Intelligent optimization method and system for photovoltaic conversion efficiency of water-photovoltaic energy storage

Abstract

The present invention provides an intelligent optimization method and system for photovoltaic conversion efficiency of water-photovoltaic energy storage, which relates to the field of electric energy storage technology. By acquiring the hydropower generation parameter space and photovoltaic generation environmental parameters, a hydropower generation optimization function is constructed, and hydropower generation parameters are optimized to obtain the optimal hydropower generation parameters, the optimal hydropower generation power, the optimal photovoltaic generation power, and the optimal basic conversion efficiency of photovoltaic generation. According to the optimal hydropower generation power and the optimal photovoltaic generation power, the optimal total power generation power is calculated to optimize energy storage scheduling, and the optimal scheduling conversion efficiency is calculated. The optimal basic conversion efficiency is corrected and calculated to obtain the optimization result of photovoltaic conversion efficiency. The technical problem that the energy storage and photovoltaic conversion efficiency in water-photovoltaic storage and the reasonable allocation and efficient utilization of electric energy are affected in the prior art is solved. The technical effect of improving the energy storage and photovoltaic conversion efficiency in water-photovoltaic storage and improving the overall performance of the water-photovoltaic energy storage platform is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage, and in particular to an intelligent optimization method and system for photovoltaic conversion efficiency of water-photovoltaic energy storage. Background Art

[0002] In the field of renewable energy, the water-solar-storage system, as an innovative solution integrating hydropower, photovoltaic power generation and energy storage technology, is increasingly becoming an important force in promoting energy transformation. However, the current water-solar-storage system has problems such as the environmental interaction between hydropower and photovoltaic power generation has not been fully optimized, resulting in a decrease in overall energy utilization. At the same time, there are problems such as low energy storage efficiency, insufficient power conversion efficiency and inefficient scheduling management, which in turn causes energy waste in the conversion and distribution process, affecting the rational distribution and efficient utilization of electric energy.

[0003] The existing technology has technical problems such as low energy storage in water-photovoltaic storage and low photovoltaic conversion efficiency in water-photovoltaic storage, energy waste, and affecting the rational distribution and efficient utilization of electric energy. Summary of the invention

[0004] The present application provides an intelligent optimization method and system for the photovoltaic conversion efficiency of water-photovoltaic storage energy, which is used to solve the technical problems in the prior art of low energy storage and power generation conversion efficiency in water-photovoltaic storage, energy waste, and affecting the rational distribution and efficient utilization of electric energy.

[0005] In view of the above problems, the present application provides an intelligent optimization method and system for photovoltaic conversion efficiency of water-photovoltaic energy storage.

[0006] The first aspect of the present application provides an intelligent optimization method for photovoltaic conversion efficiency of hydro-photovoltaic energy storage, the method comprising: obtaining a hydropower generation parameter space of a hydropower generation module, and collecting photovoltaic power generation environmental parameters of the current photovoltaic power generation module; constructing a hydropower generation optimization function for optimizing hydropower generation parameters for the purpose of increasing the total power of hydropower generation and photovoltaic power generation, and increasing the basic conversion efficiency of photovoltaic power generation; according to the hydropower generation optimization function, optimizing the hydropower generation parameters within the hydropower generation parameter space to obtain optimal hydropower generation parameters, and obtaining optimal hydropower generation power, optimal photovoltaic power generation power, and optimal basic conversion efficiency of photovoltaic power generation. The conversion efficiency is calculated, wherein the influence of the hydropower generation parameters on the photovoltaic power generation environmental parameters is predicted, and the power of the hydropower generation and the photovoltaic power generation is predicted for optimization; the optimal total power generation is calculated according to the optimal hydropower generation power and the optimal photovoltaic generation power, and the energy storage scheduling is optimized to obtain the optimal energy storage scheduling scheme, wherein the optimal energy storage scheduling scheme includes the optimal ratio of energy storage module energy storage and water energy storage for the optimal total power generation; according to the optimal energy storage scheduling scheme, the optimal scheduling conversion efficiency is calculated, the optimal basic conversion efficiency is corrected and calculated, and the optimal photovoltaic conversion efficiency is obtained as the optimization result of the photovoltaic conversion efficiency.

[0007] The second aspect of the present application provides an intelligent optimization system for photovoltaic conversion efficiency of hydro-photovoltaic energy storage, the system comprising: a power generation parameter acquisition module, the power generation parameter acquisition module is used to acquire the hydropower generation parameter space of the hydropower generation module, and collect the photovoltaic power generation environmental parameters of the current photovoltaic power generation module; a hydropower generation optimization function construction module, the hydropower generation optimization function construction module is used to construct a hydropower generation optimization function for optimizing hydropower generation parameters for the purpose of improving the total power of hydropower generation and photovoltaic power generation, and improving the basic conversion efficiency of photovoltaic power generation; a hydropower generation parameter optimization module, the hydropower generation parameter optimization module is used to optimize the hydropower generation parameters in the hydropower generation parameter space according to the hydropower generation optimization function, obtain the optimal hydropower generation parameters, and obtain the optimal hydropower generation power, the optimal photovoltaic power generation power and the photovoltaic power generation. The optimal basic conversion efficiency of power generation, wherein the influence of the hydropower generation parameters on the photovoltaic power generation environmental parameters is predicted, and the power of hydropower generation and photovoltaic power generation is predicted for optimization; an optimal energy storage scheduling scheme acquisition module, wherein the optimal energy storage scheduling scheme acquisition module is used to calculate the optimal total power generation power according to the optimal hydropower generation power and the optimal photovoltaic generation power, perform energy storage scheduling optimization, and obtain the optimal energy storage scheduling scheme, wherein the optimal energy storage scheduling scheme includes the optimal ratio of energy storage module energy storage and water energy storage for the optimal total power generation power; an optimal basic conversion efficiency correction module, wherein the optimal basic conversion efficiency correction module is used to calculate the optimal scheduling conversion efficiency according to the optimal energy storage scheduling scheme, correct the optimal basic conversion efficiency, and obtain the optimal photovoltaic conversion efficiency as the optimization result of photovoltaic conversion efficiency.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The method provided in the embodiment of the present application obtains the hydropower parameter space of the hydropower module and collects the photovoltaic power generation environmental parameters of the current photovoltaic power generation module; with the purpose of improving the total power of hydropower generation and photovoltaic power generation, and improving the basic conversion efficiency of photovoltaic power generation, a hydropower optimization function for optimizing the hydropower parameters is constructed; according to the hydropower optimization function, the hydropower parameters are optimized in the hydropower parameter space to obtain the optimal hydropower parameters, and the optimal hydropower power, the optimal photovoltaic power and the optimal basic conversion efficiency of photovoltaic power generation are obtained, wherein the influence of the hydropower parameters on the photovoltaic power generation environmental parameters is predicted, and the power of hydropower generation and photovoltaic power generation is predicted for optimization; the optimal total power generation is calculated according to the optimal hydropower power and the optimal photovoltaic power, and the energy storage scheduling optimization is performed to obtain the optimal energy storage scheduling plan, and the optimal scheduling conversion efficiency is calculated according to the optimal energy storage scheduling plan, and the optimal basic conversion efficiency is corrected and calculated to obtain the optimal photovoltaic conversion efficiency as the optimization result of the photovoltaic conversion efficiency. The technical effect of improving the energy storage and photovoltaic conversion efficiency in water-photovoltaic storage, reducing energy waste, realizing the rational distribution and efficient utilization of electric energy, and improving the overall performance of the water-photovoltaic storage energy platform has been achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flowchart of a multi-source data planning method based on the city brain provided for this application;

[0012] Figure 2 A schematic diagram of the structure of an intelligent optimization system for photovoltaic conversion efficiency of water-photovoltaic energy storage provided in this application.

[0013] Explanation of the reference numerals: 11 power generation parameter acquisition module, 12 hydropower generation optimization function construction module, 13 hydropower generation parameter optimization module, 14 optimal energy storage scheduling scheme acquisition module, 15 optimal basic conversion efficiency correction module. DETAILED DESCRIPTION

[0014] The present application provides an intelligent optimization method and system for photovoltaic conversion efficiency of water-photovoltaic energy storage, which is used to solve the technical problems of low energy storage and photovoltaic conversion efficiency in water-photovoltaic storage, energy waste, and the impact on the rational allocation and efficient use of electric energy in the prior art. The technical effect of improving the energy storage and photovoltaic conversion efficiency in water-photovoltaic storage, reducing energy waste, achieving the rational allocation and efficient use of electric energy, and improving the overall performance of the water-photovoltaic energy storage platform is achieved.

[0015] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0016] Embodiment 1, as Figure 1 As shown, the present application provides an intelligent optimization method for photovoltaic conversion efficiency of water-photovoltaic energy storage, the method is applied to a water-photovoltaic energy storage platform, the water-photovoltaic energy storage platform includes a hydropower generation module, a photovoltaic power generation module and an energy storage module, the method includes:

[0017] The hydropower generation parameter space of the hydropower generation module is obtained, and the photovoltaic power generation environmental parameters of the current photovoltaic power generation module are collected.

[0018] Specifically, the hydro-photovoltaic energy storage platform is a comprehensive energy platform that integrates hydropower generation, photovoltaic power generation and energy storage technology. The platform is mainly composed of three core parts: hydropower generation module, photovoltaic power generation module and energy storage module. The hydropower generation module is the core part of the hydro-photovoltaic energy storage platform, responsible for converting the kinetic energy of water flow into electrical energy; the photovoltaic power generation module directly converts solar energy into electrical energy through photovoltaic panels; the energy storage module is responsible for storing the electrical energy generated by the hydropower generation module and the photovoltaic power generation module, and releasing the electrical energy when needed to meet the load demand. The modules work together to form an efficient, flexible and sustainable energy supply system.

[0019] The hydropower parameter space refers to the set of key parameters that affect the efficiency of hydropower generation, including: water flow, water head, turbine speed, etc. According to the principles and practical experience of hydropower generation, professional measuring equipment is used to measure the key parameters to obtain the hydropower parameter space. The measuring equipment of the patent includes: flow meter, water level meter, etc. The flow meter is used to measure the water flow, such as electromagnetic flow meter, ultrasonic flow meter, etc. The water level meter is used to measure the water head, such as float water level meter, pressure water level meter, etc. At the same time, the photovoltaic power generation environmental parameters that affect the power generation efficiency of the current photovoltaic power generation module are collected according to the sensor, including temperature, humidity, etc. Through the collection and analysis of these data, strong data support is provided for the subsequent optimization of hydropower generation parameters, improvement of photovoltaic power generation efficiency, and optimization of energy storage scheduling.

[0020] With the aim of increasing the total power of hydropower and photovoltaic power generation, as well as improving the basic conversion efficiency of photovoltaic power generation, a hydropower optimization function is constructed to optimize the hydropower generation parameters.

[0021] Specifically, in order to increase the total power of hydropower and photovoltaic power generation and improve the basic conversion efficiency of photovoltaic power generation, a hydropower optimization function for optimizing hydropower parameters is constructed. The hydropower optimization function can automatically calculate the most suitable combination of hydropower parameters according to the characteristics of the current water conservancy module and photovoltaic power generation module, as well as the interaction between the two modules, so that the hydropower generation efficiency is the highest. This efficient operation can also create more favorable environmental conditions for photovoltaic power generation, thereby increasing the total power of the two and improving the basic conversion efficiency of photovoltaic power generation, thereby maximizing the utilization of resources and optimizing benefits.

[0022] According to the hydropower optimization function, the hydropower parameters are optimized in the hydropower parameter space to obtain the optimal hydropower parameters, and the optimal hydropower power, the optimal photovoltaic power and the optimal basic conversion efficiency of photovoltaic power generation are obtained, wherein the impact of the hydropower parameters on the photovoltaic power generation environmental parameters is predicted, and the power of hydropower and photovoltaic power generation is predicted for optimization.

[0023] Specifically, according to the constructed hydropower optimization function, parameter optimization is performed in the hydropower parameter space. The optimization function traverses various parameter combinations in the hydropower parameter space, including adjusting key parameters such as water flow, water head, and turbine speed, and calculates the possible impact of different hydropower parameter changes on photovoltaic power generation environmental parameters, such as temperature and humidity, to obtain the hydropower power under each parameter combination, and find a set of optimal hydropower parameter combinations. This combination not only optimizes the hydropower power and photovoltaic power, but also maximizes the basic conversion efficiency of photovoltaic power generation. The optimal basic conversion efficiency refers to the conversion efficiency that the photovoltaic power generation equipment itself can achieve without considering the impact of energy storage scheduling.

[0024] The optimal total power generation is calculated based on the optimal hydroelectric power generation and the optimal photovoltaic power generation, and the energy storage scheduling is optimized to obtain the optimal energy storage scheduling plan, wherein the optimal energy storage scheduling plan includes the optimal ratio of storing the optimal total power generation in the energy storage module and storing hydropower.

[0025] Specifically, after determining the optimal hydroelectric power and the optimal photovoltaic power, the optimal hydroelectric power and the optimal photovoltaic power are added together to obtain the optimal total power generation rate, that is, the highest power generation efficiency that can be achieved by the hydropower and photovoltaic power generation modules together. The power generated needs to be stored or stored in water energy. The energy storage module and water energy storage both have power loss. In order to further improve energy utilization efficiency and reduce unnecessary losses, the optimal total power generation is optimized for energy storage scheduling, and the optimal ratio of energy storage module energy storage and water energy storage for the optimal total power generation is determined. The optimal ratio is used to guide the storage of part of the electric energy through the energy storage module, while converting another part of the electric energy into water energy storage so that it can be released when needed. By finely regulating the ratio of energy storage and water energy storage, it is possible to effectively respond to fluctuations in power demand, ensure the stable operation of the power grid, and minimize the loss of energy during conversion and storage. According to the optimization of energy storage scheduling, the optimal energy storage scheduling scheme is obtained. The optimal energy storage scheduling scheme not only reduces losses, but also improves the overall utilization efficiency of energy.

[0026] According to the optimal energy storage scheduling scheme, the optimal scheduling conversion efficiency is calculated, and the optimal basic conversion efficiency is corrected and calculated to obtain the optimal photovoltaic conversion efficiency as the optimization result of the photovoltaic conversion efficiency.

[0027] Specifically, according to the optimal energy storage scheduling scheme, combined with the energy storage power loss coefficient, the water storage power loss coefficient and the hydropower generation conversion efficiency, the optimal scheduling conversion efficiency is calculated. The optimal scheduling conversion efficiency reflects the energy conversion efficiency of the entire process from power generation to energy storage and then to discharge (or utilization of hydropower) during the energy storage scheduling process. Finally, the optimal basic conversion efficiency is corrected using the calculated optimal scheduling conversion efficiency. Through the correction, the impact of energy storage scheduling on the actual available energy of photovoltaic power generation is taken into account, thereby obtaining a more accurate optimal photovoltaic conversion efficiency. The optimal photovoltaic conversion efficiency obtained is then used as the optimization result of the photovoltaic conversion efficiency, which improves the conversion efficiency of energy storage and power generation and the overall utilization rate of photovoltaic power generation, realizes the reasonable allocation and efficient utilization of electric energy, improves the overall performance of the water-light storage energy platform, and reduces energy waste.

[0028] Furthermore, the hydropower generation parameter space of the hydropower generation module is obtained, and the photovoltaic power generation environmental parameters of the current photovoltaic power generation module are collected, including: obtaining the hydropower generation parameter space of the hydropower generation module; collecting the humidity parameters and temperature parameters in the current photovoltaic power generation module environment as photovoltaic power generation environmental parameters.

[0029] Specifically, first, the hydropower generation parameter data is obtained from the professional measuring equipment and high-precision sensors deployed at the key positions of the hydropower generation module. The hydropower generation parameter data is the key parameters that directly affect the efficiency and stability of hydropower generation, including water flow, water head, turbine speed, etc. Then, based on the obtained hydropower generation parameter data, the hydropower generation parameter space is constructed using data analysis software tools and techniques. The hydropower generation parameter space is a multi-dimensional data set that contains the hydropower generation parameter values ​​at different times and under different working conditions. At the same time, the key environmental parameters in the photovoltaic power generation module environment are clarified, mainly including humidity parameters and temperature parameters. These two parameters have a significant impact on the efficiency of photovoltaic power generation. The temperature and humidity sensors deployed at the key positions of the photovoltaic power generation module are used to collect the humidity parameters and temperature parameters in the current photovoltaic power generation module environment, and the collected temperature parameters and humidity parameters are used as photovoltaic power generation environment parameters. By constructing the hydropower generation parameter space and collecting photovoltaic power generation environment parameters, the operating characteristics of the hydropower generation module and the current photovoltaic power generation specific environment data can be accurately and timely obtained, providing data support for the subsequent improvement of conversion efficiency.

[0030] Furthermore, in order to increase the total power of hydropower and photovoltaic power generation, and to improve the basic conversion efficiency of photovoltaic power generation, a hydropower optimization function for optimizing hydropower generation parameters is constructed, as shown in the following formula: ; Among them, HPF is the power generation fitness, and is the weight, is the hydropower generation power under the hydropower generation parameters, The photovoltaic power generation power under the photovoltaic power generation environmental parameters affected by the hydropower generation parameters, is the preset power generation capacity, is the light energy power of the current photovoltaic power generation module.

[0031] Specifically, a hydropower optimization function for optimizing hydropower generation parameters is constructed as follows: ; Among them, HPF is the power generation fitness, and is the weight, is the hydropower generation power under the hydropower generation parameters, The photovoltaic power generation power under the photovoltaic power generation environmental parameters affected by the hydropower generation parameters, is the preset power generation capacity, is the light energy power of the current photovoltaic power generation module.

[0032] By constructing a hydropower optimization function, the complex relationship between hydropower and photovoltaic power generation can be comprehensively considered. By adjusting the hydropower parameters, the total power of hydropower and photovoltaic power generation and the basic conversion efficiency of photovoltaic power generation can be simultaneously improved, the optimal operating state of the water-photovoltaic energy storage platform can be achieved, and energy utilization efficiency can be improved.

[0033] Furthermore, according to the hydropower optimization function, hydropower parameter optimization is performed in the hydropower parameter space to obtain optimal hydropower parameters, and optimal hydropower power, optimal photovoltaic power and optimal basic conversion efficiency of photovoltaic power generation are obtained, including: randomly generating initial hydropower parameters in the hydropower parameter space; predicting the impact on the photovoltaic power generation environmental parameters and the power generation of the hydropower module according to the initial hydropower parameters, obtaining initial influencing environmental parameters and initial hydropower power, collecting the light energy power received by the current photovoltaic power generation module, and combining the initial influencing environmental parameters , predict and obtain the initial photovoltaic power generation power; based on the hydropower optimization function, calculate and obtain the initial power generation fitness of the initial hydropower generation parameters according to the initial hydropower generation power, the initial light power and the initial photovoltaic power; continue to optimize the hydropower generation parameters in the hydropower parameter space until convergence, output the hydropower generation parameters with the maximum power generation fitness, and obtain the optimal hydropower generation parameters; obtain the optimal hydropower generation power and the optimal photovoltaic power generation power obtained based on the prediction processing of the optimal hydropower generation parameters during the optimization process, and calculate and obtain the optimal basic conversion efficiency according to the optimal photovoltaic power generation power and light power.

[0034] Specifically, first, in the constructed hydropower parameter space, a set of primary hydropower parameters are generated by a random generation algorithm, and the primary hydropower parameters include water flow, water head, turbine speed, etc. Then, the primary hydropower parameters are input into the model, and the prediction model is used to predict the potential impact of the primary hydropower parameters on the photovoltaic power generation environment, and the power generation of the hydropower module is predicted according to the prediction model. Since the hydropower module and the photovoltaic power module are integrated in the water-light energy storage platform, the water of the hydropower module will affect the humidity and temperature of the environment, and then affect the power of photovoltaic power generation. Therefore, the photovoltaic power generation power is predicted according to the environmental parameters after the impact combined with the light power parameters such as light intensity, and the primary environmental parameters and the primary hydropower power are obtained. The prediction model is a machine learning model trained with historical data. The primary environmental parameters are the potential impact of the primary hydropower parameters on the parameters such as temperature and humidity in the environment. The primary hydropower power refers to the ability of the hydropower module to convert water flow energy into electrical energy per unit time when the primary hydropower parameters are running. At the same time, the light power received by the current photovoltaic power generation module is collected in real time, and the light power reflects the maximum convertible energy under the current lighting conditions. The collected light power is combined with the predicted primary environmental parameters, which indirectly affect the photovoltaic power generation efficiency. The primary photovoltaic power generation under the influence of the primary hydropower generation parameters is predicted through the model, laying the foundation for the subsequent improvement of photovoltaic conversion efficiency.

[0035] Then, using the constructed hydropower optimization function, the initial hydropower power, the initial solar power and the initial photovoltaic power are used as inputs to calculate the power generation fitness of the initial hydropower generation parameters. The power generation fitness quantifies the comprehensive performance of the total power of hydropower and photovoltaic power generation and the photovoltaic power generation efficiency under the current combination of initial hydropower generation parameters.

[0036] In order to find the optimal combination of hydropower generation parameters, an optimization algorithm such as genetic algorithm, simulated annealing, etc. is started in the hydropower generation parameter space, and the hydropower generation parameters are updated iteratively, and the above prediction and calculation process is repeated continuously. In each iteration, the corresponding photovoltaic power generation environmental parameters, hydropower generation power and photovoltaic power generation power are predicted according to the new hydropower generation parameters, and the power generation fitness is recalculated. The optimization process continues until the power generation fitness reaches the preset convergence condition, such as the fitness change is small after multiple consecutive iterations or reaches the preset number of iterations. At this time, the hydropower generation parameters with the maximum power generation fitness are output, and the hydropower generation parameters are the optimal hydropower generation parameters under the current conditions. Finally, based on the optimal hydropower generation parameters predicted by the optimal hydropower generation parameters, and combined with the real-time collected light energy power, the optimal basic conversion efficiency is calculated. The optimal basic conversion efficiency is achieved by dividing the photovoltaic power generation power by the light energy power, which reflects the ability of photovoltaic power generation to convert light energy into electrical energy under the optimal hydropower generation parameters.

[0037] Further, according to the initial hydropower generation parameters, the influence on the photovoltaic power generation environmental parameters and the power generation of the hydropower generation module are predicted, the initial influencing environmental parameters and the initial hydropower generation power are obtained, the light energy power received by the current photovoltaic power generation module is collected, and the initial photovoltaic power generation power is predicted in combination with the initial influencing environmental parameters, including: according to the historical data of the hydropower generation module and the photovoltaic power generation module, a sample hydropower generation parameter set, a sample influencing environmental parameter set and a sample hydropower generation power set are collected; the sample hydropower generation parameter set is used as input, and the sample influencing environmental parameter set and the sample hydropower generation power set are used. The electric power sets are respectively output, and the environmental impact prediction branch and the hydropower prediction branch are trained to obtain the hydropower prediction path; the sample environmental parameter set and the sample light energy power set are collected as input, and the sample photovoltaic power generation power set is collected as output to train the photovoltaic power generation prediction path; the hydropower generation prediction path and the photovoltaic power generation prediction path are connected to predict the initial hydropower generation parameters to obtain the initial environmental parameters and the initial hydropower generation power; the light energy power received by the current photovoltaic power generation module is collected, combined with the initial environmental parameters, input into the photovoltaic power generation prediction path, and the initial photovoltaic power generation power is predicted.

[0038] Specifically, first, relevant data are extracted from the historical data of the hydropower generation module and the photovoltaic power generation module, including a sample hydropower generation parameter set, a sample influencing environment parameter set and a sample hydropower generation power set. The sample hydropower generation parameter set includes water flow, head, turbine speed, etc., and the sample influencing environment parameter set includes temperature, humidity, etc.

[0039] Then, the sample hydropower generation parameter set is used as the model input, and the sample environmental impact parameter set and the sample hydropower generation power set are respectively output. Using machine learning technology, two independent prediction models are trained. The prediction model includes an environmental impact prediction branch and a hydropower generation prediction branch. The environmental impact prediction branch is responsible for predicting future environmental conditions based on hydropower generation parameters, while the hydropower generation prediction branch predicts hydropower generation power based on current environmental conditions and hydropower generation parameters. These two branches together constitute the hydropower generation prediction path, and the prediction model is a neural network model.

[0040] Next, for the photovoltaic power generation module, similarly, a set of sample environmental parameters and a set of sample light energy power are collected as input, and the corresponding set of sample photovoltaic power generation is used as output to train the photovoltaic power generation prediction model, that is, the photovoltaic power generation prediction path. The photovoltaic power generation prediction path can predict the photovoltaic power generation power based on the current environmental conditions and light energy power.

[0041] After completing the training of the above-mentioned hydropower prediction path and photovoltaic power prediction path, the two prediction paths are connected to form a comprehensive energy generation prediction model, and the comprehensive energy generation prediction model is used to predict the initial generation of hydropower generation parameters. The given initial generation of hydropower generation parameters are predicted through the hydropower prediction path to obtain the initial generation of environmental parameters and the initial generation of hydropower generation power. Subsequently, the light energy power received by the current photovoltaic power generation module and the predicted initial generation of environmental parameters are input into the photovoltaic power generation prediction path to predict the initial generation of photovoltaic power generation power. By connecting the trained hydropower prediction path and the photovoltaic power generation prediction path, not only can the power of hydropower and photovoltaic power be predicted separately, but also a more comprehensive and accurate power generation prediction can be made under the premise of considering the mutual influence between the two, thereby improving the accuracy and efficiency of subsequent water-light storage energy photovoltaic conversion.

[0042] Furthermore, the optimal total power generation is calculated based on the optimal hydroelectric power generation and the optimal photovoltaic power generation, and energy storage scheduling optimization is performed to obtain the optimal energy storage scheduling scheme, including: calculating the optimal total power generation based on the optimal hydroelectric power generation and the optimal photovoltaic power generation; randomly allocating the optimal total power generation to energy storage modules for energy storage and hydroelectric power generation modules for water energy storage, and constructing an energy storage scheduling space; with the purpose of reducing the energy storage loss and water energy storage loss of the optimal total power generation, constructing an energy storage scheduling function, as shown in the following formula: ; Among them, ESS is the scheduling fitness, and is the weight, and The energy storage power ratio and water storage power ratio of the optimal total power generation in the energy storage scheduling scheme allocated to the energy storage module for energy storage and the hydropower module for water energy storage, The energy storage power loss coefficient of the energy storage module. is the water storage power loss coefficient of the hydropower module for water energy storage, and Q is the water energy conversion efficiency under the optimal hydropower generation parameters; in the energy storage scheduling space, randomly generate an initial energy storage scheduling plan; according to the initial energy storage power ratio and the initial water storage power ratio of the optimal total power generation in the initial energy storage scheduling plan allocated to the energy storage module for energy storage and the hydropower module for water energy storage, calculate and obtain the initial scheduling fitness based on the energy storage scheduling function; continue to optimize the energy storage scheduling until convergence, output the energy storage scheduling plan with the largest scheduling fitness, and obtain the optimal energy storage scheduling plan.

[0043] Specifically, the optimal total power generation is obtained according to the optimal hydroelectric power generation and the optimal photovoltaic power generation, that is, the optimal total power generation is obtained by adding the optimal hydroelectric power generation and the optimal photovoltaic power generation. The optimal total power generation is randomly allocated to the energy storage module for energy storage and the hydroelectric power module for water energy storage, and an energy storage scheduling space is constructed, which includes all possible power allocation schemes.

[0044] Next, in order to reduce the energy storage loss and water energy storage loss of the optimal total power generation, an energy storage scheduling function is constructed, and the energy storage scheduling function is as follows: ; Among them, ESS is the scheduling fitness, and is the weight, and The energy storage power ratio and water storage power ratio of the optimal total power generation in the energy storage scheduling scheme allocated to the energy storage module for energy storage and the hydropower module for water energy storage, The energy storage power loss coefficient of the energy storage module. is the water storage power loss coefficient of the hydropower module for water energy storage, Q is the water power conversion efficiency under the optimal hydropower parameters. Further, the energy storage power loss coefficient refers to the proportion of power loss during the energy storage process of the energy storage module, such as heat loss, line transmission loss, water storage power, the water storage power loss coefficient refers to the proportion of power loss during the water storage process, and the water power conversion efficiency refers to the ratio of water power generation power to water potential energy under the optimal hydropower parameters.

[0045] Then, a primary energy storage scheduling scheme is randomly generated in the energy storage scheduling space by a random number generation algorithm, and the primary energy storage scheduling scheme clearly states how the optimal total power generation is allocated to the power ratio of the energy storage module and the hydropower module. Based on the energy storage scheduling function, the primary energy storage power ratio and the primary water storage power ratio of the optimal total power generation in the primary energy storage scheduling scheme allocated to the energy storage module for energy storage and the hydropower module for water energy storage are calculated to obtain the primary scheduling fitness. And iterative optimization is performed according to optimization algorithms such as genetic algorithms and particle swarm optimization to generate a new energy storage scheduling scheme and calculate its scheduling fitness. The optimization process continues until the scheduling fitness is no longer significantly improved or reaches a preset number of iterations, at which time the optimization process is considered to have converged. Once the optimization process converges, the energy storage scheduling scheme with the maximum scheduling fitness is output, which is the optimal energy storage scheduling scheme. The optimal energy storage scheduling scheme refers to the best performance in reducing energy storage loss and water energy storage loss, thereby achieving efficient utilization and storage of total power generation.

[0046] Furthermore, according to the optimal energy storage scheduling scheme, the optimal scheduling conversion efficiency is calculated, and the optimal basic conversion efficiency is corrected and calculated to obtain the optimal photovoltaic conversion efficiency, including: according to the optimal energy storage power ratio and the optimal water storage power ratio in the optimal energy storage scheduling scheme, combined with the energy storage power loss coefficient, the water storage power loss coefficient and the hydropower generation conversion efficiency, the optimal scheduling conversion efficiency is calculated; according to the optimal scheduling conversion efficiency, the optimal basic conversion efficiency is corrected and calculated to obtain the optimal photovoltaic conversion efficiency.

[0047] Specifically, after obtaining the optimal energy storage scheduling scheme, the optimal scheduling conversion efficiency under the optimal energy storage scheduling scheme is calculated according to the optimal energy storage power ratio and the optimal water storage power ratio in the optimal energy storage scheduling scheme combined with the energy storage power loss coefficient, the water storage power loss coefficient and the hydropower generation conversion efficiency. The optimal scheduling conversion efficiency is the overall efficiency of the energy storage and power generation process taking into account the energy storage and hydropower storage losses.

[0048] The optimal scheduling conversion efficiency and the optimal basic conversion efficiency are compared to obtain the difference between the optimal scheduling conversion efficiency and the optimal basic conversion efficiency. According to the difference and actual needs, a correction factor is determined, and the correction factor reflects the overall efficiency improvement brought about by the optimization of energy storage scheduling. Then, the optimal basic conversion efficiency is corrected according to the correction factor, and the optimal photovoltaic conversion efficiency is calculated to improve the photovoltaic conversion efficiency in water-solar storage, effectively promote the rational distribution and efficient utilization of electric energy, and at the same time improve the overall performance of the water-solar storage energy platform, thereby maximizing the efficiency of power generation and energy storage in the entire water-solar storage.

[0049] Embodiment 2, based on the same inventive concept as the intelligent optimization method for photovoltaic conversion efficiency of water-photovoltaic energy storage in the above embodiment, such as Figure 2 As shown, the present application provides an intelligent optimization system for photovoltaic conversion efficiency of water-photovoltaic energy storage, wherein the system includes:

[0050] A power generation parameter acquisition module 11, the power generation parameter acquisition module 11 is used to obtain the hydropower generation parameter space of the hydropower generation module, and collect the photovoltaic power generation environmental parameters of the current photovoltaic power generation module; a hydropower generation optimization function construction module 12, the hydropower generation optimization function construction module 12 is used to construct a hydropower generation optimization function for optimizing hydropower generation parameters with the purpose of improving the total power of hydropower generation and photovoltaic power generation, and improving the basic conversion efficiency of photovoltaic power generation; a hydropower generation parameter optimization module 13, the hydropower generation parameter optimization module 13 is used to optimize the hydropower generation parameters in the hydropower generation parameter space according to the hydropower generation optimization function, obtain the optimal hydropower generation parameters, and obtain the optimal hydropower generation power, the optimal photovoltaic power generation power and the optimal basic conversion efficiency of photovoltaic power generation, wherein, according to the hydropower generation optimization function, The influence of hydroelectric power generation parameters on the photovoltaic power generation environmental parameters is predicted, and the power of hydroelectric power generation and photovoltaic power generation is predicted for optimization; an optimal energy storage scheduling scheme obtaining module 14, the optimal energy storage scheduling scheme obtaining module 14 is used to calculate the optimal total power generation power according to the optimal hydroelectric power generation power and the optimal photovoltaic power generation power, perform energy storage scheduling optimization, and obtain the optimal energy storage scheduling scheme, wherein the optimal energy storage scheduling scheme includes the optimal ratio of energy storage module energy storage and water energy storage for the optimal total power generation power; an optimal basic conversion efficiency correction module 15, the optimal basic conversion efficiency correction module 15 is used to calculate the optimal scheduling conversion efficiency according to the optimal energy storage scheduling scheme, correct the optimal basic conversion efficiency, and obtain the optimal photovoltaic conversion efficiency as the optimization result of photovoltaic conversion efficiency.

[0051] Furthermore, the power generation parameter acquisition module 11 is used to execute the following method: acquiring the hydropower generation parameter space of the hydropower generation module; collecting the humidity parameter and the temperature parameter in the current photovoltaic power generation module environment as the photovoltaic power generation environment parameters.

[0052] Furthermore, the hydropower generation optimization function construction module 12 is used to execute the following method: ; Among them, HPF is the power generation fitness, and is the weight, is the hydropower generation power under the hydropower generation parameters, The photovoltaic power generation power under the photovoltaic power generation environmental parameters affected by the hydropower generation parameters, is the preset power generation capacity, is the light energy power of the current photovoltaic power generation module.

[0053] Furthermore, the hydropower generation parameter optimization module 13 is used to execute the following method: randomly generate initial hydropower generation parameters in the hydropower generation parameter space; predict the impact on the photovoltaic power generation environmental parameters and the power generation power of the hydropower generation module according to the initial hydropower generation parameters, obtain the initial influencing environmental parameters and the initial hydropower generation power, collect the light energy power received by the current photovoltaic power generation module, and predict the initial photovoltaic power generation power in combination with the initial influencing environmental parameters; based on the hydropower generation optimization function, calculate the initial power generation fitness of the initial hydropower generation parameters according to the initial hydropower generation power, the initial light energy power and the initial photovoltaic power generation power; continue to optimize the hydropower generation parameters in the hydropower generation parameter space until convergence, output the hydropower generation parameters with the largest power generation fitness, and obtain the optimal hydropower generation parameters; obtain the optimal hydropower generation power and the optimal photovoltaic power generation power obtained based on the prediction processing of the optimal hydropower generation parameters during the optimization process, and calculate the optimal basic conversion efficiency according to the optimal photovoltaic power generation power and light energy power.

[0054] Furthermore, the hydropower generation parameter optimization module 13 is used to execute the following method: based on the historical data of the hydropower generation module and the photovoltaic power generation module, a sample hydropower generation parameter set, a sample influencing environment parameter set and a sample hydropower generation power set are collected; the sample hydropower generation parameter set is used as input, and the sample influencing environment parameter set and the sample hydropower generation power set are used as output, respectively, to train the environmental impact prediction branch and the hydropower generation prediction branch to obtain the hydropower generation prediction path; the sample environmental parameter set and the sample light energy power set are collected as input, and the sample photovoltaic power generation power set is collected as output to train the photovoltaic power generation prediction path; the hydropower generation prediction path and the photovoltaic power generation prediction path are connected to predict the initial hydropower generation parameters to obtain the initial influencing environment parameters and the initial hydropower generation power; the light energy power of the current photovoltaic power generation module receiving the light energy is collected, combined with the initial influencing environment parameters, input into the photovoltaic power generation prediction path, and the initial photovoltaic power generation power is predicted.

[0055] Furthermore, the optimal energy storage scheduling scheme obtaining module 14 is used to perform the following method: calculate the optimal total power generation according to the optimal hydroelectric power generation and the optimal photovoltaic power generation; randomly distribute the optimal total power generation to the energy storage module for energy storage and the hydroelectric power generation module for water energy storage, and construct an energy storage scheduling space; with the purpose of reducing the energy storage loss and water energy storage loss of the optimal total power generation, construct an energy storage scheduling function, as shown in the following formula: ; Among them, ESS is the scheduling fitness, and is the weight, and The energy storage power ratio and water storage power ratio of the optimal total power generation in the energy storage scheduling scheme allocated to the energy storage module for energy storage and the hydropower module for water energy storage, The energy storage power loss coefficient of the energy storage module. is the water storage power loss coefficient of the hydropower module for water energy storage, and Q is the water energy conversion efficiency under the optimal hydropower generation parameters; in the energy storage scheduling space, randomly generate an initial energy storage scheduling plan; according to the initial energy storage power ratio and the initial water storage power ratio of the optimal total power generation in the initial energy storage scheduling plan allocated to the energy storage module for energy storage and the hydropower module for water energy storage, calculate and obtain the initial scheduling fitness based on the energy storage scheduling function; continue to optimize the energy storage scheduling until convergence, output the energy storage scheduling plan with the largest scheduling fitness, and obtain the optimal energy storage scheduling plan.

[0056] Furthermore, the optimal basic conversion efficiency correction module 15 is used to execute the following method: according to the optimal energy storage power ratio and the optimal water storage power ratio in the optimal energy storage scheduling scheme, combined with the energy storage power loss coefficient, the water storage power loss coefficient and the hydropower generation conversion efficiency, the optimal scheduling conversion efficiency is calculated; according to the optimal scheduling conversion efficiency, the optimal basic conversion efficiency is corrected and calculated to obtain the optimal photovoltaic conversion efficiency.

[0057] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0058] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent optimization method for photovoltaic conversion efficiency of water-photovoltaic energy storage, characterized in that: The method is applied to a water-photovoltaic energy storage platform, which includes a hydroelectric power generation module, a photovoltaic power generation module and an energy storage module. The method includes: Acquire the hydropower generation parameter space of the hydropower generation module, and collect the photovoltaic power generation environmental parameters of the current photovoltaic power generation module; With the purpose of increasing the total power of hydropower and photovoltaic power generation, and improving the basic conversion efficiency of photovoltaic power generation, a hydropower optimization function is constructed to optimize the hydropower generation parameters; According to the hydropower optimization function, the hydropower parameters are optimized in the hydropower parameter space to obtain the optimal hydropower parameters, and the optimal hydropower power, the optimal photovoltaic power and the optimal basic conversion efficiency of photovoltaic power generation are obtained, wherein the influence of the hydropower parameters on the photovoltaic power generation environmental parameters is predicted, and the power of the hydropower and photovoltaic power generation is predicted for optimization; The optimal total power generation is calculated based on the optimal hydroelectric power generation and the optimal photovoltaic power generation, and the energy storage scheduling is optimized to obtain the optimal energy storage scheduling scheme, wherein the optimal energy storage scheduling scheme includes the optimal ratio of energy storage module energy storage and water energy storage for the optimal total power generation; According to the optimal energy storage scheduling scheme, the optimal scheduling conversion efficiency is calculated, and the optimal basic conversion efficiency is corrected and calculated to obtain the optimal photovoltaic conversion efficiency as the optimization result of the photovoltaic conversion efficiency; In order to increase the total power of hydropower and photovoltaic power generation, and to improve the basic conversion efficiency of photovoltaic power generation, a hydropower optimization function is constructed to optimize the hydropower generation parameters, as shown in the following formula: ； Among them, HPF is the power generation fitness, and is the weight, is the hydropower generation power under the hydropower generation parameters, The photovoltaic power generation power under the photovoltaic power generation environmental parameters affected by the hydropower generation parameters, is the preset power generation capacity, is the light energy power of the current photovoltaic power generation module; According to the hydropower optimization function, hydropower parameter optimization is performed in the hydropower parameter space to obtain optimal hydropower parameters, and to obtain optimal hydropower power, optimal photovoltaic power and optimal basic conversion efficiency of photovoltaic power generation, including: In the hydropower parameter space, randomly generating initial hydropower parameters; According to the primary hydropower generation parameters, the influence on the photovoltaic power generation environmental parameters and the power generation of the hydropower generation module are predicted, the primary influencing environmental parameters and the primary hydropower generation power are obtained, the light energy power received by the current photovoltaic power generation module is collected, and the primary photovoltaic power generation power is predicted and obtained in combination with the primary influencing environmental parameters; Based on the hydropower optimization function, the primary generation fitness of the primary generation hydropower generation parameters is calculated according to the primary generation hydropower generation power, the primary generation solar power and the primary generation photovoltaic power; Continue to optimize the hydropower generation parameters in the hydropower generation parameter space until convergence, output the hydropower generation parameters with the maximum power generation fitness, and obtain the optimal hydropower generation parameters; The optimal hydroelectric power generation power and the optimal photovoltaic power generation power are obtained based on the optimal hydroelectric power generation parameter prediction processing during the optimization process, and the optimal basic conversion efficiency is calculated based on the optimal photovoltaic power generation power and light energy power.

2. The intelligent optimization method for photovoltaic conversion efficiency of water-photovoltaic energy storage according to claim 1 is characterized in that: Obtain the hydropower generation parameter space of the hydropower generation module, and collect the photovoltaic power generation environmental parameters of the current photovoltaic power generation module, including: Obtain the hydropower generation parameter space of the hydropower generation module; The humidity parameters and temperature parameters in the current photovoltaic power generation module environment are collected as photovoltaic power generation environment parameters.

3. The intelligent optimization method for photovoltaic conversion efficiency of water-photovoltaic energy storage according to claim 1 is characterized in that: According to the primary hydropower generation parameters, the influence on the photovoltaic power generation environmental parameters and the power generation of the hydropower generation module are predicted, the primary influencing environmental parameters and the primary hydropower generation power are obtained, the light energy power received by the current photovoltaic power generation module is collected, and the primary photovoltaic power generation power is predicted and obtained in combination with the primary influencing environmental parameters, including: According to the historical data of the hydropower generation module and the photovoltaic power generation module, a sample hydropower generation parameter set, a sample environmental impact parameter set and a sample hydropower generation power set are collected; Using the sample hydropower generation parameter set as input, using the sample environmental impact parameter set and the sample hydropower generation power set as outputs, respectively, training the environmental impact prediction branch and the hydropower generation prediction branch, and obtaining a hydropower generation prediction path; Collect sample environmental parameter sets and sample light power sets as input, collect sample photovoltaic power generation sets as output, and train photovoltaic power generation prediction paths; Connecting the hydropower generation prediction path and the photovoltaic power generation prediction path, predicting the primary hydropower generation parameters, and obtaining the primary environmental influencing parameters and the primary hydropower generation power; The light energy power of the current photovoltaic power generation module receiving light energy is collected, combined with the primary generation influencing environmental parameters, input into the photovoltaic power generation prediction path, and the primary generation photovoltaic power generation power is predicted.

4. The intelligent optimization method for photovoltaic conversion efficiency of water-photovoltaic energy storage according to claim 1 is characterized in that: The optimal total power generation is calculated based on the optimal hydroelectric power generation and the optimal photovoltaic power generation, and the energy storage scheduling is optimized to obtain the optimal energy storage scheduling plan, including: Calculate the optimal total power generation according to the optimal hydroelectric power generation and the optimal photovoltaic power generation; Randomly allocating the optimal total power generation to the energy storage module for energy storage and the hydropower generation module for water energy storage, thereby constructing an energy storage scheduling space; In order to reduce the energy storage loss and water energy storage loss of the optimal total power generation, an energy storage scheduling function is constructed as follows: ； Among them, ESS is the scheduling fitness, and is the weight, and The energy storage power ratio and water storage power ratio of the optimal total power generation in the energy storage scheduling scheme allocated to the energy storage module for energy storage and the hydropower module for water energy storage, The energy storage power loss coefficient of the energy storage module. is the water storage power loss coefficient of the hydropower module for water energy storage, and Q is the water energy conversion efficiency under the optimal hydropower generation parameters; Randomly generate an initial energy storage scheduling plan in the energy storage scheduling space; The primary scheduling fitness is calculated based on the energy storage scheduling function according to the primary energy storage power ratio and the primary water storage power ratio of the optimal total power generation in the primary energy storage scheduling scheme allocated to the energy storage module for energy storage and the hydropower generation module for water energy storage; Continue to optimize the energy storage scheduling until convergence, output the energy storage scheduling plan with the largest scheduling fitness, and obtain the optimal energy storage scheduling plan.

5. The intelligent optimization method for photovoltaic conversion efficiency of water-photovoltaic energy storage according to claim 4 is characterized in that: According to the optimal energy storage scheduling scheme, the optimal scheduling conversion efficiency is calculated, and the optimal basic conversion efficiency is corrected and calculated to obtain the optimal photovoltaic conversion efficiency, including: According to the optimal energy storage power ratio and the optimal water storage power ratio in the optimal energy storage scheduling scheme, combined with the energy storage power loss coefficient, the water storage power loss coefficient and the hydropower generation conversion efficiency, the optimal scheduling conversion efficiency is calculated; According to the optimal scheduling conversion efficiency, the optimal basic conversion efficiency is corrected and calculated to obtain the optimal photovoltaic conversion efficiency.

6. An intelligent optimization system for photovoltaic conversion efficiency of water-photovoltaic energy storage, characterized in that: The steps for implementing the method according to any one of claims 1 to 5 include: A power generation parameter acquisition module, which is used to acquire the hydropower generation parameter space of the hydropower generation module and collect the photovoltaic power generation environment parameters of the current photovoltaic power generation module; A hydropower generation optimization function construction module, wherein the hydropower generation optimization function construction module is used to construct a hydropower generation optimization function for optimizing hydropower generation parameters for the purpose of increasing the total power of hydropower generation and photovoltaic power generation and increasing the basic conversion efficiency of photovoltaic power generation; A hydropower generation parameter optimization module, the hydropower generation parameter optimization module is used to optimize the hydropower generation parameters in the hydropower generation parameter space according to the hydropower generation optimization function, obtain the optimal hydropower generation parameters, and obtain the optimal hydropower generation power, the optimal photovoltaic generation power and the optimal basic conversion efficiency of photovoltaic generation, wherein the influence of the hydropower generation parameters on the photovoltaic generation environmental parameters is predicted, and the power of the hydropower generation and the photovoltaic generation is predicted for optimization; An optimal energy storage scheduling scheme obtaining module, the optimal energy storage scheduling scheme obtaining module is used to calculate the optimal total power generation according to the optimal hydropower generation power and the optimal photovoltaic generation power, perform energy storage scheduling optimization, and obtain the optimal energy storage scheduling scheme, wherein the optimal energy storage scheduling scheme includes the optimal ratio of energy storage module energy storage and water energy storage for the optimal total power generation; The optimal basic conversion efficiency correction module is used to calculate the optimal scheduling conversion efficiency according to the optimal energy storage scheduling plan, and to correct the optimal basic conversion efficiency to obtain the optimal photovoltaic conversion efficiency as the optimization result of the photovoltaic conversion efficiency.

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