Intelligent capacity optimization configuration software platform system integrated with wind and light storage load

Abstract

The invention relates to the technical field of integrated wind and light storage load optimal configuration, in particular to an integrated wind and light storage load intelligent capacity optimal configuration software platform system which comprises a data acquisition module, a data processing module, an analog simulation module, an optimal configuration module, an intelligent decision support module, a user interface module and a system management module. The method is economical and practical, can effectively solve the problems that a traditional configuration method depends on experience, lacks accurate analysis, is limited in system data processing capacity and cannot deeply mine data values, enables the economical efficiency of a configuration scheme and the energy utilization efficiency to be maximized, is high in system flexibility, and can effectively adapt to different user requirements and scene changes.

Description

Technical Field

[0001] This invention relates to the technical field of integrated wind, solar, and energy storage load optimization configuration, and is a software platform system for intelligent capacity optimization configuration of integrated wind, solar, and energy storage load. Background Technology

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, wind and solar energy have received widespread attention as important clean energy sources. However, due to the randomness and volatility of wind and solar energy, how to achieve their efficient and stable grid integration has become an urgent problem to be solved. To address this, integrated wind-solar-storage load systems have emerged. These systems combine wind power generation, solar power generation, energy storage devices, and load management to achieve optimized energy allocation and efficient utilization.

[0003] However, traditional configuration methods often rely on empirical judgment or simple mathematical models, lacking comprehensive consideration and precise analysis of complex factors. This results in suboptimal configuration schemes, making it difficult to maximize economic benefits and energy efficiency. Secondly, existing systems have limited data processing and analysis capabilities, failing to fully utilize massive amounts of data for in-depth mining and intelligent decision-making, thus limiting the system's optimization capabilities and decision support level. Summary of the Invention

[0004] The purpose of this invention is to provide an economical and practical integrated intelligent capacity optimization configuration software platform system for wind, solar and energy storage, which can effectively solve the problems of traditional configuration methods relying on experience and lacking precise analysis, as well as the limited data processing capabilities of the system and the inability to deeply mine the value of data. This maximizes the economy and energy utilization efficiency of the configuration scheme, and the system is highly flexible and can effectively adapt to different user needs and scenario changes.

[0005] The technical solution of this invention is achieved through the following measures: A software platform system for intelligent capacity optimization and configuration integrating wind, solar, and energy storage is characterized in that the system includes: a data acquisition module, a data processing module, a simulation module, an optimization and configuration module, an intelligent decision support module, a user interface module, and a system management module; The data acquisition module, as the front end of the system, is responsible for collecting various types of data on wind and solar resources, energy storage devices, and loads in real time, and transmitting these raw data to the data processing module. The data processing module performs preprocessing operations such as cleaning, transformation, and normalization on the received data to ensure data quality and consistency, and stores the processed data in the database for use by subsequent modules. The simulation module uses the processed data to build a simulation model of the wind-solar-storage system, simulates the system operation under different configuration schemes, and provides a basis for simulation verification for the optimization configuration module. The optimization configuration module optimizes the system capacity using an optimization algorithm engine based on the user-defined goals and constraints. Internally, it includes a goal function construction submodule, a constraint setting submodule, an optimization algorithm engine submodule, a result output and interpretation submodule, and a report generation submodule. The intelligent decision support module provides in-depth decision analysis and support based on optimization results and simulation data, helping users make scientific decisions. The user interface module is the window through which the system interacts with the user. It displays data, simulation results and optimization schemes through a visual interface, receives user input instructions and parameters, and transmits user feedback to the optimization configuration module and the intelligent decision support module. The system management module is responsible for monitoring, maintaining, and managing the logs of the entire system, ensuring the normal operation of each module and data security, while also providing user management and access control functions.

[0006] The data acquisition module collects various data from the wind-solar-storage system in real time and activates a large number of sensors deployed at wind farms, solar power plants, energy storage devices, and load points, including wind speed sensors, wind direction sensors, light intensity sensors, temperature sensors, humidity sensors, energy storage status sensors, and load monitoring devices. These sensors collect data at a preset high frequency to ensure the real-time performance and accuracy of the data.

[0007] The data processing module performs in-depth processing and analysis on the collected raw data. First, it cleans the data, identifies and processes outliers, missing values, and duplicate data, and uses interpolation and regression analysis methods to fill in missing values ​​to ensure data consistency and integrity. Next, it performs data conversion, unifying data from different sources and formats into the format and units required by the system, including converting wind speed from meters per second to kilometers per hour and light intensity from lux to watts per square meter.

[0008] The simulation module utilizes high-quality data provided by the data processing module to establish a simulation model of the wind-solar-storage-load system. The model includes a wind power generation model, a solar power generation model, an energy storage model, and a load model, considering various factors such as wind speed distribution, solar intensity variation, energy storage system characteristics, load demand, and grid constraints. When setting model parameters, the performance parameters and operating characteristics of actual equipment are fully considered, including the rated power of the wind turbine, cut-in wind speed, cut-out wind speed, photovoltaic panel conversion efficiency, tilt angle, and orientation, energy storage system capacity, charge / discharge efficiency, and cycle life, and the time-varying characteristics and demand response capability of the load. When running the simulation model, the system's operation over a specific time period is simulated, outputting detailed simulation results including power generation, load fulfillment rate, energy storage status changes, and grid interaction. By analyzing the simulation results, system performance, the feasibility of configuration schemes, and potential problems are evaluated, providing important reference for optimized configuration.

[0009] The objective function construction submodule of the optimization configuration module first receives the optimization objective input by the user based on the user's specific needs and preferences, and then transforms these objectives into a computable mathematical function. In this process, the module considers various cost factors, including equipment purchase costs, installation costs, and operation and maintenance costs, and combines them in an appropriate mathematical form. Finally, the constructed objective function will serve as the input to the optimization algorithm, guiding the algorithm to find the best solution that meets the conditions. The objective function is calculated using a total cost minimization algorithm, and its formula is: minC=C_wind+C_solar+C_storage+C_operation Where C_wind is the cost of wind power generation, C_solar is the cost of solar power generation, C_storage is the cost of energy storage system, and C_operation is the cost of operation and maintenance.

[0010] The constraint setting submodule of the optimization configuration module is responsible for defining the constraints that must be followed in the optimization problem to ensure the feasibility and rationality of the optimization results in practical applications. It allows users to set a series of constraints according to actual needs and system limitations, including power balance constraints, equipment capacity limits, and reliability requirements. The module will convert these constraints into mathematical expressions and, together with the objective function, form a complete mathematical model of the optimization problem. The formula for calculating power balance constraints is: P_wind+P_solar+P_storage-P_load=0 Where P_wind is the wind power generation capacity, P_solar is the solar power generation capacity, P_storage is the energy storage system charging and discharging capacity, where discharging is positive and charging is negative, and P_load is the load power.

[0011] The optimization algorithm engine submodule of the optimization configuration module uses the GA genetic algorithm to perform iterative calculations based on the objective function and constraints. The module initializes a population representing possible solutions, and then continuously evolves the population through selection, crossover, and mutation operations until it finds the optimal or near-optimal solution that satisfies the termination condition. During this process, the module evaluates the fitness of each solution, i.e., the value of the objective function, and uses the fitness to guide the search direction of the algorithm. Finally, the algorithm outputs the optimal solution or a set of excellent solutions for use by subsequent modules. The algorithm flow is as follows: S1. Initialize the population: Randomly generate a certain number of initial solution individuals, each solution representing a possible wind-solar-storage configuration scheme; S2. Selection: Based on the fitness value of individuals, select the best individuals to enter the next generation; usually, roulette wheel selection or tournament selection methods are used. S3. Crossover: Pair the selected individuals and generate new individuals through crossover operation, realizing gene recombination and information exchange; S4. Mutation: Randomly altering certain genes of new individuals to increase population diversity and avoid getting trapped in local optima; S5. Fitness Assessment: Calculate the fitness value for each individual to assess its performance. The fitness function is usually associated with the objective function; S6. Termination Check: Check if the termination condition is met. If it is, output the optimal solution; otherwise, return to step 2 to continue iteration. The fitness function formula is: ; Where F(x) is the fitness value of individual x; C(x) is the objective function value corresponding to individual x, i.e., the total cost; ϵ is a very small positive number used to avoid the case where the denominator is zero.

[0012] The result output and interpretation submodule of the optimization configuration module is responsible for presenting the results of the optimization algorithm to the user in an intuitive and easy-to-understand way, and providing necessary explanations and analyses. In terms of operation, this module first receives the results output by the optimization algorithm engine, including the optimal solution vector and the objective function value, and then organizes these results into tables and charts for display to the user. At the same time, the module will interpret the results, explaining how the optimal solution satisfies the constraints and optimization objectives, as well as the specific meaning and impact of each parameter.

[0013] The report generation submodule of the optimization configuration module is responsible for automatically compiling detailed optimization configuration reports, recording the optimization process, results, and recommended solutions for easy user review and decision-making. In terms of operation, this module summarizes information from each stage of objective function construction, constraint setting, optimization algorithm execution, and result interpretation, and generates reports according to a predetermined format and structure. The reports typically include a description of the optimization problem, the algorithm used, an overview of the optimization process, detailed parameters of the optimal solution, the value of the objective function, the satisfaction of constraints, and recommended implementation suggestions.

[0014] After receiving the results from the optimization configuration module and the simulation module, the intelligent decision support module uses data mining, pattern recognition, and predictive analysis methods to deeply mine valuable information from the data. The user interface module includes a data monitoring interface, a simulation result display interface, an optimization configuration result display interface, a parameter setting interface, and a system management interface; the data monitoring interface displays key data in real time, including wind speed, light intensity, power generation, and load demand, in a visually intuitive manner using charts and dashboards; The system management module includes several sub-modules: user management, data management, log management, and security management. The user management sub-module handles user registration, login, permission allocation, and role management, ensuring that only authorized users can access system resources and perform specific operations. The data management sub-module is responsible for data storage, backup, recovery, and deletion, employing efficient data storage strategies and disaster recovery plans to guarantee data security and integrity. The log management sub-module records all system operation logs, including user operations, system anomalies, and data changes, facilitating tracking and auditing. The security management sub-module implements various security measures, including data encryption, access control, and firewall settings, to prevent data leakage and unauthorized intrusion.

[0015] Compared with the prior art, the present invention solves the following problems and achieves beneficial technical effects: 1) It solves the problem that traditional configuration methods rely on experience and lack precise analysis.

[0016] Traditional configuration methods rely primarily on manual experience or simplified mathematical models, failing to comprehensively and accurately consider the randomness and volatility of wind speed and sunlight, as well as the coupled effects of various complex factors such as equipment performance, load changes, and power grid constraints. This invention overcomes the subjectivity and inaccuracies of empiricism by constructing refined simulation and mathematical optimization models and employing intelligent optimization algorithms to achieve scientific and precise optimization of system capacity.

[0017] 2) To address the problem that existing systems have limited data processing capabilities and cannot deeply mine the value of data.

[0018] Existing systems are insufficient for processing massive amounts of multi-source wind, solar, and energy storage data, making effective data cleaning, integration, and in-depth analysis difficult, thus limiting the level of data-driven intelligent decision-making. This invention utilizes a powerful data acquisition and processing module to preprocess raw data through cleaning, transformation, and normalization, ensuring data quality and providing a reliable data foundation for subsequent simulations and optimization calculations. Furthermore, it leverages data mining techniques to deeply extract the value from the data.

[0019] 3) To solve the problem that the economic efficiency and energy utilization efficiency of the configuration scheme cannot be maximized.

[0020] Due to the lack of systematic optimization methods, traditional configuration schemes often struggle to achieve multiple objectives simultaneously, such as lowest system lifecycle cost, highest revenue, or optimal energy efficiency, while meeting various constraints. This invention constructs an optimization model with the objective function of minimizing total cost, comprehensively considering constraints such as power balance and equipment capacity. It then utilizes intelligent algorithms, such as genetic algorithms, to solve the model, thereby generating a capacity configuration scheme that is close to optimal in terms of both economy and energy efficiency.

[0021] In this invention, the optimized configuration module significantly improves the intelligence and accuracy of the system's energy configuration through the precise collaboration of its internal sub-modules. The objective function construction sub-module allows users to customize optimization objectives based on specific needs, such as cost minimization, benefit maximization, or carbon emission reduction, ensuring a high degree of alignment between the configuration scheme and user requirements. The constraint setting sub-module considers various practical limitations, such as equipment capacity, investment budget, and policies and regulations, guaranteeing the feasibility and compliance of the configuration scheme. The optimization algorithm engine sub-module integrates advanced optimization algorithms, finding near-optimal capacity configuration schemes through efficient calculation and iteration. The result output and interpretation sub-module presents the optimization results in an intuitive way, providing detailed explanations and analysis to help users understand the advantages and disadvantages of the configuration scheme. This intelligent optimized configuration not only improves energy utilization efficiency but also reduces operating costs, enhancing the system's economics and competitiveness.

[0022] In this invention, the effective operation of the optimization configuration module enhances the decision support capabilities and user trust of the entire software platform system. Through precise data analysis and simulation, the optimization configuration module can predict system performance under different configuration schemes, providing decision-makers with a scientific basis. The report generation submodule further transforms complex optimization results into easily understandable report formats, facilitating user communication and decision-making. This transparent decision support process increases user trust in the system and improves the user experience. Furthermore, the operation of the optimization configuration module promotes continuous system improvement and optimization. Through continuous algorithm updates and parameter adjustments, the system can adapt to the ever-changing market environment and user needs, maintaining long-term competitiveness and vitality. Therefore, the optimization configuration module is not only key to achieving intelligent capacity optimization configuration but also an important guarantee for enhancing the overall value of the system and user satisfaction.

[0023] 4) Address the issues of weak decision support functions and the difficulty for users to understand and trust the optimization results.

[0024] Existing systems typically only provide simple configuration results, lacking detailed explanations of the optimization process, comparative analyses of multiple solutions, and predictions of future performance. This makes it difficult for users to understand and trust the system's recommendations, resulting in limited decision support capabilities. This invention, through an intelligent decision support module and a result output and interpretation submodule, not only provides the optimal configuration solution but also offers detailed simulation results, sensitivity analysis, risk assessment, and easy-to-understand reports, greatly enhancing the system's decision support capabilities and the transparency of its results.

[0025] 5) Address the issues of insufficient system flexibility and difficulty in adapting to different user needs and changing scenarios.

[0026] Existing systems often have fixed functions, making it difficult to flexibly adjust them according to specific user optimization goals (such as focusing on cost, reliability, or environmental protection), local resource conditions, and policy requirements. This invention, through modular design, allows users to define optimization goals, flexibly set constraints, and provides a user-friendly parameter setting interface, enabling the system to adapt to diverse application scenarios and personalized user needs. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the operation of a specific embodiment of the present invention; Figure 2 This is a schematic diagram of the composition of an optimized configuration module according to the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] Reference Figure 1-2 A software platform system integrating wind, solar and energy storage for intelligent capacity optimization and configuration, comprising: a data acquisition module, a data processing module, a simulation module, an optimization and configuration module, an intelligent decision support module, a user interface module and a system management module; The optimization configuration module has internal sub-modules for objective function construction, constraint setting, optimization algorithm engine, result output and interpretation, and report generation. As the front end of the system, the data acquisition module is responsible for collecting various data from wind and solar resources, energy storage devices, and loads in real time, and transmitting these raw data to the data processing module.

[0030] The data processing module performs preprocessing operations such as cleaning, transformation, and normalization on the received data to ensure data quality and consistency, and stores the processed data in the database for use by subsequent modules.

[0031] The simulation module uses the processed data to build a simulation model of the wind-solar-storage system, simulating the system operation under different configuration schemes, and providing a basis for simulation verification for the optimization module.

[0032] The optimization configuration module uses an optimization algorithm engine to calculate and optimize system capacity based on user-defined goals and constraints. The intelligent decision support module provides in-depth decision analysis and support based on optimization results and simulation data, helping users make scientific decisions.

[0033] The user interface module serves as the window for interaction between the system and the user. It displays data, simulation results, and optimization schemes through a visual interface, receives user input instructions and parameters, and transmits user feedback to the optimization configuration module and the intelligent decision support module.

[0034] The system management module is responsible for monitoring, maintaining, and managing the logs of the entire system, ensuring the normal operation of each module and data security, while also providing user management and access control functions.

[0035] The data acquisition module collects various data from the wind-solar-storage system in real time. It activates numerous sensors deployed at wind farms, solar power plants, energy storage devices, and load points, including wind speed sensors, wind direction sensors, light intensity sensors, temperature sensors, humidity sensors, energy storage status sensors, and load monitoring equipment. These sensors collect data at a preset high frequency (e.g., once per minute) to ensure the real-time nature and accuracy of the data.

[0036] The data processing module performs in-depth processing and analysis on the collected raw data. First, data cleaning is performed to identify and handle outliers, missing values, and duplicate data. Methods such as interpolation and regression analysis are used to fill in missing values, ensuring data consistency and integrity. Next, data transformation is performed, unifying data from different sources and formats into the system's required format and units, including converting wind speed from meters per second to kilometers per hour and light intensity from lux to watts per square meter. Then, preprocessing operations such as normalization and standardization are performed, using methods like min-max normalization and Z-score standardization to eliminate differences in data units and scales, improving data quality and usability. Finally, the processed data is stored in a high-performance, scalable database, such as MySQL, PostgreSQL, or a NoSQL database, facilitating subsequent querying, analysis, and data mining.

[0037] The simulation module utilizes high-quality data provided by the data processing module to establish a simulation model of the wind-solar-storage-load system. The model includes wind power generation, solar power generation, energy storage, and load models, considering various factors such as wind speed distribution, solar intensity variations, energy storage system characteristics, load demand, and grid constraints. When setting model parameters, the performance parameters and operating characteristics of actual equipment are fully considered, such as the rated power of wind turbines, cut-in wind speed, cut-out wind speed, photovoltaic panel conversion efficiency, tilt angle, and orientation, energy storage system capacity, charge / discharge efficiency, and cycle life, and the time-varying characteristics and demand response capability of the load. When running the simulation model, the system's operation is simulated over specific time periods (e.g., one day, one week, one year), outputting detailed simulation results such as power generation, load fulfillment rate, energy storage status changes, and grid interaction. By analyzing the simulation results, system performance, the feasibility of configuration schemes, and potential problems are evaluated, providing important reference for optimized configuration.

[0038] The objective function construction submodule, based on the user's specific needs and preferences, first receives the optimization objectives input by the user, and then transforms these objectives into a computable mathematical function. In this process, the module considers various cost factors, such as equipment purchase costs, installation costs, and operation and maintenance costs, and combines them in an appropriate mathematical form. Ultimately, the constructed objective function serves as input to the optimization algorithm, guiding it to find the optimal solution that satisfies the given conditions. The objective function is calculated using a total cost minimization algorithm, and its formula is: minC=C_wind+C_solar+C_storage+C_operation Where C_wind is the cost of wind power generation, C_solar is the cost of solar power generation, C_storage is the cost of energy storage system, and C_operation is the cost of operation and maintenance.

[0039] The constraint setting submodule is responsible for defining the constraints that must be followed in the optimization problem, ensuring the feasibility and rationality of the optimization results in practical applications. It allows users to set a series of constraints based on actual needs and system limitations, such as power balance constraints, equipment capacity limits, and reliability requirements. The module will then transform these constraints into mathematical expressions, which, together with the objective function, constitute a complete mathematical model of the optimization problem.

[0040] The formula for calculating power balance constraints is: P_wind+P_solar+P_storage-P_load=0 Where P_wind is the wind power generation capacity, P_solar is the solar power generation capacity, P_storage is the energy storage system charging and discharging capacity, where discharging is positive and charging is negative, and P_load is the load power.

[0041] The optimization algorithm engine submodule utilizes the GA (Genetic Algorithm) to iteratively calculate based on the objective function and constraints. The module initializes a population representing possible solutions and then continuously evolves the population through selection, crossover, and mutation operations until it finds the optimal or near-optimal solution that satisfies the termination condition. During this process, the module evaluates the fitness of each solution, i.e., the value of the objective function, and uses this fitness to guide the algorithm's search direction. Finally, the algorithm outputs the optimal solution or a set of excellent solutions for use by subsequent modules.

[0042] The algorithm flow is as follows: S1. Initialize the population: Randomly generate a certain number of initial solutions (individuals), each solution representing a possible wind-solar-storage configuration scheme.

[0043] S2. Selection: Based on the fitness value of individuals, select the best individuals to enter the next generation. Methods such as roulette wheel selection and tournament selection are commonly used.

[0044] S3. Crossover: Pair the selected individuals and generate new individuals through crossover, thereby achieving gene recombination and information exchange.

[0045] S4. Mutation: Randomly altering certain genes of new individuals to increase population diversity and avoid getting trapped in local optima.

[0046] S5. Fitness Assessment: Calculate the fitness value for each individual to assess its performance. The fitness function is usually associated with the objective function.

[0047] S6. Termination Check: Check if the termination conditions are met (such as reaching the maximum number of iterations, fitness convergence, etc.). If met, output the optimal solution; otherwise, return to step 2 to continue iteration.

[0048] The fitness function formula is: ; Where F(x) is the fitness value of individual x. C(x) is the objective function value for individual x, i.e., the total cost. ϵ is a very small positive number used to avoid the denominator being zero.

[0049] The Results Output and Interpretation submodule is responsible for presenting the optimization algorithm results to users in an intuitive and easy-to-understand manner, providing necessary explanations and analyses. In terms of operation, this module first receives the results output by the optimization algorithm engine, including the optimal solution vector and objective function values. It then organizes these results into tables, charts, and other formats for display to the user. Simultaneously, the module interprets the results, explaining how the optimal solution satisfies the constraints and optimization objectives, as well as the specific meaning and impact of each parameter. The module may indicate the optimal values ​​for wind power generation capacity, solar power generation capacity, and energy storage system capacity, explaining how these values ​​are achieved while satisfying power balance and cost minimization requirements. Furthermore, the module may provide sensitivity analysis to explore the impact of changes in input parameters on the results, helping users to gain a deeper understanding of the optimization results.

[0050] The report generation submodule is responsible for automatically generating detailed optimization configuration reports, recording the optimization process, results, and recommended solutions for easy user review and decision-making. In terms of operation, this module summarizes information from each stage, including objective function construction, constraint setting, optimization algorithm execution, and result interpretation, and generates a report according to a predetermined format and structure. The report typically includes a description of the optimization problem, the algorithm used, an overview of the optimization process, detailed parameters of the optimal solution, the value of the objective function, the satisfaction of constraints, and recommended implementation suggestions. The module also supports customizing the report's content and format according to user needs, such as adding a company logo or adjusting the layout. The generated report can be exported in PDF, Word, and other formats for easy saving, printing, and sharing. Through the report, users can gain a comprehensive understanding of the optimization configuration process and results, providing strong support for subsequent decision-making and implementation.

[0051] After receiving the results from the optimization configuration module and the simulation module, the intelligent decision support module uses data mining, pattern recognition, and predictive analysis to deeply extract valuable information from the data. It utilizes intelligent algorithms such as decision trees, random forests, support vector machines, and neural networks to conduct multi-dimensional and multi-level decision analysis, evaluating the risks, benefits, and feasibility of configuration schemes. Considering various factors such as market risk, technological risk, policy risk, and environmental risk, it outputs comprehensive and detailed decision recommendations, such as recommended optimal configuration schemes, risk control measures, and contingency plans. Through visualization, report generation, and interactive query functions, it helps users better understand the decision results and formulate scientific and reasonable energy configuration strategies.

[0052] The user interface modules include a data monitoring interface, a simulation results display interface, an optimization configuration results display interface, a parameter setting interface, and a system management interface. The data monitoring interface displays key data such as wind speed, solar irradiance, power generation, and load demand in real time, presented intuitively in the form of charts and dashboards.

[0053] The system management module comprises several sub-modules, including user management, data management, log management, and security management. The user management sub-module handles user registration, login, permission allocation, and role management, ensuring that only authorized users can access system resources and perform specific operations. The data management sub-module is responsible for data storage, backup, recovery, and deletion, employing efficient data storage strategies and disaster recovery plans to guarantee data security and integrity. The log management sub-module records all system operation logs, including user actions, system anomalies, and data changes, facilitating tracking and auditing. The security management sub-module implements various security measures, such as data encryption, access control, and firewall settings, to prevent data leakage and unauthorized intrusion.

[0054] In this invention, the optimized configuration module significantly improves the intelligence and accuracy of the system's energy configuration through the precise collaboration of its internal sub-modules. The objective function construction sub-module allows users to customize optimization objectives based on specific needs, such as cost minimization, benefit maximization, or carbon emission reduction, ensuring a high degree of alignment between the configuration scheme and user requirements. The constraint setting sub-module considers various practical limitations, such as equipment capacity, investment budget, and policies and regulations, guaranteeing the feasibility and compliance of the configuration scheme. The optimization algorithm engine sub-module integrates advanced optimization algorithms, finding near-optimal capacity configuration schemes through efficient calculation and iteration. The result output and interpretation sub-module presents the optimization results in an intuitive way, providing detailed explanations and analysis to help users understand the advantages and disadvantages of the configuration scheme. This intelligent optimized configuration not only improves energy utilization efficiency but also reduces operating costs, enhancing the system's economics and competitiveness.

[0055] In this invention, the effective operation of the optimization configuration module enhances the decision support capabilities and user trust of the entire software platform system. Through precise data analysis and simulation, the optimization configuration module can predict system performance under different configuration schemes, providing decision-makers with a scientific basis. The report generation submodule further transforms complex optimization results into easily understandable report formats, facilitating user communication and decision-making. This transparent decision support process increases user trust in the system and improves the user experience. Furthermore, the operation of the optimization configuration module promotes continuous system improvement and optimization. Through continuous algorithm updates and parameter adjustments, the system can adapt to the ever-changing market environment and user needs, maintaining long-term competitiveness and vitality. Therefore, the optimization configuration module is not only key to achieving intelligent capacity optimization configuration but also an important guarantee for enhancing the overall value of the system and user satisfaction.

[0056] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. An integrated wind-solar-storage capacity optimization configuration software platform system, characterized in that The system comprises a data acquisition module, a data processing module, a simulation module, an optimal configuration module, an intelligent decision support module, a user interface module and a system management module. The data acquisition module serves as the front end of the system, responsible for real-time collection of various types of data of wind, light, energy storage devices and loads, and transmission of these raw data to the data processing module. The data processing module performs cleaning, conversion and normalization preprocessing operations on the received data to ensure data quality and consistency, and stores the processed data in the database for subsequent modules. The simulation module uses the processed data to establish a simulation model of the wind, light, storage and load system, simulates the system operation under different configuration schemes, and provides a simulation verification basis for the optimal configuration module. The optimal configuration module uses an optimization algorithm engine to optimize the system capacity according to the user's set target and constraint conditions, and has a target function construction submodule, a constraint condition setting submodule, an optimization algorithm engine submodule, a result output and interpretation submodule, and a report generation submodule. The intelligent decision support module provides in-depth decision analysis and support based on the optimization results and simulation data, helping users make scientific decisions. The user interface module is the window for system and user interaction, which displays data, simulation results and optimization schemes through a visual interface, receives user input instructions and parameters, and feeds back user feedback to the optimal configuration module and intelligent decision support module. The system management module is responsible for monitoring, maintenance and log management of the entire system, ensuring normal operation of each module and data security, while providing user management and permission control functions.

2. The integrated wind-solar-storage capacity optimization configuration software platform system according to claim 1, characterized in that The data acquisition module collects various types of data of the wind, light, storage and load system in real time, starts a large number of sensors deployed in wind farms, solar power stations, energy storage devices and load points, including wind speed sensors, wind direction sensors, light intensity sensors, temperature sensors, humidity sensors, energy storage state sensors and load monitoring devices. These sensors collect data at a high frequency according to the preset frequency to ensure the real-time and accuracy of the data. 3.The integrated wind-solar-storage capacity optimization configuration software platform system according to claim 1 or 2, characterized in that The data processing module performs in-depth processing and analysis on the collected raw data. First, data cleaning is performed to identify and process outliers, missing values and duplicate data. Interpolation and regression analysis methods are used to fill in missing values to ensure data consistency and integrity. Then, data conversion is performed to convert different sources and formats of data into the required format and unit of the system, including converting wind speed from meters / second to kilometers / hour and converting light intensity from lux to watts / square meter.

4. The integrated wind-solar-storage capacity optimization configuration software platform system according to claim 3, characterized in that The simulation module uses high-quality data provided by the data processing module to establish a simulation model of the wind-solar-storage-load system. The model includes a wind power generation model, a solar power generation model, a storage model, and a load model. Various factors are considered, including wind speed distribution, changes in light intensity, storage system characteristics, load demand, and grid constraints. When setting model parameters, the performance parameters and operating characteristics of actual equipment are fully considered, including the rated power, cut-in wind speed, and cut-out wind speed of wind turbines, the conversion efficiency, inclination, and orientation of photovoltaic panels, the capacity, charging and discharging efficiency, and cycle life of the storage system, and the time-varying characteristics and demand response capabilities of the load. When running the simulation model, the system's operation over a specific time period is simulated, and detailed simulation results are output, including power generation, load satisfaction rate, storage state changes, and grid interaction. By analyzing the simulation results, the system's performance, the feasibility of the configuration scheme, and potential problems are evaluated, providing important references for optimizing the configuration.

5. The integrated wind-solar-storage capacity optimization configuration software platform system according to claim 4, characterized in that The objective function construction submodule of the optimization configuration module first receives user input optimization objectives based on user-specific needs and preferences, and then converts these objectives into calculable mathematical functions. In this process, the module considers various cost factors, including equipment purchase costs, installation costs, and operation and maintenance costs, and combines them in appropriate mathematical forms. Finally, the constructed objective function serves as input for the optimization algorithm, guiding the algorithm to find the best solution that meets the conditions. The total cost minimization algorithm is used for calculation, and the objective function formula is: min C = C_wind + C_solar + C_storage + C_operation Where C_wind is the wind power generation cost, C_solar is the solar power generation cost, C_storage is the storage system cost, and C_operation is the operation and maintenance cost.

6. The integrated wind-solar-storage capacity optimization configuration software platform system according to claim 5, characterized in that The constraint condition setting submodule of the optimization configuration module is responsible for defining the limitations that must be followed in the optimization problem, ensuring the feasibility and reasonableness of the optimization results in practical applications. Users can set a series of constraint conditions based on actual needs and system limitations, including power balance constraints, device capacity limits, and reliability requirements. The module converts these constraint conditions into mathematical expressions and, together with the objective function, forms a complete mathematical model of the optimization problem. The power balance constraint calculation formula is: P_wind + P_solar + P_storage - P_load = 0 Where P_wind is the wind power generation power, P_solar is the solar power generation power, P_storage is the storage system charging and discharging power (discharging is positive and charging is negative), and P_load is the load power.

7. The integrated wind-solar-storage capacity optimization configuration software platform system according to claim 6, characterized in that The optimization algorithm engine submodule of the optimization configuration module uses the GA genetic algorithm to perform iterative calculation according to the objective function and the constraint condition. The module initializes a population representing possible solutions, and then continuously evolves the population through selection, crossover and mutation operations until the optimal solution or the approximate optimal solution that meets the termination condition is found. In this process, the module evaluates the fitness of each solution, that is, the value of the objective function, and guides the search direction of the algorithm according to the fitness. Finally, the algorithm outputs the optimal solution or a set of excellent solutions for use by subsequent modules. The algorithm flow is as follows: S1. Initialize the population: randomly generate a certain number of initial solution individuals, each solution representing a possible wind-light-load configuration scheme; S2. Selection: select excellent individuals into the next generation according to the fitness value of the individuals; usually use roulette selection, tournament selection method; S3. Crossover: pair the selected individuals and generate new individuals through crossover operation to realize gene recombination and information exchange; S4. Mutation: randomly change some genes of the new individuals to increase the diversity of the population and avoid falling into local optimum; S5. Fitness evaluation: calculate the fitness value of each individual to evaluate its advantages and disadvantages, and the fitness function is usually associated with the objective function; S6. Termination judgment: check whether the termination condition is met; if met, output the optimal solution; otherwise, return to step 2 to continue iteration; The fitness function formula is: Where F(x) is the fitness value of individual x; C(x) is the objective function value of individual x, that is, the total cost; ε is a very small positive number to avoid the case where the denominator is zero.

8. The integrated wind-solar-storage capacity optimization configuration software platform system according to claim 7, characterized in that The result output and explanation submodule of the optimization configuration module is responsible for presenting the results of the optimization algorithm to the user in an intuitive and easy-to-understand manner, and providing necessary explanation and analysis. In terms of running method, this module first receives the results output by the optimization algorithm engine, including the optimal solution vector and the objective function value, and then arranges these results into tables and charts to display to the user. At the same time, the module explains how the optimal solution meets the constraint conditions and optimization objectives, as well as the specific meanings and influences of various parameters.

9. The integrated wind-solar-storage capacity optimization configuration software platform system according to claim 8, characterized in that The report generation submodule of the optimization configuration module is responsible for automatically preparing a detailed optimization configuration report to record the optimization process, results and recommended scheme for user reference and decision-making. In terms of running method, this module summarizes the information of each stage of the target function construction, constraint condition setting, optimization algorithm execution and result explanation, and generates a report according to the predetermined format and structure. The report usually includes the description of the optimization problem, the algorithm used, the overview of the optimization process, the detailed parameters of the optimal solution, the value of the objective function, the satisfaction of the constraint conditions and the recommended implementation suggestions.

10. The integrated wind-solar-storage capacity optimization configuration software platform system according to claim 9, characterized in that The intelligent decision support module receives the results of the optimization configuration module and the simulation simulation module, and uses data mining, pattern recognition and prediction analysis methods to deeply mine valuable information from the data. The user interface module includes data monitoring interface, simulation result display interface, optimization configuration result display interface, parameter setting interface and system management interface. The data monitoring interface displays key data in real time, including wind speed, light intensity, power generation, and load demand, in the form of charts and dashboards for intuitive display. The system management module includes multiple sub-modules such as user management, data management, log management, and security management. The user management sub-module is responsible for handling user registration, login, permission allocation, and role management, ensuring that only authorized users can access system resources and perform specific operations. The data management sub-module is responsible for data storage, backup, recovery, and deletion, using efficient data storage strategies and disaster recovery plans to ensure data security and integrity. The log management sub-module records all system operation logs, including user operations, system abnormalities, and data changes, facilitating tracking and auditing. The security management sub-module implements various security measures, including data encryption, access control, and firewall settings, to prevent data leakage and unauthorized intrusion.

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