Power generation scheduling method, device and equipment of wind-solar hybrid power generation system and medium

Abstract

The invention relates to the technical field of wind-solar hybrid power generation dispatching, and discloses a power generation dispatching method, device and equipment of a wind-solar hybrid power generation system and a medium, and the power generation dispatching method of the wind-solar hybrid power generation system comprises the steps: obtaining wind power, photoelectric and power utilization efficiency parameters; determining a first correlation coefficient of the wind power and power utilization efficiency parameters and a second correlation coefficient of the photoelectric and power utilization efficiency parameters, and determining a target similarity according to the two correlation coefficients and the weight factor; determining an initial scheduling strategy according to the target similarity; according to the target energy storage battery temperature existing in the comparison result of the energy storage battery temperature of the energy storage system and the battery safety temperature threshold value, whether the target energy storage system is a priority scheduling target or not is judged, and if yes, power generation scheduling is conducted according to the adjusted target scheduling strategy. The target scheduling strategy is determined by determining the similarity between wind and light power generation and user power utilization and judging whether the temperature of the energy storage system is safe or not, and the reasonability and accuracy of the target scheduling strategy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind-solar hybrid power generation scheduling, and in particular to a power generation scheduling method, device, equipment and medium for a wind-solar hybrid power generation system. Background Art

[0002] With the rapid development of renewable energy, wind and solar power have become key components of the global energy transition. Wind-solar hybrid power generation systems combine the advantages of wind and photovoltaic power generation, not only reducing dependence on traditional energy sources but also improving the stability and reliability of energy supply. Energy storage systems are crucial in wind-solar hybrid power generation systems. These systems store excess electricity through batteries, enabling wind-solar hybrid power generation systems to provide stable power support to the grid even when wind or sunlight is insufficient. However, the operation of wind-solar hybrid power generation systems faces multiple challenges, particularly how to efficiently dispatch and manage power generation resources to meet load demands while ensuring the safety and affordability of wind-solar hybrid power generation systems.

[0003] In related technologies, the power generation scheduling method for the wind-solar hybrid power generation system collects data on wind farms and photovoltaic power farms in the wind-solar hybrid power generation system, predicts the wind power generation and photovoltaic power generation conditions, and performs scheduling based on the prediction results.

[0004] However, this method cannot guarantee the accuracy of predicting wind power generation and photovoltaic power generation, resulting in unreasonable and inaccurate scheduling results during subsequent scheduling. Summary of the Invention

[0005] In view of this, the present invention provides a power generation scheduling method, device, equipment and medium for a wind-solar hybrid power generation system to solve the problem in the related art that the power generation scheduling method for the wind-solar hybrid power generation system causes the scheduling results to be unreasonable and inaccurate.

[0006] In a first aspect, the present invention provides a method for power generation scheduling of a wind-solar hybrid power generation system, comprising: obtaining wind power efficiency parameters of a wind turbine in the wind-solar hybrid power generation system, photovoltaic efficiency parameters of a photovoltaic array in the wind-solar hybrid power generation system, and power efficiency parameters corresponding to a power grid connected to the wind-solar hybrid power generation system; determining a first correlation coefficient between the wind power efficiency parameters and the power efficiency parameters, determining a second correlation coefficient between the photovoltaic efficiency parameters and the power efficiency parameters, and determining a target similarity based on the first correlation coefficient, the second correlation coefficient, and a first weight factor; comparing the target similarity with a preset similarity threshold, and determining an initial scheduling strategy based on the first comparison result; obtaining The first energy storage battery temperature of the wind power energy storage system corresponding to the wind turbine generator set and the second energy storage battery temperature of the photovoltaic energy storage system corresponding to the photovoltaic power generation array are respectively compared with the battery safety temperature threshold to obtain a second comparison result; if there is a target energy storage battery temperature exceeding the battery safety temperature threshold in the second comparison result, it is determined whether the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling strategy; if the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling strategy, the initial scheduling strategy is adjusted, and power generation scheduling is performed according to the adjusted target scheduling strategy.

[0007] By obtaining wind power, photovoltaic efficiency parameters, and electricity efficiency parameters and determining the correlation coefficient between them to determine target similarity, the present invention can more accurately match electricity demand with wind and solar power generation capacity, making power generation scheduling more closely aligned with actual electricity consumption, improving the rationality and stability of power supply, reducing wind and solar curtailment, and improving energy utilization. The present invention compares the target similarity with a preset similarity threshold and, based on the first comparison result, determines an initial scheduling strategy. Determining the scheduling strategy based on quantitative indicators allows power generation scheduling to more closely align with actual electricity demand, better integrates the wind and solar hybrid power generation system into grid operations, and enhances the coordination of the entire power system. The present invention obtains the first energy storage battery temperature of the wind power energy storage system corresponding to the wind turbine generator set and the second energy storage battery temperature of the photovoltaic energy storage system corresponding to the photovoltaic power generation array, and compares the first energy storage battery temperature and the second energy storage battery temperature with the battery safety temperature threshold respectively to obtain a second comparison result. If there is a target energy storage battery temperature exceeding the battery safety temperature threshold in the second comparison result, it is determined whether the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling strategy. If the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling strategy, the initial scheduling strategy is performed. The scheduling strategy is adjusted, and power generation scheduling is performed according to the adjusted target scheduling strategy. The present invention takes into account the temperature factor of the energy storage battery. When the temperature of the energy storage battery exceeds the safety threshold, the initial scheduling strategy will be adjusted. Therefore, the scheduling strategy is dynamically optimized according to the actual state of the energy storage system to cope with different operating conditions and adapt to the intermittent and fluctuating characteristics of wind and solar power generation. This real-time monitoring and timely response mechanism ensures the rationality and accuracy of scheduling. At the same time, it can effectively avoid the performance degradation, life shortening and even safety accidents of the energy storage system caused by problems such as battery overheating, and ensure the long-term reliable operation of the energy storage equipment. Compared with related technologies, the present invention does not require prediction, but determines the similarity between wind and solar power generation and electricity demand, and judges whether the temperature of the energy storage system is safe to determine the target scheduling strategy, thereby improving the rationality and accuracy of the target scheduling strategy, ensuring that the power generation scheduling strategy always adapts to the actual operating conditions of the wind and solar hybrid power generation system, and maintaining the safe and stable operation of the entire wind and solar hybrid power generation system.

[0008] In an optional embodiment, the wind power efficiency parameters include wind power generation and wind power conversion efficiency, the photovoltaic efficiency parameters include photovoltaic power generation and photovoltaic conversion efficiency, and the electricity efficiency parameters include electricity efficiency and electricity consumption; obtaining the wind power efficiency parameters of the wind turbine in the wind-solar hybrid power generation system, the photovoltaic efficiency parameters of the photovoltaic power generation array in the wind-solar hybrid power generation system, and the electricity efficiency parameters corresponding to the power grid connected to the wind-solar hybrid power generation system, include: obtaining the wind power generation data and wind power generation of the wind turbine in the wind-solar hybrid power generation system; analyzing the conversion efficiency of the wind turbine based on the wind power generation data and wind wavelength characteristic data to obtain the wind power conversion efficiency; obtaining the photovoltaic power generation data and photovoltaic power generation of the photovoltaic power generation array in the wind-solar hybrid power generation system; analyzing the conversion efficiency of the photovoltaic power generation array based on the photovoltaic power generation data, light wavelength characteristic data and loss factor to obtain the photovoltaic conversion efficiency; obtaining the electricity consumption corresponding to the power grid connected to the wind-solar hybrid power generation system; and obtaining the electricity efficiency based on the quotient of the electricity consumption and the electricity consumption time period.

[0009] This invention incorporates wind wavelength characteristic data when analyzing the conversion efficiency of wind turbines. Describing wind characteristics by wavelength helps to further explore the potential of wind energy resources and more precisely match wind turbine design and operating conditions. This allows for a more comprehensive and accurate description of the wind power conversion process, improving the accuracy of wind power conversion efficiency. This invention also considers light wavelength characteristic data and loss factors when analyzing the conversion efficiency of photovoltaic arrays, resulting in a more accurate photoelectric conversion efficiency that better reflects the actual photoelectric conversion process.

[0010] In an optional embodiment, a first correlation coefficient between a wind power efficiency parameter and an electricity efficiency parameter is determined, a second correlation coefficient between a photovoltaic efficiency parameter and an electricity efficiency parameter is determined, and a target similarity is determined based on the first correlation coefficient, the second correlation coefficient, and the first weight factor, including: determining a wind power conversion efficiency change graph for a preset time period based on the wind power conversion efficiency, and determining a wind power generation change graph for a preset time period based on the wind power generation; determining a photoelectric conversion efficiency change graph for a preset time period based on the photoelectric conversion efficiency, and determining a photoelectric generation change graph for a preset time period based on the photoelectric generation; and determining a photoelectric generation change graph for a preset time period based on the photoelectric conversion efficiency. According to the power efficiency, determine the power efficiency change graph of the preset time period, and according to the power consumption, determine the power consumption change graph of the preset time period; obtain multiple first band lengths and the average value of the first band length in the wind power conversion efficiency change graph, multiple second band lengths and the average value of the second band length in the wind power generation change graph, multiple third band lengths and the average value of the third band length in the photovoltaic conversion efficiency change graph, multiple fourth band lengths and the average value of the fourth band length in the photovoltaic power generation change graph, multiple fifth band lengths and the average value of the fifth band length in the power efficiency change graph, and multiple fifth band lengths in the power consumption change graph According to the plurality of first band lengths, the average value of the first band lengths, the plurality of fifth band lengths and the average value of the fifth band lengths, a first correlation degree is determined; according to the plurality of second band lengths, the average value of the second band lengths, the plurality of sixth band lengths and the average value of the sixth band lengths, a second correlation degree is determined; according to the first correlation degree and the second correlation degree, a first correlation coefficient is obtained; according to the plurality of third band lengths, the average value of the third band lengths, the plurality of fifth band lengths and the average value of the fifth band lengths, a third correlation degree is determined; according to the plurality of fourth band lengths, the average value of the third band lengths, the plurality of fifth band lengths and the average value of the fifth band lengths, a third correlation coefficient is obtained; The fourth correlation degree is determined by the average value of the four-band lengths, multiple sixth band lengths and the average value of the sixth band lengths, and the second correlation coefficient is obtained according to the third correlation degree and the fourth correlation degree; the first weight factor is obtained, and the first weight factor is a weight factor dynamically adjusted using the particle swarm algorithm; the first product result is obtained according to the product of the first correlation coefficient and the first weight factor; the second weight factor is obtained according to the difference between the preset value and the first weight factor; the second product result is obtained according to the product of the second correlation coefficient and the second weight factor; and the target similarity is obtained according to the sum of the first product result and the second product result.

[0011] The present invention improves waveform matching accuracy by determining the degree of correlation between wind power, photovoltaic power generation and user demand, performs similarity calculation based on the changing trend of wind power and photovoltaic power generation data and the fluctuation of user demand, optimizes the output waveform of wind and photovoltaic power generation to make it as close as possible to the user's electricity consumption waveform. Based on the similarity calculation results, the system can intelligently select wind power or photovoltaic power generation as the priority scheduling target, and at the same time decide whether to use energy storage as a backup scheduling target based on the status of the energy storage system to ensure uninterrupted power supply. When determining the target similarity, the present invention dynamically adjusts the first weight factor of wind power and photovoltaic power generation, so that the wind-solar hybrid power generation system can give full play to the complementarity of wind power and photovoltaic power generation under different meteorological conditions, improves the coordination of wind and photovoltaic power generation, and automatically adjusts the power generation weights of photovoltaic and wind power when meteorological conditions change, avoiding instability of the wind-solar hybrid power generation system caused by insufficient power generation of a single energy source, ensuring maximum energy output, and improving the matching degree of wind power and photovoltaic power generation to user demand through real-time weight optimization, avoiding energy waste.

[0012] In an optional embodiment, an initial scheduling strategy is determined based on the first comparison result, including: if the first comparison result is that the target similarity is greater than or equal to a preset similarity threshold, then the initial scheduling strategy is to use wind turbines and photovoltaic power generation arrays as priority scheduling targets, and wind power energy storage systems and photovoltaic energy storage systems as suboptimal scheduling targets; if the first comparison result is that the target similarity is less than the preset similarity threshold, then the initial scheduling strategy is to use wind power energy storage systems and photovoltaic energy storage systems as priority scheduling targets, and wind turbines and photovoltaic power generation arrays as suboptimal scheduling targets.

[0013] In an optional embodiment, the initial scheduling strategy is adjusted, including: if the target energy storage system is a wind power energy storage system, the initial scheduling strategy is adjusted to prioritize the photovoltaic power generation array and the photovoltaic energy storage system, and to prioritize the wind turbine generator set and the wind power energy storage system; if the target energy storage system is a photovoltaic energy storage system, the initial scheduling strategy is adjusted to prioritize the wind turbine generator set and the wind power energy storage system, and to prioritize the photovoltaic power generation array and the photovoltaic energy storage system.

[0014] In an optional embodiment, power generation scheduling according to the adjusted target scheduling strategy includes: when the priority scheduling target in the target scheduling strategy includes a wind power storage system or a photovoltaic energy storage system, monitoring the remaining power of multiple batteries in the wind power storage system or the photovoltaic energy storage system; dividing the multiple batteries into a superior power supply group and a secondary power supply group according to the remaining power of the multiple batteries, and adjusting the batteries in the superior power supply group and the secondary power supply group according to the remaining power of the multiple batteries monitored in real time, and using the batteries in the superior power supply group for power generation scheduling.

[0015] This invention accurately determines the power supply priority of batteries based on the real-time remaining charge of energy storage batteries, reducing overuse and unnecessary battery loss, extending the overall service life of the energy storage system, and reducing the maintenance and replacement costs of the energy storage component of the wind-solar hybrid power generation system. The invention adjusts the batteries in the group based on the real-time monitoring of the remaining battery charge, ensuring that the division of power supply groups always matches the actual battery charge status. This dynamic optimization mechanism can adapt to the changes in the energy storage battery charge over time, continuously ensuring reliable power supply, ensuring the stability of the power supply, and reducing power outages or fluctuations caused by insufficient battery charge.

[0016] In a second aspect, the present invention provides a power generation scheduling device for a wind-solar hybrid power generation system, comprising: a parameter acquisition module for acquiring wind power efficiency parameters of wind turbines in the wind-solar hybrid power generation system, photovoltaic efficiency parameters of photovoltaic arrays in the wind-solar hybrid power generation system, and power efficiency parameters corresponding to the power grid connected to the wind-solar hybrid power generation system; a similarity determination module for determining a first correlation coefficient between wind power efficiency parameters and power efficiency parameters, determining a second correlation coefficient between photovoltaic efficiency parameters and power efficiency parameters, and determining a target similarity based on the first correlation coefficient, the second correlation coefficient, and a first weight factor; an initial strategy determination module for comparing the target similarity with a preset similarity threshold, and determining an initial scheduling strategy based on the first comparison result. strategy; a temperature comparison module, used to obtain the first energy storage battery temperature of the wind power energy storage system corresponding to the wind turbine generator set and the second energy storage battery temperature of the photovoltaic energy storage system corresponding to the photovoltaic power generation array, and compare the first energy storage battery temperature and the second energy storage battery temperature with the battery safety temperature threshold respectively to obtain a second comparison result; a power generation scheduling module, used to determine whether the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling strategy based on the presence of a target energy storage battery temperature exceeding the battery safety temperature threshold in the second comparison result; if the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling strategy, the initial scheduling strategy is adjusted, and power generation scheduling is performed according to the adjusted target scheduling strategy.

[0017] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the power generation scheduling method of the wind-solar hybrid power generation system of the first aspect or any corresponding embodiment thereof.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the power generation scheduling method for a wind-solar hybrid power generation system according to the first aspect or any corresponding embodiment thereof.

[0019] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the power generation scheduling method for a wind-solar hybrid power generation system according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 4 is a flow chart of a method for power generation scheduling of a wind-solar hybrid power generation system according to an embodiment of the present invention.

[0022] Figure 2 4 is a flow chart of another power generation scheduling method for a wind-solar hybrid power generation system according to an embodiment of the present invention.

[0023] Figure 3 4 is a flow chart of another power generation scheduling method for a wind-solar hybrid power generation system according to an embodiment of the present invention.

[0024] Figure 4 4 is a structural block diagram of a power generation scheduling device for a wind-solar hybrid power generation system according to an embodiment of the present invention.

[0025] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0027] With the rapid development of renewable energy, wind and solar power have become key components of the global energy transition. Wind-solar hybrid power systems, which combine the advantages of wind and photovoltaic power generation, are becoming increasingly popular. These systems not only reduce dependence on traditional energy sources but also improve the stability and reliability of energy supply. However, the operation of wind-solar hybrid power systems faces multiple challenges, particularly how to efficiently dispatch and manage power generation resources to meet load demands while ensuring system safety and cost-effectiveness.

[0028] Energy storage systems play a crucial role in wind-solar hybrid power generation systems. By storing excess electrical energy in energy storage batteries, wind-solar hybrid power generation systems can provide stable power support to the grid in situations where wind or sunlight is insufficient. However, temperature management of energy storage batteries is a critical issue. Excessively high battery temperatures not only affect energy storage efficiency but can also pose safety risks such as battery overheating, failure, or fire. Dynamically scheduling photovoltaic arrays, battery energy storage, and wind power generation can maximize the efficiency of power generation resources while ensuring the safety of batteries and equipment. Therefore, how to rationally schedule photovoltaic arrays, battery energy storage, and other energy resources for power generation to ensure the efficient and safe operation of wind-solar hybrid power generation systems has become a pressing issue.

[0029] When determining traditional wind power conversion efficiency, most of the focus is on basic factors such as wind speed and blade area, with insufficient consideration of the angle between wind direction and the axis of the wind turbine. Other relevant factors, such as the wake velocity loss coefficient, are not considered. This can lead to large errors in calculating the overall power generation of a wind farm, often overestimating the actual power generation. At the same time, wind volatility is a key characteristic of wind power generation. The speed and direction of the wind are not constant, but rather have certain fluctuation characteristics. When determining traditional wind power conversion, the relevant technology may not consider the wavelength related to the wind's fluctuation characteristics, and cannot accurately reflect the impact of wind changes on different time scales on power generation efficiency. This fluctuation can lead to unstable output power of the wind turbine, and the relevant technology cannot capture this dynamic change when determining traditional wind power conversion.

[0030] In the related art, when determining the photoelectric conversion efficiency, the internal loss factor of the material and the external environmental loss factor may not be fully considered. In the actual photoelectric conversion process, the characteristics of the material itself and the external environment (such as temperature, humidity, etc.) have a significant impact on the photoelectric conversion efficiency. Ignoring these factors will lead to inaccurate calculation results. At the same time, in the related art, when determining the photoelectric conversion efficiency, the spectral distribution of the light source and the spectral response function of the photovoltaic array may not be considered in detail. The conversion efficiency of light of different wavelengths in photovoltaic devices is different. Ignoring these spectral characteristics will lead to inaccurate evaluation of the photoelectric conversion efficiency.

[0031] When determining similarity in related technologies, the weighting coefficients for wind power and photovoltaic power are typically static and cannot be dynamically adjusted based on actual conditions at different time periods. The relative importance of photovoltaic and wind power often changes with seasons, weather changes, or daytime and nighttime power demand, but related technologies cannot adaptively adjust these weights when determining similarity.

[0032] Although the fixed temperature threshold strategy in related technologies can ensure basic safety in energy storage systems, it lacks adaptability to different environments, seasons, load fluctuations and other factors, which may lead to the following problems: ignoring seasonal changes. For example, in summer, the external temperature is high. If the fixed temperature threshold is not adaptively adjusted, the energy storage battery may approach or exceed the set temperature threshold due to overheating of the external environment, thereby affecting the battery's service life and safety; when the temperature is low in winter, the battery's charging efficiency will decrease, and the battery may operate at too low a temperature, thus failing to achieve optimal performance, and even causing the battery to fail to charge normally.

[0033] In related technologies, the maintenance and management of energy storage batteries usually rely on manual intervention or regular inspections. However, such management methods may result in the inability to detect faulty batteries in a timely manner, thereby delaying maintenance and affecting the stability of the entire system. Dynamic optimization based on the actual operating performance of the battery is impossible, resulting in some batteries being damaged prematurely due to frequent use as suboptimal targets.

[0034] An embodiment of the present invention provides a wind-solar hybrid power generation system method, which determines the target scheduling strategy by determining the similarity between wind-solar power generation and user electricity consumption, and judging whether the temperature of the energy storage system is safe, so as to achieve the effect of improving the rationality and accuracy of the target scheduling strategy.

[0035] According to an embodiment of the present invention, an embodiment of a power generation scheduling method for a wind-solar hybrid power generation system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] In this embodiment, a power generation scheduling method for a wind-solar hybrid power generation system is provided, which can be used for computer equipment. Figure 1 is a flow chart of a power generation scheduling method for a wind-solar hybrid power generation system according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0037] Step S101 , obtaining wind power efficiency parameters of wind turbines in a wind-solar hybrid power generation system, photovoltaic efficiency parameters of photovoltaic arrays in the wind-solar hybrid power generation system, and power efficiency parameters corresponding to a power grid connected to the wind-solar hybrid power generation system.

[0038] Among them, the wind-solar hybrid power generation system is an electricity production system that comprehensively utilizes wind energy and solar energy resources to generate electricity and is equipped with an energy storage system; a wind turbine is a device that converts wind energy into electrical energy; wind power efficiency parameters are parameters that measure the power generation performance and energy conversion capacity of a wind turbine, and wind power efficiency parameters include wind power generation and wind power conversion efficiency; a photovoltaic power generation array is a power generation unit composed of multiple solar photovoltaic components (such as photovoltaic panels) connected in series, parallel or in series and parallel; photoelectric efficiency parameters are parameters that measure the power generation performance and energy conversion capacity of a photovoltaic power generation array, and photoelectric efficiency parameters include photoelectric power generation and photoelectric conversion efficiency; electricity efficiency parameters are parameters that measure the energy utilization efficiency and electricity consumption characteristics of the power consumption system, and electricity efficiency parameters include electricity efficiency and electricity consumption.

[0039] In some optional embodiments, wind power efficiency parameters include wind power generation and wind power conversion efficiency, photovoltaic efficiency parameters include photovoltaic power generation and photovoltaic conversion efficiency, and electricity efficiency parameters include electricity efficiency and electricity consumption; obtaining wind power efficiency parameters of wind turbines in a wind-solar hybrid power generation system, photovoltaic efficiency parameters of photovoltaic power generation arrays in a wind-solar hybrid power generation system, and electricity efficiency parameters corresponding to a power grid connected to the wind-solar hybrid power generation system include: obtaining wind power generation data and wind power generation of wind turbines in the wind-solar hybrid power generation system; analyzing the conversion efficiency of wind turbines based on the wind power generation data and wind wavelength characteristic data to obtain wind power conversion efficiency; obtaining photovoltaic power generation data and photovoltaic power generation of photovoltaic power generation arrays in the wind-solar hybrid power generation system; analyzing the conversion efficiency of photovoltaic power generation arrays based on photovoltaic power generation data, light wavelength characteristic data, and loss factors to obtain photovoltaic conversion efficiency; obtaining electricity consumption corresponding to the power grid connected to the wind-solar hybrid power generation system; and obtaining electricity efficiency based on the quotient of electricity consumption and electricity consumption time period.

[0040] Among them, wind power generation data is data related to wind power generation, including: wind turbine current, air density, wind speed, area swept by wind turbine blades, power coefficient, mechanical rotation efficiency, generator efficiency, angle between wind direction and wind turbine axis, etc.; wind wavelength characteristic data is data related to wind fluctuation characteristics, including: wavelength related to wind fluctuation characteristics, wind energy spectrum distribution, and wind turbine response function to different wind characteristics, etc.; photovoltaic power generation data is data related to photovoltaic power generation, including: current generated by photovoltaic devices under light, speed of light, power of light irradiating photovoltaic devices, total amount of light energy irradiating photovoltaic devices, and final electrical energy output by photovoltaic devices, etc.; light wavelength characteristic data is data related to photovoltaic fluctuation characteristics, including: wavelength of light, spectral distribution of light source, spectral response function of photovoltaic array, etc.; loss factor includes internal material loss factor and external environmental loss factor; power usage time period can be 24 hours.

[0041] In some optional implementations, wind power generation, photovoltaic power generation, and power consumption are obtained through a power grid monitoring system.

[0042] In some optional embodiments, the conversion efficiency of the wind turbine generator set is analyzed based on the wind power generation data and the wind wavelength characteristic data to obtain the wind power conversion efficiency, including: obtaining a third product result based on the product of air density, wind speed and the area swept by the wind turbine blades; obtaining a first division result based on the quotient of the current generated by the wind turbine and the third product result; obtaining a fourth product result based on the product of the target preset value, the Boltzmann constant and the temperature; obtaining a fifth product result based on the product of the air density, the square of the air, the area swept by the wind turbine blades and the wavelength related to the wind fluctuation characteristics; obtaining a second division result based on the quotient of the fourth product result and the fifth product result. division result; integrating the product of the energy spectrum distribution of the wind and the response function of the wind turbine to different wind characteristics to obtain a first integral result; integrating the energy spectrum distribution of the wind to obtain a second integral result; obtaining a third division result according to the quotient of the first integral result and the second integral result; obtaining a first subtraction result according to the difference between the preset value and the wake velocity loss coefficient; obtaining a sixth product result according to the product of the power coefficient, the mechanical transmission efficiency, the generator efficiency, the wind direction correction coefficient and the first subtraction result; obtaining a wind power conversion efficiency according to the product of the first division result, the second division result, the third division result, the sixth product result and 100%; wherein the preset value is 1 and the target preset value is 2.

[0043] For example, the formula for determining wind power conversion efficiency is:

[0044]

[0045] Among them, η w is the wind power conversion efficiency, I w is the current generated by the wind turbine, ρ is the air density, v is the wind speed, A is the area swept by the wind turbine blades, k is the Boltzmann constant, T is the temperature, λ w is the wavelength related to the wave characteristics of the wind, S wind (λ w ) is the energy spectrum distribution of wind, R turbine (λ w ) is the response function of the wind turbine to different wind characteristics, C p is the power coefficient, η mech is the mechanical transmission efficiency, η gen is the generator efficiency, k(θ) is the wind direction correction coefficient, θ is the angle between the wind direction and the axis of the wind turbine, C w is the wake velocity loss coefficient.

[0046] In this embodiment of the present invention, the introduction of wavelength into the wind power conversion efficiency formula helps better coordinate the operation of wind power and other energy sources. By comprehensively analyzing the wavelength characteristics of wind and the characteristics of other energy sources, it is possible to optimize the configuration and scheduling strategies of multi-energy systems, thereby improving the stability and reliability of the entire energy system. For example, in a wind-solar hybrid system, the output of wind power and photovoltaic power can be rationally allocated based on the variations in wind wavelength and solar radiation, achieving efficient complementarity and a stable supply of energy.

[0047] In some optional embodiments, the conversion efficiency of the photovoltaic power generation array is analyzed based on the photovoltaic power generation data, the light wavelength characteristic data, and the loss factor to obtain the photoelectric conversion efficiency, including: obtaining a fourth division result based on the quotient of the current generated by the photovoltaic device under light and the amount of electron charge; obtaining a seventh product result based on the product of the Planck constant and the speed of light; obtaining an eighth product result based on the product of the power of the light irradiated on the photovoltaic device and the wavelength of the light; obtaining a fifth division result based on the quotient of the seventh product result and the eighth product result; integrating the product of the spectral distribution of the light source and the spectral response function of the photovoltaic array to obtain The third integration result; integrating the spectral distribution of the light source to obtain a fourth integration result; obtaining a sixth division result based on the quotient of the third integration result and the fourth integration result; obtaining a second subtraction result based on the difference between the preset value and the loss factor inside the material; obtaining a third subtraction result based on the difference between the preset value and the external environment loss factor; obtaining a seventh division result based on the quotient of the electric energy finally output by the photovoltaic device and the total amount of light energy irradiated on the photovoltaic device; obtaining the photoelectric conversion efficiency based on the product of the fourth division result, the fifth division result, the sixth division result, the second subtraction result, the third subtraction result, the seventh division result and 100%.

[0048] Exemplarily, the formula for determining the photoelectric conversion efficiency is:

[0049]

[0050] Among them, η l is the photoelectric conversion efficiency, I l is the current generated by the photovoltaic device under light, q is the electron charge, h is the Planck constant, c is the speed of light, P light is the power of light irradiating the photovoltaic device, λ l is the wavelength of light, S light (λ l ) is the spectral distribution of the light source, R device (λ l ) is the spectral response function of the photovoltaic array, L ma is the loss factor inside the material, L en is the external environment loss factor, P out is the final output power of the photovoltaic device, P in is the total amount of light energy incident on the photovoltaic device.

[0051] In some optional implementations, the wind power conversion efficiency, the photovoltaic conversion efficiency, and the electricity efficiency need to be calculated once at the end of each time unit.

[0052] Step S102 : determining a first correlation coefficient between the wind power efficiency parameter and the electricity efficiency parameter, determining a second correlation coefficient between the photovoltaic efficiency parameter and the electricity efficiency parameter, and determining target similarity based on the first correlation coefficient, the second correlation coefficient, and the first weight factor.

[0053] In some optional embodiments, a first correlation coefficient between a wind power efficiency parameter and an electricity efficiency parameter is determined, a second correlation coefficient between a photovoltaic efficiency parameter and an electricity efficiency parameter is determined, and a target similarity is determined based on the first correlation coefficient, the second correlation coefficient, and the first weight factor, including: determining a wind power conversion efficiency variation graph for a preset time period based on the wind power conversion efficiency, and determining a wind power generation variation graph for a preset time period based on the wind power generation; determining a photoelectric conversion efficiency variation graph for a preset time period based on the photoelectric conversion efficiency, and determining a photoelectric generation variation graph for a preset time period based on the photoelectric generation; and determining a target similarity based on the first correlation coefficient, the second correlation coefficient, and the first weight factor. According to the power efficiency, determine the power efficiency change graph of the preset time period, and according to the power consumption, determine the power consumption change graph of the preset time period; obtain multiple first band lengths and the average value of the first band length in the wind power conversion efficiency change graph, multiple second band lengths and the average value of the second band length in the wind power generation change graph, multiple third band lengths and the average value of the third band length in the photovoltaic conversion efficiency change graph, multiple fourth band lengths and the average value of the fourth band length in the photovoltaic power generation change graph, multiple fifth band lengths and the average value of the fifth band length in the power efficiency change graph, and multiple fifth band lengths in the power consumption change graph According to the plurality of first band lengths, the average value of the first band lengths, the plurality of fifth band lengths and the average value of the fifth band lengths, a first correlation degree is determined; according to the plurality of second band lengths, the average value of the second band lengths, the plurality of sixth band lengths and the average value of the sixth band lengths, a second correlation degree is determined; according to the first correlation degree and the second correlation degree, a first correlation coefficient is obtained; according to the plurality of third band lengths, the average value of the third band lengths, the plurality of fifth band lengths and the average value of the fifth band lengths, a third correlation degree is determined; according to the plurality of fourth band lengths, the average value of the third band lengths, the plurality of fifth band lengths and the average value of the fifth band lengths, a third correlation coefficient is obtained; The fourth correlation degree is determined by the average value of the four-band lengths, multiple sixth band lengths and the average value of the sixth band lengths, and the second correlation coefficient is obtained according to the third correlation degree and the fourth correlation degree; the first weight factor is obtained, and the first weight factor is a weight factor dynamically adjusted using the particle swarm algorithm; the first product result is obtained according to the product of the first correlation coefficient and the first weight factor; the second weight factor is obtained according to the difference between the preset value and the first weight factor; the second product result is obtained according to the product of the second correlation coefficient and the second weight factor; and the target similarity is obtained according to the sum of the first product result and the second product result.

[0054] Among them, the preset time period can be 24 hours, the wind power conversion efficiency change graph is a change graph of wind power conversion efficiency over time within 24 hours, the horizontal axis of the wind power conversion efficiency change graph is time, the time interval can be 1 hour, and the vertical axis of the wind power conversion efficiency change graph is wind power conversion efficiency; the wind power generation change graph is a change graph of wind power generation over time within 24 hours, the horizontal axis of the wind power generation change graph is time, the time interval can be 1 hour, and the vertical axis of the wind power generation change graph is wind power generation; the photoelectric conversion efficiency change graph is a change graph of photoelectric conversion efficiency over time within 24 hours, the horizontal axis of the photoelectric conversion efficiency change graph is time, and the time interval can be 1 hour. The vertical axis of the photoelectric conversion efficiency change graph is the photoelectric conversion efficiency; the photovoltaic power generation change graph is a graph showing the change of photovoltaic power generation over time within 24 hours, the horizontal axis of the photovoltaic power generation change graph is time, the time interval can be 1 hour, and the vertical axis of the photovoltaic power generation change graph is photovoltaic power generation; the electricity consumption efficiency change graph is a graph showing the change of electricity consumption efficiency over time within 24 hours, the horizontal axis of the electricity consumption efficiency change graph is time, the time interval can be 1 hour, and the vertical axis of the electricity consumption efficiency change graph is electricity efficiency; the electricity consumption change graph is a graph showing the change of electricity consumption over time within 24 hours, the horizontal axis of the electricity consumption change graph is time, the time interval can be 1 hour, and the vertical axis of the electricity consumption change graph is electricity consumption.

[0055] In some optional embodiments, a first correlation degree is determined based on multiple first band lengths, an average value of the first band lengths, multiple fifth band lengths, and an average value of the fifth band lengths; a second correlation degree is determined based on multiple second band lengths, an average value of the second band lengths, multiple sixth band lengths, and an average value of the sixth band lengths; and a first correlation coefficient is obtained based on the first correlation degree and the second correlation degree, including: obtaining multiple fourth subtraction results based on the difference between the multiple first band lengths and the average value of the first band lengths; obtaining multiple fifth subtraction results based on the difference between the multiple fifth band lengths and the average value of the fifth band lengths; summing the products of the multiple fourth subtraction results and the corresponding fifth subtraction results to obtain a first summation result; summing the squares of the multiple fourth subtraction results to obtain a second summation result; taking the square root of the second summation result to obtain a first square root result; summing the squares of the multiple fifth subtraction results to obtain a third summation result; taking the square root of the third summation result to obtain a second square root result; and According to the product of the first square root result and the second square root result, a ninth product result is obtained; according to the quotient of the first summation result and the ninth product result, a first correlation degree is obtained; according to the difference between the multiple second band lengths and the average value of the second band lengths, multiple sixth subtraction results are obtained; according to the difference between the multiple sixth band lengths and the average value of the sixth band lengths, multiple seventh subtraction results are obtained; the products of the multiple sixth subtraction results and the corresponding seventh subtraction results are summed to obtain a fourth summation result; the squares of the multiple sixth subtraction results are summed to obtain a fifth summation result; the square root of the fourth summation result is obtained to obtain a third square root result; the squares of the multiple seventh subtraction results are summed to obtain a sixth summation result; the square root of the sixth summation result is obtained to obtain a fourth square root result; according to the product of the third square root result and the fourth square root result, a tenth product result is obtained; according to the quotient of the fourth summation result and the tenth product result, a second correlation degree is obtained; the sum of the first correlation degree and the second correlation degree and multiplied by one half is obtained to obtain a first correlation coefficient.

[0056] Exemplarily, the formula for determining the first correlation coefficient is:

[0057]

[0058] Among them, ρ w,u is the first correlation coefficient, a i is the length of the first band of the i-th wave, is the average length of the first band, c i is the length of the i-th fifth band, is the average length of the fifth band, s is the total number of bands, a j ′ is the length of the j-th second band, is the average length of the second band, c j ′is the length of the jth sixth band, is the average length of the sixth band.

[0059] In some optional embodiments, a third correlation degree is determined based on multiple third band lengths, an average value of the third band lengths, multiple fifth band lengths, and an average value of the fifth band lengths; a fourth correlation degree is determined based on multiple fourth band lengths, an average value of the fourth band lengths, multiple sixth band lengths, and an average value of the sixth band lengths; and a second correlation coefficient is obtained based on the third correlation degree and the fourth correlation degree, including: obtaining multiple eighth subtraction results based on the differences between the multiple third band lengths and the average value of the third band lengths; summing the products of the multiple eighth subtraction results and the corresponding fifth subtraction results to obtain a seventh summation result; summing the squares of the multiple eighth subtraction results to obtain an eighth summation result; taking the square root of the eighth summation result to obtain a fifth square root result; and obtaining the square root of the eighth summation result based on the third correlation degree and the fourth correlation degree. The product of the fifth square root result and the second square root result obtains the eleventh product result; the third correlation degree is obtained according to the quotient of the seventh summation result and the eleventh product result; the multiple ninth subtraction results are obtained according to the difference between the multiple fourth band lengths and the average value of the fourth band lengths; the multiple ninth subtraction results are summed with the product of the corresponding seventh subtraction results to obtain the ninth summation result; the squares of the multiple fourth subtraction results are summed to obtain the tenth summation result; the square root of the tenth summation result is obtained to obtain the sixth square root result; the twelfth product result is obtained according to the product of the sixth square root result and the fourth square root result; the fourth correlation degree is obtained according to the quotient of the ninth summation result and the twelfth product result; the sum of the third correlation degree and the fourth correlation degree and multiplied by one half to obtain the second correlation coefficient.

[0060] Exemplarily, the formula for determining the second correlation coefficient is:

[0061]

[0062] Among them, ρ l,u is the second correlation coefficient, b i is the length of the i-th third band, is the average length of the third band, c i is the length of the i-th fifth band, is the average length of the fifth band, s is the total number of bands, b j ′ is the length of the jth fourth band, is the average length of the fourth band, c j ′ is the length of the jth sixth band, is the average length of the sixth band.

[0063] In some optional implementations, the formula for determining target similarity is:

[0064]

[0065] Among them, S final is the target similarity, is the first weight factor, ρ w,u is the first correlation coefficient, ρ l,u is the second correlation coefficient.

[0066] In the embodiment of the present invention, the first weight factor represents the relative importance of photovoltaic power and wind power. like Trend 1 means that photovoltaic power generation contributes more to wind-solar hybrid power generation. Tends to 0, which means that wind power generation contributes more to the wind-solar hybrid power generation. If the proportion of photovoltaic and wind power in the system is relatively close, The value can be 0.5. If photovoltaic power generation is more important, the value can be adjusted accordingly. If, during certain periods, the photovoltaic power generation or power generation fluctuates greatly, it is possible to consider dynamically adjusting the first weight factor, for example, according to the weather forecast of the day or the wind speed and light intensity monitored in real time.

[0067] In an embodiment of the present invention, the first weight factor is a weight factor that is dynamically adjusted using a particle swarm algorithm. The particle swarm algorithm is a group intelligence optimization algorithm that simulates the foraging behavior of a flock of birds and is particularly suitable for continuous optimization problems, such as adjusting the relative importance of photovoltaic and wind power in a wind-solar power generation system (i.e., the first weight factor).

[0068] Specifically, the particle swarm optimization algorithm includes: generating a particle swarm, where each particle corresponds to a first weight factor; initializing the particle position, that is, the initial value, which can be randomly generated or a reasonable initial value estimated based on historical data; initializing the particle speed, which represents the rate of adjustment of the first weight factor, usually set to zero or a small random value; initializing the personal best position and the global best position.

[0069] For each particle, calculate its current position, which is the first weight factor The corresponding target similarity S final ; Update the personal best position and the global best position, and set the initial position of each particle to its personal best position; if the current particle's position fitness is better than its historical personal best fitness, then update the particle's personal best position; if the current particle's position fitness is better than the global best position fitness of all particles, then update the global best position.

[0070] The update formula is:

[0071]

[0072] in, is the update speed of the i-th particle, is the updated position of the i-th particle, w is the inertia weight, which controls the inertia of the particle, c1 is the first acceleration constant, c2 is the second acceleration constant, c1 and c2 are used to control the movement of the particle toward the personal best position and the global best position, r1 is the first random number, r2 is the second random number, r1 and r2 are random numbers in the range of [0, 1], which are used to increase randomness, P best is the personal best position of the i-th particle, X i is the current position information of the i-th particle, G best is the global best position among all particles.

[0073] When the preset maximum number of iterations is reached or the change in the objective function value is lower than the set threshold, the algorithm terminates and the global optimal position G of the particle swarm is best This is the optimal solution, that is, the adjusted first weight factor, which is used to adjust the relative importance of wind power and photovoltaic power generation in the wind-solar power generation combination.

[0074] The embodiment of the present invention uses a particle swarm algorithm to determine the optimal first weight factor, which can adaptively adjust the relative importance of photovoltaic and wind power, thereby more accurately matching the fluctuations of wind and solar power generation with user electricity demand, and dynamically adjusting the first weight factor according to actual meteorological conditions to maximize the similarity between the combined waveform of wind and solar power generation and the waveform of user electricity demand.

[0075] Step S103: compare the target similarity with a preset similarity threshold, and determine an initial scheduling strategy based on a first comparison result.

[0076] The preset similarity threshold may be set according to actual conditions. For example, the preset similarity threshold may be 95%.

[0077] In some optional embodiments, an initial scheduling strategy is determined based on the first comparison result, including: if the first comparison result is that the target similarity is greater than or equal to a preset similarity threshold, then the initial scheduling strategy is to use wind turbines and photovoltaic power generation arrays as priority scheduling targets, and wind power energy storage systems and photovoltaic energy storage systems as suboptimal scheduling targets; if the first comparison result is that the target similarity is less than the preset similarity threshold, then the initial scheduling strategy is to use wind power energy storage systems and photovoltaic energy storage systems as priority scheduling targets, and wind turbines and photovoltaic power generation arrays as suboptimal scheduling targets.

[0078] For example, if the target similarity S final≥95%, it means that the similarity between the power generation of wind turbines and photovoltaic arrays and the power consumption of users is large. Therefore, wind and solar power generation can better meet user needs. The initial scheduling strategy is to prioritize wind turbines and photovoltaic arrays, and wind power storage systems and photovoltaic energy storage systems as suboptimal scheduling targets. If the target similarity S final <95%, it means that the similarity between the power generation of wind turbines and photovoltaic arrays and the electricity consumption of users is not very large. Therefore, wind and solar power generation is not well matched with user demand. The initial scheduling strategy is to prioritize wind power energy storage systems and photovoltaic energy storage systems, and wind turbines and photovoltaic arrays as suboptimal scheduling targets.

[0079] Step S104: obtaining the temperature of a first energy storage battery of the wind power storage system corresponding to the wind turbine generator set and the temperature of a second energy storage battery of the photovoltaic energy storage system corresponding to the photovoltaic power generation array, and comparing the first energy storage battery temperature and the second energy storage battery temperature with the battery safety temperature threshold to obtain a second comparison result.

[0080] Among them, a first temperature sensor is installed in the wind power storage system corresponding to the wind turbine generator set, which is used to monitor the temperature of the first energy storage battery of the wind power storage system in real time; a second temperature sensor is installed in the photovoltaic energy storage system corresponding to the photovoltaic power generation array, which is used to monitor the temperature of the second energy storage battery of the photovoltaic energy storage system in real time.

[0081] In some optional embodiments, the battery safety temperature threshold can be dynamically adjusted based on actual conditions. For example, the battery safety temperature threshold is dynamically adjusted based on environmental factors, grid load, and the health status of the energy storage battery. Dynamic adjustment of the battery safety temperature threshold can make the temperature control process of the energy storage battery more flexible and intelligent, avoiding energy waste or equipment damage caused by traditional fixed temperature thresholds. The temperature safety threshold of the energy storage battery can be automatically adjusted according to external environmental conditions (such as season and weather) and grid load fluctuations, thereby optimizing the charging and discharging efficiency and safety of the energy storage battery.

[0082] In some optional embodiments, the second comparison result includes four situations. The first is that the temperature of the first energy storage battery exceeds the battery safety temperature threshold, and the temperature of the second energy storage battery exceeds the battery safety temperature threshold; the second is that the temperature of the first energy storage battery exceeds the battery safety temperature threshold, and the temperature of the second energy storage battery does not exceed the battery safety temperature threshold; the third is that the temperature of the first energy storage battery does not exceed the battery safety temperature threshold, and the temperature of the second energy storage battery exceeds the battery safety temperature threshold; the fourth is that the temperature of the first energy storage battery does not exceed the battery safety temperature threshold, and the temperature of the second energy storage battery does not exceed the battery safety temperature threshold.

[0083] In step S105, if the second comparison result contains a target energy storage battery temperature that exceeds the battery safety temperature threshold, it is determined whether the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling policy. If the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling policy, the initial scheduling policy is adjusted, and power generation scheduling is performed according to the adjusted target scheduling policy.

[0084] Among them, if there is a target energy storage battery temperature exceeding the battery safety temperature threshold in the second comparison result, there are three situations: the first situation is that in the second comparison result, the first energy storage battery temperature exceeds the battery safety temperature threshold, and the second energy storage battery temperature exceeds the battery safety temperature threshold, then the first energy storage battery temperature and the second energy storage battery temperature are both the target energy storage battery temperature; the second situation is that in the second comparison result, the first energy storage battery temperature exceeds the battery safety temperature threshold, and the second energy storage battery temperature does not exceed the battery safety temperature threshold, then the first energy storage battery temperature is the target energy storage battery temperature; the third situation is that in the second comparison result, the first energy storage battery temperature does not exceed the battery safety temperature threshold, and the second energy storage battery temperature exceeds the battery safety temperature threshold, then the second energy storage battery temperature is both the target energy storage battery temperature.

[0085] In some optional implementations, the initial scheduling strategy is adjusted, including: if the target energy storage system is a wind power energy storage system, the initial scheduling strategy is adjusted to prioritize the photovoltaic power generation array and the photovoltaic energy storage system, and to prioritize the wind turbine generator set and the wind power energy storage system; if the target energy storage system is a photovoltaic energy storage system, the initial scheduling strategy is adjusted to prioritize the wind turbine generator set and the wind power energy storage system, and to prioritize the photovoltaic power generation array and the photovoltaic energy storage system.

[0086] Exemplarily, if the second comparison result is that the temperature of the first energy storage battery exceeds the battery safety temperature threshold, and the temperature of the second energy storage battery does not exceed the battery safety temperature threshold, and the initial scheduling strategy is to prioritize the wind power storage system and the photovoltaic energy storage system as scheduling targets, and the wind turbine generator set and the photovoltaic power generation array as suboptimal scheduling targets, then it means that the first energy storage battery temperature is the target energy storage battery temperature, and the target energy storage system corresponding to the first energy storage battery temperature is the wind power storage system, and the wind power storage system is the priority scheduling target in the initial scheduling strategy, indicating that the wind power storage system is currently used for power generation, but the temperature of the wind power storage system is too high and is no longer suitable as the priority scheduling target. The initial scheduling strategy is adjusted to prioritize the photovoltaic power generation array and the photovoltaic energy storage system, and to prioritize the wind turbine generator set and the wind power storage system as suboptimal scheduling targets, so as to avoid overheating of the wind power storage battery and ensure its safe operation.

[0087] For example, if the second comparison result shows that the temperature of the first energy storage battery does not exceed the battery safety temperature threshold, and the temperature of the second energy storage battery exceeds the battery safety temperature threshold, and the initial scheduling strategy is to prioritize the wind power energy storage system and the photovoltaic energy storage system as scheduling targets, and the wind turbine generator set and the photovoltaic power generation array as suboptimal scheduling targets, then it means that the second energy storage battery temperature is the target energy storage battery temperature, and the target energy storage system corresponding to the second energy storage battery temperature is the photovoltaic energy storage system, and the photovoltaic energy storage system is the priority scheduling target in the initial scheduling strategy, indicating that the photovoltaic energy storage system is currently used for power generation, but the temperature of the photovoltaic energy storage system is too high and is no longer suitable as the priority scheduling target. The initial scheduling strategy is adjusted to prioritize the wind turbine generator set and the wind power energy storage system as scheduling targets, and the photovoltaic power generation array and the photovoltaic energy storage system as suboptimal scheduling targets, so as to avoid overheating of the photovoltaic energy storage battery and ensure its safe operation.

[0088] In some optional embodiments, if there is no target energy storage battery temperature exceeding the battery safety temperature threshold in the second comparison result, and / or if the target energy storage system corresponding to the target energy storage battery temperature is a suboptimal scheduling target in the initial scheduling strategy, a prompt message is generated and the temperature of the first energy storage battery of the wind power energy storage system and the temperature of the second energy storage battery of the photovoltaic energy storage system are continuously monitored, and temperature and priority judgments are continuously performed.

[0089] The power generation scheduling method for a wind-solar hybrid power generation system provided in this embodiment obtains wind power, photovoltaic efficiency parameters, and power efficiency parameters, and determines the correlation coefficient between them to determine the target similarity. This method can more accurately match power demand with wind and photovoltaic power generation capacity, making power generation scheduling more consistent with actual power consumption, improving the rationality and stability of power supply, reducing wind and photovoltaic curtailment, and improving energy utilization. This embodiment of the present invention compares the target similarity with a preset similarity threshold, determines an initial scheduling strategy based on the first comparison result, and determines the scheduling strategy based on quantitative indicators. This allows power generation scheduling to be more consistent with actual power demand, better integrates the wind-solar hybrid power generation system into power grid operation, and enhances the coordination of the entire power system. The embodiment of the present invention obtains the first energy storage battery temperature of the wind power energy storage system corresponding to the wind turbine generator set and the second energy storage battery temperature of the photovoltaic energy storage system corresponding to the photovoltaic power generation array, and compares the first energy storage battery temperature and the second energy storage battery temperature with the battery safety temperature threshold respectively to obtain a second comparison result. If there is a target energy storage battery temperature exceeding the battery safety temperature threshold in the second comparison result, it is determined whether the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling strategy. If the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling strategy, the initial scheduling strategy is performed. The scheduling strategy is adjusted, and power generation scheduling is performed according to the adjusted target scheduling strategy. The embodiment of the present invention takes into account the temperature factor of the energy storage battery. When the temperature of the energy storage battery exceeds the safety threshold, the initial scheduling strategy will be adjusted. Therefore, the scheduling strategy is dynamically optimized according to the actual state of the energy storage system to cope with different operating conditions and adapt to the intermittent and fluctuating characteristics of wind and solar power generation. This real-time monitoring and timely response mechanism ensures the rationality and accuracy of scheduling. At the same time, it can effectively avoid the performance degradation, life shortening and even safety accidents of the energy storage system caused by problems such as battery overheating, and ensure the long-term reliable operation of the energy storage equipment. Compared with the related art, the embodiment of the present invention does not require prediction, but determines the similarity between wind and solar power generation and electricity demand, and judges whether the temperature of the energy storage system is safe to determine the target scheduling strategy, thereby improving the rationality and accuracy of the target scheduling strategy, ensuring that the power generation scheduling strategy always adapts to the actual operating conditions of the wind and solar hybrid power generation system, and maintaining the safe and stable operation of the entire wind and solar hybrid power generation system.

[0090] In this embodiment, a power generation scheduling method for a wind-solar hybrid power generation system is provided, which can be used for computer equipment. Figure 2 FIG. 1 is a flow chart of another power generation scheduling method for a wind-solar hybrid power generation system according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0091] Step S201: Obtain the wind power efficiency parameters of the wind turbine generator set in the wind-solar hybrid power generation system, the photovoltaic efficiency parameters of the photovoltaic array in the wind-solar hybrid power generation system, and the power efficiency parameters corresponding to the power grid connected to the wind-solar hybrid power generation system. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0092] Step S202: Determine the first correlation coefficient between the wind power efficiency parameter and the electricity efficiency parameter, determine the second correlation coefficient between the photovoltaic efficiency parameter and the electricity efficiency parameter, and determine the target similarity based on the first correlation coefficient, the second correlation coefficient and the first weight factor. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0093] Step S203: compare the target similarity with the preset similarity threshold, and determine the initial scheduling strategy based on the first comparison result. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0094] Step S204: Obtain the temperature of the first energy storage battery of the wind power storage system corresponding to the wind turbine generator set and the temperature of the second energy storage battery of the photovoltaic energy storage system corresponding to the photovoltaic array, and compare the first energy storage battery temperature and the second energy storage battery temperature with the battery safety temperature threshold to obtain a second comparison result. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0095] In step S205, if the second comparison result contains a target energy storage battery temperature that exceeds the battery safety temperature threshold, it is determined whether the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling policy. If the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling policy, the initial scheduling policy is adjusted, and power generation scheduling is performed according to the adjusted target scheduling policy.

[0096] Specifically, the above step S205 includes:

[0097] Step S2051: If the second comparison result contains a target energy storage battery temperature that exceeds the battery safety temperature threshold, it is determined whether the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling policy. If the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling policy, the initial scheduling policy is adjusted.

[0098] Step S2052: When the priority scheduling target in the target scheduling strategy includes a wind power storage system or a photovoltaic energy storage system, the remaining power of multiple batteries in the wind power storage system or the photovoltaic energy storage system is monitored.

[0099] A fixed monitoring period is set to regularly monitor the remaining power of multiple batteries in the wind power energy storage system or the photovoltaic energy storage system. Exemplarily, the monitoring period may be one hour.

[0100] In step S2053, the multiple batteries are divided into a superior power supply group and a secondary power supply group according to the remaining power of the multiple batteries, and the batteries in the superior power supply group and the secondary power supply group are adjusted according to the remaining power of the multiple batteries monitored in real time, and the batteries in the superior power supply group are used for power generation scheduling.

[0101] Among them, according to the remaining power of multiple batteries, multiple batteries are divided into a superior power supply group and a secondary power supply group, including: setting an upper limit threshold of the secondary power supply target interval and a lower limit threshold of the secondary power supply target interval. If the remaining power of the battery is less than the lower limit threshold of the secondary power supply target interval, or the remaining power of the battery is greater than the upper limit threshold of the secondary power supply target interval, the battery is divided into the superior power supply group; if the remaining power of the battery is greater than or equal to the lower limit threshold of the secondary power supply target interval, and the remaining power of the battery is less than or equal to the upper limit threshold of the secondary power supply target interval, the battery is divided into the secondary power supply group.

[0102] Exemplarily, the upper limit threshold of the secondary power supply target interval can be any one of the intervals [80%, 100%], the upper limit threshold of the secondary power supply target interval can be 80%, and the lower limit threshold of the secondary power supply target interval can be any one of the intervals [0, 20%], the lower limit threshold of the secondary power supply target interval can be 20%.

[0103] In some optional implementations, when selecting multiple batteries that are divided into the superior power supply group, the battery with the largest remaining power or the least remaining power will be preferentially selected as the first-round power supply target until the remaining power of the current battery is exhausted or the remaining power falls within the judgment condition of the secondary power supply interval threshold, and then the battery is removed from the superior power supply target group; in the embodiment of the present invention, the batteries in these superior power supply groups are first selected from the battery with the largest remaining power or the battery with the least remaining power. Whether to select the largest or the least depends on the scheduling strategy. Usually, the battery with the largest power is selected to extend the power supply time.

[0104] In some optional embodiments, when selecting multiple batteries divided into the suboptimal power supply group, the battery with the largest remaining power is selected as the first round of power supply target until the remaining power of the current battery is reduced to the same as the battery with the least remaining power in the secondary power supply group, and then the battery is removed from the secondary power supply target group.

[0105] For example, assuming that a certain energy storage system includes 4 batteries, namely the first battery, the second battery, the third battery and the fourth battery, the remaining power of the first battery is 18%, which is less than 20%, then the first battery is divided into the superior power supply group, and the low-power battery is consumed first to avoid being in a low-power state for a long time and affecting its life. The remaining power of the second battery is 35%, which is between 20% and 80%, then the second battery is divided into the secondary power supply group, the remaining power of the third battery is 90%, which is greater than 80%, then the third battery is divided into the superior power supply group, the remaining power of the fourth battery is 65%, which is between 20% and 80%, then the second battery is divided into the secondary power supply group, then the superior power supply group The primary power supply group includes the first battery and the third battery, and the secondary power supply group includes the second battery and the fourth battery; when the primary power supply group is supplying power, if the remaining power of the first battery is the least, the first battery can be used as the first-round power supply target; if the remaining power of the third battery is the largest, the third battery can be used as the first-round power supply target; illustratively, when the third battery is first used as the first-round power supply target to start supplying power, the power of the third battery will gradually be consumed until one of the following two situations occurs: the remaining power of the third battery is completely exhausted; the remaining power of the third battery drops below the lower limit threshold (20%) of the set secondary power supply interval threshold, and the third battery is removed from the primary power supply group and no longer participates in the subsequent power supply scheduling.

[0106] In some optional embodiments, the power generation scheduling method of the wind-solar hybrid power generation system also includes: recording the number of times each battery is divided into the optimal power supply group and the number of times each battery is divided into the suboptimal power supply group, and treating the batteries that are divided into the optimal power supply group the most and the least times, and the batteries that are divided into the suboptimal power supply group the most and the least times, as batteries that need maintenance, and providing real-time feedback to the client.

[0107] The power generation scheduling method for a wind-solar hybrid power generation system provided in this embodiment accurately determines the power supply priority of batteries based on the real-time remaining power of energy storage batteries, reducing overuse and unnecessary loss of batteries, extending the service life of the energy storage system as a whole, and reducing the maintenance and replacement costs of the energy storage component of the wind-solar hybrid power generation system. The present invention adjusts the batteries in the group based on the real-time monitored remaining battery power, ensuring that the division of power supply groups always matches the actual battery power status. This dynamic optimization mechanism can adapt to the changes in the energy storage battery power over time, continuously ensuring reliable power supply, ensuring the stability of the power supply, and reducing power outages or fluctuations caused by insufficient battery power.

[0108] In this embodiment, a power generation scheduling method for a wind-solar hybrid power generation system is provided, which can be used for computer equipment. Figure 3 FIG. 1 is a flow chart of another power generation scheduling method for a wind-solar hybrid power generation system according to an embodiment of the present invention. Figure 3As shown, the process includes the following steps:

[0109] Step S301: collect daily wind power generation data of wind turbines and daily photovoltaic power generation data of photovoltaic arrays in the wind-solar hybrid power generation system, and obtain daily user electricity consumption data of users connected to the wind-solar hybrid power generation system. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0110] Step S302: Determine the wind power conversion efficiency and wind power generation of the wind turbine generator set based on the collected daily wind power generation data, and determine the photovoltaic conversion efficiency and photovoltaic power generation of the photovoltaic array based on the collected daily photovoltaic power generation data. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0111] Step S303 , generating a daily wind power generation trend change graph, a daily photovoltaic power generation trend change graph, and a daily user electricity consumption trend change graph based on wind power conversion efficiency, wind power generation, photovoltaic conversion efficiency, photovoltaic power generation, and daily user electricity consumption data.

[0112] Among them, the daily wind power generation trend change graph includes the wind power conversion efficiency change graph and the wind power generation change graph, the daily photovoltaic power generation trend change graph includes the photoelectric conversion efficiency change graph and the photoelectric power generation change graph, and the daily user electricity consumption trend change graph includes the electricity efficiency change graph and the electricity consumption change graph. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0113] Step S304, based on the trend change graph, calculate the similarity between the daily wind power generation trend change graph and the daily photovoltaic power generation trend change graph and the daily user electricity consumption trend change graph respectively, use an adaptive weighted algorithm to determine the target similarity, and analyze and judge the priority scheduling target and the suboptimal scheduling target through a preset similarity threshold.

[0114] For details, please see Figure 1 Step S102 and step S103 of the illustrated embodiment will not be described in detail here.

[0115] Step S305: Real-time monitoring of the temperature of the energy storage batteries connected to the wind turbines and photovoltaic arrays in the wind-solar hybrid power generation system, setting a battery safety temperature threshold and comparing it with the real-time energy storage battery temperature, and switching the scheduling target accordingly based on the priority scheduling target. Figure 1 Step S104 and step S105 of the illustrated embodiment will not be described in detail here.

[0116] Step S306: Real-time monitoring and collection of the remaining power of the energy storage battery and setting the energy storage battery supply constraint conditions. According to the energy storage battery supply constraint conditions, when the energy storage battery meets the constraint conditions and is determined to be a dispatch target, it transmits power to the grid users connected to the wind-solar hybrid power station. For details, please refer to Figure 2 Step S205 of the illustrated embodiment will not be described in detail here.

[0117] Step S307: Count the number of times each energy storage battery is divided into the superior power supply group and the inferior power supply group based on the energy supply constraint conditions. By analyzing the performance of each energy storage battery during the power supply process, the battery that needs maintenance is identified. Figure 2 Step S205 of the illustrated embodiment will not be described in detail here.

[0118] In this embodiment, a power generation scheduling device for a wind-solar hybrid power generation system is also provided. The device is used to implement the above-mentioned embodiments and preferred implementation methods. The details that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0119] This embodiment provides a power generation scheduling device for a wind-solar hybrid power generation system. Figure 4 Shown, including:

[0120] The parameter acquisition module 401 is used to obtain the wind power efficiency parameters of the wind turbine in the wind-solar hybrid power generation system, the photovoltaic efficiency parameters of the photovoltaic array in the wind-solar hybrid power generation system, and the power efficiency parameters corresponding to the power grid connected to the wind-solar hybrid power generation system.

[0121] The similarity determination module 402 is configured to determine a first correlation coefficient between the wind power efficiency parameter and the electricity efficiency parameter, determine a second correlation coefficient between the photovoltaic efficiency parameter and the electricity efficiency parameter, and determine a target similarity based on the first correlation coefficient, the second correlation coefficient, and a first weight factor.

[0122] The initial strategy determination module 403 is configured to compare the target similarity with a preset similarity threshold and determine an initial scheduling strategy based on a first comparison result.

[0123] The temperature comparison module 404 is used to obtain the temperature of a first energy storage battery of the wind power storage system corresponding to the wind turbine generator set and the temperature of a second energy storage battery of the photovoltaic energy storage system corresponding to the photovoltaic power generation array, and compare the first energy storage battery temperature and the second energy storage battery temperature with the battery safety temperature threshold to obtain a second comparison result.

[0124] The power generation scheduling module 405 is configured to determine, based on the presence of a target energy storage battery temperature exceeding a battery safety temperature threshold in the second comparison result, whether the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling policy; if the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling policy, adjust the initial scheduling policy and perform power generation scheduling according to the adjusted target scheduling policy.

[0125] In some optional implementations, the parameter acquisition module 401 includes:

[0126] The wind power data acquisition unit is used to obtain wind power generation data and wind power generation of wind turbines in the wind-solar hybrid power generation system.

[0127] The wind power conversion efficiency analysis unit is used to analyze the conversion efficiency of the wind turbine generator set based on wind power generation data and wind wavelength characteristic data to obtain the wind power conversion efficiency.

[0128] The photovoltaic data acquisition unit is used to obtain the photovoltaic power generation data and photovoltaic power generation of the photovoltaic power generation array in the wind-solar hybrid power generation system.

[0129] The photoelectric conversion efficiency analysis unit is used to analyze the conversion efficiency of the photovoltaic power generation array according to the photovoltaic power generation data, the light wavelength characteristic data and the loss factor to obtain the photoelectric conversion efficiency.

[0130] The power consumption acquisition unit is used to acquire the power consumption corresponding to the power grid connected to the wind-solar hybrid power generation system.

[0131] The power consumption efficiency determining unit is configured to obtain the power consumption efficiency according to the quotient of the power consumption and the power consumption time period.

[0132] In some optional implementations, the similarity determination module 402 includes:

[0133] The change diagram determination unit is used to determine the wind power conversion efficiency change diagram for a preset time period based on the wind power conversion efficiency, and determine the wind power generation change diagram for a preset time period based on the wind power generation; determine the photoelectric conversion efficiency change diagram for a preset time period based on the photoelectric conversion efficiency, and determine the photoelectric power generation change diagram for a preset time period based on the photoelectric power generation; determine the power consumption efficiency change diagram for a preset time period based on the power consumption efficiency, and determine the power consumption change diagram for a preset time period based on the power consumption.

[0134] The wavelength data acquisition unit is used to obtain multiple first band lengths and the average value of the first band length in the wind power conversion efficiency change graph, multiple second band lengths and the average value of the second band length in the wind power generation change graph, multiple third band lengths and the average value of the third band length in the photoelectric conversion efficiency change graph, multiple fourth band lengths and the average value of the fourth band length in the photovoltaic power generation change graph, multiple fifth band lengths and the average value of the fifth band length in the power consumption efficiency change graph, and multiple sixth band lengths and the average value of the sixth band length in the power consumption change graph.

[0135] The first correlation coefficient determination unit is used to determine a first correlation degree based on multiple first band lengths, an average value of the first band lengths, multiple fifth band lengths, and an average value of the fifth band lengths; determine a second correlation degree based on multiple second band lengths, an average value of the second band lengths, multiple sixth band lengths, and an average value of the sixth band lengths; and obtain a first correlation coefficient based on the first correlation degree and the second correlation degree.

[0136] The second correlation coefficient determination unit is used to determine the third correlation degree based on multiple third band lengths, the average value of the third band lengths, multiple fifth band lengths and the average value of the fifth band lengths; determine the fourth correlation degree based on multiple fourth band lengths, the average value of the fourth band lengths, multiple sixth band lengths and the average value of the sixth band lengths; and obtain the second correlation coefficient based on the third correlation degree and the fourth correlation degree.

[0137] The weight factor determination unit is used to obtain a first weight factor, where the first weight factor is a weight factor dynamically adjusted using a particle swarm algorithm.

[0138] The target similarity determination unit is used to obtain a first product result based on the product of the first correlation coefficient and the first weight factor; obtain a second weight factor based on the difference between a preset value and the first weight factor; obtain a second product result based on the product of the second correlation coefficient and the second weight factor; and obtain the target similarity based on the sum of the first product result and the second product result.

[0139] In some optional implementations, the initial strategy determination module 403 includes:

[0140] The initial strategy determination module unit is used to determine, based on the first comparison result, that the target similarity is greater than or equal to a preset similarity threshold, that the initial scheduling strategy is to use wind turbines and photovoltaic power generation arrays as priority scheduling targets, and wind power energy storage systems and photovoltaic energy storage systems as suboptimal scheduling targets; and to determine, based on the first comparison result, that the target similarity is less than a preset similarity threshold, that the initial scheduling strategy is to use wind power energy storage systems and photovoltaic energy storage systems as priority scheduling targets, and wind turbines and photovoltaic power generation arrays as suboptimal scheduling targets.

[0141] In some optional implementations, the power generation scheduling module 405 includes:

[0142] The scheduling strategy adjustment unit is used to adjust the initial scheduling strategy to prioritize the photovoltaic power generation array and the photovoltaic energy storage system and the wind power energy storage system as the suboptimal scheduling targets based on the target energy storage system being the wind power energy storage system; if the target energy storage system is the photovoltaic energy storage system, the initial scheduling strategy is adjusted to prioritize the wind power generation array and the wind power energy storage system and the photovoltaic energy storage system as the suboptimal scheduling targets.

[0143] A remaining power monitoring unit is used to monitor the remaining power of multiple batteries in the wind power storage system or the photovoltaic energy storage system according to the priority scheduling target in the target scheduling strategy including the wind power storage system or the photovoltaic energy storage system;

[0144] The power generation scheduling unit is used to divide multiple batteries into a superior power supply group and a secondary power supply group according to the remaining power of the multiple batteries, adjust the batteries in the superior power supply group and the secondary power supply group according to the remaining power of the multiple batteries monitored in real time, and use the batteries in the superior power supply group for power generation scheduling.

[0145] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0146] The power generation scheduling device of the wind-solar hybrid power generation system in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0147] The embodiment of the present invention also provides a computer device having the above Figure 4 The power generation scheduling device of the wind-solar hybrid power generation system shown.

[0148] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0149] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0150] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0151] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0152] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0153] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0154] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0155] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0156] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A power generation scheduling method for a wind-solar hybrid power generation system, characterized in that: The method comprises: Obtaining wind power efficiency parameters of the wind turbine in the wind-solar hybrid power generation system, photovoltaic efficiency parameters of the photovoltaic array in the wind-solar hybrid power generation system, and power efficiency parameters corresponding to the power grid connected to the wind-solar hybrid power generation system; determining a first correlation coefficient between the wind power efficiency parameter and the electricity efficiency parameter, determining a second correlation coefficient between the photovoltaic efficiency parameter and the electricity efficiency parameter, and determining a target similarity based on the first correlation coefficient, the second correlation coefficient, and a first weighting factor; Comparing the target similarity with a preset similarity threshold, and determining an initial scheduling strategy based on a first comparison result; Obtaining a first energy storage battery temperature of a wind power energy storage system corresponding to the wind turbine generator set and a second energy storage battery temperature of a photovoltaic energy storage system corresponding to the photovoltaic power generation array, and comparing the first energy storage battery temperature and the second energy storage battery temperature with a battery safety temperature threshold, respectively, to obtain a second comparison result; If the second comparison result contains a target energy storage battery temperature exceeding the battery safety temperature threshold, it is determined whether the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling strategy. If the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling strategy, the initial scheduling strategy is adjusted, and power generation scheduling is performed according to the adjusted target scheduling strategy.

2. The method according to claim 1, characterized in that The wind power efficiency parameters include wind power generation and wind power conversion efficiency, the photovoltaic efficiency parameters include photovoltaic power generation and photovoltaic conversion efficiency, and the electricity efficiency parameters include electricity efficiency and electricity consumption; the method of obtaining the wind power efficiency parameters of the wind turbine in the wind-solar hybrid power generation system, the photovoltaic efficiency parameters of the photovoltaic power generation array in the wind-solar hybrid power generation system, and the electricity efficiency parameters corresponding to the power grid connected to the wind-solar hybrid power generation system includes: Acquiring wind power generation data and wind power generation of the wind turbine generator set in the wind-solar hybrid power generation system; Analyzing the conversion efficiency of the wind turbine generator set according to the wind power generation data and the wind wavelength characteristic data to obtain the wind power conversion efficiency; Acquiring photovoltaic power generation data and the photovoltaic power generation of the photovoltaic power generation array in the wind-solar hybrid power generation system; Analyzing the conversion efficiency of the photovoltaic power generation array according to the photovoltaic power generation data, the light wavelength characteristic data, and the loss factor to obtain the photoelectric conversion efficiency; Obtaining the power consumption corresponding to the power grid connected to the wind-solar hybrid power generation system; The power consumption efficiency is obtained according to the quotient of the power consumption and the power consumption time period.

3. The method according to claim 2, characterized in that The determining of a first correlation coefficient between the wind power efficiency parameter and the electricity efficiency parameter, determining a second correlation coefficient between the photovoltaic efficiency parameter and the electricity efficiency parameter, and determining target similarity based on the first correlation coefficient, the second correlation coefficient, and a first weighting factor, includes: Determine a wind power conversion efficiency variation diagram for a preset time period based on the wind power conversion efficiency, and determine a wind power generation variation diagram for the preset time period based on the wind power generation; determine a photoelectric conversion efficiency variation diagram for the preset time period based on the photoelectric conversion efficiency, and determine a photoelectric generation variation diagram for the preset time period based on the photoelectric generation; determine a power consumption efficiency variation diagram for the preset time period based on the power consumption efficiency, and determine a power consumption variation diagram for the preset time period based on the power consumption; Obtaining a plurality of first band lengths and an average value of the first band lengths in the wind power conversion efficiency change graph, a plurality of second band lengths and an average value of the second band lengths in the wind power generation change graph, a plurality of third band lengths and an average value of the third band lengths in the photoelectric conversion efficiency change graph, a plurality of fourth band lengths and an average value of the fourth band lengths in the photoelectric power generation change graph, a plurality of fifth band lengths and an average value of the fifth band lengths in the power consumption efficiency change graph, and a plurality of sixth band lengths and an average value of the sixth band lengths in the power consumption change graph; determining a first correlation degree based on a plurality of first band lengths, an average value of the first band lengths, a plurality of fifth band lengths, and an average value of the fifth band lengths; determining a second correlation degree based on a plurality of second band lengths, an average value of the second band lengths, a plurality of sixth band lengths, and an average value of the sixth band lengths; and obtaining a first correlation coefficient based on the first correlation degree and the second correlation degree; determining a third correlation degree based on the plurality of third band lengths, the average of the third band lengths, the plurality of fifth band lengths, and the average of the fifth band lengths; determining a fourth correlation degree based on the plurality of fourth band lengths, the average of the fourth band lengths, the plurality of sixth band lengths, and the average of the sixth band lengths; and obtaining the second correlation coefficient based on the third correlation degree and the fourth correlation degree; Obtaining the first weight factor, where the first weight factor is a weight factor dynamically adjusted using a particle swarm algorithm; Obtaining a first product result according to the product of the first correlation coefficient and the first weight factor; Obtaining a second weighting factor according to a difference between a preset value and the first weighting factor; Obtaining a second product result according to the product of the second correlation coefficient and the second weight factor; The target similarity is obtained according to the sum of the first multiplication result and the second multiplication result.

4. The method according to any one of claims 1 to 3, characterized in that Determining the initial scheduling strategy according to the first comparison result includes: If the first comparison result is that the target similarity is greater than or equal to the preset similarity threshold, the initial scheduling strategy is to use the wind turbine generator set and the photovoltaic power generation array as priority scheduling targets, and the wind power energy storage system and the photovoltaic energy storage system as suboptimal scheduling targets; If the first comparison result is that the target similarity is less than the preset similarity threshold, the initial scheduling strategy is to use the wind power energy storage system and the photovoltaic energy storage system as priority scheduling targets, and the wind turbine generator set and the photovoltaic power generation array as suboptimal scheduling targets.

5. The method according to any one of claims 1 to 3, characterized in that The adjusting the initial scheduling strategy includes: If the target energy storage system is the wind power energy storage system, the initial scheduling strategy is adjusted to take the photovoltaic power generation array and the photovoltaic energy storage system as the priority scheduling targets, and take the wind turbine generator set and the wind power energy storage system as the suboptimal scheduling targets; If the target energy storage system is the photovoltaic energy storage system, the initial scheduling strategy is adjusted to take the wind turbine generator set and the wind power energy storage system as the priority scheduling targets, and the photovoltaic power generation array and the photovoltaic energy storage system as the suboptimal scheduling targets.

6. The method according to any one of claims 1 to 3, characterized in that The power generation scheduling according to the adjusted target scheduling strategy includes: When the priority scheduling target in the target scheduling strategy includes the wind power energy storage system or the photovoltaic energy storage system, monitoring the remaining power of multiple batteries in the wind power energy storage system or the photovoltaic energy storage system; According to the remaining power of multiple batteries, the multiple batteries are divided into a superior power supply group and a secondary power supply group, and the batteries in the superior power supply group and the secondary power supply group are adjusted according to the remaining power of multiple batteries monitored in real time, and the batteries in the superior power supply group are used for power generation scheduling.

7. A power generation dispatching device for a wind-solar hybrid power generation system, characterized in that: The device comprises: A parameter acquisition module is used to obtain wind power efficiency parameters of the wind turbine in the wind-solar hybrid power generation system, photovoltaic efficiency parameters of the photovoltaic array in the wind-solar hybrid power generation system, and power efficiency parameters corresponding to the power grid connected to the wind-solar hybrid power generation system; a similarity determination module, configured to determine a first correlation coefficient between the wind power efficiency parameter and the electricity efficiency parameter, determine a second correlation coefficient between the photovoltaic efficiency parameter and the electricity efficiency parameter, and determine a target similarity based on the first correlation coefficient, the second correlation coefficient, and a first weighting factor; an initial strategy determination module, configured to compare the target similarity with a preset similarity threshold, and determine an initial scheduling strategy based on a first comparison result; a temperature comparison module, configured to obtain a first energy storage battery temperature of the wind power energy storage system corresponding to the wind turbine generator set and a second energy storage battery temperature of the photovoltaic energy storage system corresponding to the photovoltaic power generation array, and respectively compare the first energy storage battery temperature and the second energy storage battery temperature with a battery safety temperature threshold to obtain a second comparison result; a power generation scheduling module configured to determine, based on the presence of a target energy storage battery temperature exceeding the battery safety temperature threshold in the second comparison result, whether a target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling policy; if the target energy storage system corresponding to the target energy storage battery temperature is a priority scheduling target in the initial scheduling policy, adjust the initial scheduling policy and perform power generation scheduling according to the adjusted target scheduling policy.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the power generation scheduling method of the wind-solar hybrid power generation system according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the power generation scheduling method for the wind-solar hybrid power generation system according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the power generation scheduling method for a wind-solar hybrid power generation system according to any one of claims 1 to 6.

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