power generation forecast curves per unit capacity for each type of bracket. The future dynamic electricity price generation module is used to generate a dynamic electricity price time series for future periods by performing time series analysis on historical electricity price data, regional electricity load, and meteorological factors based on a pre-trained long-short-term memory network prediction model; An efficiency function construction module is used to weightedly couple the annual unit capacity power generation forecast curve with the dynamic electricity price time series hour by hour to determine the electricity price weighted unit capacity efficiency function corresponding to the photovoltaic power generation device of each photovoltaic bracket type; a coupled optimization model solving module for constructing a ternary coupled optimization model of photovoltaics, energy storage, and electricity prices based on the electricity price weighted unit capacity efficiency function, taking the installed capacity of the fixed-tilt bracket, vertical mounting bracket, and flat single-axis tracking bracket type photovoltaic power generation device and the capacity of the electrochemical energy storage system as joint optimization variables, and maximizing the project internal rate of return as the core goal, and solving a candidate configuration solution set; A candidate configuration solution set ranking module is used to calculate and visualize the economic indicators of the candidate configuration solution set, and generate weighted scoring ranking results based on user preference setting parameters; The joint configuration auxiliary recommendation module is used to output a recommended solution for the joint configuration of photovoltaic power generation devices and energy storage for each photovoltaic bracket type based on the weighted score sorting result.

[0015] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the photovoltaic and energy storage joint configuration auxiliary decision-making method described above is implemented.

[0016] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the photovoltaic and energy storage joint configuration auxiliary decision-making method according to any one of the above items is implemented.

[0017] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects: The photovoltaic and energy storage joint configuration auxiliary decision-making method in the example embodiment of the present disclosure, on the one hand, realizes the organic combination of power generation timing characteristics and electricity price fluctuations through the weighted coupling calculation of the hourly power generation power model of the unit capacity of photovoltaic power generation devices of different photovoltaic bracket types and the dynamic electricity price time series, so that the power generation contribution of photovoltaic power generation devices of each photovoltaic bracket type in different electricity price ranges can be finely identified, avoiding the distortion problem of the traditional solution that evaluates the economic feasibility only based on the annual average power generation, and can effectively reduce the loss of income caused by the mismatch between power generation peak and low electricity price; on the other hand, by incorporating the installed capacity of photovoltaic power generation devices of each photovoltaic bracket type and the capacity of the electrochemical energy storage system into the ternary coupling optimization model, the power generation timing characteristics, The interactive mechanism between electricity price fluctuations and energy storage scheduling can match the charging and discharging strategies of energy storage with photovoltaic power generation output, achieving a flexible response to dynamic electricity prices, thereby overcoming the limitations of related technologies in which energy storage capacity configuration relies on experience values ​​and has low utilization rates, and improving the overall economic return rate of energy storage resources. On the other hand, through the calculation and visualization of multi-dimensional economic indicators of candidate configuration solutions, and combined with user preference weights for comprehensive scoring and ranking, the advantages and disadvantages of different configuration solutions can be presented in an intuitive and data-based manner, breaking through the shortcomings of traditional solutions that rely on static accounting and manual experience judgment, and can provide flexible and transparent solution comparison and optimization basis for different investment preferences, ensuring that the decision-making process is more targeted and operational.

[0018] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0020] Figure 1 A schematic diagram of a photovoltaic and energy storage joint configuration auxiliary decision-making method according to some embodiments of the present disclosure is schematically shown.

[0021] Figure 2 The flowchart of determining the annual unit capacity power generation prediction curve according to some embodiments of the present disclosure is schematically shown.

[0022] Figure 3 The following schematically illustrates a flow chart of determining a candidate configuration solution set based on a ternary coupling optimization model according to some embodiments of the present disclosure.

[0023] Figure 4 The schematic diagram shows the structure of a photovoltaic and energy storage joint configuration auxiliary decision-making device according to some embodiments of the present disclosure.

[0024] Figure 5 A schematic structural diagram of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0025] Figure 6 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0026] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0027] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of this specification, as detailed in the appended claims.

[0028] Furthermore, the drawings are schematic illustrations only and are not necessarily drawn to scale. The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically separate entities. In other words, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0029] In this example embodiment, a method for assisting decision-making in the joint configuration of photovoltaics and energy storage is first provided. This method for assisting decision-making in the joint configuration of photovoltaics and energy storage can be applied to terminal devices, such as mobile phones, computers and other electronic devices, and can also be applied to servers. This embodiment is not limited to this, and the following description will take the execution of this method by a server as an example. Figure 1The following schematically illustrates a flow chart of a photovoltaic and energy storage joint configuration auxiliary decision-making method according to some embodiments of the present disclosure. Figure 1 As shown, the photovoltaic and energy storage joint configuration auxiliary decision-making method may include the following steps: Step S110, obtaining historical meteorological data, grid spot electricity price data, and basic project parameters for the target site, establishing hourly power generation models per unit capacity for fixed-tilt mounts, vertical-mount mounts, and horizontal single-axis tracking mounts, and generating annual unit capacity power generation prediction curves for each type of photovoltaic mount. Step S120 , based on the pre-trained long short-term memory network prediction model, perform time series analysis on historical electricity price data, regional electricity load and meteorological factors to generate a dynamic electricity price time series for future time periods; Step S130, weightedly coupling the annual unit capacity power generation prediction curve with the dynamic electricity price time series hour by hour to determine the electricity price weighted unit capacity efficiency function corresponding to each photovoltaic bracket type photovoltaic power generation device; Step S140, based on the electricity price weighted unit capacity efficiency function, using the installed capacity of the fixed-tilt support, vertical mounting support, and flat single-axis tracking support type photovoltaic power generation devices and the capacity of the electrochemical energy storage system as joint optimization variables, and taking maximizing the project internal rate of return as the core goal, constructing a ternary coupled optimization model of photovoltaics, energy storage, and electricity price, and solving a candidate configuration solution set; Step S150 , calculating and visualizing the economic performance indicators of the candidate configuration solution set, and generating a weighted scoring ranking result in combination with user preference setting parameters; Step S160: outputting a recommended solution for the combined configuration of photovoltaic power generation devices and energy storage for each photovoltaic bracket type based on the weighted score ranking result.

[0030] According to the auxiliary decision-making method for the joint configuration of photovoltaic and energy storage in this example embodiment, on the one hand, the weighted coupling calculation of the hourly power generation model of the unit capacity of photovoltaic power generation devices of different photovoltaic bracket types and the dynamic electricity price time series is realized to achieve an organic combination of power generation timing characteristics and electricity price fluctuations, so that the power generation contribution of photovoltaic power generation devices of each bracket type in different electricity price ranges can be finely identified, avoiding the distortion problem of the traditional solution that evaluates the economic feasibility only based on the annual average power generation, and can effectively reduce the loss of income caused by the mismatch between power generation peak and low electricity price; on the other hand, by incorporating the installed capacity of photovoltaic power generation devices of each photovoltaic bracket type and the capacity of the electrochemical energy storage system into the ternary coupling optimization model, the power generation timing characteristics, electricity The interactive mechanism between price fluctuations and energy storage scheduling can match the charging and discharging strategies of energy storage with photovoltaic power generation output, achieve flexible response to dynamic electricity prices, and thus overcome the limitations of related technologies in which energy storage capacity configuration relies on experience values ​​and has low utilization rates, thereby improving the overall economic return rate of energy storage resources. On the other hand, through the calculation and visualization of multi-dimensional economic indicators of candidate configuration solutions, and combined with user preference weights for comprehensive scoring and ranking, the advantages and disadvantages of different configuration solutions can be presented in an intuitive and data-based manner, breaking through the shortcomings of traditional solutions that rely on static accounting and manual experience judgment, and can provide flexible and transparent solution comparison and optimization basis for different investment preferences, ensuring that the decision-making process is more targeted and operational.

[0031] The photovoltaic and energy storage joint configuration auxiliary decision-making method in this example embodiment will be further described below.

[0032] In step S110, historical meteorological data, grid spot electricity price data and basic project parameters of the target site are obtained, and hourly power generation models per unit capacity are established for photovoltaic power generation devices of fixed tilt brackets, vertical mounting brackets and horizontal single-axis tracking bracket types, respectively, and a full-year unit capacity power generation prediction curve corresponding to each bracket type photovoltaic power generation device is generated.

[0033] In an exemplary embodiment of the present disclosure, the target site refers to a specific geographical location where a photovoltaic power station is planned to be built or has been built, and its meteorological conditions directly determine the actual power generation capacity of the photovoltaic modules.

[0034] Historical meteorological data may include, but are not limited to, hourly total solar radiation, direct radiation, diffuse radiation, ambient temperature, sunshine duration, air quality, wind speed, humidity and other parameters. By collecting this data from long-term monitoring stations or authoritative meteorological databases, high-precision environmental inputs can be provided for subsequent power generation prediction models. Grid spot electricity price data refers to the hourly electricity price curve formed in electricity spot market transactions, which is usually released by the power dispatching center or trading platform, and its time resolution can reach the hourly level. Basic project parameters may include engineering information such as the rated power of photovoltaic modules, module efficiency, number of modules, inverter model and its efficiency curve, installation angle and azimuth, etc., which are used to accurately simulate the power generation characteristics of photovoltaic systems.

[0035] A fixed-tilt bracket type photovoltaic power generation device refers to a photovoltaic power generation device in which the tilt angle of the photovoltaic module is fixed. Its advantages are simple structure and easy maintenance; a vertically mounted bracket type photovoltaic power generation device refers to a photovoltaic power generation device in which the photovoltaic module is installed in a vertical state, which is suitable for specific geographical conditions or bifacial module gain utilization; a flat single-axis tracking bracket type photovoltaic power generation device refers to a photovoltaic module that tracks the path of the sun within a certain range around a horizontal or near-horizontal axis, thereby increasing the daily power generation.

[0036] Using historical meteorological data from the target site, grid spot electricity prices, and basic project parameters, hourly power generation models per unit capacity were developed for each of the three PV mounting types. This modeling process, based on a 1 kW rated power, calculated power generation over different time periods. This modeling process can utilize PV array simulation software (such as PVsyst) or a custom mathematical model, taking into account factors such as the module's photovoltaic conversion efficiency, temperature correction factor, dust obstruction factor, reflection factor, and inverter conversion losses.

[0037] Generating a full-year unit capacity power generation forecast curve involves expanding the hourly power generation model over a time series of 8,760 hours per year to generate an hourly power generation capacity forecast curve that covers seasonal variations. Alternatively, a full-year unit capacity power generation forecast curve can be generated using methods such as solar irradiation inversion from satellite remote sensing data or power generation fitting methods based on artificial intelligence models, although this exemplary embodiment does not impose any particular limitations on this method.

[0038] In step S120, based on the pre-trained long short-term memory network prediction model, a time series analysis is performed on historical electricity price data, regional electricity load and meteorological factors to generate a dynamic electricity price time series for future time periods.

[0039] In an example embodiment of the present disclosure, a Long Short-Term Memory (LSTM) network is a special recurrent neural network structure whose core advantage is its ability to capture long-term dependencies in time series, making it suitable for forecasting nonlinear fluctuations such as electricity prices. A pre-trained LSTM prediction model utilizes existing large-scale historical electricity price and load data to train the LSTM model offline, optimizing the model's weights and parameters to accurately capture the temporal patterns of electricity prices. In a specific offline training implementation, a sliding window input sequence of a certain length (such as electricity price and load data for the past 72 hours) is selected to output a forecasted electricity price sequence for the next 24 hours. The mean squared error (MSE) is used as the loss function, and the network weights are adjusted through a backpropagation algorithm. Alternatively, a prediction model can be trained using a gated recurrent unit (GRU), an attention-based time series prediction model, or a hybrid ARIMA-LSTM model.

[0040] Historical electricity price data forms the foundation of model training, reflecting price fluctuations in the electricity market over different time periods. Regional electricity load refers to the amount of electricity demand in a region during a specific time period. Combined with historical electricity price data, this data can help predict changes in the supply and demand relationship for electricity prices. Meteorological factors such as temperature, wind speed, and light intensity can also affect the supply of electricity at the power generation end of the electricity market, indirectly influencing price fluctuations. Generating a dynamic electricity price time series for future periods refers to the output of the model's hourly price forecasts for the future, typically with a minimum time resolution of hours and covering the next 24, 48, or even longer periods.

[0041] In step S130, the annual unit capacity power generation prediction curve is weightedly coupled with the dynamic electricity price time series hourly to determine the electricity price weighted unit capacity efficiency function corresponding to the photovoltaic power generation device of each photovoltaic bracket type.

[0042] In an example embodiment of the present disclosure, the annual unit capacity power generation forecast curve reflects the power generation of each photovoltaic bracket type photovoltaic power generation device under hourly conditions throughout the year, and the dynamic electricity price time series reflects the price signal of the future hourly electricity market. Hourly weighted coupling refers to multiplying the power generation value by the corresponding electricity value at each time point, and accumulating or integrating them throughout the year to obtain an indicator that comprehensively reflects the potential for power generation revenue. The electricity price weighted unit capacity efficiency function is a quantitative indicator used to characterize the weighted revenue value that can be brought by unit installed capacity under different photovoltaic bracket types photovoltaic power generation devices. Optionally, an electricity price weight coefficient can be introduced to perform nonlinear amplification on peak and valley electricity prices, or a seasonal correction factor can be introduced for segmented weighting. This embodiment does not specifically limit this.

[0043] In step S140, based on the electricity price weighted unit capacity efficiency function, the installed capacity of the photovoltaic power generation devices of the fixed tilt bracket, vertical mounting bracket, and flat single-axis tracking bracket type and the capacity of the electrochemical energy storage system are used as joint optimization variables, and with maximizing the project internal rate of return as the core goal, a ternary coupling optimization model of photovoltaics, energy storage and electricity price is constructed, and the candidate configuration solution set is solved.

[0044] In an example embodiment of the present disclosure, the electricity price weighted unit capacity efficiency function is used as an input indicator to measure the potential profit of power generation configuration, and is used to guide the economic evaluation of the configuration scheme of photovoltaic power generation devices of various photovoltaic bracket types. The joint optimization variable refers to the installed capacity of photovoltaic power generation devices of three photovoltaic bracket types and the capacity of the electrochemical energy storage system as parameters to be optimized. There is a complementary relationship between these variables: the timing characteristics of photovoltaic power generation output affect the charging and discharging strategy of energy storage, while the energy storage capacity determines the ability to adjust to electricity price fluctuations. The electrochemical energy storage system can use lithium-ion batteries, sodium-sulfur batteries or flow batteries, with a capacity range from hundreds of kilowatt-hours to hundreds of megawatt-hours.

[0045] The project's internal rate of return (IRR) is a core economic indicator of the optimization objective, used to measure the return on project investment. When constructing a ternary coupled optimization model, it is necessary to comprehensively consider the photovoltaic power generation curve, electricity price time series, and energy storage dynamic equations, so that power generation, electricity price, and energy storage scheduling form a three-dimensional constraint and interactive relationship. For example, linear programming, nonlinear programming, or genetic algorithms can be used to transform the multi-objective optimization problem into a weighted single-objective iterative solution, or a multi-objective evolutionary algorithm with Pareto non-inferiority can be employed. Of course, solutions can also be obtained using heuristic algorithms, simulated annealing algorithms, or particle swarm optimization algorithms. This embodiment does not specifically limit the specific method for solving the candidate configuration solution set. The established ternary coupled optimization model can quickly screen out the candidate solution that optimally balances benefits and costs from numerous combinations, providing a high-quality solution set for subsequent economic ranking and decision-making.

[0046] In step S150, the economic performance index of the candidate configuration solution set is calculated and visualized, and a weighted scoring ranking result is generated in combination with the user preference setting parameters.

[0047] In an example embodiment of the present disclosure, the candidate configuration solution set refers to multiple groups of photovoltaic bracket type photovoltaic power generation device installed capacity and energy storage capacity combinations selected by the optimization algorithm, each combination representing a feasible configuration solution.

[0048] Economic indicators may include internal rate of return, net present value (NPV), levelized cost of electricity (LCOE), and payback period, etc. These indicators can comprehensively evaluate the long-term profitability, cost level, and capital recovery speed of the solution. Of course, other economic indicators that can evaluate the long-term profitability, cost level, and capital recovery speed of the solution can also be used, and this example embodiment does not specifically limit this.

[0049] The calculation process for economic indicators can be based on a standard discounted cash flow model, combining construction investment, operating costs, and power generation revenue over the project lifecycle. Visualization can generate economic indicator radar charts, configuration ratio diagrams, and energy storage dispatch curves, presenting complex economic indicator information in intuitive graphics, making it easy to compare the advantages and disadvantages of different solutions.

[0050] User preference parameters refer to the weights assigned by users to various economic indicators based on their investment strategies, such as a preference for high returns, low costs, or short payback periods. By combining these weights with the economic performance indicators of a set of candidate solutions, a composite score is generated, and solutions are ranked based on the resulting score. Alternatively, machine learning models can be used to automatically model user preferences, or dynamic weighting adjustments can be implemented through an interactive interface.

[0051] In step S160, a recommended solution for the combined configuration of photovoltaic power generation devices and energy storage for each photovoltaic bracket type is output based on the weighted score ranking result.

[0052] In an exemplary embodiment of the present disclosure, a recommended solution is a preferred configuration selected based on the ranking results, including the installed capacity ratio of photovoltaic power generation devices, energy storage capacity, and expected economic performance indicators for each photovoltaic bracket type. The recommended solution can be output as a text report, a visual interface, or a data file, allowing users to directly apply it to project planning and investment evaluation.

[0053] By combining the hourly power generation model of the unit capacity of photovoltaic power generation devices of different photovoltaic bracket types with the weighted coupling calculation of the dynamic electricity price time series, the organic combination of power generation timing characteristics and electricity price fluctuations is achieved, so that the power generation contribution of each bracket type in different electricity price ranges can be finely identified, avoiding the distortion problem of traditional solutions that evaluate economic feasibility based solely on annual average power generation, and effectively reducing the loss of revenue caused by the mismatch between peak power generation and low electricity prices; by incorporating the installed capacity of photovoltaic power generation devices of each photovoltaic bracket type and the capacity of the electrochemical energy storage system into the ternary coupling optimization model, an interactive mechanism between power generation timing characteristics, electricity price fluctuations and energy storage scheduling is formed, which can By matching the charging and discharging strategies of energy storage with photovoltaic power generation output, a flexible response to dynamic electricity prices can be achieved, thereby overcoming the limitations of related technologies in which energy storage capacity configuration relies on experience values ​​and has low utilization rates, and improving the overall economic return rate of energy storage resources. By calculating and visualizing the multi-dimensional economic indicators of candidate configuration solutions and combining them with user preference weights for comprehensive scoring and ranking, the advantages and disadvantages of different configuration solutions can be presented in an intuitive and data-based manner, breaking through the shortcomings of traditional solutions that rely on static accounting and manual experience judgment, and providing flexible and transparent solution comparison and optimization basis for different investment preferences, ensuring that the decision-making process is more targeted and operational.

[0054] The contents of step S110 to step S160 are described in detail below.

[0055] In an exemplary embodiment of the present disclosure, Figure 2 The steps in the above are used to generate the annual unit capacity power generation forecast curve corresponding to each photovoltaic bracket type photovoltaic power generation device, refer to Figure 2 Specifically, it may include: Step S210, collecting the hourly incident radiation and ambient temperature data of the target site; Step S220, calculating the component photoelectric conversion efficiency corresponding to the fixed tilt bracket, the vertical mounting bracket and the flat single-axis tracking bracket type photovoltaic power generation device based on the hourly incident radiation and ambient temperature; Step S230, determining the hourly power generation according to the photoelectric conversion efficiency of each component and the temperature correction factor, dust shielding factor and inverter efficiency; Step S240, normalizing the hourly power generation according to the unit capacity to form a unit capacity hourly power generation model of the fixed tilt bracket, the vertical mounting bracket and the flat single-axis tracking bracket type photovoltaic power generation device; Step S250, expanding the unit capacity hourly power generation model over the entire year to obtain the annual unit capacity power generation prediction curve corresponding to each photovoltaic bracket type photovoltaic power generation device.

[0056] Hourly incident irradiance refers to the amount of solar radiation perpendicular to the ground per unit time, typically expressed in watts per square meter (W / m²). Acquiring hourly data allows for detailed understanding of the light conditions received by photovoltaic modules at different points in time. Hourly incident irradiance can be monitored in real time using a sun irradiance meter or photoelectric sensor installed at the target site, or extracted using historical observation data provided by meteorological authorities or satellite inversion data. This embodiment does not impose any specific limitations on this.

[0057] Ambient temperature data, which can include hourly variations in air temperature, is a key parameter affecting PV module temperature and photovoltaic conversion efficiency. Ambient temperature data can be measured by a weather station or transmitted remotely through an automated meteorological data collection system; this is not specifically limited in this embodiment. To ensure data accuracy, preprocessing methods such as multi-point sampling, outlier removal, and data interpolation can be employed. Alternatively, meteorological reanalysis data generated by a numerical weather prediction (NWP) model can be used as input for hourly incident radiation and ambient temperature.

[0058] The photovoltaic conversion efficiency of a module refers to the proportion of incident solar energy converted into electrical energy by a photovoltaic module under specific operating conditions. This efficiency is affected by factors such as radiation intensity, module temperature, spectral distribution, and angle of incidence. The installation method of different photovoltaic brackets directly determines the incident angle between the photovoltaic module and the sunlight, thereby affecting the effective component of the incident radiation. The photovoltaic conversion efficiency of the module of a fixed-angle bracket can be calculated by combining the radiation component at a fixed incident angle with the correction factor of the module efficiency under standard test conditions. Vertically mounted brackets can take into account light losses at different solar altitudes throughout the year. They are usually less efficient but can be used with bifacial modules to increase overall power generation. The flat single-axis tracking bracket can automatically adjust the orientation of the module through a single axis to make it as perpendicular to the incident direction of sunlight as possible, thereby improving the hourly radiation utilization rate.

[0059] The calculation of the component photoelectric conversion efficiency can be based on a component efficiency model, such as a photovoltaic component temperature model (NOCT model) and a spectral correction factor. Of course, the component photoelectric conversion efficiency can also be determined based on three-dimensional illumination simulation software or by using measured power data to regress and fit efficiency parameters. This example embodiment is not limited to this.

[0060] The temperature correction factor describes the linear or nonlinear attenuation of module efficiency with temperature changes and is generally determined by the temperature coefficient provided by the module manufacturer. The dust obscuration factor reflects the decrease in light transmittance caused by dust accumulation or contamination. Its value can be corrected through long-term monitoring or empirical formulas. Inverter efficiency refers to the energy conversion ratio when converting direct current to alternating current, typically between 95% and 99%. It can be calculated based on the inverter rating parameters corresponding to each photovoltaic power generation device type.

[0061] Normalization of hourly generated power refers to dividing the actual generated power by the rated installed capacity so as to make a unified comparison under different capacity scales. The hourly generated power model per unit capacity is based on the standard installed capacity of 1 kW or 1 MW, which can fully reflect the power generation capacity of the unit capacity photovoltaic system in different time periods under various meteorological conditions and installation methods. In specific implementation, the calculated hourly generated power sequence can be normalized according to each photovoltaic bracket type photovoltaic power generation device, so as to obtain the hourly power curve of photovoltaic power generation devices of different photovoltaic bracket types. The establishment of the hourly generated power model per unit capacity needs to take into account factors such as the attenuation rate of photovoltaic modules, the partial load effect of the inverter, and the line loss to ensure the accuracy of the results. In some optional implementations, the unit capacity output power curve can be directly generated by simulation software, or the model can be calibrated by measured data.

[0062] For example, the hourly power generation model per unit capacity can be expressed by the following relationship: ;in, Can indicate the Photovoltaic power generation device of the type of photovoltaic bracket The hourly power generation per unit capacity of an hour, Can indicate the Photovoltaic power generation device of the type of photovoltaic bracket Hourly incident irradiance per hour, It can indicate the reference efficiency of photovoltaic modules under standard test conditions. It can represent the temperature correction factor of photovoltaic modules, Can indicate the Ambient temperature for hours, Can represent the dust occlusion factor, It can represent the comprehensive system loss factor, which can include inverter efficiency, cable loss, etc.

[0063] Annual time series expansion involves accumulating and displaying hourly unit capacity power models over a year's time series, encompassing the combined impact of the four seasons, day and night, and varying meteorological conditions on power generation capacity. In practice, the unit capacity power model is gradually calculated based on 8760 hours (i.e., 365 days x 24 hours) of hourly meteorological data to generate a full-year unit capacity power generation forecast curve. This full-year unit capacity power generation forecast curve clearly describes the temporal characteristics of power generation throughout the year, including peak and off-peak periods, and seasonal variations. To further improve forecast accuracy, weather forecast data or data from a typical meteorological year can be incorporated into the time series expansion process to reflect future trends in the operating environment. Alternatively, a full-year hourly power generation curve can be directly generated using a photovoltaic power generation simulation platform (such as SAM or PVlib).

[0064] By collecting hourly incident radiation and ambient temperature data, and combining the module photoelectric conversion efficiency, temperature correction factor, dust shielding factor and inverter efficiency, an hourly power generation model per unit capacity of photovoltaic power generation devices with different photovoltaic bracket types is established and an annual prediction curve is generated. This can finely model the impact of the bracket installation method on the power generation timing, avoiding the deviation caused by the traditional estimation of only the annual average radiation, thereby providing reliable basic data for the accurate benefit comparison of different bracket schemes.

[0065] In an exemplary embodiment of the present disclosure, a ternary coupling optimization model of photovoltaics, energy storage, and electricity prices may be constructed through the following steps, which may specifically include: A storage energy dynamic equation can be established, which is used to describe the charging power, discharging power and state of charge of the electrochemical energy storage system in each time period. The charging and discharging efficiency and the charging and discharging mutual exclusion constraints are introduced into the storage energy dynamic equation to form an energy storage sub-model. The annual unit capacity power generation forecast curve and the electricity price weighted unit capacity efficiency function are combined with the energy storage sub-model to construct a ternary coupling optimization model.

[0066] The energy storage dynamics equation is a mathematical model used to characterize the time-varying energy state of an energy storage system. Its core variables include charging power, discharging power, and state of charge (SOC). SOC refers to the ratio of the energy storage system's current stored charge to its rated capacity, typically expressed as a percentage. Charge and discharge efficiency is the loss factor during energy conversion in the energy storage system, typically ranging from 90% to 98%, depending on the type of energy storage technology. To prevent simultaneous charging and discharging of the energy storage system, a mutual exclusion constraint must be incorporated into the equation. This means that when the charging power is greater than 0, the discharging power is 0, and vice versa. This mutual exclusion constraint can be controlled by binary logic variables to switch between charging and discharging. In practical implementation, this equation can be solved jointly with photovoltaic power generation forecasts and electricity price signals using optimization methods based on linear programming or mixed integer programming. Alternatively, a more refined energy dynamics equation can be developed using an equivalent circuit model (ECM), or the equation parameters can be updated using a data-driven charge and discharge efficiency prediction model.

[0067] The ternary coupling optimization model integrates the timing characteristics of photovoltaic power generation, dynamic electricity price signals, and energy storage dispatch capabilities into a unified optimization framework. The annual unit capacity power generation forecast curve provides the model with hourly power input, which is used to determine the distribution of power available for energy storage charging and direct grid access. The price-weighted unit capacity efficiency function serves as an indicator function for evaluating profitability, providing an economic reference for each power generation allocation and energy storage dispatch strategy. The energy storage submodel constrains the upper and lower bounds of energy flow through state of charge, charge and discharge power constraints, and efficiency parameters, allowing the optimization process to consider the actual availability of energy storage in different time periods. For example, the non-dominated sorting genetic algorithm (NSGA-II), particle swarm optimization, or other multi-objective evolutionary algorithms can be used to iteratively solve the ternary coupling model as the objective function. Optimization can also be performed using mathematical modeling methods based on linear programming (LP) or mixed integer linear programming (MILP), which are not specifically limited in this embodiment.

[0068] For example, the dynamic electricity price time series for future periods can be determined by the following relationship: ;in, It can represent the predicted dynamic electricity price time series of the future t+h hours, It can represent input sequences, which may include historical electricity price data, regional electricity load, meteorological factors and other characteristic quantities. A mapping function that can represent a long short-term memory network prediction model.

[0069] The electricity price weighted unit capacity efficiency function can be determined by the following relationship: ;in, It can represent the electricity price weighted unit capacity efficiency value of the photovoltaic power generation device of the type of photovoltaic bracket, Can indicate the Photovoltaic power generation device of the type of photovoltaic bracket The hourly power generation per unit capacity of an hour, Can indicate the The hourly forecast electricity price is calculated from the dynamic electricity price time series. It can represent the total number of hours in a year, usually 8760.

[0070] The energy storage dynamic equation, i.e. the energy storage sub-model, can be expressed by the following relationship: ; in, It can be said that the electrochemical energy storage system The state of charge at the moment, It can be said that the electrochemical energy storage system The state of charge at the moment, Can indicate the Charging power at the moment, Can indicate the The discharge power at the moment, It can represent the charging efficiency, usually with a value of 0.9–0.98. It can represent the discharge efficiency, usually with a value of 0.9–0.98. It can represent the time step, It can represent the mutual exclusion constraint of charge and discharge, that is, the constraint ensures that the energy storage system cannot be charged and discharged at the same time in the same time period.

[0071] For example, the three-element coupled optimization model can be expressed by the following objective function: in, It can be expressed as the internal rate of return of the project. It can be said that the photovoltaic system The power generation at the moment, Can indicate the Charging power at the moment, Can indicate the The discharge power at the moment, Can indicate the Hourly predicted electricity price, Can indicate the Hours of operation and maintenance costs, It can represent the additional investment cost of energy storage configuration, It can represent the initial project investment amount, It can represent the total number of hours in a year, usually 8760. It can represent the rated capacity of the electrochemical energy storage system. Can indicate the maximum charging power, Can indicate the maximum discharge power, Can indicate the Annual net cash flow, It can represent the project life cycle, that is, the total number of years from the completion and commissioning of the project to the end of operation, which can usually be 20-25 years and is related to the life of photovoltaic modules, the life of the energy storage system and the economic feasibility assessment cycle.

[0072] By establishing a dynamic energy equation for energy storage, introducing charging and discharging efficiency and mutual exclusion constraints, and combining it with the annual unit capacity power generation forecast curve and the electricity price weighted efficiency function to construct a ternary coupling optimization model, it is possible to simultaneously consider the characteristics of photovoltaic power generation, dynamic changes in electricity prices, and energy storage scheduling capabilities, achieve dynamic matching between power generation and energy storage, avoid energy losses caused by disordered charging and discharging of the energy storage system, and thus provide a scheduling model with reasonable constraints for subsequent comprehensive benefit optimization.

[0073] In an exemplary embodiment of the present disclosure, Figure 3 Solve the candidate configuration solution set by the steps in Figure 3 Specifically, it may include: Step S310, inputting the installed capacity of the fixed tilt bracket, the vertical mounting bracket, the flat single-axis tracking bracket type photovoltaic power generation device and the capacity of the electrochemical energy storage system into the ternary coupling optimization model; Step S320, performing a multi-objective evolutionary search using a non-dominated sorting genetic algorithm based on the ternary coupling optimization model; Step S330, performing iterative calculation of the internal rate of return in the multi-objective evolutionary search process, and calculating the internal rate of return for each set of joint optimization variables; Step S340 , feeding back the internal rate of return as a fitness value to the evolutionary search process of the ternary coupling optimization model, generating a Pareto non-inferior solution set, and using the Pareto non-inferior solution set as a candidate configuration solution set.

[0074] Installed capacity refers to the total power of photovoltaic modules deployed in the project for each type of photovoltaic power generation device, typically expressed in kilowatts or megawatts. Different installed capacities directly determine the peak power generation and the amplitude of the overall output curve, while the mounting method of each mount can affect its power generation timing characteristics. The capacity of an electrochemical energy storage system refers to the rated energy storage capacity of the energy storage device, typically expressed in kilowatt-hours (kWh) or megawatt-hours (MWh). This parameter directly affects the system's dispatch depth and peak-shaving and valley-filling capabilities. Inputting these parameters into the ternary coupled optimization model involves parameterizing the combination of the installed capacity of each type of photovoltaic power generation device and the energy storage capacity into a variable vector, which serves as the design space for the model solution. For example, structured data input can be used, such as by using programming interfaces such as Python, Matlab, or C++ to import the installed capacity and energy storage capacity into the optimization algorithm module in matrix or vector format. Alternatively, project parameters can be automatically retrieved and input values ​​dynamically updated through a database or cloud-based configuration platform. This is not specifically limited in this embodiment.

[0075] The non-dominated sorting genetic algorithm (NSGA-II) is an improved multi-objective evolutionary optimization algorithm capable of simultaneously optimizing multiple conflicting objective functions. For example, in a ternary coupled optimization model, it is necessary to consider indicators such as economic benefits and resource utilization. The principle of the non-dominated sorting genetic algorithm is to ensure the diversity and convergence of solutions by performing rapid non-dominated sorting on the population, calculating the congestion distance, and retaining the elite strategy. For example, an initial population containing installed capacity and energy storage capacity can be encoded, with each individual representing a configuration plan. New solutions are then generated using genetic operations such as crossover and mutation, and fitness values ​​are evaluated based on the unit capacity efficiency function weighted by electricity price, internal rate of return, and other economic indicators. Finally, non-dominated sorting is used to screen out the Pareto frontier solution and enter the next generation of iterations.

[0076] During the evolutionary search process, each candidate PV and energy storage combination is iteratively calculated using cash flow calculations based on lifecycle power generation, dynamic electricity prices, and investment costs. This calculation can be performed using numerical methods such as the bisection method and Newton iteration, or automatically using the financial analysis module. Alternative implementations include a rate of return matrix evaluation method that combines net present value and internal rate of return, or a risk-return analysis based on Monte Carlo simulation.

[0077] The Pareto non-inferior solution set refers to a set of solutions that do not exist in all solutions and are superior to other solutions in all optimization objectives. The solutions in this set represent the optimal solutions under the trade-offs between different objectives. The internal rate of return is fed back to the evolutionary search process as a fitness value to guide the selection, crossover and mutation operations of the algorithm, thereby promoting the population to gradually converge to the Pareto frontier. In specific implementation, after each generation of iteration, non-inferior solutions are screened out by fitness ranking and congestion ranking, and the final solution set is output when the iteration termination condition is met. Optionally, the Pareto solution set can be streamlined using hierarchical clustering, or the coverage of the solution set can be improved by a multi-objective optimization method based on distribution estimation, but this example embodiment is not limited to this.

[0078] By inputting the installed capacity of photovoltaic power generation devices and electrochemical energy storage capacity of various photovoltaic bracket types into a ternary coupled optimization model and performing a multi-objective evolutionary search based on a non-dominated sorting genetic algorithm, it is possible to simultaneously optimize multiple configuration combinations within a large parameter space. Combined with iterative calculation of the internal rate of return and the use of economic indicators as fitness feedback, this effectively generates a Pareto non-inferior solution set that balances benefits and costs, thereby providing a richer and higher-quality set of configuration solutions for multiple option selection.

[0079] In an exemplary embodiment of the present disclosure, the following steps may be performed to generate a weighted score ranking result in combination with user preference setting parameters, which may specifically include: The user-set preference weight parameters can be obtained, including weight coefficients for internal rate of return, net present value, cost per kilowatt-hour, and payback period. The economic indicators of the candidate configuration solution set are weighted with the preference weight parameters to obtain a comprehensive score for each solution. The solutions in the candidate configuration solution set are ranked according to the comprehensive score to obtain a weighted score ranking result.

[0080] Among them, the preference weight parameter set by the user refers to the priority ratio assigned by the user to different economic indicators in the actual project evaluation, which is used to reflect the investor's return target and risk preference. The internal rate of return measures the capital return rate of the project, the net present value reflects the net income over the entire life cycle of the project, the cost per kilowatt-hour is used to evaluate the comprehensive cost per unit of electricity, and the investment payback period is used to reflect the speed of capital recovery. In specific implementation, the user can input the weight value of each indicator through the visual interface, and the system can ensure that the sum of the weights is 1 or meets the preset distribution ratio through input verification. Of course, it is also possible to automatically recommend weight distribution based on the user's historical preference data using a machine learning algorithm, or quickly configure weight parameters for different users through the default investment strategy template. This embodiment does not specifically limit this.

[0081] The candidate configuration solution set is generated by the optimization model as multiple feasible configurations of photovoltaic power generation devices and energy storage capacity using different types of photovoltaic racks. Each solution corresponds to a set of economic indicators. Weighted calculation involves multiplying the standardized value of each economic indicator by a user-defined weight coefficient, and then summing the results to obtain a comprehensive score for the solution. Indicators of different dimensions can be converted into dimensionless, comparable values ​​using range normalization or Z-score normalization. This can be implemented using matrix operations in programming languages, such as Python's NumPy or Pandas libraries. Alternatively, multi-criteria decision-making methods (MCDM), such as the Analytic Hierarchy Process (AHP) or Grey Relational Analysis (GRA), can be used to complete the weighted score calculation.

[0082] Sorting involves arranging the comprehensive scores of all candidate configuration solutions from high to low to identify the optimal or top-ranked solutions. In specific implementations, a quick sort or merge sort algorithm can be used, and the score and corresponding solution configuration parameters are retained in the sorting results for subsequent display and decision-making. The scoring results can also be categorized and graded, for example, by categorizing solutions into different levels such as excellent, good, and fair based on the score range, to facilitate user screening based on investment strategies. The sorting process can also be combined with user-defined constraints, such as the upper limit of the investment budget and the maximum payback period, to perform secondary filtering, which is not specifically limited in this embodiment.

[0083] By using user-defined preference weights, the multi-dimensional economic indicators of candidate configuration solutions are weighted and ranked against these weights. This structured approach transforms complex economic data into intuitively comparable comprehensive scores. This process enables personalized configuration recommendations tailored to different investment preferences, avoiding the biased selection of options based on a single economic indicator, thereby improving the pertinence and accuracy of decision-making.

[0084] In an exemplary embodiment of the present disclosure, economic performance index calculation for a candidate configuration solution set may be implemented through the following steps, which may specifically include: The internal rate of return and net present value of the project life cycle can be calculated based on the installed capacity and energy storage capacity in the candidate configuration solution set; a comprehensive economic indicator matrix is ​​generated based on the internal rate of return, net present value, cost per kilowatt-hour, and investment payback period, and the comprehensive economic indicator matrix is ​​used as the economic indicator of the candidate configuration solution set.

[0085] The project lifecycle refers to the entire cycle from construction, commissioning, to decommissioning, typically lasting 20 to 25 years. The internal rate of return (IRR) measures project profitability by calculating the discount rate that results in a zero net present value. The net present value (NPV) is calculated by converting the annual cash flows over the project's lifecycle to the current value at a preset discount rate. This is used to assess the project's overall return on investment. The calculation takes into account the hourly power generation of the photovoltaic power generation device, electricity price revenue, energy storage charging and discharging benefits, equipment depreciation, operating and maintenance costs, and investment costs.

[0086] Cost per kilowatt-hour (CLE) refers to the ratio of the total investment and operating costs of a photovoltaic power plant over its entire lifecycle to the total power generated. It is used to evaluate the equilibrium level of power generation costs. The payback period, which refers to the time required for a project's net cash flow to reach zero, is commonly used to measure the speed of capital recovery. A comprehensive economic indicator matrix organizes indicators such as internal rate of return, net present value, CLE, and payback period into column vectors or matrices, mapping each candidate configuration's economic indicators to a single value, thus forming a multidimensional data set. In specific implementations, each indicator can first be normalized and standardized to eliminate dimensional differences. The indicator matrix can then be constructed using matrix operation tools (such as Python's Pandas, Excel, or Matlab). Alternatively, a weighted scoring matrix can be introduced to directly generate a weighted matrix by weighting each economic indicator according to user preferences, or economic indicators can be hierarchically aggregated using multi-criteria decision analysis methods.

[0087] For example, the internal rate of return can be calculated using the following relationship: ; in, Can indicate the Annual net cash flow, It can represent the life cycle of the project. It can be expressed as the internal rate of return of the project. It can represent the initial project investment amount.

[0088] The net present value can be calculated using the following relationship: ; in, It can represent the discount rate.

[0089] The cost per kilowatt-hour can be determined using the following relationship: ; in, It can represent the cost per kilowatt-hour. Can indicate the Annual investment cost amortization, It can represent the operation and maintenance costs, Can indicate the Annual power generation.

[0090] By calculating economic indicators such as internal rate of return, net present value, cost per kilowatt-hour, and payback period based on the installed capacity and energy storage capacity of candidate configuration solutions, and generating a comprehensive economic indicator matrix, the long-term economic performance of configuration solutions can be evaluated from multiple dimensions. This matrix provides a unified data platform for subsequent weighted scoring and visualization, making the economic performance comparison of configuration solutions more comprehensive and intuitive.

[0091] In an exemplary embodiment of the present disclosure, the following steps may be performed to visualize the candidate configuration solution set, which may include: The economic indicator radar chart, configuration ratio chart and energy storage behavior trajectory chart of the candidate configuration solution set can be generated; after receiving the user's adjustment operation on the installed capacity or energy storage capacity of photovoltaic power generation devices of each photovoltaic bracket type, the economic indicator matrix is ​​recalculated based on the adjusted installed capacity and energy storage capacity; the updated economic indicator matrix is ​​fed back to the economic indicator radar chart, configuration ratio chart and energy storage behavior trajectory chart in real time to achieve dynamic visual update.

[0092] The economic indicator radar chart is a multidimensional visualization method that maps indicators such as internal rate of return, net present value, cost per kilowatt-hour, and payback period onto axes of equal angles. By connecting the scoring points for different indicators to form polygons, it can be used to visually demonstrate the differences in the performance of candidate solutions across multiple dimensions. The configuration ratio chart is used to reflect the installed capacity ratio of photovoltaic power generation devices and the allocation of energy storage capacity for each photovoltaic bracket type. The structural characteristics of the solution can be presented using bar charts, stacked charts, or donut charts. The energy storage behavior trajectory chart is used to display the charge and discharge power curves and state of charge (SOC) changes of the energy storage system over different time periods, which can intuitively demonstrate the temporal characteristics of energy storage scheduling. In specific implementations, these graphics can be generated using Python's Matplotlib, Plotly, or commercial visualization tools. Alternative implementations include using a web-based interactive graphical interface and dynamic rendering of graphics using front-end frameworks such as ECharts or D3.js.

[0093] User adjustments are typically made through a human-computer interaction interface (HMI) by dragging sliders, entering numerical values, or selecting preset templates to modify the installed capacity ratio or energy storage capacity of the candidate PV mounting system. Based on the user-entered adjustment parameters, the system invokes the existing power generation forecast model and energy storage dynamic equations to recalculate hourly power generation and energy storage scheduling strategies. Based on the adjusted results, economic indicators such as internal rate of return, net present value, cost per kilowatt-hour, and payback period are then updated. In specific implementations, a backend calculation module can be used to rapidly recalculate sub-model data related to the adjustment parameters to improve response speed. Alternative implementation methods include utilizing a cache mechanism to pre-store key calculation results or using a machine learning-based regression model to rapidly predict the changing trends of economic indicators. This step ensures that the visualized data is synchronized with the user's dynamic needs, ensuring the real-time and accuracy of the analysis results.

[0094] Real-time feedback means that when the economic indicator matrix is ​​updated, the system automatically triggers a refresh of the front-end visualization components, mapping the latest economic results to radar charts, scale charts, and trajectory charts, ensuring that the displayed content is consistent with the back-end calculation results. Dynamic visualization updates can be achieved through data binding and event-driven mechanisms. For example, a two-way data binding framework (such as Vue.js or React) can be used in the web front-end to automatically link data updates with interface rendering. Alternative implementation methods include API calls based on the desktop visualization application or establishing a real-time data channel between the front-end and back-end via WebSocket.

[0095] By generating economic indicator radar charts, configuration ratio charts, and energy storage behavior trajectory charts, and dynamically updating the economic indicator matrix and charts after users adjust PV or energy storage parameters, the system can visualize and provide instant feedback on the economic performance of candidate solutions. This interactive and dynamic display allows users to intuitively understand the changing trends in returns after adjustments, thereby improving the efficiency of solution evaluation and decision-making.

[0096] It should be noted that although the steps of the method disclosed herein are depicted in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in that particular order, or that all steps must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one, and / or one step may be decomposed into multiple steps.

[0097] In addition, in this exemplary embodiment, a photovoltaic and energy storage joint configuration auxiliary decision-making device is also provided. Figure 4 As shown, the photovoltaic and energy storage joint configuration auxiliary decision-making device 400 includes: The power generation prediction curve generation module 410 is used to obtain historical meteorological data, grid spot electricity price data, and basic project parameters of the target site, establish hourly power generation models per unit capacity for fixed-tilt brackets, vertical-mount brackets, and horizontal single-axis tracking brackets, and generate annual power generation prediction curves per unit capacity for each type of bracket. The future dynamic electricity price generation module 420 is used to perform time series analysis on historical electricity price data, regional electricity load, and meteorological factors based on a pre-trained long short-term memory network prediction model to generate a dynamic electricity price time series for future periods; The utility function construction module 430 is used to weightedly couple the annual unit capacity power generation prediction curve with the dynamic electricity price time series hour by hour to determine the electricity price weighted unit capacity utility function corresponding to each photovoltaic bracket type photovoltaic power generation device; A coupled optimization model solving module 440 is configured to construct a ternary coupled optimization model of photovoltaics, energy storage, and electricity prices based on the electricity price weighted unit capacity efficiency function, using the installed capacity of the fixed-tilt support, vertical mounting support, and flat single-axis tracking support type photovoltaic power generation devices and the capacity of the electrochemical energy storage system as joint optimization variables, with maximizing the project internal rate of return as the core goal, and solve a candidate configuration solution set; The candidate configuration solution set ranking module 450 is used to calculate and visualize the economic indicators of the candidate configuration solution set, and generate a weighted scoring ranking result based on the user's preference setting parameters; The joint configuration auxiliary recommendation module 460 is used to output a recommended solution for the joint configuration of photovoltaic power generation devices and energy storage for each photovoltaic bracket type based on the weighted score sorting result.

[0098] In some exemplary embodiments of the present disclosure, based on the aforementioned solution, the power generation prediction curve generation module 410 is configured as follows: Collecting hourly incident radiation and ambient temperature data of the target site; Based on the hourly incident radiation and the ambient temperature, respectively calculating the photoelectric conversion efficiency of the components corresponding to the photovoltaic power generation device of the fixed tilt bracket, the vertical mounting bracket, and the flat single-axis tracking bracket type; Determining hourly generated power based on the photoelectric conversion efficiency of each component, a temperature correction factor, a dust shielding factor, and inverter efficiency; Normalizing the hourly generated power according to unit capacity to form hourly generated power per unit capacity models for the fixed tilt bracket, the vertical mounting bracket, and the flat single-axis tracking bracket type photovoltaic power generation devices; The hourly power generation model per unit capacity is expanded over the entire year time series to obtain the annual unit capacity power generation prediction curve corresponding to each photovoltaic power generation device of each photovoltaic bracket type.

[0099] In some example embodiments of the present disclosure, based on the aforementioned solution, the coupled optimization model solving module 440 is configured as follows: Establishing an energy storage dynamic equation, which is used to describe the charging power, discharging power and state of charge of the electrochemical energy storage system in each time period, and introducing charging and discharging efficiency and charging and discharging mutual exclusion constraints into the energy storage dynamic equation to form an energy storage submodel; The annual unit capacity power generation forecast curve and the electricity price weighted unit capacity efficiency function are combined with the energy storage sub-model to construct a ternary coupling optimization model.

[0100] In some example embodiments of the present disclosure, based on the aforementioned solution, the coupling optimization model solving module 440 is configured to: Inputting the installed capacity of the fixed tilt bracket, the vertical mounting bracket, the flat single-axis tracking bracket type photovoltaic power generation device and the capacity of the electrochemical energy storage system into the ternary coupling optimization model; Based on the ternary coupling optimization model, a non-dominated sorting genetic algorithm is used to perform multi-objective evolutionary search; Performing iterative calculation of internal rate of return during the multi-objective evolutionary search process, and calculating the internal rate of return for each set of joint optimization variables; The internal rate of return is fed back as a fitness value to the evolutionary search process of the ternary coupling optimization model to generate a Pareto non-inferior solution set, and the Pareto non-inferior solution set is used as a candidate configuration solution set.

[0101] In some example embodiments of the present disclosure, based on the aforementioned solution, the candidate configuration solution set sorting module 450 is configured to: Obtaining preference weight parameters set by a user, wherein the preference weight parameters include weight coefficients for internal rate of return, net present value, cost per kilowatt-hour, and investment payback period; Performing weighted calculation on each economic indicator of the candidate configuration solution set and the preference weight parameter to obtain a comprehensive score value for each solution; The solutions in the candidate configuration solution set are sorted according to the comprehensive score value to obtain a weighted score sorting result.

[0102] In some example embodiments of the present disclosure, based on the aforementioned solution, the candidate configuration solution set sorting module 450 is configured to: Calculating the internal rate of return and net present value of the project life cycle based on the installed capacity and energy storage capacity in the candidate configuration solution set; A comprehensive economic indicator matrix is ​​generated according to the internal rate of return, the net present value, the cost per kilowatt-hour, and the investment payback period, and the comprehensive economic indicator matrix is ​​used as the economic indicator of the candidate configuration solution set.

[0103] In some example embodiments of the present disclosure, based on the aforementioned solution, the candidate configuration solution set sorting module 450 is configured to: Generate an economic indicator radar chart, a configuration ratio chart, and an energy storage behavior trajectory chart for the candidate configuration solution set; After receiving the user's adjustment operation on the installed capacity or energy storage capacity of the photovoltaic power generation device of each photovoltaic bracket type, recalculating the economic indicator matrix based on the adjusted installed capacity and energy storage capacity; The updated economic indicator matrix is ​​fed back to the economic indicator radar chart, the configuration ratio chart and the energy storage behavior trajectory chart in real time to achieve dynamic visual update.

[0104] The specific details of each module of the above-mentioned photovoltaic and energy storage joint configuration decision-making support device have been described in detail in the corresponding photovoltaic and energy storage joint configuration decision-making support method, so they will not be repeated here.

[0105] It should be noted that while the detailed description above mentions several modules or units of the photovoltaic and energy storage joint configuration decision support device, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in a single module or unit. Conversely, the features and functions of a single module or unit described above can be further divided and embodied by multiple modules or units.

[0106] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned photovoltaic and energy storage joint configuration auxiliary decision-making method is also provided.

[0107] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods, or program products. Therefore, various aspects of the present disclosure may be implemented in the following forms: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which may be collectively referred to herein as a "circuit," "module," or "system."

[0108] Refer to the following Figure 5 hereinafter, an electronic device 500 according to such an embodiment of the present disclosure is described. Figure 5 The electronic device 500 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0109] like Figure 5As shown, electronic device 500 is implemented as a general-purpose computing device. Components of electronic device 500 may include, but are not limited to, the aforementioned at least one processing unit 510, the aforementioned at least one storage unit 520, a bus 530 connecting various system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0110] The storage unit stores program codes, which can be executed by the processing unit 510, so that the processing unit 510 performs the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit 510 can perform the following steps: Figure 1 In step S110, the historical meteorological data, grid spot electricity price data and project basic parameters of the target site are obtained, and hourly power generation models of fixed tilt bracket, vertical mounting bracket and horizontal single-axis tracking bracket type photovoltaic power generation devices are established respectively, and the annual unit capacity power generation forecast curve corresponding to each photovoltaic bracket type photovoltaic power generation device is generated; in step S120, based on the pre-trained long short-term memory network prediction model, the historical electricity price data, regional electricity load and meteorological factors are analyzed in time series to generate a dynamic electricity price time series for the future period; in step S130, the annual unit capacity power generation forecast curve is weightedly coupled with the dynamic electricity price time series hourly to determine the annual unit capacity power generation forecast curve of each photovoltaic bracket type photovoltaic power generation device. Corresponding electricity price weighted unit capacity efficiency function; step S140, based on the electricity price weighted unit capacity efficiency function, taking the installed capacity of the fixed-tilt bracket, vertical mounting bracket, and flat single-axis tracking bracket type photovoltaic power generation device and the capacity of the electrochemical energy storage system as joint optimization variables, and taking maximizing the project internal rate of return as the core goal, constructing a ternary coupling optimization model of photovoltaics, energy storage and electricity price, and solving the candidate configuration solution set; step S150, calculating and visualizing the economic indicators of the candidate configuration solution set, and generating a weighted score ranking result in combination with the user preference setting parameters; step S160, outputting a recommended solution for the joint configuration of photovoltaic power generation device and energy storage for each photovoltaic bracket type according to the weighted score ranking result.

[0111] The storage unit 520 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM) 521 and / or a cache memory unit 522 , and may further include a read-only memory unit (ROM) 523 .

[0112] The storage unit 520 may also include a program / utility 524 having a set (at least one) of program modules 525, such program modules 525 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0113] Bus 530 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0114] The electronic device 500 can also communicate with one or more external devices 570 (e.g., a keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 500, and / or any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., a router, modem, etc.). This communication can occur via an input / output (I / O) interface 550. Furthermore, the electronic device 500 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 560. As shown, the network adapter 560 communicates with other modules of the electronic device 500 via a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 500, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0115] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes several instructions for causing a computing device (such as a personal computer, server, terminal device, or network device) to execute the methods according to the embodiments of the present disclosure.

[0116] In exemplary embodiments of the present disclosure, a computer-readable storage medium is also provided, on which is stored a program product capable of implementing the aforementioned methods of this specification. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product comprising program code. When the program product is executed on a terminal device, the program code is configured to cause the terminal device to execute the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present disclosure.

[0117] refer to Figure 6As shown, a program product 600 for implementing the aforementioned photovoltaic and energy storage joint configuration decision support method according to an embodiment of the present disclosure is described. This program product can be implemented in a portable compact disc read-only memory (CD-ROM) and include program code, and can be executed on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0118] The program product may utilize any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0119] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0120] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0121] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0122] Furthermore, the figures above are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the figures above do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0123] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes several instructions to enable a computing device (such as a personal computer, server, touch terminal, or network device) to execute the methods according to the embodiments of the present disclosure.

[0124] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0125] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A photovoltaic and energy storage joint configuration auxiliary decision-making method, characterized in that: include: Obtain historical meteorological data, grid spot electricity price data, and basic project parameters for the target site, establish hourly power generation models per unit capacity for fixed-tilt mounts, vertically mounted mounts, and horizontal single-axis tracking mounts, and generate annual unit capacity power generation forecast curves for each type of mount. Based on the pre-trained long short-term memory network prediction model, the system conducts time series analysis on historical electricity price data, regional electricity load and meteorological factors to generate dynamic electricity price time series for future periods. The annual unit capacity power generation prediction curve is weightedly coupled with the dynamic electricity price time series hour by hour to determine the electricity price weighted unit capacity efficiency function corresponding to the photovoltaic power generation device of each photovoltaic bracket type; Based on the electricity price weighted unit capacity efficiency function, the installed capacity of the fixed-tilt bracket, vertical mounting bracket, and flat single-axis tracking bracket photovoltaic power generation device and the capacity of the electrochemical energy storage system are used as joint optimization variables, and with maximizing the project internal rate of return as the core goal, a ternary coupled optimization model of photovoltaics, energy storage, and electricity price is constructed, and a solution set of candidate configurations is solved; Calculate and visualize the economic indicators of the candidate configuration solution set, and generate weighted scoring ranking results based on user preference setting parameters; According to the weighted scoring ranking results, a recommended solution for the combined configuration of photovoltaic power generation devices and energy storage for each photovoltaic bracket type is output.

2. The method according to claim 1, characterized in that The generating of the annual unit capacity power generation prediction curve corresponding to each type of photovoltaic power generation device includes: Collecting hourly incident radiation and ambient temperature data of the target site; Based on the hourly incident radiation and the ambient temperature, respectively calculating the photoelectric conversion efficiency of the components corresponding to the photovoltaic power generation device of the fixed tilt bracket, the vertical mounting bracket, and the flat single-axis tracking bracket type; Determining hourly generated power based on the photoelectric conversion efficiency of each component, a temperature correction factor, a dust shielding factor, and inverter efficiency; Normalizing the hourly generated power according to unit capacity to form hourly generated power per unit capacity models for the fixed tilt bracket, the vertical mounting bracket, and the flat single-axis tracking bracket type photovoltaic power generation devices; The hourly power generation model per unit capacity is expanded over the entire year time series to obtain the annual unit capacity power generation prediction curve corresponding to each photovoltaic power generation device of each photovoltaic bracket type.

3. The method according to claim 1, characterized in that The construction of a ternary coupling optimization model of photovoltaics, energy storage and electricity prices includes: Establishing an energy storage dynamic equation, which is used to describe the charging power, discharging power and state of charge of the electrochemical energy storage system in each time period, and introducing charging and discharging efficiency and charging and discharging mutual exclusion constraints into the energy storage dynamic equation to form an energy storage submodel; The annual unit capacity power generation forecast curve and the electricity price weighted unit capacity efficiency function are combined with the energy storage sub-model to construct a ternary coupling optimization model.

4. The method according to claim 1, wherein The candidate configuration solution set includes: Inputting the installed capacity of the fixed tilt bracket, the vertical mounting bracket, the flat single-axis tracking bracket type photovoltaic power generation device and the capacity of the electrochemical energy storage system into the ternary coupling optimization model; Based on the ternary coupling optimization model, a non-dominated sorting genetic algorithm is used to perform multi-objective evolutionary search; Performing iterative calculation of internal rate of return during the multi-objective evolutionary search process, and calculating the internal rate of return for each set of joint optimization variables; The internal rate of return is fed back as a fitness value to the evolutionary search process of the ternary coupling optimization model to generate a Pareto non-inferior solution set, and the Pareto non-inferior solution set is used as a candidate configuration solution set.

5. The method according to claim 1, wherein The step of generating a weighted scoring ranking result by combining user preference setting parameters includes: Obtaining preference weight parameters set by a user, wherein the preference weight parameters include weight coefficients for internal rate of return, net present value, cost per kilowatt-hour, and investment payback period; Performing weighted calculation on each economic indicator of the candidate configuration solution set and the preference weight parameter to obtain a comprehensive score value for each solution; The solutions in the candidate configuration solution set are sorted according to the comprehensive score value to obtain a weighted score sorting result.

6. The method according to claim 1, characterized in that Calculating economic indicators for the candidate configuration solution set includes: Calculating the internal rate of return and net present value of the project life cycle based on the installed capacity and energy storage capacity in the candidate configuration solution set; A comprehensive economic indicator matrix is ​​generated according to the internal rate of return, the net present value, the cost per kilowatt-hour, and the investment payback period, and the comprehensive economic indicator matrix is ​​used as the economic indicator of the candidate configuration solution set.

7. The method according to claim 1, characterized in that Visually display the candidate configuration solution set, including: Generate an economic indicator radar chart, a configuration ratio chart, and an energy storage behavior trajectory chart for the candidate configuration solution set; After receiving the user's adjustment operation on the installed capacity or energy storage capacity of the photovoltaic power generation device of each photovoltaic bracket type, recalculating the economic indicator matrix based on the adjusted installed capacity and energy storage capacity; The updated economic indicator matrix is ​​fed back to the economic indicator radar chart, the configuration ratio chart and the energy storage behavior trajectory chart in real time to achieve dynamic visual update.

8. A photovoltaic and energy storage joint configuration auxiliary decision-making device, characterized in that: include: The power generation forecast curve generation module is used to obtain historical meteorological data, grid spot electricity price data and basic project parameters of the target site, establish hourly power generation models per unit capacity for fixed-tilt brackets, vertical-mount brackets, and horizontal single-axis tracking brackets, and generate annual power generation forecast curves per unit capacity for each type of bracket. The future dynamic electricity price generation module is used to generate a dynamic electricity price time series for future periods by performing time series analysis on historical electricity price data, regional electricity load, and meteorological factors based on a pre-trained long-short-term memory network prediction model; An efficiency function construction module is used to weightedly couple the annual unit capacity power generation forecast curve with the dynamic electricity price time series hour by hour to determine the electricity price weighted unit capacity efficiency function corresponding to the photovoltaic power generation device of each photovoltaic bracket type; a coupled optimization model solving module for constructing a ternary coupled optimization model of photovoltaics, energy storage, and electricity prices based on the electricity price weighted unit capacity efficiency function, taking the installed capacity of the fixed-tilt bracket, vertical mounting bracket, and flat single-axis tracking bracket type photovoltaic power generation device and the capacity of the electrochemical energy storage system as joint optimization variables, and maximizing the project internal rate of return as the core goal, and solving a candidate configuration solution set; A candidate configuration solution set ranking module is used to calculate and visualize the economic indicators of the candidate configuration solution set, and generate weighted scoring ranking results based on user preference setting parameters; The joint configuration auxiliary recommendation module is used to output a recommended solution for the joint configuration of photovoltaic power generation devices and energy storage for each photovoltaic bracket type based on the weighted score sorting result.

9. An electronic device, characterized in that: include: processor; and A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the photovoltaic and energy storage joint configuration auxiliary decision-making method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the photovoltaic and energy storage joint configuration auxiliary decision-making method as described in any one of claims 1 to 7 is implemented.

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