Solar power generation algorithm combining single-axis tracking and inverse tracking

Abstract

The invention relates to the technical field of solar power generation tracking, and discloses a solar power generation algorithm combining single-axis tracking and inverse tracking, which realizes collaborative optimization of single-axis tracking and inverse tracking, effectively avoids the problems of technology isolation and difficulty in later collaboration in the traditional design, and improves the power generation efficiency. Therefore, the performance of the photovoltaic system is comprehensively optimized at the initial stage of a project, the power generation efficiency is maximized, and the power generation capacity prediction of the system under different environmental conditions is more accurate by simulating the characteristics and control logic of the single-axis tracking and inverse tracking system in details and combining actual power station data to calibrate model parameters. Powerful data support is provided for scientific planning, design and performance evaluation of a photovoltaic project, and the industry can be developed more efficiently and sustainably.

Description

Technical Field

[0001] The present invention relates to the field of solar power generation tracking technology, and in particular to a solar power generation algorithm combining single-axis tracking and reverse tracking. Background Art

[0002] As the global demand for renewable energy continues to grow, solar energy, as a clean, renewable energy form, has received widespread attention and application. In photovoltaic power generation technology, improving the power generation efficiency of photovoltaic modules is the key to reducing power generation costs and improving energy utilization. As two effective photovoltaic tracking technologies, single-axis tracking technology and reverse tracking technology have shown unique advantages in improving the lighting efficiency of photovoltaic modules and controlling power generation.

[0003] Single-axis tracking technology uses high-precision sun position sensors and intelligent control systems to monitor the changes in the sun's position in real time, and drives the motor to adjust the angle of the photovoltaic module so that it is always as close to the sun's rays as possible throughout the day. This technology significantly improves the lighting efficiency of photovoltaic modules, thereby increasing power generation. Globally, single-axis tracking technology has become a research hotspot in the photovoltaic field and has been widely used in many fields such as large-scale ground photovoltaic power stations and distributed photovoltaic roof systems. However, single-axis tracking technology still has some shortcomings in practical applications, such as tracking accuracy limitations under extreme weather conditions, high initial investment costs, and challenges in adaptability to complex terrain and climatic conditions.

[0004] Reverse tracking technology is a device or method that adjusts the plane angle of photovoltaic modules to deviate from the direct direction of the sun under specific conditions to improve the module conversion rate, temperature control and adjust the power generation power. This technology is particularly suitable for scenes such as excessive solar radiation, high ambient temperature or cloudy areas. By optimizing the lighting angle of photovoltaic modules, more efficient energy conversion can be achieved. Although reverse tracking technology has shown certain advantages and potential, its overall technical maturity still needs to be improved. At present, reverse tracking technology faces problems such as insufficient technical maturity, unstable power generation efficiency and a gap between theoretical models and practical applications in practical applications.

[0005] More importantly, in existing photovoltaic projects, single-axis tracking technology and reverse tracking technology are mostly used separately, lacking effective integration and coordinated optimization. The control strategies and operating parameters of the two technologies have not been organically combined, resulting in the performance of the entire photovoltaic system not being optimal. In addition, the existing photovoltaic simulation design system has obvious deficiencies in the simulation and analysis of the two tracking technologies, and cannot accurately predict key indicators such as power generation and system stability after the combined application of the two technologies. This brings blindness to the planning and design of photovoltaic projects and increases the investment risk of the project. Therefore, a solar power generation algorithm combining single-axis tracking and reverse tracking is proposed. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a solar power generation algorithm combining single-axis tracking and reverse tracking to solve the background technical problems.

[0007] To achieve the above object, the present invention provides the following technical solution: a solar power generation algorithm combining single-axis tracking and reverse tracking, comprising the following steps: 1. Data collection and analysis, and drawing of photovoltaic design schemes Step 1.1: Collect basic data Small step 1.1.1: Establish a stable data connection interface with Meteonorm and Solargis professional meteorological databases to obtain detailed meteorological data for the past 10 years at the location of the PV power station, including but not limited to hourly light intensity, temperature, wind speed, wind direction, air pressure and humidity information; at the same time, obtain long-term climate statistics for the area, including but not limited to annual average sunshine hours, average temperature and frequency of extreme weather.

[0008] Step 1.2: Build a photovoltaic power plant model Small step 1.2.1: Draw an accurate plane simulation model of the PV power station based on the collected geographic information and actual site conditions.

[0009] 2. Select single-axis tracking and reverse tracking methods for the photovoltaic design and improve the inverter string and cable configuration including but not limited to Step 2.1: Select tracking method Small step 2.1.1: Based on the geographical location, meteorological conditions, PV module technical parameters and the layout design of the PV solution collected in the early stage, conduct a comprehensive evaluation and select different tracking methods, single-axis tracking or reverse tracking, or both.

[0010] Step 2.2: Calculate the inverter capacity and configure, and improve the cable configuration Small step 2.2.1: Select the inverter model and set the capacity ratio to cope with the output power fluctuations of the components under different environmental conditions and ensure that the inverter can operate efficiently for a long time. The simulation model designs the string distribution method according to the parameters of the PV components and the input requirements of the inverter.

[0011] 3. Pre-generate power generation analysis report Step 3.1: Analyze power generation data Small step 3.1.1: Organize the simulation results, and simulate and calculate the power generation of the PV power station in different time periods based on the established PV power station model and the selected tracking method.

[0012] Step 3.2: Generate power generation analysis report Small step 3.2.1: Based on the power generation data and analysis results, write a detailed power generation analysis report, which includes the basic information of the PV power station, power generation simulation results, and consumption analysis conclusions. Preferably, in step 1.1, the following steps are also included: Small step 1.1.2: Communicate in depth with electricity users and collect detailed electricity consumption parameters, including but not limited to daily electricity consumption, monthly electricity consumption, electricity load curve, and peak and valley time distribution information; for photovoltaic power stations connected to the grid, determine the grid access requirements and local electricity price policies; based on the above electricity consumption parameters, design the installed capacity of the photovoltaic power station and the power generation scheduling strategy.

[0013] Preferably, in step 1.2, the following steps are also included: Small step 1.2.2: Set the basic parameters of the PV panels, including but not limited to the panel model, installation method, installation angle, and shadow information.

[0014] Preferably, in step 2.2, the following steps are also included: Small step 2.2.2: Select the string distribution method, and configure the inverter, AC combiner box, high and low voltage cabinets on the power station plane, intelligently generate the cable path diagram, and calculate its length.

[0015] Small step 2.2.3: Improve the cable configuration and transformer selection according to the inverter and string configuration.

[0016] Preferably, in step 3.1, the following steps are also included: Small step 3.1.2: Analyze in detail the various loss factors that affect power generation, including but not limited to inverter efficiency loss, over-capacity peak shaving loss, and component aging loss.

[0017] Preferably, in the report of the sub-step 3.2.1, charts and tables are used to intuitively display the data and analysis results.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention realizes the coordinated optimization of single-axis tracking and reverse tracking, effectively avoiding the problems of technical isolation and difficulty in later coordination in traditional design, thereby ensuring that the performance of the photovoltaic system is fully optimized at the beginning of the project and maximizing the power generation efficiency. By exhaustively simulating the characteristics and control logic of the single-axis tracking and reverse tracking systems and calibrating the model parameters with actual power station data, the system's power generation prediction under different environmental conditions is more accurate, providing strong data support for the scientific planning, design and performance evaluation of photovoltaic projects, and contributing to a more efficient and sustainable development of the industry.

[0019] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flowchart of the solar power generation algorithm combining single-axis tracking and reverse tracking of the present invention; Figure 2 It is a specific schematic diagram of single-axis tracking and reverse tracking of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 technical field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1-2 The present invention proposes a solar power generation algorithm that combines single-axis tracking and reverse tracking. The algorithm integrates photovoltaic single-axis tracking technology and reverse tracking technology into a photovoltaic simulation design system, which is both complementary and common, providing a more rigorous shadow analysis and power generation simulation analysis for the photovoltaic design system, aiming to improve the accuracy of photovoltaic simulation design, improve power generation efficiency and system stability.

[0023] 1. Preliminary data collection and analysis and drawing of photovoltaic design plans Step 1.1: Collect basic data Small step 1.1.1: Establish a stable data connection interface with professional meteorological databases such as Meteonorm and Solargis to obtain detailed meteorological data for at least the past 10 years at the location of the PV power station, including hourly light intensity (direct irradiance, diffuse irradiance), temperature (ambient temperature, component temperature), wind speed, wind direction, air pressure, humidity and other information; at the same time, obtain long-term climate statistical data for the area, such as average annual sunshine hours, average temperature, frequency of extreme weather, etc., to provide a comprehensive meteorological basis for subsequent PV power station design and power generation forecast.

[0024] Small step 1.1.2: Communicate in depth with the electricity users and collect detailed electricity parameters, including daily electricity consumption, monthly electricity consumption, electricity load curve (power demand at different times), peak and valley time distribution, etc. For photovoltaic power stations connected to the power grid, it is also necessary to understand the grid access requirements (such as voltage level, power factor, harmonic requirements, etc.) and local electricity price policies (time-of-use electricity prices, subsidy policies, etc.). Based on these electricity parameters, the installed capacity and power generation scheduling strategy of the photovoltaic power station are reasonably designed to ensure that the photovoltaic power generation can meet the electricity demand to the greatest extent and maximize economic benefits.

[0025] Step 1.2: Build a photovoltaic power plant model Small step 1.2.1: Draw an accurate plane model of the PV power station based on the collected geographic information and the actual site conditions. Accurately mark the arrangement of PV modules (row spacing, column spacing), module orientation, and the location and size of obstacles (such as buildings, trees, and poles). Set the terrain attributes of different areas in the model (such as slope and altitude changes) to facilitate shadow analysis and power generation simulation.

[0026] Small step 1.2.2: Set the basic parameters of the PV modules, such as the module model, installation method (horizontal or vertical), installation angle, shadow shielding (whether there is no shielding all year round), etc. These module parameters will directly affect the power generation performance of the PV system and the accuracy of the simulation results.

[0027] 2. Select single-axis tracking and reverse tracking methods for the photovoltaic design and improve the configuration of inverter strings, cables, etc. Step 2.1: Select tracking method Small step 2.1.1: Based on the geographical location, meteorological conditions, PV module technical parameters, and PV solution layout design collected in the early stage, a comprehensive evaluation is conducted to select different tracking methods, single-axis tracking or reverse tracking, or both. Figure 2 As shown, for example, for photovoltaic power stations located in high latitudes (such as above 50° north latitude) with relatively uniform sunshine time distribution throughout the year and large changes in solar altitude angle, single-axis tracking is preferred to make full use of solar radiation resources in different seasons and time periods. In low-latitude areas (such as between 20°-30° north latitude), if there are more cloudy days, abundant scattered light resources, high summer temperatures, and high solar radiation intensity, a combination of single-axis tracking and reverse tracking can be considered. For example, in some photovoltaic power stations in Southeast Asia, this combination is used, with single-axis tracking to improve power generation efficiency on sunny days, and reverse tracking for temperature control and scattered light utilization in hot or cloudy weather.

[0028] Step 2.2: Calculate inverter capacity and configure, improve cable configuration, etc. Small step 2.2.1: Select the inverter model and set the capacity ratio (generally between 1.1-1.3, determined according to the actual situation) to cope with the output power fluctuations of the components under different environmental conditions and ensure that the inverter can operate efficiently for a long time. Simulate the system and reasonably design the string distribution method based on the parameters of the PV components (open circuit voltage, short circuit current, maximum power point voltage, maximum power point current) and the input requirements of the inverter (maximum input voltage, maximum input current, number of MPPT paths, etc.).

[0029] Small step 2.2.2: Select the string distribution method, and configure the inverter, AC combiner box, high and low voltage cabinets on the power station plane, intelligently generate the cable path diagram, and calculate its length.

[0030] Small step 2.2.3: Improve the cable configuration, transformer selection, etc. according to the inverter and string configuration.

[0031] 3. Pre-generate power generation analysis report Step 3.1: Analyze power generation data Small step 3.1.1: Organize the simulation results, and simulate and calculate the power generation of the photovoltaic power station in different time periods (first year, 25-year average) based on the established photovoltaic power station model and the selected tracking method. When calculating the monthly power generation, consider the impact of factors such as sunshine duration, solar radiation intensity, and temperature changes in different months on power generation efficiency. Conduct a consumption analysis, and evaluate the consumption of photovoltaic power generation based on the power load curve of the power user and the acceptance capacity of the local power grid to determine whether there is a phenomenon of abandoned light and the size of the abandoned light rate.

[0032] Small step 3.1.2: Analyze in detail the various loss factors that affect power generation, including inverter efficiency loss (calculate the efficiency loss under different input powers based on the inverter conversion efficiency curve), over-capacity peak-shaving loss (the loss caused by the inability to fully utilize the component output power during certain periods due to inverter over-capacity), component aging loss (calculate the reduction in power generation caused by the decline in component power year by year based on the component attenuation rate curve), etc.

[0033] Step 3.2: Generate power generation analysis report Small step 3.2.1: Based on the power generation data and analysis results, write a detailed power generation analysis report, which includes the basic information of the photovoltaic power station (geographic location, installed capacity, component type, tracking method, etc.), power generation simulation results (first year power generation, 25-year average power generation, monthly power generation curve, etc.), and consumption analysis conclusions (abandonment, consumption rate, etc.). In the report, use charts (such as bar charts, line charts, etc.) and tables to intuitively display the data and analysis results, making the report easier to understand and read.

[0034] The present invention applies photovoltaic single-axis tracking technology and reverse tracking technology to the photovoltaic simulation design system, realizing the organic combination of the two technologies in the photovoltaic system. By considering the collaborative work of the two technologies in the simulation design stage, the system performance can be fully optimized in the project planning and design stage, avoiding the problem of two technologies being considered separately and difficult to coordinate in the later stage in traditional design. The simultaneous application of the two technologies is conducive to maximizing the efficiency of photovoltaic power generation. For example, under normal lighting and temperature conditions, the single-axis tracking system plays a role, actively tracking the sun and increasing power generation. However, when extremely hot weather occurs or the temperature of the photovoltaic module is too high, the system can switch to reverse tracking mode, temporarily sacrificing a certain amount of power generation efficiency to protect the photovoltaic module, extend its service life, and reduce the risk of failures such as hot spots.

[0035] By introducing photovoltaic single-axis tracking technology and reverse tracking technology, the photovoltaic simulation design system can more accurately simulate the power generation state of the photovoltaic system and predict key performance indicators such as the power generation and power output curve of the photovoltaic system. The model not only includes the basic component models of photovoltaic components, inverters, cables, etc. in the traditional photovoltaic system simulation model, but also simulates the kinematic characteristics of the single-axis tracking system and the control logic of the reverse tracking system in detail. By collecting and analyzing the actual photovoltaic power station operation data, the parameters in the model are continuously calibrated and optimized, so that the simulation model can accurately predict the prediction under different environmental conditions with higher accuracy. Provide a scientific basis for the planning, design, and performance evaluation of photovoltaic projects.

Claims

1. A solar power generation algorithm combining single-axis tracking and reverse tracking, characterized in that: The following steps are involved:

1. Data collection and analysis, and drawing of photovoltaic design schemes Step 1.1: Collect basic data Small step 1.1.1: Establish a stable data connection interface with Meteonorm and Solargis professional meteorological databases to obtain detailed meteorological data for at least the past 10 years at the location of the PV power station, including but not limited to hourly light intensity, temperature, wind speed, wind direction, air pressure and humidity information; at the same time, obtain long-term climate statistics for the area, including but not limited to annual average sunshine hours, average temperature and frequency of extreme weather; Step 1.2: Build a photovoltaic power plant model Small step 1.2.1: Draw an accurate plane simulation model of the photovoltaic power station based on the collected geographical information and the actual site conditions; 2. Select single-axis tracking and reverse tracking methods for the photovoltaic design and improve the inverter string and cable configuration including but not limited to Step 2.1: Select tracking method Small step 2.1.1: Based on the geographical location, meteorological conditions, PV module technical parameters and PV solution layout design factors collected in the early stage, conduct a comprehensive evaluation and select different tracking methods, single-axis tracking or reverse tracking, or both; Step 2.2: Calculate the inverter capacity and configure, and improve the cable configuration Small step 2.2.1: Select the inverter model and set the capacity ratio to cope with the output power fluctuations of the components under different environmental conditions, ensuring that the inverter can operate efficiently for a long time; the simulation model designs the string distribution method according to the parameters of the PV components and the input requirements of the inverter; 3. Pre-generate power generation analysis report Step 3.1: Analyze power generation data Small step 3.1.1: Organize the simulation results, and simulate and calculate the power generation of the photovoltaic power station in different time periods according to the established photovoltaic power station model and the selected tracking method; Step 3.2: Generate power generation analysis report Small step 3.2.1: Based on the power generation data and analysis results, write a detailed power generation analysis report, which includes the basic information of the PV power station, power generation simulation results, and consumption analysis conclusions.

2. According to claim 1, a solar power generation algorithm combining single-axis tracking and reverse tracking is characterized in that: In step 1.1, the following steps are also included: Small step 1.1.2: Communicate in depth with electricity users and collect detailed electricity consumption parameters, including but not limited to daily electricity consumption, monthly electricity consumption, electricity load curve, and peak and valley time distribution information; for photovoltaic power stations connected to the grid, determine the grid access requirements and local electricity price policies; based on the above electricity consumption parameters, design the installed capacity of the photovoltaic power station and the power generation scheduling strategy.

3. According to the claim, the solar power generation algorithm combining single-axis tracking and reverse tracking is characterized in that: In step 1.2, the following steps are also included: Small step 1.2.2: Set the basic parameters of the PV panels, including but not limited to the panel model, installation method, installation angle, and shadow information.

4. According to claim 1, the solar power generation algorithm combining single-axis tracking and reverse tracking is characterized in that: In step 2.2, the following steps are also included: Small step 2.2.2: Select the string distribution method, and configure the inverter, AC combiner box, high and low voltage cabinets on the power station plane, intelligently generate the cable path diagram, and calculate its length; Small step 2.2.3: Improve the cable configuration and transformer selection according to the inverter and string configuration.

5. According to the claim, the solar power generation algorithm combining single-axis tracking and reverse tracking is characterized in that: In step 3.1, the following steps are also included: Small step 3.1.2: Analyze in detail the various loss factors that affect power generation, including but not limited to inverter efficiency loss, over-capacity peak shaving loss, and component aging loss.

6. According to the claim, the solar power generation algorithm combining single-axis tracking and reverse tracking is characterized in that: In the report of step 3.2.1, use charts and tables to visually present the data and analysis results.

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