A wind farm site selection system based on wind, solar, and storage power complementarity for new energy bases

Abstract

The present invention relates to the technical field of wind farm site selection, and discloses a wind farm site selection system for a new energy base based on wind, solar, and storage output complementarity. By comprehensively considering the power generation fluctuation difference and the wind-solar complementarity ratio index at the candidate site selection location, a comprehensive complementarity index is calculated, and the predetermined site selection location is screened out accordingly. This can effectively quantify the complementarity of wind and solar resources and the power generation fluctuation, thereby significantly improving the scientificity and rationality of the site selection of wind farms in new energy bases. Furthermore, a target optimization function is constructed based on the wind energy time complementarity index and the stability index, and the target optimization algorithm is used to solve the optimal solution to determine the most preferred site selection location, thereby maximizing the operating efficiency of the wind, solar, and storage system. This method can significantly reduce the impact of the volatility of new energy power generation on the power grid, improve the overall coordination ability of the wind, solar, and storage system, and provide stronger support for the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm site selection, and in particular to a wind farm site selection system based on wind, solar, storage and output complementarity of new energy bases. Background Art

[0002] As the global energy mix shifts toward a low-carbon economy, wind and solar power, as important renewable energy sources, continue to increase their share in the power system. However, the intermittent and volatile nature of wind and photovoltaic power generation poses challenges to the stable operation of the power grid. To address this issue, wind-solar-storage hybrid systems have emerged, combining wind and solar power with energy storage technologies to achieve smooth power output. Energy storage systems are a key component of wind-solar-storage hybrid systems, facilitating the variability of wind and solar power generation, providing backup capacity to meet sudden load demands, and enabling time-shifting of energy (e.g., storing excess daytime photovoltaic power for use at night). Currently, commonly used energy storage technologies include battery storage (such as lithium-ion batteries), pumped hydro, and compressed air storage. However, wind-solar-storage hybrid technology, by integrating the synergistic effects of wind, solar, and storage systems, can significantly improve the overall power generation efficiency and stability of new energy bases.

[0003] Traditional wind farm site selection relies primarily on an assessment of wind resources, including key factors such as wind speed, wind direction, and topography. While this approach played an important role in the early development of renewable energy, its limitations are becoming increasingly apparent, especially in the context of large-scale renewable energy base construction. On the one hand, sites are often selected in areas with high wind speeds and stable wind directions to maximize power generation. However, this assessment ignores the intermittent and fluctuating nature of wind power generation and its potential complementary relationship with solar power generation. On the other hand, wind and solar power generation outputs are naturally complementary in time. For example, solar power output is high during the day when sunlight is abundant, while wind power output is high at night when wind speeds are high. If site selection is based solely on wind resources, opportunities to utilize solar power may be missed, resulting in a decrease in overall power generation efficiency. Given these technical issues, it is necessary to propose a wind farm site selection system for renewable energy bases based on the complementary output of wind, solar, and storage. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a wind farm site selection system based on the complementary output of wind, solar and storage in a new energy base, including the following modules:

[0005] Target area data acquisition module: used to obtain target area data, including wind speed and light intensity;

[0006] Candidate site selection location screening module: connected to the target area data acquisition module, used to obtain the preset wind speed and preset light intensity according to the wind speed and light intensity, and determine the candidate site selection location in the target area;

[0007] Predetermined site selection location screening module: connected to the candidate site selection location screening module, used to define the wind-solar complementarity ratio index and the power generation fluctuation difference, and screen the predetermined site selection location from the candidate site selection locations based on the wind-solar complementarity ratio index and the power generation fluctuation difference;

[0008] The optimal site location determination module is connected to the predetermined site location screening module, and is used to determine the time complementarity index and stability index, construct the target optimization function based on the time complementarity index and stability index, and use the target optimization algorithm to solve the optimal solution of the target optimization function to obtain the optimal site location.

[0009] Furthermore, the predetermined site selection location screening module includes the following submodules:

[0010] Wind-solar complementarity ratio index acquisition submodule: used to obtain the power generation fluctuation difference of each candidate site location, and calculate the wind-solar complementarity ratio index of each candidate site location based on the power generation fluctuation difference;

[0011] Comprehensive complementary index construction submodule: used to calculate the comprehensive complementary index based on the power generation fluctuation difference and wind-solar complementary ratio index, and arrange the comprehensive complementary index from high to low, and take the candidate site locations corresponding to the top 3 / 5 comprehensive complementary indexes as the predetermined site locations.

[0012] Furthermore, the wind-solar complementarity ratio indicator acquisition submodule includes the following units:

[0013] Time series data acquisition unit: used to acquire the time series data of wind power generation and solar power generation of each candidate site location, wherein the wind power generation time series data and / or solar power generation time series data are collected once every hour, with the hour as the collection period;

[0014] Power generation ratio calculation unit: used to calculate the power generation ratio of wind power generation and solar power generation in each collection period based on the wind power generation time series data and the solar power generation time series data;

[0015] Hourly ratio calculation unit: used to calculate the ratio of hours corresponding to power generation ratios greater than 0.4 and less than 0.6 to the total number of hours as hourly ratio;

[0016] Correlation factor calculation unit: used to calculate the correlation factor between wind power generation and solar power generation based on wind power generation time series data and solar power generation time series data;

[0017] Power generation fluctuation difference calculation unit: used to calculate the power generation fluctuation difference of each candidate site location;

[0018] Fluctuation factor calculation unit: used to calculate the fluctuation factor based on the wind power generation time series data and the solar power generation time series data;

[0019] Wind-solar complementary ratio index acquisition unit: used to calculate the wind-solar complementary ratio index based on the hourly ratio, fluctuation factor, correlation factor and power generation fluctuation difference.

[0020] Furthermore, the power generation fluctuation difference calculation unit includes the following subunits:

[0021] Period correlation factor calculation unit: used to calculate the period correlation factor between wind power generation and solar power generation in each acquisition period;

[0022] Power generation fluctuation difference calculation unit: used to calculate power generation fluctuation difference according to wind power generation in each collection cycle, solar power generation in each collection cycle and cycle-related factors.

[0023] Furthermore, the optimal address location determination module includes the following submodules:

[0024] Time complementarity index acquisition submodule: used to obtain time complementarity index;

[0025] Stability index acquisition module: used to obtain stability index;

[0026] Target optimization function construction module: used to construct the target optimization function based on the time complementarity index and stability index, use the target optimization algorithm to solve the optimal solution of the target optimization function, and obtain the optimal address location.

[0027] Furthermore, the time complementarity index acquisition submodule includes the following units:

[0028] Peak moment acquisition unit: used to obtain the time point when the load peak occurs at the predetermined location from the power grid dispatching center as the peak moment;

[0029] Load dynamic weight acquisition unit: used to obtain the current grid load demand and maximum grid load demand of the predetermined site from the grid dispatching center, and calculate the load dynamic weight based on the current grid load demand, maximum grid load demand and peak time;

[0030] Time complementarity index calculation unit: used for calculating the time complementarity index according to the load dynamic weight, the wind power generation in each collection cycle and the solar power generation in each collection cycle.

[0031] Furthermore, the stability index acquisition module includes the following units:

[0032] Charge and discharge depth standard deviation acquisition unit: used to obtain the charge and discharge depth standard deviation of the energy storage system in the wind-solar storage system;

[0033] Health index acquisition unit: used to obtain the failure rate and power generation efficiency of the wind, solar and storage system, and calculate the health index based on the failure rate and power generation efficiency;

[0034] Grid frequency deviation acquisition unit: used to acquire grid frequency deviation;

[0035] Stability index calculation unit: used to calculate the stability index based on the load dynamic weight, health index, grid frequency deviation and charge and discharge depth standard deviation.

[0036] Furthermore, the calculation formula for the comprehensive complementary index is:

[0037] ;

[0038] ;

[0039] ;

[0040] In the above formula, represents the comprehensive complementary index of the i-th candidate location, Represents the wind-solar complementarity ratio index of the i-th candidate site location, represents the power generation fluctuation difference of the i-th candidate site, Represents the impact coefficient of the wind-solar complementarity ratio index, Represents the influence coefficient of power generation fluctuation difference, N represents the number of hours corresponding to the power generation ratio greater than 0.4 and less than 0.6, represents the volatility factor, represents the influence coefficient of the fluctuation factor, Represents the maximum value operation, represents the wind energy generation in the tth collection period, represents the solar power generation in the tth collection cycle, represents the correlation factor, represents the exponential decay coefficient, represents the influence coefficient of the correlation factor, m represents the total number of acquisition cycles, Represents the cycle correlation factor of the tth acquisition cycle.

[0041] Furthermore, the objective optimization function is:

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] In the above formula, F represents the target optimization function, represents the time complementarity index, Represents the stability index, represents the influence coefficient of the time complementarity index, represents the influence coefficient of the stability index, represents the dynamic weight of the load, represents the standard deviation of the depth of charge and discharge, Represents the grid frequency deviation, Represents the peak moment, represents the influencing factor of the ratio of health index to grid frequency deviation, Q represents the failure rate, Represents power generation efficiency, is a constant greater than 0, represents the first exponential decay coefficient, represents the second exponential decay coefficient, represents the adjustment coefficient of the standard deviation of the charge and discharge depth, Represents the grid load demand at the current moment, Represents the maximum grid load demand.

[0047] The embodiments of the present invention have the following technical effects:

[0048] The present invention calculates a comprehensive complementarity index by comprehensively considering the power generation fluctuation difference and the wind-solar complementarity ratio index at the candidate site selection location, and selects the predetermined site selection location based on this, which can effectively quantify the complementarity of wind and solar resources and the fluctuation of power generation, thereby significantly improving the scientificity and rationality of the site selection of wind farms in new energy bases. Furthermore, a target optimization function is constructed based on the wind energy time complementarity index and the stability index, and the target optimization algorithm is used to solve the optimal solution to determine the optimal site selection location, thereby maximizing the operating efficiency of the wind-solar-storage system. This method can significantly reduce the impact of the volatility of new energy power generation on the power grid, improve the overall coordination ability of the wind-solar-storage system, and provide stronger support for the power grid. In addition, through multi-dimensional and multi-level evaluation methods, the present invention can also achieve accurate optimization of site selection plans under complex and changeable natural conditions, significantly reduce the occurrence of wind and solar power abandonment, improve energy utilization, and provide strong technical support for the planning and construction of new energy bases. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. 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.

[0050] Figure 1 This is a structural diagram of a wind farm site selection system for a new energy base based on wind, solar, and storage power complementarity provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0052] Figure 1 This is a structural diagram of a wind farm site selection system based on wind, solar, and storage power complementarity provided by an embodiment of the present invention. Figure 1 , specifically including the following modules:

[0053] Target area data acquisition module: used to obtain target area data, including wind speed and light intensity.

[0054] In practical applications, the selection of target areas requires comprehensive consideration of geographical, climatic, economic, and social factors. The following are the specific implementation steps:

[0055] A. Geographical Scope Delineation

[0056] Based on the needs of new energy base construction, a preliminary geographical scope should be defined. For example, areas with abundant wind resources and good sunlight conditions can be selected, such as the Gobi Desert or desert areas in northwest China (such as the Hexi Corridor in Gansu and Hami in Xinjiang). These areas typically have high wind speeds and long sunshine hours, making them suitable for wind-solar hybrid power generation.

[0057] B. Climate conditions screening

[0058] Based on historical meteorological data, we filter out areas that meet the following conditions:

[0059] Annual average wind speed ≥6 m / s (suitable for wind power development).

[0060] The total annual sunshine hours are ≥2500 hours (suitable for photovoltaic development).

[0061] The seasonal changes in wind speed and light intensity are complementary to each other (e.g., higher wind speed and weaker light in winter, and stronger light and lower wind speed in summer).

[0062] C. Assessment of economic and social factors

[0063] The target areas can be further narrowed down based on regional economic development levels, grid access conditions, and land use. For example, priority can be given to areas close to load centers or with existing transmission lines to reduce power transmission costs.

[0064] Through the above steps, one or more target areas are finally determined as the objects of subsequent data analysis.

[0065] In addition to wind speed and light intensity, target area data can also include: temperature, humidity, solar radiation angle, incident angle, landform characteristics, land use type, etc.

[0066] Candidate site selection location screening module: connected to the target area data acquisition module, used to obtain the preset wind speed and preset light intensity based on the wind speed and light intensity, and determine the candidate site selection location in the target area.

[0067] This embodiment uses the preset wind speed and preset light intensity as screening conditions. Preferably, this embodiment also considers the substation location information as a screening condition. The preset wind speed is: greater than or equal to 6m / s; the preset light intensity is: greater than or equal to 1200kWh / m 2 The light intensity is calculated; the 50km circular range covered by the substation is also used as a screening condition.

[0068] Specifically: take each square kilometer within the target area as a small area, obtain the annual average wind speed and annual average sunlight intensity of each small area within the target area, mark the substation location information of the small area, generate the annual average wind speed distribution map and the annual average sunlight intensity distribution map and superimpose them, and filter out the areas within the 50km circular area covered by the substation with an annual average wind speed greater than or equal to 6m / s and an annual average sunlight intensity greater than or equal to 1200kWh / m 2 areas as candidate locations.

[0069] Predetermined site selection location screening module: connected to the candidate site selection location screening module, used to define the wind-solar complementary ratio index and the power generation fluctuation difference, and screen out the predetermined site selection location from the candidate site selection locations based on the wind-solar complementary ratio index and the power generation fluctuation difference.

[0070] The scheduled site selection location screening module includes the following submodules:

[0071] Wind-solar complementarity ratio index acquisition submodule: used to obtain the power generation fluctuation difference of each candidate site selection location, and calculate the wind-solar complementarity ratio index of each candidate site selection location based on the power generation fluctuation difference.

[0072] The wind-solar complementary ratio indicator acquisition submodule includes the following units:

[0073] Time series data acquisition unit: used to obtain the time series data of wind power generation and solar power generation at each candidate site.

[0074] The wind power generation time series data and / or solar power generation time series data are collected once every hour, with the hour being the collection period.

[0075] Power generation ratio calculation unit: used to calculate the power generation ratio of wind power generation and solar power generation in each collection period based on wind power generation time series data and solar power generation time series data.

[0076] ;in, represents the proportion of power generation in the tth collection cycle, represents the wind energy generation in the tth collection period, Represents the solar power generation in the tth collection cycle.

[0077] Hour ratio calculation unit: used to calculate the ratio of the number of hours corresponding to the power generation ratio greater than 0.4 and less than 0.6 to the total number of hours as the hour ratio: N / m, where N represents the number of hours corresponding to the power generation ratio greater than 0.4 and less than 0.6, and m represents the total number of acquisition cycles (that is, the total number of hours in the entire time series).

[0078] The hourly ratio is calculated by counting the number of hours in each hour when the ratio of wind energy to solar energy generation falls within the range of 0.4 to 0.6, and calculating its proportion to the total number of hours, which can intuitively reflect the temporal complementarity of wind energy and solar energy generation. Specifically, when When it is close to 0.5, it indicates that the power generation of wind energy and solar energy is almost equal in that hour, and the output of the two energy sources is in a relatively balanced state; The further the ratio deviates from 0.5 (i.e., closer to 0 or 1), the more dominant wind or solar power generation is at a given moment, indicating weaker complementarity. Therefore, a larger hourly ratio indicates that the ratio of wind and solar power generation approaches equilibrium over a wider range of time periods, demonstrating greater temporal complementarity. This equilibrium helps mitigate the volatility of renewable energy generation, reduces the need for energy storage systems or grid regulation, and improves the stability and economic efficiency of the entire wind-solar hybrid system. In other words, a larger hourly ratio not only indicates better resource matching but also provides a scientific basis for the site selection and operational optimization of renewable energy bases.

[0079] Correlation factor calculation unit: used to calculate the correlation factor between wind power generation and solar power generation based on wind power generation time series data and solar power generation time series data.

[0080] Power generation fluctuation difference calculation unit: used to calculate the power generation fluctuation difference of each candidate site location;

[0081] The power generation fluctuation difference calculation unit includes the following subunits:

[0082] Period correlation factor calculation subunit: used to calculate the period correlation factor between wind power generation and solar power generation in each acquisition period;

[0083] Power generation fluctuation difference calculation subunit: used to calculate power generation fluctuation difference based on wind power generation in each collection cycle, solar power generation in each collection cycle and cycle-related factors.

[0084] ;

[0085] in, represents the power generation fluctuation difference of the i-th candidate site, represents the wind energy generation in the tth collection period, represents the solar power generation in the tth collection cycle, Represents the cycle correlation factor of the tth acquisition cycle.

[0086] The difference in power generation fluctuation is also highly correlated with the complementarity of power generation. In addition to calculating the absolute difference between wind and solar power generation, the periodic correlation factor between wind and solar power generation in each collection cycle is also considered. When it is close to 1, it means that the power generation of the two energy sources is highly correlated during this period and the complementarity is poor; on the contrary, when When it is close to 0, the complementarity is good. Therefore, the larger This means that power generation fluctuates more, while smaller This indicates relatively stable power generation and good complementarity. This indicator plays a key role in the subsequent fluctuation factor calculation, helping to identify candidate sites with small power generation fluctuations and good complementarity, thereby optimizing the site selection of new energy bases and improving the overall stability of the system.

[0087] Fluctuation factor calculation unit: used to calculate the fluctuation factor based on the wind power generation time series data and the solar power generation time series data.

[0088] .

[0089] Wind-solar complementary ratio index acquisition unit: used to calculate the wind-solar complementary ratio index based on the hourly ratio, fluctuation factor, correlation factor and power generation fluctuation difference.

[0090] ;

[0091] in, Represents the wind-solar complementarity ratio index of the i-th candidate site location, represents the influence coefficient of the fluctuation factor, Represents the maximum value operation, represents the influence coefficient of the correlation factor, m represents the total number of acquisition cycles, and N represents the number of hours corresponding to the power generation ratio greater than 0.4 and less than 0.6. represents the correlation factor, represents the exponential decay coefficient.

[0092] Comprehensive complementary index construction submodule: used to calculate the comprehensive complementary index based on the power generation fluctuation difference and wind-solar complementary ratio index, and arrange the comprehensive complementary index from high to low, and take the candidate site locations corresponding to the top 3 / 5 comprehensive complementary indexes as the predetermined site locations.

[0093] ;

[0094] In the above formula, represents the comprehensive complementary index of the i-th candidate location, Represents the impact coefficient of the wind-solar complementarity ratio index, The coefficient representing the impact of power generation fluctuation differences.

[0095] This embodiment comprehensively considers the complementarity and volatility of wind and solar power generation to obtain a comprehensive complementarity index, comprehensively evaluates the wind-solar complementary characteristics of candidate site locations, helps identify areas with smaller power generation fluctuations and better complementarity, and thus optimizes the site selection plan for new energy bases.

[0096] The optimal site location determination module is connected to the predetermined site location screening module, and is used to determine the time complementarity index and stability index, construct the target optimization function based on the time complementarity index and stability index, and use the target optimization algorithm to solve the optimal solution of the target optimization function to obtain the optimal site location.

[0097] The optimal address location determination module includes the following submodules:

[0098] Time complementarity index acquisition submodule: used to obtain time complementarity index.

[0099] Stability index acquisition module: used to obtain stability indicators.

[0100] Among them, the time complementarity index acquisition submodule includes the following units:

[0101] Peak moment acquisition unit: used to obtain the time point when the load peak occurs at the predetermined site from the power grid dispatching center as the peak moment.

[0102] Peak time reflects the changing pattern of grid load demand and is a key basis for evaluating the temporal complementarity of wind and solar power generation. By comparing the temporal distribution of wind and solar power generation, it can be determined whether both can provide sufficient support at peak time. Therefore, this embodiment obtains historical load data from the grid dispatch center, analyzes its annual load curve, and thus determines the peak load time of each year. .

[0103] Load dynamic weight acquisition unit: used to obtain the current grid load demand and maximum grid load demand of the predetermined site from the grid dispatching center, and calculate the load dynamic weight based on the current grid load demand, maximum grid load demand and peak time.

[0104] ;

[0105] in, represents the dynamic weight of the load, Represents the peak moment, represents the first exponential decay coefficient, Represents the grid load demand at the current moment, Represents the maximum grid load demand.

[0106] Dynamic weights can highlight the importance of critical periods (such as peak load) to system operation, ensuring that the output of wind, solar and storage systems better matches grid demand during critical periods.

[0107] Time complementarity index calculation unit: used to calculate the time complementarity index based on the load dynamic weight, wind power generation in each collection cycle, and solar power generation in each collection cycle .

[0108] .

[0109] This embodiment derives dynamic load weights based on peak times to reflect changes in grid load demand during different collection cycles, enabling the temporal complementarity index to more accurately match actual load demand. The complementary characteristics of wind and solar energy are optimized during peak and trough load periods, ensuring sufficient solar power generation during high-load periods (such as daytime or evening) and stable wind power generation during low-load periods (such as nighttime). This index, combined with wind and solar power generation, quantifies the temporal matching between wind and solar energy. This index can be used to identify candidate locations where wind and solar power generation can effectively complement each other during different time periods, ensuring that new energy bases can maintain stable power output under all-weather conditions and reducing reliance on a single energy source.

[0110] The stability index acquisition module includes the following units:

[0111] Charge and discharge depth standard deviation acquisition unit: used to obtain the charge and discharge depth standard deviation of the energy storage system in the wind-solar-storage system.

[0112] The standard deviation of the depth of charge and discharge reflects the operating status of the energy storage system. A large standard deviation may indicate that the energy storage system frequently undergoes high-depth charge and discharge, accelerating battery aging and affecting the long-term stability of the system. Therefore, this embodiment considers the standard deviation of the depth of charge and discharge during site selection. The standard deviation of the depth of charge and discharge is calculated by collecting depth of charge and discharge data from the energy storage system's battery management system (BMS).

[0113] Health index acquisition unit: used to obtain the failure rate and power generation efficiency of the wind, solar and storage system, and calculate the health index G based on the failure rate and power generation efficiency.

[0114] Failure rates are obtained from historical maintenance records of wind, solar, and storage systems. Power generation efficiency refers to the device's ability to convert input energy into electricity. During actual operation, power generation efficiency is calculated by collecting input and output data from the device.

[0115] ; where Q represents the failure rate, Represents power generation efficiency. The health index comprehensively assesses the overall performance of a wind, solar, and energy storage system. A higher health index indicates stable and efficient system operation, helping to identify the optimal site location.

[0116] Grid frequency deviation acquisition unit: used to acquire grid frequency deviation.

[0117] Multiple grid frequency data points are collected in real time from the grid dispatch center, and the grid frequency deviation is calculated using a deviation calculation method. The grid frequency deviation reflects the grid's stability requirements. Wind, solar, and storage systems must have rapid response capabilities to help maintain grid frequency stability.

[0118] Stability index calculation unit: used to calculate the stability index based on the load dynamic weight, health index, grid frequency deviation and charge and discharge depth standard deviation.

[0119] ;

[0120] in, Represents the stability index, represents the standard deviation of the depth of charge and discharge, Represents the grid frequency deviation, represents the impact factor, is a constant greater than 0, represents the second exponential decay coefficient, Represents the adjustment factor of the standard deviation of the charge and discharge depth.

[0121] These parameters comprehensively assess the operational performance and adaptability of wind, solar, and storage systems from various perspectives: dynamic load weighting ensures that site selection matches the actual grid demand; standard deviation of charge and discharge depth reflects the operating status of the energy storage system, preventing equipment aging caused by frequent, high-depth charge and discharge; grid frequency deviation indicates the system's ability to support grid stability; and failure rate and power generation efficiency measure equipment reliability and energy conversion efficiency. These parameters can be used to screen candidate sites that not only possess superior resource conditions but also demonstrate greater stability and economic efficiency in actual operation, providing strong support for the scientific planning and construction of new energy bases.

[0122] Target optimization function construction module: used to construct the target optimization function based on the time complementarity index and stability index, use the target optimization algorithm to solve the optimal solution of the target optimization function, and obtain the optimal address location.

[0123] The target optimization function is:

[0124] ;

[0125] In the above formula, F represents the target optimization function, represents the time complementarity index, Represents the stability index, represents the influence coefficient of the time complementarity index, Represents the influence coefficient of the stability index.

[0126] This embodiment first identifies pre-selected locations based on wind and solar resource complementarity and power generation complementarity. It then further optimizes candidate locations based on stability and temporal complementarity. These two factors complement each other, providing a scientific basis for site selection for wind farms in new energy bases. This ensures that the final selected location not only offers superior resource conditions but also achieves efficient energy utilization and stable power output in actual operation.

[0127] The layout of the wind farm is as follows:

[0128] 1. Arrange fans according to the "determinant" or "radiant" layout principle:

[0129] Column-row layout: Suitable for flat terrain with a single wind direction. The fans are arranged along the dominant wind direction, with row spacing of 5 to 9 times the impeller diameter and column spacing of 3 to 5 times the impeller diameter.

[0130] Radial layout: Suitable for areas with complex terrain or multiple wind directions, with wind turbines arranged radially with the center point as the center of the circle.

[0131] Example: In this embodiment, the most preferred site location is in the southeast area with the highest wind speed. Wind turbines can be concentrated in this area and their specific locations can be adjusted according to the terrain.

[0132] 2. Arrange photovoltaic modules according to the "matrix" layout principle:

[0133] The row and column spacing must meet the requirement of minimizing shadow occlusion. Usually the row spacing is 1.2 to 1.5 times the component length.

[0134] In areas with large terrain undulations, a block layout can be adopted to divide the photovoltaic array into several sub-areas, each of which is designed independently.

[0135] Example: The area with the strongest sunlight is located in the southwest. The photovoltaic arrays can be concentrated in this area and the inclination angle can be adjusted according to the terrain.

[0136] 3. Deploy energy storage systems close to substations to facilitate rapid response to grid demand. In areas with complex terrain, energy storage systems can be dispersed across multiple sub-regions for increased flexibility.

[0137] 4. According to the distribution of wind farms and photovoltaic power stations, rationally plan the location of substations to ensure the shortest transmission distance and minimum loss.

[0138] 5. Design transmission lines of different voltage levels based on the output characteristics of wind farms and photovoltaic power plants. In areas with higher load demands, prioritize high-voltage transmission lines.

[0139] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A wind farm site selection system based on wind, solar, and storage power complementarity for new energy bases, characterized by: Includes the following modules: Target area data acquisition module: used to obtain target area data, including wind speed and light intensity; Candidate site selection location screening module: connected to the target area data acquisition module, used to obtain the preset wind speed and preset light intensity according to the wind speed and light intensity, and determine the candidate site selection location in the target area; Predetermined site selection location screening module: connected to the candidate site selection location screening module, used to define the wind-solar complementarity ratio index and the power generation fluctuation difference, and screen the predetermined site selection location from the candidate site selection locations based on the wind-solar complementarity ratio index and the power generation fluctuation difference; Optimal site location determination module: connected to the predetermined site location screening module, used to determine the time complementarity index and stability index, build a target optimization function based on the time complementarity index and stability index, and use the target optimization algorithm to solve the optimal solution of the target optimization function to obtain the optimal site location; The predetermined site location screening module includes the following submodules: Wind-solar complementarity ratio index acquisition submodule: used to obtain the power generation fluctuation difference of each candidate site location, and calculate the wind-solar complementarity ratio index of each candidate site location based on the power generation fluctuation difference; Comprehensive complementary index construction submodule: used to calculate the comprehensive complementary index based on the power generation fluctuation difference and wind-solar complementary ratio index, and sort the comprehensive complementary index from high to low, and select the candidate site locations corresponding to the top 3 / 5 comprehensive complementary indexes as the predetermined site locations; Among them, the calculation formula group of comprehensive complementary index is: ； ； ； In the above formula, represents the comprehensive complementary index of the i-th candidate location, Represents the wind-solar complementarity ratio index of the i-th candidate site location, represents the power generation fluctuation difference of the i-th candidate site, Represents the impact coefficient of the wind-solar complementarity ratio index, Represents the influence coefficient of power generation fluctuation difference, N represents the number of hours corresponding to the power generation ratio greater than 0.4 and less than 0.6, represents the volatility factor, represents the influence coefficient of the fluctuation factor, Represents the maximum value operation, represents the wind energy generation in the tth collection period, represents the solar power generation in the tth collection cycle, represents the correlation factor, represents the exponential decay coefficient, represents the influence coefficient of the correlation factor, m represents the total number of acquisition cycles, represents the cycle correlation factor of the tth acquisition cycle; The target optimization function is: ； ； ； ； In the above formula, F represents the target optimization function, represents the time complementarity index, Represents the stability index, represents the influence coefficient of the time complementarity index, represents the influence coefficient of the stability index, represents the dynamic weight of the load, represents the standard deviation of the depth of charge and discharge, Represents the grid frequency deviation, Represents the peak moment, represents the influencing factor of the ratio of health index to grid frequency deviation, Q represents the failure rate, Represents power generation efficiency, is a constant greater than 0, represents the first exponential decay coefficient, represents the second exponential decay coefficient, represents the adjustment coefficient of the standard deviation of the charge and discharge depth, Represents the grid load demand at the current moment, Represents the maximum grid load demand.

2. A wind farm site selection system based on wind, solar, and storage-based new energy bases according to claim 1, characterized in that: The wind-solar complementary ratio indicator acquisition submodule includes the following units: Time series data acquisition unit: used to acquire the time series data of wind power generation and solar power generation of each candidate site location, wherein the wind power generation time series data and / or solar power generation time series data are collected once every hour, with the hour as the collection period; Power generation ratio calculation unit: used to calculate the power generation ratio of wind power generation and solar power generation in each collection period based on the wind power generation time series data and the solar power generation time series data; Hourly ratio calculation unit: used to calculate the ratio of hours corresponding to power generation ratios greater than 0.4 and less than 0.6 to the total number of hours as hourly ratio; Correlation factor calculation unit: used to calculate the correlation factor between wind power generation and solar power generation based on wind power generation time series data and solar power generation time series data; Power generation fluctuation difference calculation unit: used to calculate the power generation fluctuation difference of each candidate site location; Fluctuation factor calculation unit: used to calculate the fluctuation factor based on the wind power generation time series data and the solar power generation time series data; Wind-solar complementary ratio index acquisition unit: used to calculate the wind-solar complementary ratio index based on the hourly ratio, fluctuation factor, correlation factor and power generation fluctuation difference.

3. A wind farm site selection system based on wind, solar, and storage-based new energy bases according to claim 2, characterized in that: The power generation fluctuation difference calculation unit includes the following subunits: Period correlation factor calculation unit: used to calculate the period correlation factor between wind power generation and solar power generation in each acquisition period; Power generation fluctuation difference calculation unit: used to calculate power generation fluctuation difference according to wind power generation in each collection cycle, solar power generation in each collection cycle and cycle-related factors.

4. A wind farm site selection system based on wind, solar, and storage-based new energy bases according to claim 3, characterized in that: The optimal address location determination module includes the following submodules: Time complementarity index acquisition submodule: used to obtain time complementarity index; Stability index acquisition module: used to obtain stability index; Target optimization function construction module: used to construct the target optimization function based on the time complementarity index and stability index, use the target optimization algorithm to solve the optimal solution of the target optimization function, and obtain the optimal address location.

5. The wind farm site selection system based on wind, solar, and storage-based new energy base according to claim 4 is characterized in that: The time complementarity index acquisition submodule includes the following units: Peak moment acquisition unit: used to obtain the time point when the load peak occurs at the predetermined location from the power grid dispatching center as the peak moment; Load dynamic weight acquisition unit: used to obtain the current grid load demand and maximum grid load demand of the predetermined site from the grid dispatching center, and calculate the load dynamic weight based on the current grid load demand, maximum grid load demand and peak time; Time complementarity index calculation unit: used for calculating the time complementarity index according to the load dynamic weight, the wind power generation in each collection cycle and the solar power generation in each collection cycle.

6. A wind farm site selection system based on wind, solar, and storage-based new energy bases according to claim 5, characterized in that: The stability index acquisition module includes the following units: Charge and discharge depth standard deviation acquisition unit: used to obtain the charge and discharge depth standard deviation of the energy storage system in the wind-solar storage system; Health index acquisition unit: used to obtain the failure rate and power generation efficiency of the wind, solar and storage system, and calculate the health index based on the failure rate and power generation efficiency; Grid frequency deviation acquisition unit: used to acquire grid frequency deviation; Stability index calculation unit: used to calculate the stability index based on the load dynamic weight, health index, grid frequency deviation and charge and discharge depth standard deviation.

Images