Source network load storage system extreme wind and light output characteristic analysis method

Abstract

The invention relates to the field of power system analysis, in particular to a source network load storage system extreme wind and light output characteristic analysis method, which comprises the following steps of: acquiring multi-year long-sequence climate data and actual measurement data of a project location; analyzing and screening long-sequence climate data, interpolating measured data, and prolonging the measured data into long-sequence data; constructing a wind power output calculation model and a photovoltaic output calculation model; inputting the long-sequence climate data into a wind power output calculation model and a photovoltaic output calculation model to obtain a wind power output curve graph and a photovoltaic output curve graph; analyzing calculation results output by the wind power output calculation model and the photovoltaic output calculation model; and according to the extreme value, obtaining an output characteristic curve of the extreme wind-light year and the year where the extreme wind-light month is located, substituting the output characteristic curve into the source network load storage model, and optimizing the configuration of the source network load storage system under the extreme climate condition. The method has the effect of improving the reliability of the wind and light output characteristic analysis result.

Description

Technical Field

[0001] The present invention relates to the field of power system analysis, and in particular to a method for analyzing extreme wind-solar output characteristics of a source-grid-load-storage system. Background Art

[0002] In the field of renewable energy generation, wind and solar energy are the main forms of new energy, and their output characteristics are affected by a variety of climate factors, including wind speed, wind direction, temperature, solar radiation, etc. Especially in the plateau cold arid climate zone, extreme climate conditions (such as extreme low temperature, low pressure, strong ultraviolet rays, thunderstorms, etc.) put forward higher requirements on the operating efficiency and reliability of wind turbines and photovoltaic modules. Traditional power and electricity balance analysis methods are often based on short-term meteorological data, which makes it difficult to accurately reflect the wind and solar output characteristics under long-term climate conditions and their impact on the reliability of the power supply system. Summary of the invention

[0003] In order to solve the problem of insufficient reliability of wind-solar output characteristics analysis results, the present invention provides an extreme wind-solar output characteristics analysis method for a source-grid-load-storage system.

[0004] The present invention provides a method for analyzing the extreme wind-solar output characteristics of a source-grid-load-storage system, which adopts the following technical solutions: A method for analyzing extreme wind-solar output characteristics of a source-grid-load-storage system comprises the following steps: Obtain long-term climate data for many years at the project site; Analyze and screen long-series climate data; Based on long-sequence climate data, wind power output calculation models and photovoltaic output calculation models are constructed; Inputting long-sequence climate data into a wind power output calculation model and a photovoltaic output calculation model to obtain a wind power output curve diagram and a photovoltaic output curve diagram; Analyze the calculation results output by the wind power output calculation model and the photovoltaic output calculation model, and filter out extreme values ​​in the calculation results; Based on the extreme values, the output characteristic curves of the extreme wind-light year and the extreme wind-light month are obtained, and they are substituted into the source-grid-load-storage model to optimize the configuration of the source-grid-load-storage system under extreme climatic conditions.

[0005] In a specific feasible implementation plan, if there are measured data at the project location, when obtaining the multi-year long series climate data at the project location, the measured data at the project location are obtained; After obtaining the measured data, the measured data is extended and interpolated to be extended into long sequence data.

[0006] In a specific feasible implementation scheme, when interpolating measured data, the selected mesoscale data uses correlation as a priority criterion. The higher the correlation between the hourly data of the mesoscale data and the hourly data of the measured data, the higher the priority of the mesoscale data.

[0007] In a specific implementation scheme, the multi-year long series climate data includes hourly wind speed and wind direction data at the hub height of the wind tower for a full year, as well as 20-30 years of ERA5 mesoscale 100m height hourly wind speed, temperature, air pressure, precipitation, and NASA hourly radiation data at that location.

[0008] In a specific feasible implementation plan, inputting long-sequence climate data into a wind power output calculation model and a photovoltaic output calculation model to obtain a wind power output curve diagram and a photovoltaic output curve diagram includes the following steps: Calculate the theoretical output and reduction factor, obtain the actual output based on the reduction factor, and obtain the wind power output curve based on the actual output; According to the hourly data in the long-sequence climate data, the project system efficiency is calculated and the long-sequence hourly photovoltaic power generation of the project is output; the photovoltaic output calculation model calculates the preliminary multi-year photovoltaic unit hourly output data based on the hourly photovoltaic power generation; according to the statistical multi-year average unit output, it is converted to the same numerical level as the representative annual hours to obtain the multi-year unit hourly photovoltaic output data, and the photovoltaic output curve is generated based on the multi-year unit hourly photovoltaic output data.

[0009] In a specific implementation plan, the calculation formula for the multi-year hourly photovoltaic output is:

[0010] In the above formula, Provides photovoltaic power for many years on an hourly basis; It is the hourly data of electricity consumption for many years; is the number of hours per year; is the project capacity; For the number of years.

[0011] In a specific feasible implementation plan, the calculation results also include annual wind power output data, annual photovoltaic power output data, wind power and photovoltaic combined output data, and statistical charts of the output changes of wind and photovoltaic combined output month by month in each year.

[0012] In a specific possible implementation scheme, the extreme values ​​include the year with the lowest output, the year with the highest output, the season with the lowest output, and the month with the lowest output.

[0013] In summary, the present invention has the following beneficial effects: Comprehensively consider the long-term climate data of many years to accurately evaluate the output characteristics and power supply reliability of the wind-solar hybrid power supply system under extreme climate conditions. Improve the adaptability and reliability of the system for the planning and design of energy projects under extreme climate conditions such as plateau cold and arid climate zones. Provide the direction of load optimization strategy through the power balance analysis of the source-grid-load-storage system, improve the power supply reliability under extreme climate conditions, and reduce the system operation and maintenance costs. Provide a scientific basis for the planning, design, operation and management of renewable energy power generation projects, and promote the sustainable development of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of the analysis method of extreme wind and solar power output characteristics of the source-grid-load-storage system.

[0015] Figure 2 This is the wind power output curve for each year.

[0016] Figure 3 This is the wind power output curve for each year and month.

[0017] Figure 4 It is the photovoltaic output curve for each year.

[0018] Figure 5 It is the photovoltaic output curve of each year and month.

[0019] Figure 6 It is a statistical chart of the changes in the combined output of wind and solar power on a monthly basis each year. DETAILED DESCRIPTION

[0020] The following is combined with Figure 1-6 The present invention is described in further detail.

[0021] Reference Figure 1 The method for analyzing the extreme wind-solar output characteristics of the source-grid-load-storage system includes the following steps: S100, obtain long-term climate data and measured data for the project location over many years.

[0022] The multi-year long series climate data includes hourly wind speed and direction data at the hub height of the wind tower for a full year, as well as 20-30 years of ERA5 mesoscale hourly wind speed, temperature, air pressure, precipitation, NASA hourly radiation data at the location at 100m. The measured data includes data obtained from wind farms and photovoltaic power stations. Since the collection of measured data is not carried out at every project site, the measured data can be selected according to the actual situation.

[0023] S200, analyzes and screens long series climate data.

[0024] The long-sequence climate data and the mesoscale data of the measured data are preprocessed, including data cleaning, outlier processing, and extended interpolation. The measured data are generally wind and light data measured in a short period of time. Therefore, it is necessary to combine the mesoscale data of the long-sequence climate data for extended interpolation, and perform cleaning, verification, and normalization to extend the measured data into long-sequence data. When interpolating the measured data, the selected mesoscale data uses correlation as the priority judgment standard. The higher the correlation between the hourly data of the mesoscale data and the hourly data of the measured data, the higher the priority of the mesoscale data.

[0025] Through data interpolation, the short-term measured representative annual wind speed data can be extended and interpolated into hourly long series data from 1990 to 2023 using the long-series ERA5 wind speed data.

[0026] S300 builds wind power output calculation models and photovoltaic output calculation models based on long-series climate data of no less than 20 years.

[0027] S400, inputting the long sequence climate data into a wind power output calculation model and a photovoltaic output calculation model to obtain a wind power output curve diagram and a photovoltaic output curve diagram.

[0028] The wind power output model includes theoretical output calculation and actual output calculation. The theoretical output calculation is based on long-sequence climate data and the power curve of the wind turbine; the actual output calculation is based on the calculation result of the theoretical output and the reduction factor. The reduction factor is calculated by reducing factors such as air density, wake effect, and field loss. The wind power output curves for each year and each month are obtained based on the actual output.

[0029] For example: Refer to Figure 2 and Figure 3 The wind turbine equipment has a rotor diameter of 195m and a single unit capacity of 5MW. The theoretical output curve is calculated based on the long-sequence climate data and wind turbine power curve at the site. The theoretical output curve is further reduced by wind speed segment according to the designed power generation, with a comprehensive reduction factor of 72%, and the wind power output curves for each year and each month are generated.

[0030] The hourly data of plane radiation, diffuse radiation, temperature, wind speed, precipitation, etc. in the long-sequence climate data are input into the PVsyst software, and the photovoltaic modules, inverters and photovoltaic scale designed in the source-grid-load-storage project are used to set the parameters such as inclination angle, layout spacing, ground reflectivity and field loss to calculate the project system efficiency, and output the long-sequence hourly photovoltaic power generation of the project. After the hourly power generation is output, it is imported into the photovoltaic output calculation model. The project capacity and representative annual hours are set in the model. The model obtains preliminary multi-year photovoltaic unit hourly output data by dividing the hourly power generation by the capacity. According to the statistics of the multi-year average unit output, it is converted to the same numerical level as the representative annual hours, and the multi-year unit hourly photovoltaic output data is obtained. The calculation formula is:

[0031] In the above formula, Provides photovoltaic power for many years on an hourly basis; It is the hourly data of electricity consumption for many years; is the number of hours per year; is the project capacity; For the number of years.

[0032] The data statistics in the Excel-based model are used to generate photovoltaic output curves for each year and photovoltaic output curves for each year and month.

[0033] Reference Figure 4 and Figure 5 After inputting the long-term series radiation from 1990 to 2023 into the photovoltaic output calculation model, the photovoltaic output curves for each year and the photovoltaic output curves for each year and month are generated.

[0034] S500, analyzing calculation results output by the wind power output calculation model and the photovoltaic output calculation model, and screening out extreme values ​​in the calculation results.

[0035] The calculation results also include wind power annual output data, photovoltaic annual output data, wind power photovoltaic combined output data, and output change statistics of wind and photovoltaic combined output in each year and month. Extreme values ​​include the year with the lowest output, the year with the highest output, the season with the lowest output, and the month with the lowest output.

[0036] Combination Figure 2-Figure 5 , we can analyze that in the past 20 years, the output of wind power was the lowest in 2022, the highest in 2009, and close to average wind years in 2006 and 2016. The output of photovoltaic power was the highest in 20 years in 2004, and the lowest in 2017. In the four quarters, the output of wind power was the lowest in autumn, and the output of photovoltaic power was the lowest in summer; August was the lowest month for wind power output over the years, and January was the lowest month for photovoltaic power output over the years.

[0037] Combination Figure 6, an analysis of the combined output of wind and solar power was conducted with a ratio of 500,000 kW for wind power and 700,000 kW for photovoltaic power. It was found that the combined output of wind and solar power was the smallest in 2022 and the largest in 2009. July 2017 was the month with the smallest combined output of wind and solar power in many years, and March 2004 was the month with the largest combined output of wind and solar power in many years.

[0038] S600, based on the extreme values ​​screened out above, obtain the output characteristic curve of the year with extreme wind and light years and extreme wind and light months, substitute it into the source-grid-load-storage model, and optimize the configuration of the source-grid-load-storage system under extreme climatic conditions.

[0039] The hourly data of the year with the minimum combined output of wind and solar power, as well as the hourly data of the year with the minimum combined output of wind and solar power over many years, are input into the source-grid-load-storage model to obtain the wind-solar power output under extreme climate conditions, and the wind-solar power output under extreme climate conditions is compared with the representative annual power balance analysis results. Based on the comparison results, the wind-solar power output changes for each year, month, and quarter are calculated, and the power balance analysis is performed in combination with the load data. Based on the analysis results, the configuration of the source-grid-load-storage system under extreme climate conditions is optimized. The optimized projects include wind-solar installed capacity, energy storage, emergency power supply configuration, etc.

[0040] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing the extreme wind-solar output characteristics of a source-grid-load-storage system, characterized by: The steps include: Obtain long-term climate data for many years at the project site; Analyze and screen long-series climate data; Based on long-sequence climate data, wind power output calculation models and photovoltaic output calculation models are constructed; Inputting long-sequence climate data into a wind power output calculation model and a photovoltaic output calculation model to obtain a wind power output curve diagram and a photovoltaic output curve diagram; Analyze the calculation results output by the wind power output calculation model and the photovoltaic output calculation model, and filter out extreme values ​​in the calculation results; Based on the extreme values, the output characteristic curves of the extreme wind-light year and the extreme wind-light month are obtained, and they are substituted into the source-grid-load-storage model to optimize the configuration of the source-grid-load-storage system under extreme climatic conditions.

2. The method for analyzing extreme wind-solar output characteristics of a source-grid-load-storage system according to claim 1 is characterized by: If there are measured data at the project location, then when obtaining the multi-year long series climate data at the project location, obtain the measured data at the project location; After obtaining the measured data, the measured data is extended and interpolated to be extended into long sequence data.

3. The method for analyzing extreme wind-solar output characteristics of a source-grid-load-storage system according to claim 2 is characterized by: When interpolating measured data, the selected mesoscale data uses correlation as the priority criterion. The higher the correlation between the hourly data of the mesoscale data and the hourly data of the measured data, the higher the priority of the mesoscale data.

4. The method for analyzing extreme wind-solar output characteristics of a source-grid-load-storage system according to claim 1 is characterized by: The multi-year long series climate data include hourly wind speed and direction data at the hub height of the wind tower for a full year, as well as 20-30 years of ERA5 mesoscale 100m height hourly wind speed, temperature, air pressure, precipitation, and NASA hourly radiation data at that location.

5. The method for analyzing extreme wind-solar output characteristics of a source-grid-load-storage system according to claim 1 is characterized by: Inputting long-sequence climate data into the wind power output calculation model and the photovoltaic output calculation model to obtain the wind power output curve and the photovoltaic output curve includes the following steps: Calculate the theoretical output and reduction factor, obtain the actual output based on the reduction factor, and obtain the wind power output curve based on the actual output; According to the hourly data in the long-sequence climate data, the project system efficiency is calculated and the long-sequence hourly photovoltaic power generation of the project is output; the photovoltaic output calculation model calculates the preliminary multi-year photovoltaic unit hourly output data based on the hourly photovoltaic power generation; according to the statistical multi-year average unit output, it is converted to the same numerical level as the representative annual hours to obtain the multi-year unit hourly photovoltaic output data, and the photovoltaic output curve is generated based on the multi-year unit hourly photovoltaic output data.

6. The method for analyzing extreme wind-solar output characteristics of a source-grid-load-storage system according to claim 5 is characterized by: The calculation formula for the hourly photovoltaic output over many years is: In the above formula, Provides photovoltaic power for many years on an hourly basis; It is the hourly data of electricity consumption for many years; represents the number of hours per year; is the project capacity; For the number of years.

7. The method for analyzing extreme wind-solar output characteristics of a source-grid-load-storage system according to claim 1 is characterized by: The calculation results also include annual wind power output data, annual photovoltaic power output data, wind power and photovoltaic combined output data, and statistical charts of the output changes of wind and photovoltaic combined output on a monthly basis in each year.

8. The method for analyzing extreme wind-solar output characteristics of a source-grid-load-storage system according to claim 1 is characterized by: Extreme values ​​include the year with the lowest output, the year with the highest output, the season with the lowest output, and the month with the lowest output.

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