Method for evaluating regional renewable energy complementarity and related device

Abstract

The application discloses a kind of regional renewable energy complementarity evaluation methods and related equipment.The method comprises: obtaining the meteorological time series data associated with the renewable energy of target area;Based on the meteorological time series data, the first renewable energy power output and the second renewable energy power output of target area are calculated respectively;The length of the process line of the first renewable energy power output, the second renewable energy power output and the joint power output under the target time scale is calculated to evaluate the relative process line complementary index of the first renewable energy and the second renewable energy of the target area based on the process line length.Can solve the problem that existing analysis method cannot identify more complex nonlinear or hysteresis complementary mode between wind power and photovoltaic renewable energy, and sensitivity and stability performance difference is larger under different time scales, leading to difficult to compare or cross-scale application.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the computer field, and more particularly, to a regional renewable energy complementarity evaluation method and related equipment. BACKGROUND

[0002] Renewable energy such as wind power and photovoltaic power generation is highly dependent on meteorological conditions, and the power output has significant randomness and volatility, which poses a challenge to the frequency control, peak regulation and frequency regulation, and reserve capacity configuration of the power grid. In order to better realize the utilization and configuration of renewable energy, it is necessary to analyze and quantify the complementarity of renewable energy, but the existing methods for quantifying the complementarity of renewable energy lack intuitive physical meaning, cannot reasonably estimate the optimal ratio of different renewable energies, and it is also difficult to judge whether the joint output curve has the actual peak clipping and valley filling ability. The sensitivity and stability performance are quite different at different time scales, which makes it difficult to compare or apply across scales. SUMMARY

[0003] A series of simplified concepts are introduced in the summary section, which will be described in further detail in the specific embodiments section. The summary section of the present application does not mean to attempt to limit the key features and essential technical features of the claimed technical solutions, nor does it mean to determine the protection scope of the claimed technical solutions.

[0004] The method for quantifying and analyzing the complementarity of renewable energy can include correlation, cross frequency and standard deviation complementarity. However, the current correlation calculation can only reflect the linear positive / negative correlation relationship, and cannot identify more complex nonlinear or hysteresis complementarity patterns between renewable energies such as wind power and photovoltaic power. Cross frequency only identifies complementarity by counting the frequency of adjacent time periods showing opposite changes in statistical output, but ignores the amplitude information of the fluctuations, and even if the output changes very little, it is also considered as a complementary event, which is easy to overestimate the complementarity. The standard deviation complementarity only focuses on the size of the overall fluctuation amplitude, and cannot capture the dynamic complementarity characteristics and extreme fluctuation behavior of renewable energy.

[0005] In order to solve the problem that the existing analysis method cannot identify more complex nonlinear or hysteresis complementarity patterns between renewable energies such as wind power and photovoltaic power, and the sensitivity and stability performance are quite different at different time scales, which makes it difficult to compare or apply across scales, in a first aspect, the present application provides a regional renewable energy complementarity evaluation method, the method comprising:

[0006] Obtaining meteorological time series data associated with the renewable energy in the target region;

[0007] Based on the meteorological time series data, calculating the first renewable energy power output and the second renewable energy power output of the target region, respectively.

[0008] calculating lengths of process lines of the first renewable energy power output, the second renewable energy power output and the joint power output over time at a target time scale, respectively, to evaluate a relative process line complementary index of the first renewable energy and the second renewable energy of the target region based on the lengths of the process lines, the length of the process line being an arc length of a curve of time series data within a preset time period to indicate joint fluctuation characteristics of different renewable energies.

[0009] Optionally, the evaluating the complementary index of the first renewable energy and the second renewable energy of the target region based on the lengths of the process lines comprises:

[0010] based on a formula calculating the relative process line complementary index of the first renewable energy and the second renewable energy of the target region, wherein, is a relative process line length of the first renewable energy power sequence, used to represent a fluctuation intensity of the wind power itself, is a relative process line length of the second renewable energy power sequence, used to represent a fluctuation intensity of the photovoltaic power itself, is a relative process line length of the first renewable energy and the second renewable energy combined by weights, used to represent a fluctuation intensity of joint output of the first renewable energy and the second renewable energy, and are weight factors of the installed capacities of the first renewable energy and the second renewable energy, respectively.

[0011] Optionally, the relative process line length is obtained by calculation, wherein, is a process line length, representing a path length of actual power output of the renewable energy over time; is a rated power process line length, representing a path length of the renewable energy over time when the renewable energy is operated at a rated power, the process line length is obtained by an approximate calculation through a Euclidean distance between adjacent output points in the time series.

[0012] Optionally, the process line length is obtained by a formula calculation;

[0013] the rated power process line length is obtained by a formula calculation;

[0014] the joint power is represented as ;

[0015] Combined rated power is expressed as ;

[0016] wherein, X is the renewable energy type, is the time interval resolution, is the time scale of the minimum calculation unit; is the power difference between adjacent time periods, is the rated power of the renewable energy.

[0017] Optionally, in the case that the first renewable energy is wind energy and the second renewable energy is solar energy, the meteorological time series data comprises wind speed time series data, ground level irradiance time series data and air temperature time series data;

[0018] In the case that the target time scale is 24 hours, the smooth path of solar night zero output is eliminated to avoid underestimating the renewable energy complementarity.

[0019] Optionally, the eliminating the smooth path of solar night zero output in the case that the target time scale is 24 hours comprises:

[0020] In the case that the target time scale is 24 hours, the length of the process line of solar energy is obtained by the formula ,

[0021] wherein, is the time interval resolution, is the time scale of the minimum calculation unit, is the power difference between adjacent time periods, is the rated power of the photovoltaic component, and when the photovoltaic output of continuous time points is all zero, the index , the corresponding incremental term becomes 0, and the corresponding rated process line length may be adjusted to .

[0022] Optionally, the method further comprises:

[0023] In the case that PLCI =0, it is indicated that there is no complementary effect between the renewable energies;

[0024] In the case that 0 PLCI <1, it is indicated that there is a complementary effect between the renewable energies, and the closer the value is to 1, the better the effect is;

[0025] In the case that PLCI <0, it is indicated that there is a negative complementarity between the renewable energies.

[0026] In a second aspect, the present application further provides a device for evaluating regional renewable energy complementarity, comprising:

[0027] an acquisition unit configured to acquire meteorological time series data associated with the renewable energy in a target region;

[0028] a calculation unit configured to calculate first renewable energy power output and second renewable energy power output of the target region based on the meteorological time series data, respectively;

[0029] an evaluation unit configured to calculate length of a process line of the first renewable energy power output, the second renewable energy power output and joint power output of the target region varying with time in a target time scale, and evaluate relative process line complement index of the first renewable energy and the second renewable energy of the target region based on the length of the process line, the length of the process line being an arc length of a curve of time series data in a preset time period.

[0030] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement steps of the method for evaluating regional renewable energy complementarity according to any one of the first aspect when executing the computer program stored in the memory.

[0031] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the method for evaluating regional renewable energy complementarity according to any one of the first aspect.

[0032] In summary, the regional renewable energy complementarity evaluation method provided in the application comprises the following steps: obtaining meteorological time series data associated with the renewable energy in a target region; calculating first renewable energy power output and second renewable energy power output of the target region based on the meteorological time series data; calculating the length of a process line of the first renewable energy power output, the second renewable energy power output and joint power output at a target time scale with respect to time variation; and evaluating the relative process line complementarity index of the first renewable energy and the second renewable energy of the target region based on the length of the process line. The length of the process line is the arc length of a curve of time series data within a preset time period, which is used to indicate the joint fluctuation characteristics of different renewable energies. The method is suitable for key application scenarios such as renewable energy site selection planning, energy structure optimization configuration, energy storage system capacity design and multi-energy collaborative operation. The method quantitatively characterizes the complementarity characteristics of renewable energy output with respect to time variation based on the length of the process line, and quantifies the complementarity between different energies. The relative process line complementarity index is more dynamically sensitive and physically intuitive when quantifying complementarity, and can reflect the change range of joint fluctuation and its potential impact on system operation. The method is suitable for complementarity evaluation of time series data, and is particularly suitable for application in the field of renewable energy, including resource comprehensive evaluation, site optimization selection, energy storage configuration, system dispatching optimization and operation risk management scenarios. In addition, the method can also be extended to meteorological process analysis, economic variable complementarity evaluation and other interdisciplinary fields, and has good universality and practical value.

[0033] The regional renewable energy complementarity evaluation method of the application, other advantages, objects and characteristics of the application will be embodied in part by the following description, and will be understood by those skilled in the art through research and practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0034] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of preferred embodiments and are not meant to limit the present description. Moreover, the same reference numerals are used throughout the accompanying drawings to represent similar or same components. In the drawings:

[0035] Figure 1 A regional renewable energy complementarity evaluation method flowchart is provided for the embodiments of the application;

[0036] Figure 2 A 4-seat wind-solar complementary power station is provided for the embodiments of the application. The daily output sequence of wind power and photovoltaic power generation in 2020 is shown in the figure;

[0037] Figure 3 The wind power and photovoltaic power output monthly process line complementarity index of the 4-seat wind-solar complementary power station provided for the embodiments of the application;

[0038] Figure 4 A regional renewable energy complementarity evaluation device structure schematic diagram provided by an embodiment of the present application;

[0039] Figure 5 A regional renewable energy complementarity evaluation electronic device structure schematic diagram provided by an embodiment of the present application. DETAILED DESCRIPTION

[0040] The terms "first", "second", "third", "fourth" and the like in the description and claims of the present application and in the above-mentioned drawings (if any) are used to distinguish similar objects, not necessarily described in a particular order or sequence. It should be understood that the data thus used can be interchanged, where appropriate, so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a list of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments.

[0041] In order to solve the problem that the existing analysis method cannot identify the more complex nonlinear or hysteresis complementary mode between wind power and photovoltaic and other renewable energy, and the sensitivity and stability are quite different at different time scales, which leads to the difficulty of unified comparison or cross-scale application, please refer to Figure 1 A regional renewable energy complementarity evaluation method flowchart provided by an embodiment of the present application, which can specifically include steps S110 to S130.

[0042] S110, obtaining meteorological time series data associated with the renewable energy of the target region.

[0043] S120, calculating the first renewable energy power output and the second renewable energy power output of the target region based on the meteorological time series data.

[0044] S130, calculating process line lengths of the first renewable energy power output, the second renewable energy power output and the joint power output varying with time under a target time scale respectively, to evaluate a relative process line complementarity index of the first renewable energy and the second renewable energy of the target region based on the process line lengths, the process line length being an arc length of a curve of time series data within a preset time period to indicate joint fluctuation characteristics of different renewable energies.

[0045] It can be understood that the embodiments of the present application define an index quantifying renewable energy complementarity, i.e., a relative process line complementarity index (Path Length Complementarity Index, PLCI ). Taking wind energy and solar energy as examples, the index representation formula is as follows:

[0046] (1)

[0047] wherein, is the relative process line length of the wind power sequence, used to measure the fluctuation intensity of the wind power itself; is the relative process line length of the photovoltaic power sequence, used to measure the fluctuation intensity of the photovoltaic itself; is the relative process line length of the wind energy and solar energy combined according to the weight, used to describe the fluctuation intensity of the joint output of the wind energy and the solar energy; and are weight factors of the installed capacities of the wind energy and the solar energy respectively.

[0048] For time series data, the relative process line length can be calculated by the following formula:

[0049] (2)

[0050] wherein, is the process line length, indicating the path length of the actual power output of the renewable energy varying with time; is the rated power process line length, indicating the path length of the renewable energy varying with time when running at rated power.

[0051] The process line length can be approximately calculated by the Euclidean distance between adjacent output points in the time series:

[0052] (3)

[0053] (4)

[0054] wherein, X is the renewable energy type, for time interval resolution (in hours), for time scale of minimum calculation unit (in day scale hours); for power difference of adjacent time period, for renewable energy rated power.

[0055] It can be understood that the PLCI measures whether the path roughness of the joint curve is complementarily reduced, for each energy output time series, first use the process line length, that is, the Euclidean path of the power-time curve, to measure how tortuous the curve is, and then use the relative process line length to make it dimensionless; finally, use formula (1) to compare the polyline degree of the joint curve with the weighted average of the polyline degree of each curve. If the ups and downs after the combination are offset by each other, the joint curve is smoother, the relative denominator is smaller, the PLCI rises, and the complementarity is quantified as greater. Conversely, it decreases or even becomes negative. This definition directly explains whether the complementarity smooths the fluctuations as a comparison of the curve geometric length, so it is intuitive and physically interpretable.

[0056] ​The relative profile length essentially measures how jagged the power-time curve is. It accumulates the up-and-down changes in power over each small time interval and normalizes it by the rated power and the time step, resulting in a dimensionless roughness. When the power fluctuations in adjacent time periods are not extreme, this roughness is positively correlated with the intensity of all small-magnitude ramps and dips, that is, the greater the incremental energy, the more jagged the curve, and the greater the relative profile length; conversely, the smoother the curve, the closer the relative profile length to the baseline. This property directly corresponds the computable cumulative ramp intensity to the seemingly smoother joint output, which is the result of the superposition of each energy source weighted by its share. After superposition, the amount of change in each time step is also superimposed: if the changes in the two energy sources at the same time are in opposite directions, for example, the wind fluctuates at night, and the photovoltaic is basically unchanged or falls, their changes will offset each other, making the jaggedness of the joint curve significantly reduced; if they change in the same direction at the same time, the joint curve is more jitter. PLCI is the weighted average of the roughness of the joint curve compared with the roughness of each curve: the more offset, the smoother the joint, the higher the PLCI, indicating stronger complementarity; conversely, the PLCI decreases or even becomes negative, indicating that the superposition amplifies the fluctuations. Since this comparison is made at the level of the amount of change in each time step, it naturally captures the complementary behavior at multiple time scales. Thus, since the correlation (p) assesses whether the horizontal values are synchronized. It first normalizes the sequence, and then sees if it is high at the same time and low at the same time. This method is weak in distinguishing between situations with the same mean and variance but completely different intra-day structures; for example, photovoltaic fluctuates during the day and is stable at night, wind power is active at night and relatively stable during the day, from the horizontal value, the two may not be strongly negatively correlated, but from the amount of change, they are often opposite. Correlation is prone to miss this slope complementarity, while PLCI does not. Cross frequency (PR) only counts the number of times the direction is opposite, but does not distinguish the magnitude. Many small-magnitude reverse changes and a few large-magnitude reverse changes look similar to PR; while PLCI gives substantial impact to the magnitude: large-magnitude cancellation significantly reduces the roughness of the joint curve, thereby increasing the complementarity index, which is closer to the concern of dispatching whether the key ramp is offset. The quasi-difference complementarity rate (R_SD) assesses whether the overall volatility after superposition decreases, but it is not sensitive to time order. As long as the overall variance is close, it will be considered similar, even if one sequence is a few large ramps and the other is frequent small jitter, R_SD is difficult to reflect the process information of when and how the two occur and superimpose.

[0057] It should be noted that since the photovoltaic power is zero at night, the time component is accumulated every hour in formula (3), so that the long-time stable zero output at night also contributes to the profile length, thereby artificially reducing the volatility measure of photovoltaic. This will cause the denominator in formula (1) to be smaller, resulting in a relative profile complementarity index PLCIThe reading is too low, thus underestimating the degree of wind-solar complementarity. In reality, wind power output at night precisely compensates for the periods of zero photovoltaic output, and should be considered a complementary effect. Therefore, to avoid falsely reducing the photovoltaic fluctuation metric due to zero nighttime output, and to ensure that it only measures the path length of the actual change segment, the photovoltaic process line length can be calculated using the following formula:

[0058] (5)

[0059] (6)

[0060] Here, when the photovoltaic output is zero for consecutive moments, the index The corresponding increment term becomes 0, meaning the time component is no longer accumulated. Simultaneously, the corresponding rated process line length... It can be adjusted to:

[0061] (7)

[0062] so This only reflects the process line length of photovoltaic power generation during the daytime sunshine period. In scenarios where wind power compensates for photovoltaic power at night, wind power fluctuations will be reflected in the combined output. In the process line length calculation, photovoltaics itself does not contribute additional to the stable path at night. This ensures that the positive effect of wind power output at night on complementarity is reflected, and the lack of photovoltaic output at night does not reduce its own volatility measurement. Since a correction for zero photovoltaic output at night is specified: if the long, unchanging stable segments at night are also accumulated along the time axis, it will falsely reduce the volatility measurement of photovoltaics, thus lowering its value in the denominator of the PLCI. And it underestimates complementarity. Therefore, indicators are used. By eliminating the accumulation of time during these stable periods, the process line lengths of solar output and rated power are accumulated only during periods of actual change. PLCI thus more accurately reflects the compensation of photovoltaic power by wind at night. This step is also a key engineering detail in establishing PLCI as a measure of continuous time fluctuations.

[0063] PLCI preserves the temporal order and adjacent changes, thus reflecting the process characteristics of when and to what extent they occur and cancel each other out. PLCI calculations rely solely on power changes at adjacent time steps and the selected time resolution. Replacing hours with days, weeks, or months simply coarses the statistical roughness; the methodology and criteria remain completely consistent. Furthermore, the removal of long-term, stable, zero-value solar power at night avoids misinterpreting flat nighttime segments with no actual change as curve smoothness, enabling fair comparisons even in regions with significant seasonal and latitudinal differences. Therefore, PLCI can be stably used across multiple scales—hourly, daily, monthly, seasonal, and annual—and is easy to perform for cross-scale horizontal comparisons and longitudinal tracking.

[0064] Example, for the combined power of wind energy and solar energy With the combined rated power It can be expressed as follows:

[0065] (8)

[0066] (9)

[0067] Example, first, the measured data or reanalysis data set such as ERA5 hourly wind turbine height, such as 100m height position wind speed time series data , surface horizontal irradiance And air temperature data ;Second, the hourly wind turbine power output of the observation point or grid cell can be calculated And photovoltaic module power output The formula for calculating wind power output is as follows:

[0068] (10)

[0069] Where, , And The cut-in wind speed of the wind turbine (2.5ms -1 ), cut-out wind speed (20ms -1 ) and rated wind speed (10.5ms -1 ) respectively; The rated power of the wind turbine.

[0070] The formula for calculating photovoltaic power output is as follows:

[0071] (11)

[0072] (12)

[0073] (13)

[0074] Where, PF Indicates the effective component area per unit land area, The rated power of the photovoltaic module (300W), The total irradiance on the photovoltaic horizontal or component inclined surface, i.e. the sum of direct and diffuse scattering, The photovoltaic system efficiency coefficient, considering the power loss from power generation to grid connection, which can be 82% in this model; The cell temperature (°C), Temperature coefficient of peak power (-0.41% / °C); Tilt angle of the photovoltaic panel, NOCT Standard operating temperature of the module, for example 45°C in this model.

[0075] For example, firstly, the demand can be assessed according to the renewable energy complementarity, and the required assessment time period can be selected and the data time resolution (e.g. hour or day, etc.). Secondly, the renewable energy power output and the joint power can be calculated by formula based on the selected time resolution data (hour or day). ; and then the relative process line complementarity index of the renewable energy complementarity is calculated according to the definition PLCI .

[0076] For example, the renewable energy complementarity calculation can be quantified based on the process line length of different time scales (e.g. day, month or year).

[0077] It can be understood that when PLCI=0, it means that there is no complementary effect between the renewable energies, i.e. the fluctuation is neither weakened nor amplified; when 0<PLCI<1, it means that there is a complementary effect between the renewable energies, and the fluctuation is reduced, and the closer the value is to 1, the better the effect is; when PLCI<0, it means that the fluctuation between the renewable energies is increased, and it is negatively complementary.

[0078] In summary, the regional renewable energy complementarity evaluation method provided by the embodiments of the present application obtains meteorological time series data associated with the renewable energy of a target region; calculates first renewable energy power output and second renewable energy power output of the target region based on the meteorological time series data; calculates the length of the process line of the first renewable energy power output, the second renewable energy power output and the joint power output under a target time scale with respect to time change; and evaluates the relative process line complementarity index of the first renewable energy and the second renewable energy of the target region based on the length of the process line. The length of the process line is the arc length of the curve of the time series data within a preset time period, which indicates the joint fluctuation characteristics of different renewable energies. The method is suitable for key application scenarios such as renewable energy site selection planning, energy structure optimization configuration, energy storage system capacity design and multi-energy collaborative operation. The method quantitatively characterizes the complementarity characteristics of renewable energy output with respect to time change based on the length of the process line, and quantifies the complementarity between different energies. The relative process line complementarity index is more dynamically sensitive and physically intuitive when quantifying complementarity, and can reflect the change range of joint fluctuations and its potential impact on system operation. The method is suitable for complementarity evaluation of time series data, especially suitable for application in the field of renewable energy, including resource comprehensive evaluation, site optimization selection, energy storage configuration, system dispatching optimization and operation risk management scenarios. In addition, the method can also be extended to meteorological process analysis, economic variable complementarity evaluation and other interdisciplinary fields, and has good universality and practical value.

[0079] According to some embodiments, taking wind power and photovoltaic power complementarity analysis as an example, four representative wind-solar complementary projects in China are selected as evaluation objects, including a wind-solar complementary power station in Ningxia, a wind-solar complementary power station in Qinghai, a wind-solar complementary power station in Xinjiang and a wind-solar complementary power station in Gansu, as shown in Table 1, to verify the scientificity and practicability of the proposed relative process line complementarity index (PLCI) in actual application. Figure 2 The daily output sequence of wind power and photovoltaic power of the above four wind-solar complementary power stations in 2020 is shown. Among them, the wind power output has strong overall volatility, presents high-frequency and irregular changes, and there are a large number of peak values with sudden increases and sudden decreases; the photovoltaic output shows high regularity, presents clear seasonal changes, that is, the summer production capacity is the strongest and the winter production capacity is the weakest. Based on the observed wind speed and solar radiation data, the annual complementarity index of wind energy and solar energy in 2020 is calculated. PLCI As shown in Table 1, there are obvious regional differences in the wind-solar complementarity of the four wind-solar complementary power stations. Among them, the wind-solar complementarity of the wind-solar complementary project in Qinghai is the highest, with an annual average PLCI reaching 0.2748, indicating that the volatility after joint operation of wind power and photovoltaic power is reduced by about 27.48% compared with independent operation; the annual PLCI0.1483, which is a 14.83% decrease in volatility compared to each individual system; the annual average PLCI 0.1351 and 0.1106, respectively, indicating relatively low complementarity. Table 1 shows the basic information of the wind-solar complementary projects and their relative process line complementarity index (PLCI).

[0080]

[0081] Table 1

[0082] In addition, PLCI The complementarity of renewable energy sources at different time scales can be evaluated. Figure 3 The monthly wind-solar complementarity index of four wind-solar complementary power stations in 2020 is shown. The results show that, PLCI show significant seasonal variation, with a bimodal characteristic, with higher complementarity in spring (February-April) and autumn (September-October), such as the wind-solar complementary power station in Qinghai, which has a PLCI 0.34, which means that in October, wind power and photovoltaic power through complementarity reduced the overall volatility by 34%; this means that the combined wind-solar operation reduced the volatility by 34% in that month; while in May-August, the complementarity is the lowest, such as the Xinjiang power station in May, PLCIThe PLCI is only 0.07, indicating that the combined operation of wind and solar power in this month only reduces the volatility by 7%, reflecting the weak complementarity of wind and solar power in spring and summer. Overall, the wind-solar complementary effect of a certain power plant in Qinghai is the best, while that of a certain power plant in Xinjiang is relatively poor. The proposed PLCI evaluation method not only accurately quantifies the complementarity of renewable energy power output, but also dynamically monitors its complementary characteristics on multiple time scales. By adjusting the installed capacity ratio of wind power and photovoltaic power, PLCI can also be used to simulate the complementarity performance under different matching schemes, providing quantitative basis for renewable energy project planning and site selection, energy storage system configuration optimization, and grid dispatching strategy formulation, which helps to improve the operation efficiency and stability of renewable energy systems. It can be understood that the calculation of PLCI is linear scanning, that is, the time series is traversed step by step, and the roughness index of each energy and the joint roughness index can be obtained by accumulating several simple quantities. For multi-energy scenarios, only the same scanning is done for each sequence, and then a linear total is done, the overall complexity increases linearly with the number of energy types × time steps, which is naturally suitable for parallel processing. Compared with methods that require frequency domain transformation or multi-window analysis, PLCI does not rely on complex transformations or repeated segmentation, and is suitable for large-scale, multi-year batch evaluation. When PLCI increases, it means that the ups and downs of the joint curve are more offset by each other. This directly leads to a reduction in the system's demand for fast backup and adjustment actions under the same load conditions. Under the same energy transfer target, the peak of charging and discharging is reduced, the required rated power segment can be smaller or the time period under the same power can be wider. The number and amplitude of large gradients and sudden changes are also reduced, which is beneficial to frequency stability and equipment life. Because the definition of PLCI directly describes whether the change is offset, it can not only be used for resource evaluation and site selection, but also naturally extend to energy storage configuration, grid dispatching and safety analysis, and can convert how much PLCI is improved into an operational index such as reducing how much climbing pressure.

[0083] It should be noted that the power system is not only concerned about whether it is complementary, but also whether it can alleviate fluctuations and reduce risks after being complementary. The relative process line complementarity index directly reflects the degree of fluctuation smoothing through the length change of the joint curve, so it can reflect whether the joint output is more stable in peak regulation and frequency modulation; whether it can reduce the charging and discharging frequency in energy storage configuration; and whether it reduces the standby capacity demand in operation risk management. This is closer to engineering applications than simple correlation indicators.

[0084] It can be understood that it also includes:

[0085] By adjusting the installed capacity ratio of wind power and photovoltaic power;

[0086] Based on the installed capacity ratio, calculate the process line length of wind power output, photovoltaic power output and their joint power output over time under different matching schemes;

[0087] The relative process line complementary index is calculated according to the process line length, so as to evaluate the complementary performance under different matching schemes, and the evaluation results are used for planning and site selection of renewable energy projects, configuration optimization of energy storage systems and scheduling strategy making of power grids, so as to improve the operation efficiency and stability of the renewable energy system.

[0088] For example, wind speed, wind direction, air pressure, air density (or air temperature), global horizontal irradiance (GHI), plane irradiance, ambient temperature, component temperature, etc. unified time-stamped weather sequences can be obtained in the target region, with a minimum granularity of 5-15 minutes; time alignment, missing data filling, outlier removal and resampling are performed to ensure that multi-source data is on the same time grid. Prepare data copies with different application scales (such as daily, ten-day, monthly, seasonal, and annual) to support complementary evaluation at multiple time scales. The electrical power output sequences of wind power and photovoltaic power can be calculated based on weather data. On the wind power side, the wind speed sequence can be projected onto the unit power curve, which can include cut-in / rated / cut-out wind speeds and be corrected according to air density differences; considering wind shear and wake, a simplified power loss coefficient can be introduced. On the photovoltaic side, irradiance and temperature models such as the NOCT / single-diode model can be used to calculate the direct current power of the component, and then multiplied by the inverter efficiency to obtain the alternating current power; if only relative evaluation is required, both source powers can be normalized to the rated value of the respective installed capacity. Set the ratio, scale and superimpose the single-source power according to the planned installed capacity, and if necessary, add grid-connected / power limitation / creep constraints and local load coupling to form a net load sequence. By scanning the candidate set of ratios r, such as 0.2:0.1 steps to 3.0, the joint power curve under each ratio scheme can be obtained. The installed capacity planning variable is explicitly mapped to the shape difference of the joint sequence. On the selected time scale (such as daily / hourly, daily / ten-day, monthly / seasonal), the process line length of the wind / light / joint power sequence is calculated respectively. To suppress measurement noise or extremely short-term spikes, a light band-limited filter / slide median can be performed on the power, without changing the trend and introducing phase delay; then the discrete arc length is calculated using the above formula. Multi-scale parallel calculation can reveal the scale difference of complementary good but seasonal bad / contrary, supporting hierarchical optimization. For each ratio scheme, calculate the PLCI, the larger the value, the smoother the joint, the stronger the complementarity. At the same time, the sensitivity curve can be output to identify the sensitive interval of the complementary benefit to the ratio change, guiding the fine tuning of the installed capacity. Based on the PLCI result, site selection, energy storage configuration and dispatching strategy are supported. For planning site selection, under the historical weather driving of multiple candidate sites, the PLCI peak value and robustness (cross-year / seasonal difference) under the same ratio scanning are compared, and the site and ratio combination with high PLCI and cross-year robustness are selected. For energy storage configuration, the higher the PLCI, the smoother the joint curve, which is equivalent to the reduction of climbing and high-frequency components; the net climbing energy that needs to be smoothed and the energy storage power / capacity can be corresponded through frequency band decomposition or net load fluctuation integration, so as to down-push the energy storage rated power, equivalent cycle and capacity. For dispatching optimization, taking minimizing the joint process line length or maximizing the PLCI as a soft target, and combining constraints such as standby, climbing and power limitation, the quantifiable reduction of daily / weekly unit combination and AGC burden is obtained.For operational risk management, the rolling estimation of PLCI can be coupled with weather forecasts to identify periods of complementary weakness, such as PLCI decline, and schedule reserve and gas turbine start-stop strategies in advance to reduce capacity and ramping risk. Thus, the process line complementary index has the advantages of dynamic sensitivity and physical intuition. The change in process line length essentially reflects the cumulative degree of power curve slope. If the output of wind power and photovoltaic power fluctuates in opposite directions within a certain period, the joint curve slope after their superposition will offset each other, so that the total length of the joint curve is significantly shortened, which means that the complementarity is enhanced. Compared with relying only on statistical indicators such as correlation coefficients, this method can more truly depict the magnitude of fluctuation ramping and the size of variable energy, and thus is closer to the physical process in grid peak shaving and energy storage applications. In addition, this method supports robust evaluation across time scales. A region may have outstanding wind-solar complementarity on an intraday scale, but relatively weak complementarity on a seasonal scale, or vice versa. By calculating the complementary index at different time scales respectively, these differences can be intuitively revealed, avoiding false decisions caused by single-scale analysis. For example, if the intraday complementarity is strong, it is more suitable to configure power-type storage to alleviate the ramping pressure; if the seasonal complementarity is more significant, it is more suitable to configure energy-type storage to balance the dry and wet differences. Moreover, this method has a quantifiable link with energy storage and grid dispatching. The shortening of the curve length essentially represents the overall reduction of power change rate, which means that the burden of the system in frequency modulation, ramping control and energy storage charging and discharging is simultaneously reduced. That is, the higher the complementary index, the lower the adjustment cost and energy storage cycle intensity required in grid operation, and the higher the efficiency and stability of the system.

[0089] It can be understood that under extreme weather conditions, such as gusts, rapid cloud shadows passing through photovoltaic arrays, etc., the power curve will have sharp fluctuations smaller than the sampling interval. The conventional PLCI ignores these disturbances due to insufficient sampling frequency, and the evaluation result is optimistic, but the system still needs to bear these hidden ramping risks.

[0090] To solve the above problems, according to some embodiments, the method further comprises:

[0091] obtaining high-frequency meteorological observation time series data of the target area, wherein the high-frequency meteorological observation data comprises laser wind measurement radar data and panoramic cloud image data;

[0092] performing sub-interval interpolation and reconstruction on the renewable energy power curve of the target area based on the high-frequency meteorological observation time series data, to supplement the high-frequency fluctuation components not embodied in the low-frequency sampling power curve;

[0093] A process line length of a renewable energy power curve containing the high frequency fluctuation component is calculated at a target time scale over time, a secondary correction term for correction is introduced in the arc length calculation, and a relative process line complementary index of a target area is evaluated based on the corrected process line length.

[0094] Exemplary, high-frequency disturbances, such as gusts, cloud shadows, often occur at minute or even second level, but the conventional SCADA / resource assessment data is usually at 5-15 minutes resolution, directly calculating the process line arc length will miss the ramping and peaks in sub-intervals, leading to the relative process line complementary index (PLCI) being overestimated, and thus the demand for energy storage and backup is judged to be overly optimistic. High-frequency meteorological observations represented by laser wind radar and panoramic cloud images can be used as a magnifying glass to interpolate and physically constrain the low-frequency power curve at sub-interval, filling in the high-frequency components swallowed by averaging; then introduce a secondary correction term in the arc length calculation to count the reconstructed arc length increment into the total arc length, making the PLCI more sensitive to the true joint fluctuations and closer to the running physics. High-frequency meteorological observation time series data of the target area can be obtained. High-frequency wind field and cloud field observations are synchronously collected in the target area. Laser wind radar is preferred for wind side, and wind profile and turbulence intensity are obtained by azimuth / elevation scanning, which is combined with wind towers for time and amplitude calibration. Panoramic cloud camera is preferred for light side, supplemented by cloud base height meter or radiation station for cloud height and irradiance benchmark calibration. All sensor timestamps are unified to the same clock source, and missing, occlusion and abnormal frames are removed, and a high-frequency driving set of minute-level wind speed / wind vector / turbulence indicators, cloud amount / cloud motion vector / hidden area mask, and surface and component plane effective irradiance estimates is generated, providing physical basis for subsequent sub-interval reconstruction of power curve. Then, the renewable energy power curve of the target area is interpolated and reconstructed at sub-interval based on the high-frequency meteorological observation time series data. The wind profile on the wind side can be extrapolated to the hub height of the unit and combined with the turbulence index and gust factor to obtain the minute-level wind speed trajectory; the trajectory is mapped to the minute-level wind power through the unit power curve and dynamic response constraints, including cut-in / rated / cut-out, ramping constraints, rotor inertia and control lag; the light side estimates the cloud motion in the last few tens of minutes based on the optical flow field of the panoramic cloud image, projects the cloud shadow to the power station plane combined with the cloud base height to obtain the minute-level irradiance disturbance, and then superimposes the temperature effect, inverter limit and series-parallel shading effect to reconstruct the minute-level photovoltaic power. For the low-frequency SCADA sequence of wind and light, replace the simple linear interpolation with the physically driven subdivided trajectory in each 5-15 minute sampling window, while applying energy consistency constraints, reconstruct the minute value average equal to the original window average, and constraints such as non-negativity, limit not exceeding, ramping not jumping, etc. to form single-source power curve containing high-frequency components and its joint power curve. Then, calculate the process line length of the power curve containing the high-frequency fluctuation component at the target time scale, and introduce a secondary correction term in the arc length calculation to evaluate the relative process line complementary index.On selected time scales, the length of the time-varying process line of wind power, photovoltaic power and their combined power curve is calculated respectively; to avoid underestimation caused by sampling and reconstruction model uncertainty, a secondary correction term is set, which is derived from the cumulative value of the arc length difference between the reconstructed curve and the linear interpolation curve within the window, or is converted according to the confidence weight of the reconstructed high-frequency component energy ratio, and is added to the total arc length to form the corrected arc length. Finally, the relative process line complementary index is calculated based on the corrected wind, light and combined arc length, and the confidence interval and data quality identifier are output at the same time for planning and scheduling side. Thus, the reconstruction driven by high-frequency observation makes the averaged climbing and peak explicit, and the corrected arc length is closer to the real fluctuation than the original arc length, so that the PLCI can identify the special case of seemingly smooth but actually flickering. The introduction of unit dynamics and climbing constraints on the wind side, and the introduction of inverter limits and cloud shadow on the light side, make the PLCI no longer be misled by the ideal curve. The corrected arc length has a source-explained correction term and a confidence output, which can be directly linked with backup capacity assessment and energy storage life model to reduce investment and operation deviation.

[0095] It can be understood that in some areas, wind-solar complementation does make the total output smoother, but the smoothed curve is out of position with the local load peak, such as wind power combined with photovoltaic power in summer night forming a concave valley, which leads to weak peak clipping and valley filling ability.

[0096] To solve the above problems, according to some embodiments, the method further comprises:

[0097] obtaining meteorological time series data associated with the renewable energy in the target area and load time series data of the target area;

[0098] calculating first renewable energy power output and second renewable energy power output of the target area based on the meteorological time series data respectively;

[0099] In calculating the length of the time-varying process line of the first renewable energy power output, the second renewable energy power output and the combined power output on the target time scale, the load peak period weight determined by the load time series data is introduced to give higher weight to the arc length fluctuation in the load peak period;

[0100] evaluating the relative process line complementary index of the first renewable energy and the second renewable energy of the target area based on the length of the process line based on the introduced load peak period weight.

[0101] For example, in the target area, first obtain meteorological time series data related to renewable energy sources such as wind power, photovoltaic, etc., such as wind speed, wind direction, air pressure, irradiance, temperature, etc., which are the driving factors for calculating power output. At the same time, the load time series data of the regional power grid is collected synchronously, and the data timestamp is aligned to ensure that the corresponding relationship between resource output and load demand in the same period can be reflected. Through data cleaning and exception elimination, a high-quality meteorological and load joint database is formed. The effectiveness of complementarity depends not only on the fluctuation relationship between wind and light, but also on the matching degree with load demand. It can ensure that the subsequently calculated complementarity index can truly reflect the combination of power supply side smoothness and load side adaptability. Based on the above meteorological time series data, the power curve model of wind turbine and the power prediction model of photovoltaic module are established respectively, considering factors such as air density, inverter efficiency, temperature effect, etc. The wind power output and photovoltaic power output sequence in the target area are calculated. In order to ensure the calculation accuracy, the power can be normalized according to the actual installed capacity. The meteorological conditions are converted into renewable energy output through physical and empirical models, so that the power time series can accurately reflect the resource fluctuation characteristics. Provide basic data input for subsequent joint power calculation and complementarity evaluation. When calculating the length of the process line of wind power output, photovoltaic power output and their joint power output changing with time in the target time scale, additional load time series information is introduced, and higher weight is given to the load peak period. For example, when the power grid is in the evening peak or the summer air conditioning load peak, the fluctuation of the joint power curve significantly enhances the impact on system operation, so the arc length increment in these periods is amplified. The operation risk of the power system is not equal in all periods, but the fluctuation in the load peak period is more likely to cause insufficient reserve, frequency deviation or voltage fluctuation. Therefore, through weighting, the complementarity index better reflects the actual adaptation degree to the key load period, rather than the average fluctuation smoothing. Finally, based on the above process line length introduced by the load peak period weight, the modified relative process line complementarity index is calculated and used to evaluate the wind power and photovoltaic complementarity performance in the target area. Compared with traditional methods, this complementarity index not only reflects the joint fluctuation reduction of the resource side, but also quantifies the effectiveness of this reduction in the load key period. Combining power supply complementarity with load demand, from physical smoothing to load adaptation. It can provide more valuable complementarity metrics for planning and operation, such as whether a certain wind-light ratio can stabilize power supply in the evening peak load period during power grid dispatching, thereby providing accurate basis for energy storage configuration, reserve capacity arrangement and dispatching strategy.

[0102] It can be understood that when wind power and photovoltaic are complementary in statistics, but the energy dominant frequency bands of their fluctuations are different, such as daily low frequency of wind power and daily high frequency of photovoltaic, the PLCI rises after superposition, but only low frequency ramping can be responded by a certain type of device such as a thermal power unit, and has no relief effect.

[0103] To address the above problems, according to some embodiments, the method further includes:

[0104] Obtain meteorological time-series data of the target area associated with the renewable energy source;

[0105] The first renewable energy power output and the second renewable energy power output of the target area are calculated based on the meteorological time series data.

[0106] Frequency band decomposition is performed on the time series of the first renewable energy power output, the second renewable energy power output, and the combined power output at the target time scale to obtain power output components in different frequency bands;

[0107] The process line length of the power output components in different frequency bands as a function of time is calculated, and weighted processing is performed in combination with the response capability of the system's adjustable unit in the corresponding frequency band.

[0108] The effective relative process line complementarity index of the first and second renewable energy sources in the target region is evaluated based on the weighted process line length.

[0109] For example, acquire meteorological time-series data related to renewable energy within the target area, such as wind speed, wind direction, atmospheric pressure, irradiance, and temperature. The collected data needs to cover a sufficiently long time span and have a sufficiently fine sampling resolution to reflect both seasonality and intraday patterns, as well as capture short-term fluctuations. Data preprocessing also requires time alignment, missing data completion, and anomaly removal to ensure the continuity and accuracy of the time series. Power output is directly affected by meteorological conditions; high-quality meteorological data is a prerequisite for obtaining reliable power curves. This provides a reliable data foundation for subsequent frequency band decomposition and process line calculations. Based on the aforementioned meteorological time-series data, calculate the wind power and photovoltaic power output sequences for the target area. For wind power, calculate using the wind turbine power curve combined with wind speed and air density, considering cut-in, rated, and cut-out wind speed limitations; for photovoltaic power, calculate component output based on irradiance and temperature models, and obtain AC power by combining inverter conversion efficiency. To ensure comparability between different energy sources, per-unit processing can be performed. Mapping meteorological conditions to physical power sequences makes the data engineering interpretable, yielding power time series that can be directly used for complementarity assessment. After obtaining wind and solar power sequences, they are decomposed into frequency bands at the target time scale, for example, into low-frequency components to reflect intraday trends and seasonal variations, and high-frequency components to reflect minute-level and hourly fluctuations. Frequency band decomposition can be achieved through methods such as filtering, empirical mode decomposition, or wavelet analysis. The operating costs and risks of the power system are sensitive to fluctuations of different frequencies differently. High-frequency fluctuations mainly affect frequency regulation and short-term energy storage demand, while low-frequency fluctuations determine peak shaving and energy storage demand. Thus, the originally mixed fluctuations are decomposed, making subsequent complementarity assessments more targeted. The process lengths of different frequency bands are calculated and weighted, and the process lengths of the power components obtained from frequency band decomposition are calculated for their time-varying characteristics, yielding length indices corresponding to low-frequency and high-frequency fluctuations. Subsequently, weighted processing is performed based on the characteristics of the system's adjustable units. For example, fast-response energy storage corresponds to high-frequency components, while thermal power or pumped storage corresponds to low-frequency components. Higher weights can be given to the high-frequency components to reflect their greater impact on operating costs. Different regulatory resources have different time response capabilities, and high-frequency fluctuations are more expensive. Therefore, the process length needs to be adjusted through weighting. Complementarity assessment can be directly mapped to system operating costs and resource allocation, making it more practical. Finally, the weighted process length can be used to calculate the relative process length complementarity index, thus obtaining an effective complementarity index of wind power and photovoltaics under different frequency bands. Unlike traditional methods, this complementarity index not only considers whether wind and solar fluctuations are statistically complementary but also further reflects whether this complementarity is effective at the actual system regulation level. Through frequency band decomposition and weight adjustment, physical complementarity is transformed into effective complementarity closely related to system operation, enabling this index to be directly used to guide energy storage configuration, unit combination, and reserve arrangements, thereby improving the engineering application value of the complementarity index.This allows us to distinguish the complementary effects of wind and solar power on slow-changing trends and rapid-changing fluctuations, avoiding the misjudgment of low-cost, slow-changing complementarity as high-value, rapid-changing complementarity. Through weighted processing, the complementarity evaluation is aligned with the response capabilities of different regulatory resources, better reflecting the costs and risks in actual operation. It can help determine whether energy storage should be power-oriented or energy-oriented, and also clarify whether reserve capacity should focus on dealing with high-frequency or low-frequency fluctuations.

[0110] For example, the aforementioned Relative Process Line Complementarity Index (PLCI) defaults to using power output as the characterization variable when assessing renewable energy complementarity, reflecting the actual impact of the energy system on the power grid. However, the PLCI possesses good versatility and can be replaced with indicators such as wind speed, solar radiation, capacity factor, or energy density per unit area, depending on specific research objectives and data availability, for resource-level complementarity analysis. Furthermore, the PLCI is not only applicable to assessing the complementarity of the same type of renewable energy between different geographical regions, but can also be extended to the complementarity analysis between two or more energy types, such as jointly analyzing the output complementarity characteristics between wind, solar, and hydropower. This flexibility gives it broad application potential in multi-energy synergistic optimization, regional power system planning, and integrated energy system design.

[0111] Please see Figure 4 One embodiment of the regional renewable energy complementarity assessment device in this application may include:

[0112] Acquisition unit 21 is used to acquire meteorological time series data of the target area associated with the renewable energy source;

[0113] Calculation unit 22 is used to calculate the first renewable energy power output and the second renewable energy power output of the target area based on the meteorological time series data;

[0114] Evaluation unit 23 is used to calculate the process line lengths of the first renewable energy power output, the second renewable energy power output, and the combined power output as a target time scale change over time, so as to evaluate the relative process line complementarity index of the first renewable energy and the second renewable energy in the target area based on the process line lengths. The process line length is the arc length of the curve of the time series data within a preset time period to indicate the combined fluctuation characteristics of different renewable energy sources.

[0115] In summary, the regional renewable energy complementarity assessment device provided in this application acquires meteorological time-series data associated with the renewable energy sources in the target region; calculates the power output of the first renewable energy source and the power output of the second renewable energy source in the target region based on the meteorological time-series data; and calculates the process line lengths of the power output of the first renewable energy source, the power output of the second renewable energy source, and the combined power output over time at the target time scale. Based on these process line lengths, the relative process line complementarity index of the first renewable energy source and the second renewable energy source in the target region is assessed. The process line length is the arc length of the curve of the time-series data within a preset time period to indicate the joint fluctuation characteristics of different renewable energy sources. This method is applicable to key application scenarios such as renewable energy site selection planning, energy structure optimization, energy storage system capacity design, and multi-energy collaborative operation. This method quantitatively characterizes the complementarity characteristics of renewable energy output based on the process line length of the renewable energy output over time, quantifying the complementarity between different energy sources. The relative process line complementarity index is more dynamically sensitive and physically intuitive when quantifying complementarity, and can reflect the magnitude of joint fluctuations and their potential impact on system operation. This method is applicable to the complementarity assessment of time series data, and is particularly suitable for applications in the renewable energy sector, including comprehensive resource assessment, site selection optimization, energy storage configuration, system scheduling optimization, and operational risk management. Furthermore, this method can also be extended to interdisciplinary fields such as meteorological process analysis and economic variable complementarity assessment, demonstrating good versatility and practical value.

[0116] like Figure 5 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-mentioned methods for assessing the complementarity of regional renewable energy:

[0117] Obtain meteorological time-series data of the target area associated with the renewable energy source;

[0118] Calculate the first renewable energy power output and the second renewable energy power output of the target area based on the meteorological time series data;

[0119] The process line lengths of the first renewable energy power output, the second renewable energy power output, and the combined power output at the target time scale are calculated as time changes, so as to evaluate the relative process line complementarity index of the first renewable energy and the second renewable energy in the target area based on the process line lengths. The process line length is the arc length of the curve of the time series data within a preset time period.

[0120] Since the electronic device described in this embodiment is the device used to implement a regional renewable energy complementarity assessment device in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0121] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the implementation methods in the corresponding embodiments:

[0122] Obtain meteorological time-series data of the target area associated with the renewable energy source;

[0123] Calculate the first renewable energy power output and the second renewable energy power output of the target area based on the meteorological time series data;

[0124] The process line lengths of the first renewable energy power output, the second renewable energy power output, and the combined power output at the target time scale are calculated as time changes, so as to evaluate the relative process line complementarity index of the first renewable energy and the second renewable energy in the target area based on the process line lengths. The process line length is the arc length of the curve of the time series data within a preset time period.

[0125] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0126] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... ​ The process for assessing the regional renewable energy complementarity in the corresponding embodiment.

[0131] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0132] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0134] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0135] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0136] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0137] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for assessing the complementarity of regional renewable energy, characterized in that, The method comprises: acquiring meteorological time series data associated with the renewable energy in a target region; calculating first renewable energy power output and second renewable energy power output of the target region based on the meteorological time series data respectively; calculating the length of a process line of the first renewable energy power output, the second renewable energy power output and the combined power output at a target time scale respectively to evaluate the relative process line complementary index of the first renewable energy and the second renewable energy in the target region based on the length of the process line, the length of the process line being the arc length of the curve of the time series data within a preset time period to indicate the combined fluctuation characteristics of different renewable energies; the evaluation of the complementary index of the first renewable energy and the second renewable energy in the target region based on the length of the process line comprises: Based on the formula a relative process line complementary index of the first renewable energy and the second renewable energy of the target region is calculated, wherein, is a relative process line length of the first renewable energy power sequence, used to characterize the self fluctuation intensity of wind power, is a relative process line length of the second renewable energy power sequence, used to characterize the self fluctuation intensity of photovoltaic, is a relative process line length of the first renewable energy and the second renewable energy after being combined by weight, used to characterize the fluctuation intensity of the combined output of the first renewable energy and the second renewable energy, and are weight factors of the installed capacity of the first renewable energy and the second renewable energy, respectively; The relative process line length Through The calculation yielded, where, The process line length represents the path length of the actual power output of renewable energy as it changes over time. The rated power process line length represents the path length over time that a renewable energy source travels at rated power. It is approximately calculated using the Euclidean distance between adjacent output points in the time series; the process line length is calculated by the formula ; the rated power course line length is calculated by the formula ; Joint power Indicated as ; Combined rated power Indicated as ; wherein X is a renewable energy type, is a time interval resolution, is a time scale of the evaluation minimum calculation unit; is a power difference of adjacent time periods, is a renewable energy rated power, said is a power output of a wind turbine, said is a power output of a photovoltaic assembly.

2. The method of claim 1, wherein, in the case that the first renewable energy is wind energy and the second renewable energy is solar energy, the meteorological time series data comprises wind speed time series data, ground surface horizontal irradiance time series data and air temperature time series data; in the case that the target time scale is 24 hours, a smooth path of zero night output of solar energy is removed to avoid underestimating the renewable energy complementarity.

3. The method of claim 1, wherein, the removal of the smooth path of zero night output of solar energy in the case that the target time scale is 24 hours comprises: In the case of a target time scale of 24 hours, the length of the process line for solar energy is calculated by the formula is calculated by the formula wherein is the time interval resolution, is the time scale of the evaluation minimum calculation unit, is the power difference of adjacent time periods, is the rated power of the photovoltaic assembly, and has: where the indicator corresponding incremental term becomes 0, the corresponding nominal process line length is adjusted to .

4. The method of any one of claims 1 to 3, wherein, The method further comprises: In PLCI = 0 indicates no complementary effects between renewable energy sources; At 0 PLCI <1, it indicates that there is a complementary effect between the renewable energy sources, and the closer the value is to 1, the better the effect is. In PLCI In the case of <0>, it indicates that there is negative complementarity between renewable energies.

5. A regional renewable energy complementarity assessment device, characterized in that, The device comprises: an acquisition unit configured to acquire meteorological time series data associated with the renewable energy in a target region; a calculation unit configured to calculate first renewable energy power output and second renewable energy power output of the target region based on the meteorological time series data respectively; an evaluation unit configured to calculate the length of a process line of the first renewable energy power output, the second renewable energy power output and the combined power output at a target time scale respectively to evaluate the relative process line complementary index of the first renewable energy and the second renewable energy in the target region based on the length of the process line, the length of the process line being the arc length of the curve of the time series data within a preset time period to indicate the combined fluctuation characteristics of different renewable energies.

6. An electronic device comprising: A memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the method for evaluating regional renewable energy complementarity according to any one of claims 1-4 when executing the computer program stored in the memory.

7. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executable on the processor to implement the method for evaluating regional renewable energy complementarity according to any one of claims 1-4.

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