Method and system for determining floating photovoltaic layout scale threshold value in semi-closed sea area

Abstract

The invention relates to the technical field of photovoltaic industry, in particular to a method and a system for determining a floating photovoltaic layout scale threshold value in a semi-closed sea area, and the method comprises the steps: taking the long-term electricity demand power of a power supply area of a photovoltaic power station as a calculation index when designing the photovoltaic layout scale threshold value; according to the method, the layout scale of the photovoltaic power station matrix is obtained through calculation in combination with the power supply power of the photovoltaic single bodies, the photovoltaic layout scale threshold determination method different from the prior art is provided, the photovoltaic layout scale threshold determination efficiency is improved, and when the photovoltaic power station is designed, the long-term electricity demand power serves as a parameter for calculating the layout scale, so that the layout scale of the photovoltaic power station matrix is calculated. And under the combined allocation with an energy storage system of a photovoltaic power station and other power supplies, the long-distance power utilization requirement of a power utilization area is met, and the proportion of clean energy in the energy supply structure can be ensured to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power station design, and in particular to a method and system for determining a threshold value for a floating photovoltaic deployment in a semi-enclosed sea area. Background Art

[0002] Solar energy is considered one of the most promising alternative energy sources due to its ubiquity and sustainability. Recent developments in photovoltaic technology have made solar energy cost competitive. From 1977 to 2020, the production cost of solar panels has dropped 300 times, further promoting the rapid spread of photovoltaic power generation technology. Due to the low-density power generation characteristics of solar energy, the installation of traditional land-based photovoltaic power generation systems requires a large amount of land, which will increase competition for land resources, damage the ecosystem and increase the cost of photovoltaic power generation, which is a huge challenge for areas with scarce land resources. Considering the distribution characteristics of photovoltaic energy and the high cost of land utilization, floating photovoltaics on water have become the focus of attention. Floating photovoltaics is a photovoltaic power generation technology that can arrange photovoltaic facilities on the surface of water bodies such as oceans, lakes, reservoirs, tailings ponds, and agricultural irrigation reservoirs. Compared with traditional land-based photovoltaic systems, floating photovoltaics integrate existing solar power generation technology and water floating technology. In recent years, the number of floating photovoltaic power stations, the scale of single power stations, and the total installed capacity have all developed extremely rapidly.

[0003] In the prior art, when designing the scale of floating photovoltaic deployment, the deployment scale is generally determined only based on the geographical conditions of the deployment area, such as whether the scale is too large to affect the waterway, and the needs of the power supply area are not considered, resulting in inaccurate scale design. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for determining a threshold value for the deployment scale of floating photovoltaic power plants in a semi-enclosed sea area, which are used to improve the accuracy of determining the threshold value for the deployment scale of photovoltaic power plants.

[0005] The present invention provides a method and system for determining a threshold value for floating photovoltaic deployment in a semi-enclosed sea area, comprising:

[0006] S1: Obtain the average power generation of floating photovoltaic power generation units in the sea area to be predicted;

[0007] S2: Determine the long-term demand power of photovoltaic power generation in the power supply area of ​​the floating photovoltaic power station;

[0008] S3: Obtaining a scale threshold of the photovoltaic power station according to the long-term demand power and the average power generation power of the photovoltaic power generation units.

[0009] Preferably, a long short-term memory network model is used to obtain the average power generation power of the floating photovoltaic power generation units in the sea area to be predicted.

[0010] Preferably, the average power generation power of the floating photovoltaic power generation unit in the sea area to be predicted using the long short-term memory network model is specifically:

[0011] Sa: establishing the long short-term memory network model;

[0012] Sb: Obtain a training set to train the long short-term memory network model;

[0013] Sc: Obtain the data of factors affecting photovoltaic power generation in the sea area to be predicted;

[0014] Sd: Input the data of factors affecting photovoltaic power generation in the sea area to be predicted into the long short-term memory network model to obtain the average power generation of floating photovoltaic power generation units in the sea area to be predicted.

[0015] Preferably, in the Sa, the long short-term memory network model includes: a forget gate, an input gate, an output gate and a unit state.

[0016] Preferably, in said Sb, said training set includes: photovoltaic power generation power influencing factor data and photovoltaic power generation power data.

[0017] Preferably, the photovoltaic power generation power influencing factor data include: solar radiation data, wind data, water flow data in semi-enclosed waters, temperature data, humidity data, photovoltaic installation angle data, and photovoltaic power generation efficiency data.

[0018] Preferably, when training the long short-term memory network model, whether to terminate the long short-term memory network model training is determined by judging whether a preset number of training times is reached or whether the loss function converges;

[0019] Preferably, the long-term power demand is calculated based on the annual power consumption growth rate of the power consumption area to calculate the power demand of the power consumption area 20 years later.

[0020] Preferably, the scale threshold of the photovoltaic power station is the ratio of the long-term required power to the average power generation power of the photovoltaic power generation unit.

[0021] According to another aspect of the present invention, a system for determining a threshold value for floating photovoltaic deployment in a semi-enclosed sea area is provided. The system adopts the above-mentioned method for determining a threshold value for floating photovoltaic deployment in a semi-enclosed sea area. The system comprises:

[0022] The photovoltaic monomer average power generation calculation module is used to obtain the average power generation of the floating photovoltaic power generation monomer in the sea area to be predicted;

[0023] A power supply area future demand power calculation module, used to determine the future demand power of photovoltaic power generation in the power supply area of ​​the floating photovoltaic power station;

[0024] The photovoltaic power station scale threshold calculation module is used to obtain the photovoltaic power station scale threshold according to the long-term demand power and the average power generation power of the photovoltaic power generation monomers.

[0025] The embodiments of the present invention have the following technical effects:

[0026] When designing the photovoltaic layout scale threshold, the present invention uses the long-term power demand power of the power supply area of ​​the photovoltaic power station as a calculation index, and calculates the layout scale of the photovoltaic power station matrix in combination with the power supply power of the photovoltaic monomer, thereby providing a method for determining the photovoltaic layout scale threshold that is different from the prior art, and improving the accuracy of determining the photovoltaic layout scale threshold.

[0027] When calculating the power generation power of a photovoltaic cell, the present invention not only considers the environmental factors of the photovoltaic power station, but also considers the installation angle of the photovoltaic cell and the power generation efficiency of the photovoltaic cell; the factors are considered comprehensively, and when obtaining the environmental factor index, the statistical quantity is used as the specific value of each index in the environmental factor, which is more scientific;

[0028] When designing a photovoltaic power station, the long-term electricity demand power is used as a parameter for calculating the layout scale. Through the joint deployment with the photovoltaic power station's energy storage system and other power supply sources, the long-term electricity demand of the power consumption area can be met, and the proportion of clean energy in the energy supply structure can be guaranteed to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 is a flow chart of a method for determining a threshold value for deployment of floating photovoltaic power in a semi-enclosed sea area provided in Example 1 of the present invention;

[0031] Figure 2 This is a flow chart of predicting the average power generation of floating photovoltaic power generation units in a sea area using a long short-term memory network model provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0033] Embodiment 1, Figure 1 1 is a flow chart of a method for determining a threshold value for floating photovoltaic deployment in a semi-enclosed sea area provided in Example 1 of the present invention. Figure 1 , a method for determining the threshold value of floating photovoltaic deployment in semi-enclosed sea areas, specifically comprising:

[0034] S1: Obtain the average power generation of floating photovoltaic power generation units in the sea area to be predicted;

[0035] The floating photovoltaic power station is composed of a combination of photovoltaic power generation monomers. Therefore, determining the average power generation of the floating photovoltaic power generation monomer is an important parameter in the design of the floating photovoltaic power station.

[0036] Among them, the long short-term memory network model is used to obtain the average power generation of floating photovoltaic power generation units in the sea area to be predicted;

[0037] Specifically, Figure 2 As shown, the average power generation power of the floating photovoltaic power generation unit in the sea area to be predicted using the long short-term memory network model is specifically:

[0038] Sa: establishing the long short-term memory network model;

[0039] The Long Short-Term Memory (LSTM) network model is an improvement on the Recurrent Neural Network (RNN) model. Although RNN has a good effect on the processing of time series problems, as the length of the input time series increases, RNN will produce problems such as gradient vanishing or gradient explosion. To solve the above problems, LSTM introduces a gate structure based on RNN. By updating, maintaining, and deleting cell state information through the gate structure, LSTM has achieved good results in processing long time series problems. It has been widely used in power forecasting, load forecasting and other fields. LSTM can retain and transmit information between different time steps of the sequence by introducing a gating mechanism.

[0040] Specifically, the long short-term memory network model includes: a forget gate, an input gate, an output gate and a unit state;

[0041] Sb: Obtain a training set to train the long short-term memory network model;

[0042] Wherein, the training set includes: photovoltaic power generation power influencing factor data and photovoltaic power generation power data;

[0043] The photovoltaic power generation factor data include: solar radiation data, wind data, water flow data in semi-enclosed waters, temperature data, humidity data, photovoltaic installation angle data, photovoltaic power generation efficiency data;

[0044] The solar radiation data, wind data, water flow data of semi-enclosed waters, temperature data, and humidity data are not instantaneous data, but statistical data in a statistical sense. For example, the solar radiation data not only changes periodically at different times of the day, but also changes with the month and season. In this embodiment, the statistical value of the solar radiation data characterizing the target area is obtained in units of years, which can be one of the annual average radiation data or the annual median radiation data; similarly, the wind data, water flow data of semi-enclosed waters, temperature data, and humidity data are consistent with the solar radiation data;

[0045] Solar radiation data is the most important factor affecting the power of floating photovoltaic power generation. During the transmission of solar radiation in the air, it is often affected by clouds, airborne particles and water vapor on the water surface, resulting in the volatility and randomness of the solar radiation received by the floating photovoltaic, which in turn causes the change of the floating photovoltaic power generation efficiency. This embodiment collects solar radiation data from different regions as a factor for photovoltaic power generation prediction, which can effectively improve the prediction accuracy of photovoltaic power generation.

[0046] It is worth noting that, compared with environmental factors such as solar radiation data, wind data, water flow data in semi-enclosed waters, temperature data, and humidity data, photovoltaic installation inclination data and photovoltaic power generation efficiency data are closely related to photovoltaic power generation equipment; this embodiment predicts photovoltaic power generation through environmental factors and photovoltaic equipment factors, which can effectively improve the prediction accuracy of photovoltaic power generation; at the same time, when obtaining environmental factor indicators, using statistics as specific values ​​of each indicator in the environmental factor is more scientific;

[0047] The photovoltaic power generation data is the average power generation of photovoltaic power generation units obtained by statistical calculation;

[0048] When training the long short-term memory network model, determining whether to end the long short-term memory network model training by judging whether a preset number of training times has been reached or judging whether the loss function has converged;

[0049] Sc: Obtain the data of factors affecting photovoltaic power generation in the sea area to be predicted;

[0050] The photovoltaic power generation power influencing factor data of the sea area to be predicted is obtained by looking up the records of the local meteorological station;

[0051] Sd: Input the data of factors affecting photovoltaic power generation in the sea area to be predicted into the long short-term memory network model to obtain the average power generation of floating photovoltaic power generation units in the sea area to be predicted.

[0052] S2: Determine the long-term demand power of photovoltaic power generation in the power supply area of ​​the floating photovoltaic power station;

[0053] Generally, photovoltaic power generation is characterized by large fluctuations. Therefore, for a certain power consumption area or microgrid, there will not be only photovoltaic power generation. Generally, there will be at least photovoltaic power generation and thermal power or hydropower co-generation. Therefore, the total future demand power of the power supply area can be calculated first, and then the future demand power of photovoltaic power generation in the power supply area of ​​the floating photovoltaic power station can be determined according to the minimum power generation operation scenario of other power supply sources;

[0054] In addition, the long-term power demand is calculated based on the annual power consumption growth rate of the power consumption area in 20 years;

[0055] In this embodiment, since the annual electricity consumption in the power consumption area is generally an increasing area, the annual electricity consumption growth rate of the power consumption area can be calculated based on the historical electricity consumption data of the power consumption area; and, for floating photovoltaic power stations in semi-enclosed sea areas, compared with reservoirs, fish ponds, and lakes, they are more affected by waves, salinity, etc., and the design life is generally 20 years. Therefore, when calculating the future demand power, this embodiment uses the power demand power of the power supply area 20 years later as the future demand power.

[0056] S3: Obtaining a scale threshold of the photovoltaic power station according to the long-term required power and the average power generation power of the photovoltaic power generation units;

[0057] The scale threshold of the photovoltaic power station is the ratio of the long-term demand power to the average power generation power of the photovoltaic power generation unit;

[0058] When designing a photovoltaic power station, the long-term electricity demand power is used as a parameter for calculating the layout scale. Through the joint deployment with the photovoltaic power station's energy storage system and other power supply sources, the long-term electricity demand of the power consumption area can be met, and the proportion of clean energy in the energy supply structure can be guaranteed to a certain extent.

[0059] Embodiment 2, this embodiment discloses a system for determining a threshold value for the deployment scale of floating photovoltaic power generation in a semi-enclosed sea area, the system adopts the method for determining a threshold value for the deployment scale of floating photovoltaic power generation in a semi-enclosed sea area of ​​embodiment 1, and the system comprises:

[0060] The photovoltaic monomer average power generation calculation module is used to obtain the average power generation of the floating photovoltaic power generation monomer in the sea area to be predicted;

[0061] A power supply area future demand power calculation module, used to determine the future demand power of photovoltaic power generation in the power supply area of ​​the floating photovoltaic power station;

[0062] The photovoltaic power station scale threshold calculation module is used to obtain the photovoltaic power station scale threshold according to the long-term demand power and the average power generation power of the photovoltaic power generation monomers.

[0063] Embodiment 3: This embodiment discloses an electronic device, which includes one or more processors and a memory.

[0064] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0065] The memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may run the program instructions to implement a method for determining a threshold value for the deployment scale of floating photovoltaics in a semi-enclosed sea area in any embodiment of the present application described above and / or other desired functions. Various contents such as initial external parameters, thresholds, etc. may also be stored in the computer-readable storage medium.

[0066] In one example, the electronic device may further include: an input device and an output device, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). The input device may include, for example, a keyboard, a mouse, etc. The output device may output various information to the outside, including early warning information, braking force, etc. The output device may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0067] In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0068] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute each or all steps of a method for determining a threshold value for the deployment scale of floating photovoltaic systems in a semi-enclosed sea area provided in any embodiment of the present application.

[0069] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0070] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps for determining the threshold value of the scale of floating photovoltaic deployment in a semi-enclosed sea area provided in any embodiment of the present application.

[0071] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0072] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0073] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the threshold value of floating photovoltaic deployment in semi-enclosed sea areas, characterized in that: include: S1: Obtain the average power generation of floating photovoltaic power generation units in the sea area to be predicted; S2: Determine the long-term demand power of photovoltaic power generation in the area supplied by the floating photovoltaic power station; S3: Obtaining a scale threshold of the photovoltaic power station according to the long-term demand power and the average power generation power of the photovoltaic power generation units.

2. The method for determining the threshold value of floating photovoltaic deployment in semi-enclosed sea areas according to claim 1, characterized in that: The long short-term memory network model is used to obtain the average power generation of floating photovoltaic power generation units in the sea area to be predicted.

3. The method for determining the threshold value of floating photovoltaic deployment in semi-enclosed sea areas according to claim 2, characterized in that: The specific method of using the long short-term memory network model to obtain the average power generation power of the floating photovoltaic power generation monomer in the sea area to be predicted is: Sa: establishing the long short-term memory network model; Sb: Obtain a training set to train the long short-term memory network model; Sc: Obtain the data of factors affecting photovoltaic power generation in the sea area to be predicted; Sd: Input the data of factors affecting photovoltaic power generation in the sea area to be predicted into the long short-term memory network model to obtain the average power generation of floating photovoltaic power generation units in the sea area to be predicted.

4. A method for determining a threshold value for deployment of floating photovoltaic power in a semi-enclosed sea area according to claim 3, characterized in that: In the Sa, the long short-term memory network model includes: a forget gate, an input gate, an output gate and a unit state.

5. The method for determining the threshold value of floating photovoltaic deployment in semi-enclosed sea areas according to claim 3, characterized in that: In the Sb, the training set includes: photovoltaic power generation power influencing factor data and photovoltaic power generation power data.

6. A method for determining a threshold value for deployment of floating photovoltaic power in a semi-enclosed sea area according to claim 5, characterized in that: The photovoltaic power generation power influencing factor data include: solar radiation data, wind data, water flow data in semi-enclosed waters, temperature data, humidity data, photovoltaic installation angle data, and photovoltaic power generation efficiency data.

7. The method for determining the threshold value of floating photovoltaic deployment in semi-enclosed sea areas according to claim 3, characterized in that: When training the LSTM network model, whether to terminate the LSTM network model training is determined by judging whether a preset number of training times is reached or whether the loss function converges.

8. The method for determining the threshold value of floating photovoltaic deployment in semi-enclosed sea areas according to claim 3, characterized in that: The long-term power demand is obtained by calculating the power demand of the power consumption area 20 years later according to the annual power consumption growth rate of the power consumption area.

9. The method for determining the threshold value of floating photovoltaic deployment in semi-enclosed sea areas according to claim 1, characterized in that: The scale threshold of the photovoltaic power station is the ratio of the long-term required power to the average power generation power of the photovoltaic power generation unit.

10. A system for determining the threshold value of floating photovoltaic deployment in semi-enclosed sea areas, the system adopts the method for determining the threshold value of floating photovoltaic deployment in semi-enclosed sea areas according to any one of claims 1 to 9, characterized in that: The system comprises: The photovoltaic monomer average power generation calculation module is used to obtain the average power generation of the floating photovoltaic power generation monomer in the sea area to be predicted; A power supply area future demand power calculation module, used to determine the future demand power of photovoltaic power generation in the power supply area of ​​the floating photovoltaic power station; The photovoltaic power station scale threshold calculation module is used to obtain the photovoltaic power station scale threshold according to the long-term demand power and the average power generation power of the photovoltaic power generation monomers.

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