Regional renewable energy complementarity evaluation method and related equipment

Abstract

The invention discloses a regional renewable energy complementarity evaluation method and related equipment. The method comprises the following steps: acquiring meteorological time sequence data, associated with renewable energy sources, of a target area; calculating first renewable energy electric power output and second renewable energy electric power output of a target area based on the meteorological time sequence data; and calculating the time-varying process line lengths of the first renewable energy source electric power output, the second renewable energy source power output and the joint power output under the target time scale, so as to evaluate the relative process line complementation index of the first renewable energy source and the second renewable energy source of the target area based on the process line lengths. The method can solve the problems that an existing analysis method cannot recognize a more complex nonlinear or hysteresis complementary mode between renewable energy sources such as wind power and photovoltaic energy, and due to the fact that the sensitivity and stability performance difference is large under different time scales, unified comparison or cross-scale application is difficult to achieve.

Description

Technical Field

[0001] This application relates to the field of computers, and more specifically, to a method and related equipment for assessing the complementarity of regional renewable energy. Background Technology

[0002] Renewable energy sources such as wind and solar power are highly dependent on weather conditions, and their power output exhibits significant randomness and volatility, posing challenges to grid frequency control, peak shaving, and reserve capacity allocation. To better utilize and allocate renewable energy, it is necessary to analyze and quantify the complementarity of renewable energy sources. However, existing methods for quantifying renewable energy complementarity lack intuitive physical meaning, cannot reasonably estimate the optimal ratio between different renewable energy sources, and are difficult to determine whether the combined output curve possesses actual peak shaving and valley filling capabilities. Furthermore, their sensitivity and stability vary significantly across different time scales, making unified comparisons or cross-scale applications difficult. Summary of the Invention

[0003] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. The summary section of this invention is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0004] Methods for quantifying and analyzing renewable energy complementarity can include correlation, cross-frequency, and standard deviation complementarity ratio. However, current correlation calculations can only reflect linear positive / negative correlations and cannot identify more complex nonlinear or lag-based complementarity patterns between renewable energy sources such as wind power and solar power. Cross-frequency identifies complementarity solely by counting the frequency of inverse changes in output between adjacent time periods, but ignores the magnitude of fluctuations, treating even minimal output changes as complementary events, which can easily lead to an overestimation of complementarity. The standard deviation complementarity ratio only focuses on the overall magnitude of fluctuations and cannot capture the dynamic complementarity characteristics and extreme fluctuation behaviors of renewable energy sources.

[0005] To address the shortcomings of existing analytical methods in identifying more complex nonlinear or hysteresis-based complementary modes between renewable energy sources such as wind and solar power, and the significant differences in sensitivity and stability across different time scales that hinder unified comparisons or cross-scale applications, this invention proposes a regional renewable energy complementarity assessment method. This method includes: Obtain meteorological time-series data of the target area associated with the renewable energy source; 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; 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, respectively, in order 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 joint fluctuation characteristics of different renewable energy sources.

[0006] Optionally, the assessment of the complementarity index of the first and second renewable energy sources in the target area based on the process line length includes: Based on formula Calculate the relative process line complementarity index of the primary and secondary renewable energy sources in the target region, where, The relative process line length of the first renewable energy power series is used to characterize the intensity of wind power fluctuations. The relative process line length of the second renewable energy power series is used to characterize the intensity of photovoltaic fluctuations. The relative process line length, obtained by combining the first and second renewable energy sources according to their weights, is used to characterize the fluctuation intensity of the combined output of the first and second renewable energy sources. and These are the weighting factors for the installed capacity of the first and second renewable energy sources, respectively.

[0007] Optionally, 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.

[0008] Optionally, the process line length Through formula Obtained through calculation; The rated power process line length Through formula Obtained through calculation; Combined power Represented as ; Combined rated power Represented as ; in, X It is a type of renewable energy. For time interval resolution, To evaluate the time scale of the smallest computational unit; The power difference between adjacent time periods. Rated power for renewable energy.

[0009] Optionally, when the first renewable energy source is wind energy and the second renewable energy source is solar energy, the meteorological time series data includes wind speed time series data, surface horizontal irradiance time series data, and temperature time series data; With a target timescale of 24 hours, the smooth path of zero solar output at night is eliminated to avoid underestimating the complementarity of renewable energy.

[0010] Optionally, the step of eliminating stable solar power output paths at night when the target time scale is 24 hours includes: Given a target timescale of 24 hours, the process length of solar energy Through formula Calculated in, For time interval resolution, To evaluate the time scale of the smallest computational unit, The power difference between adjacent time periods. The rated power of the photovoltaic module, and the index is defined as follows: when the photovoltaic output is zero for consecutive moments. The corresponding incremental term becomes 0, and the corresponding rated process line length... Can be adjusted to .

[0011] Optional, also includes: exist PLCI When =0, it indicates that there is no complementary effect between renewable energy sources; In 0< PLCI When the value is less than 1, it indicates that there is a complementary effect between renewable energy sources, and the closer the value is to 1, the better the effect. exist PLCI When <0, it indicates that renewable energy sources are negatively complementary.

[0012] Secondly, the present invention also proposes a regional renewable energy complementarity assessment device, comprising: An acquisition unit is used to acquire meteorological time-series data of the target area associated with the renewable energy source; The calculation unit 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. The evaluation unit 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 over time at a target time scale, 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.

[0013] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the regional renewable energy complementarity assessment method as described in any of the first aspects above.

[0014] Fourthly, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the regional renewable energy complementarity assessment method of any of the preceding claims of the first aspect.

[0015] In summary, the regional renewable energy complementarity assessment method proposed 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 and second renewable energy sources 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 in 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.

[0016] The regional renewable energy complementarity assessment method of the present invention, other advantages, objectives and features of the present invention will be apparent in part from the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic diagram of a method for assessing regional renewable energy complementarity provided in this application embodiment; Figure 2 A schematic diagram showing the daily output sequence of wind power and photovoltaic power generation of four wind-solar hybrid power stations provided in this application embodiment in 2020; Figure 3 The monthly process line complementarity index of wind power and photovoltaic power output of the four wind-solar hybrid power stations provided in this application embodiment; Figure 4 A schematic diagram of a regional renewable energy complementarity assessment device provided in this application embodiment; Figure 5 This is a schematic diagram of an electronic device for assessing regional renewable energy complementarity, provided as an embodiment of this application. Detailed Implementation

[0018] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0019] To address the limitations of existing analytical methods in identifying more complex nonlinear or hysteresis-based complementary modes between renewable energy sources such as wind and solar power, and the significant differences in sensitivity and stability across different time scales that hinder unified comparisons or cross-scale applications, please refer to [link to relevant documentation]. Figure 1 This is a schematic diagram of a regional renewable energy complementarity assessment method provided in an embodiment of this application, which may specifically include steps S110 to S130.

[0020] S110, Obtain meteorological time series data of the target area associated with the renewable energy source.

[0021] S120, 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.

[0022] S130, 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 function of time at the target time scale, 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.

[0023] It is understood that this application defines an index for quantifying the complementarity of renewable energy, namely: the Path Length Complementarity Index. PLCI Taking wind and solar energy as examples, the formula for this index is: (1) in, It is the relative process line length of the wind power series, used to measure the intensity of wind power fluctuations. It is the relative process line length of the photovoltaic power series, used to measure the intensity of photovoltaic fluctuations. The relative process line length obtained by combining wind and solar energy according to weights is used to describe the intensity of fluctuations in the combined output of wind and solar energy. and These are the weighting factors for wind power and solar power installed capacity, respectively.

[0024] For time series data, the relative process length It can be calculated using the following formula: (2) in, 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 length represents the path length of renewable energy as it operates at rated power over time.

[0025] Process line length It can be approximated by the Euclidean distance between adjacent output points in the time series: (3) (4) in, X It is a type of renewable energy. The time interval resolution (in hours). To evaluate the time scale of the smallest computational unit (on a daily scale) Hour); The power difference between adjacent time periods. Rated power for renewable energy.

[0026] Understandably, PLCI measures whether the path roughness of the joint curve is complementaryly reduced for each energy source. The output time series is first measured by the Euclidean path of the power-time curve, i.e., the length of the process line, and then dimensionlessized using the relative length of the process line. Finally, formula (1) is used to compare the degree of the broken line of the combined curve with the weighted average of the degree of the broken line of each individual curve. If the fluctuations are mutually canceled after the two are combined, and the combined curve is smoother, then... A smaller relative denominator leads to a higher PLCI, quantifying complementarity as greater. Conversely, a larger PLCI leads to a lower PLCI or even a negative PLCI. This definition directly interprets whether complementarity smooths out fluctuations as a comparison of the geometric lengths of curves, making it intuitive and physically explainable.

[0027] The relative process length essentially measures how tortuous the power-time curve is. It sums up the power fluctuations over small time intervals and benchmarks them against rated power and time step size, resulting in a dimensionless roughness. When power fluctuations between adjacent time periods are not extreme, this roughness is positively correlated with the sum of the strengths of all small ramps and dips; that is, the larger the incremental energy, the more jagged the curve and the greater the relative process length; conversely, the smoother the curve, the closer the relative process length is to the baseline. This property directly correlates the seemingly smoother combined output with the calculable cumulative ramp intensity. Combined output is the result of weighted superposition of each energy source. After superposition, the changes at each time step are also superimposed: if the two energy sources change in opposite directions at the same moment, for example, wind fluctuates at night while photovoltaic power remains relatively constant or declines, their changes will cancel each other out, significantly reducing the jaggedness of the combined curve; if they change in the same direction at the same moment, the combined curve will be more jagged. PLCI compares the roughness of the joint curve with the weighted average of the roughness of the individual curves: the more offsetting, the smoother the joint, the higher the PLCI, indicating stronger complementarity; conversely, a lower PLCI or even a negative PLCI indicates that superposition amplifies fluctuations. Since this comparison is performed at the level of change at each time step, it naturally captures complementary behavior across multiple time scales. Correlation (ρ) assesses whether level values ​​are synchronized. It first standardizes the series and then examines whether high values ​​are simultaneously high and low values ​​are simultaneously low. This method is weak in distinguishing cases with the same mean and variance but completely different intraday structures; for example, photovoltaic power fluctuates during the day and is stable at night, while wind power is active at night and relatively stable during the day. From the level values, they may not be strongly negatively correlated, but from the perspective of change, they are often opposite. Correlation easily misses this kind of 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-amplitude counter-variations and a few large-amplitude counter-variations appear similar in PR; however, PLCI has a substantial impact on amplitude: large-amplitude cancellation significantly reduces the roughness of the joint curve, thereby increasing the complementarity index, which is closer to the scheduler's focus on whether critical climbs are offset. The standard complementarity ratio (R_SD) assesses whether the overall volatility decreases after superposition, but it is insensitive to time order. As long as the overall variances are similar, they are considered similar; even if one sequence has a few large climbs and the other has frequent small fluctuations, R_SD is difficult to reflect the process information of when and how the two occur and how they superimpose.

[0028] It should be noted that since photovoltaic power is zero at night, the time component is accumulated every hour in formula (3), which means that the long-term stable zero output at night also contributes to the process line length, thus artificially reducing the measure of photovoltaic volatility. This will lead to a smaller denominator in formula (1), resulting in a lower relative process line 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: (5) (6) 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: (7) 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.

[0029] 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.

[0030] For example, the combined power of wind and solar energy With combined rated power It can be expressed by the following formula: (8) (9) For example, one can first obtain measured data or reanalyze datasets such as hourly wind turbine height data in ERA5, such as wind speed time series data at a height of 100m. Horizontal irradiance of the ground and temperature data Secondly, it can calculate the hourly power output of wind turbines at observation points or grid cells. and the power output of photovoltaic modules The formula for calculating wind power output is as follows: (10) in, , and These are the cut-in wind speeds of the wind turbines (2.5 m / s). -1 ), cut off the wind speed (20ms) -1 ) and rated wind speed (10.5 m / s) -1 ) This refers to the rated power of the wind turbine.

[0031] The formula for calculating photovoltaic power output is as follows: (11) (12) (13) in, PF This represents the effective component area per unit land area. Rated power of photovoltaic modules (300W). This refers to the total irradiance on the horizontal plane or the tilted surface of the photovoltaic module, which is the sum of direct and diffuse irradiance. This is the efficiency coefficient of the photovoltaic system, which takes into account the power loss during the process from power generation to grid connection. In this model, it can be 82%. The cell temperature is (°C). The temperature coefficient of peak power is −0.41% / °C. The tilt angle of the photovoltaic panel. NOCT This refers to the standard operating temperature of the component, which could be 45°C in this model.

[0032] For example, the required assessment period can be selected based on the assessment needs for renewable energy complementarity. And the data time resolution (e.g., hourly or daily). Secondly, based on the selected time resolution data (hourly or daily), the renewable energy power output can be calculated using a formula. with combined power the process line length varying with time ; then calculate the relative process line complementary index of renewable energy complementarity according to the definition PLCI .

[0033] Exemplarily, the calculation of renewable energy complementarity can be quantified based on the process line lengths at different time scales (such as daily, monthly or yearly).

[0034] It can be understood that when PLCI = 0, it means there is no complementary effect between renewable energies, that is, the fluctuations are neither weakened nor amplified; when 0 < PLCI < 1, it means there is a complementary effect between renewable energies, and their fluctuations decrease, and the closer the value is to 1, the better the effect; when PLCI < 0, it means the fluctuations between renewable energies increase, showing a negative complementarity.

[0035] In summary, the regional renewable energy complementarity assessment method provided by the embodiments of this application obtains the meteorological time series data associated with the renewable energy in the target area; calculates the first renewable energy electric power output and the second renewable energy power output of the target area based on the meteorological time series data; calculates the process line lengths of the first renewable energy electric power output, the second renewable energy power output and the combined power output varying with time under the target time scale, so as to evaluate the relative process line complementary index of the first renewable energy and the second renewable energy in the target area, and 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 energies. It is applicable to key application scenarios such as the site selection and planning of renewable energies, the optimal allocation of energy structures, the capacity design of energy storage systems, and the coordinated operation of multiple energies. This method quantitatively characterizes the complementary characteristics based on the process line length of the output of renewable energies varying with time, and quantifies the complementarity between different energies. The relative process line complementary index is more dynamically sensitive and physically intuitive when quantifying complementarity, and can reflect the change range of combined fluctuations and their potential impact on system operation. This method is applicable to the complementarity assessment of time series data, especially suitable for applications in the field of renewable energies, including scenarios such as comprehensive resource assessment, optimal site selection, energy storage configuration, system scheduling optimization, and operation risk management. In addition, this method can also be extended to interdisciplinary fields such as meteorological process analysis and economic variable complementarity assessment, and has good generality and practical value.

[0036] Based on some embodiments, taking the complementarity analysis of wind power and photovoltaic power generation as an example, four representative wind-solar complementary projects in my country were 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 (see Table 1 for details), to verify the scientificity and practicality of the proposed relative process line complementarity index (PLCI) in practical applications. Figure 2 This paper presents the daily output sequences of wind and solar power generation from the aforementioned four wind-solar hybrid power plants in 2020. Wind power output exhibits strong overall volatility, displaying high-frequency, irregular changes with numerous sharp spikes and drops. Solar power output, on the other hand, shows high regularity, exhibiting clear seasonal variations, with the strongest output in summer and the weakest in winter. Based on surface-observed wind speed and solar radiation data, the annual complementarity index of wind and solar energy in 2020 was calculated. PLCI As shown in Table 1, there are significant regional differences in the wind-solar complementarity of the four wind-solar hybrid power stations. Among them, a certain wind-solar hybrid project in Qinghai has the highest wind-solar complementarity, with an annual average of PLCI Reaching 0.2748 indicates that the volatility of wind and solar power combined operation decreased by approximately 27.48% compared to independent operation; the annual wind and solar power volatility of a wind-solar hybrid power station in Ningxia... PLCI The volatility was 0.1483, a decrease of 14.83% compared to independent operation; the annual average volatility of a wind-solar hybrid power station in Gansu and a wind-solar hybrid project in Xinjiang was... PLCI The values ​​are 0.1351 and 0.1106 respectively, indicating relatively low complementarity. Table 1 shows the basic information of wind-solar hybrid projects and their relative process line complementarity index (PLCI).

[0037] Table 1 also, PLCI It allows for the assessment of renewable energy complementarity at different time scales. Figure 3 The monthly wind-solar hybrid power station data for four wind-solar hybrid power stations was presented in 2020. The results show that... PLCI It exhibits significant seasonal variation, displaying a bimodal characteristic, with complementarity being higher in spring (February-April) and autumn (September-October). For example, a wind-solar hybrid power station in Qinghai showed higher complementarity in October. PLCI Reaching 0.34 means that in October, the complementary nature of wind and solar power reduced the overall volatility by 34%; this implies that the volatility of combined wind and solar operation decreased by 34% that month. Complementarity was lowest between May and August, as seen in the case of a power station in Xinjiang in May. PLCIA value of only 0.07 indicates that the combined wind and solar operation in that month only reduced volatility by 7%, reflecting the weak wind-solar complementarity in spring and summer. Overall, a power station in Qinghai showed the best wind-solar complementarity, while a power station in Xinjiang performed relatively poorly. The proposed PLCI assessment method can not only accurately quantify the complementarity of renewable energy power output, but also dynamically monitor its complementary characteristics across multiple time scales. By adjusting the installed capacity ratio of wind power and photovoltaics, PLCI can also be used to simulate the complementarity performance under different ratio schemes, providing quantitative basis for the planning and site selection of renewable energy projects, the optimization of energy storage system configuration, and the formulation of grid dispatch strategies, which helps to improve the operating efficiency and stability of renewable energy systems. It is understandable that the calculation of PLCI is a linear scanning process, that is, it iterates through the time series step by step, and accumulates several simple quantities to obtain the roughness index of each energy source and the joint roughness index. For multi-energy scenarios, it simply performs the same scan on each series separately and then performs a linear summation. The overall complexity increases linearly with the number of energy types × the number of time steps, which is naturally suitable for parallel processing. Compared to methods requiring frequency domain transformation or multi-window analysis, PLCI does not rely on complex transformations or repeated segmentation, making it suitable for large-scale, multi-year batch assessments. An increased PLCI means that the ramp-up and pullback of the joint curve are more effectively canceled out. This directly reduces the system's need for rapid standby and regulation under the same load conditions. Under the same energy transfer target, the peak intensity of charging and discharging is reduced, requiring a smaller rated power range or a wider time period for the same power. Furthermore, the number and magnitude of large gradients and sudden changes are reduced, which is beneficial for frequency stability and equipment lifespan. Because the definition of PLCI directly describes whether changes are canceled out, it can be used not only for resource assessment and site selection but also naturally extended to energy storage configuration, grid dispatch, and security analysis. Moreover, the target increase in PLCI can be translated into actionable indicators such as the reduction in ramp-up pressure.

[0038] It's important to note that power systems are concerned not only with whether there is complementarity, but more importantly with whether complementarity can mitigate fluctuations and reduce risks. The relative process line complementarity index, through changes in the length of the joint curve, directly reflects the degree to which fluctuations are smoothed. This indicates whether the combined output is more stable in peak and frequency regulation; whether charging and discharging frequencies can be reduced in energy storage configurations; and whether reserve capacity requirements are lowered in operational risk management. This is closer to engineering applications than simple correlation indicators.

[0039] Understandably, this also includes: By adjusting the installed capacity ratio of wind power and photovoltaic power; The process line lengths of wind power output, photovoltaic power output and combined power output as a function of time are calculated based on the installed capacity ratio for different power ratio schemes. The relative process line complementarity index is calculated based on the process line length to evaluate the complementarity performance under different ratio schemes. The evaluation results are used for the planning and site selection of renewable energy projects, the optimization of energy storage system configuration, and the formulation of grid dispatch strategies to improve the operating efficiency and stability of renewable energy systems.

[0040] For example, meteorological time-series data associated with renewable energy in the target area can be acquired. This includes acquiring meteorological sequences with uniform timestamps for wind speed, wind direction, air pressure, air density (or temperature), total ground irradiance (GHI), planar irradiance, ambient temperature, and module temperature in the target area, with a minimum granularity of 5–15 minutes. Time alignment, missing data imputation, outlier removal, and resampling are performed to ensure that multi-source data are on the same time grid. Data copies with appropriate resolutions are prepared according to different application scales (e.g., intraday, decadal, monthly, quarterly, annual) to support complementary evaluations across multiple time scales. Power output sequences for wind power and photovoltaics can be calculated separately based on meteorological data. On the wind power side, the wind speed sequence can be projected onto the turbine power curve, including cut-in / rated / cut-out wind speeds, and corrected for differences in air density. A simplified power loss factor can be introduced when considering wind shear and wake. On the photovoltaic side, irradiance and temperature models, such as the NOCT / single diode model, can be used to calculate the DC power of the modules, and then multiplied by the inverter efficiency to obtain the AC power. If only relative evaluation is required, the power of both sources can be normalized to the per-unit value of their respective installed capacity. A power ratio is set, and the power of each source is scaled and superimposed according to the planned installed capacity. If necessary, grid connection / curtailment / ramp constraints can be added to couple with local loads to form a net load sequence. By scanning the candidate set of power ratio r, such as from 0.2:0.1 to 3.0, the combined power curve under each power ratio scheme can be obtained. This explicitly maps the installed capacity planning variables to the shape differences of the combined sequence. At the selected time scale (e.g., intraday / hourly, daily / ten-day, monthly / quarterly), the process line lengths of the wind / solar / combined power sequences are calculated respectively. To suppress measurement noise or very short-term spikes, a slight band-limited filter / sliding median can be applied to the power first, without changing the trend or introducing phase delay; then the discrete arc length is calculated using the above formula. Multi-scale parallel computing can reveal scale differences where intraday complementarity is good but seasonality is poor / opposite, supporting hierarchical optimization. For each mix design, the PLCI is calculated; a higher value indicates a smoother joint and stronger complementarity. Sensitivity curves can also be output to identify the sensitivity range of complementary benefits to mix design changes, guiding fine-tuning of installed capacity. PLCI results support site selection, energy storage configuration, and scheduling strategy formulation. For site selection, driven by historical weather conditions at multiple candidate sites, the peak PLCI and robustness (cross-year / cross-seasonal differences) under the same mix design scan are compared, selecting sites and mix design combinations with high PLCI and cross-year robustness. For energy storage configuration, a higher PLCI results in a smoother joint curve, equivalent to reduced ramp-up and high-frequency component energy; the net ramp-up energy to be smoothed can be correlated with energy storage power / capacity through frequency band decomposition or net load fluctuation integration, thereby pushing down the rated energy storage, equivalent cycle, and capacity. For scheduling optimization, with the goal of minimizing the combined process line length or maximizing PLCI as the soft objective, constraints such as standby, ramping, and power curtailment are combined to obtain a quantifiable reduction in intraday / day-ahead unit combination and AGC load.For operational risk management, rolling estimates of the Power Process Complement (PLCI) can be coupled with weather forecasts to identify periods of weakened complementarity, such as a downward trend in the PLCI. This allows for advance planning of backup and gas turbine start-up / shutdown strategies, reducing capacity and ramp-up risks. Therefore, the relative process line complementarity index has the advantages of dynamic sensitivity and physical intuition. Changes in the process line length essentially reflect the cumulative degree of the power curve slope. If wind and solar power outputs fluctuate in opposite directions within a certain period, the slopes of their combined curve will cancel each other out, significantly shortening the total length of the combined curve, indicating enhanced complementarity. Compared to relying solely on statistical indicators such as correlation coefficients, this method more accurately depicts the magnitude of fluctuations and ramp-up, as well as the size of energy changes, thus being closer to the physical processes in grid peak shaving and energy storage applications. Furthermore, this method supports robust assessment across time scales. A region may exhibit strong wind-solar complementarity on an intraday scale but relatively weaker on a seasonal scale, or vice versa. By calculating the complementarity index separately at different time scales, these differences can be intuitively revealed, avoiding erroneous decisions caused by single-scale analysis. For example, if intraday complementarity is strong, power-type energy storage is more suitable to alleviate ramp-up pressure; if seasonal complementarity is more significant, energy-type energy storage is more suitable to balance the differences between wet and dry seasons. Furthermore, this method has a quantifiable connection with energy storage and grid dispatch. The shortening of the curve length essentially represents an overall reduction in the rate of power change, which means a simultaneous decrease in the system's burden in frequency regulation, ramp-up control, and energy storage charging and discharging. In other words, the higher the complementarity index, the lower the regulation costs and energy storage cycle intensity required for grid operation, and the higher the system's operating efficiency and stability.

[0041] Understandably, in extreme weather conditions, such as gusts of wind or cloud shadows rapidly passing over photovoltaic arrays, the power curve will exhibit sharp fluctuations shorter than the sampling interval. Conventional PLCI systems ignore these disturbances due to insufficient sampling frequency, resulting in overly optimistic assessments, but the system still needs to bear these hidden ramp-up risks.

[0042] To address the above problems, according to some embodiments, the method further includes: Acquire high-frequency meteorological observation time series data of the target area, including laser wind radar data and panoramic cloud image data; Based on the high-frequency meteorological observation time series data, sub-interval interpolation and reconstruction are performed on the renewable energy power curve of the target area to supplement the high-frequency fluctuation components not reflected in the low-frequency sampled power curve; The length of the process line of the renewable energy power curve containing the high-frequency fluctuation component is calculated as a function of time on the target time scale. A secondary correction term is introduced in the arc length calculation to evaluate the relative process line complementarity index of the target region based on the corrected process line length.

[0043] For example, high-frequency disturbances, such as gusts and cloud shadows, often occur on a minute or even second-by-minute scale. However, conventional SCADA / resource assessment data is mostly at a 5-15 minute resolution. Directly calculating the process arc length based on this data will miss the ramp-up and peaks within sub-intervals, leading to an overestimation of the relative process line complementarity index (PLCI) and consequently an overly optimistic assessment of energy storage and reserve requirements. High-frequency meteorological observations, such as those using laser wind radar and panoramic cloud images, can be used as a magnifying glass to perform sub-interval interpolation and physical constraint reconstruction of the low-frequency power curve, supplementing the high-frequency components swallowed by averaging. Subsequently, a secondary correction term is introduced into the arc length calculation, incorporating the reconstructed arc length increment into the total arc length, making the PLCI more sensitive to actual joint fluctuations and closer to operational physics. High-frequency meteorological observation time series data for the target area can be obtained. High-frequency wind field and cloud field observations can be simultaneously acquired in the target area. For the wind side, a laser wind-measuring radar is preferred, scanning by azimuth / elevation to acquire wind profiles and turbulence intensity. This radar, in conjunction with a wind-measuring tower, is used for time and amplitude calibration. For the light side, a panoramic cloud image camera is preferred, supplemented by a cloud base altimeter or radiation station for cloud height and irradiance benchmark calibration. All sensor timestamps are unified to the same clock source. Missing, obstructed, and abnormal frames are removed, and a high-frequency driving set is generated, providing minute-level wind speed / wind vector / turbulence indices, cloud cover / cloud motion vector / hidden area masking, and effective irradiance estimates for the surface and component planes. This provides the physical basis for subsequent sub-interval reconstruction of power curves. Then, based on the aforementioned high-frequency meteorological observation time series data, sub-interval interpolation and reconstruction of the renewable energy power curves for the target area are performed. The wind profile on the wind side can be extrapolated to the turbine hub height and combined with turbulence indices and gust factors to obtain a minute-level wind speed trajectory. This trajectory is then mapped to minute-level wind power by applying turbine power curves and dynamic response constraints, including cut-in / rated / cut-out, ramp constraints, rotor inertia, and control lag. On the solar side, the cloud movement of the last few tens of minutes is estimated using the optical flow field of a panoramic cloud image. Combined with the cloud base height, the cloud shadow is projected onto the power station plane to obtain minute-level irradiance disturbance. This is then superimposed with temperature effects, inverter limits, and series-parallel shading effects to reconstruct minute-level photovoltaic power. For the low-frequency SCADA sequences of both wind and solar power, simple linear interpolation is replaced with physically driven subdivided trajectories within each 5–15 minute sampling window. Simultaneously, energy consistency constraints are applied. The average of the reconstructed minute values ​​is equal to the original window average and impossible domain constraints such as non-negativity, no limit exceedance, and no ramp jump, forming a single-source power curve containing high-frequency components and its joint power curve. Next, the process line length of the power curve containing the high-frequency fluctuation component is calculated at the target time scale, and a secondary correction term is introduced in the arc length calculation to evaluate the relative process line complementarity index.At a selected time scale, the process lengths of wind power, photovoltaic power, and their combined power curves are calculated over time. To avoid underestimation due to uncertainties in the sampling and reconstruction model, a secondary correction term is set. This term is derived from the cumulative difference in arc length between the reconstructed curve and the linear interpolation curve within the same window, or from a conversion based on the confidence weight of the energy proportion of the reconstructed high-frequency components. This value is then added to the total arc length to form the corrected arc length. Finally, the relative process length complementarity index is calculated using the corrected wind, solar, and combined arc lengths, and confidence intervals and data quality indicators are output simultaneously for use by the planning and scheduling sides. Thus, high-frequency observation-driven reconstruction makes the averaged ramps and peaks explicit. The corrected arc length is closer to the actual fluctuations than the original arc length, enabling PLCI to identify special scenarios that appear smooth but are actually flickering. The introduction of unit dynamics and ramp constraints on the wind side, and inverter limits and cloud shadowing on the solar side, prevents PLCI from being misled by ideal curves. The corrected arc length has its own interpretable correction term and confidence output, which can be directly linked with reserve capacity assessment and energy storage lifetime models to reduce investment and operational deviations.

[0044] Understandably, in some regions, wind-solar hybridization does make the total output smoother, but the smoothed curve is out of sync with the local load peak. For example, in summer, wind power at night combined with solar power during the day forms a valley, resulting in a very weak peak shaving and valley filling capacity.

[0045] To address the above problems, according to some embodiments, the method further includes: Acquire meteorological time-series data of the target area associated with the renewable energy source and load time-series data of the target area; 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. When calculating the process line lengths of the first renewable energy power output, the second renewable energy power output, and the combined power output over time at the target time scale, the load peak period option weights determined by the load time series data are introduced to give higher weights to the arc length fluctuations during the load peak period. The relative process line complementarity index of the first and second renewable energy sources in the target area is evaluated based on the process line length of the introduced peak load option.

[0046] For example, within the target area, meteorological time-series data related to renewable energy sources such as wind power and photovoltaics are first acquired, including wind speed, wind direction, air pressure, irradiance, and temperature. These data are the driving factors for calculating power output. Simultaneously, load time-series data of the regional power grid is collected, requiring data timestamp alignment to ensure the correspondence between resource output and load demand within the same time period. Through data cleaning and anomaly removal, a high-quality joint meteorological and load database is formed. The effectiveness of complementarity depends not only on the fluctuation relationship between wind and solar power but also on the degree of matching with load demand. This ensures that the subsequently calculated complementarity indicators accurately reflect the combination of power source smoothness and load-side adaptability. Based on the aforementioned meteorological time-series data, power curve models for wind turbines and power prediction models for photovoltaic modules are established, considering factors such as air density, inverter efficiency, and temperature effects, to calculate the wind power output and photovoltaic power output sequences within the target area. To ensure calculation accuracy, power can be normalized according to the actual installed capacity. Meteorological conditions are converted into renewable energy output through physical and empirical models, enabling the power time series to accurately reflect resource fluctuation characteristics. This provides fundamental data input for subsequent joint power calculations and complementarity assessments. When calculating the process line lengths of wind power output, photovoltaic power output, and their combined power output over time at the target time scale, additional load time series information is introduced, assigning higher weight to peak load periods. For example, when the grid is in peak evening electricity consumption or summer air conditioning load peaks, the impact of fluctuations in the combined power curve on system operation is significantly amplified; therefore, the arc length increments during these periods are magnified. The operational risks of the power system are not equal across all periods, but fluctuations during peak load periods are more likely to trigger reserve shortages, frequency deviations, or voltage fluctuations. Therefore, a weighted approach is used to ensure that the complementarity index better reflects the actual adaptation to critical load periods, rather than simply smoothing out averaged fluctuations. Finally, based on the process line lengths weighted for peak load periods, a modified relative process line complementarity index is calculated and used to assess the wind and photovoltaic complementarity performance in the target area. Compared to traditional methods, this complementarity index not only reflects the reduction of joint fluctuations on the resource side but also quantifies the effectiveness of this reduction during critical load periods. This combines power complementarity with load demand, elevating the process from physical smoothing to load adaptation. It can provide more valuable complementary metrics for planning and operation. For example, in grid dispatching, it can clarify whether a certain wind and solar power ratio can provide stable power supply during the evening peak load period, thus providing a precise basis for energy storage configuration, reserve capacity arrangement and dispatch strategy formulation.

[0047] It is understandable that when wind power and solar power are statistically complementary, but the dominant frequency bands of their energy fluctuations are different, such as wind power having low frequency during the day and solar power having high frequency during the day, the PLCI increases after being superimposed. However, for a certain type of equipment, such as thermal power units, it can only respond to low frequency ramp-up and has no mitigating effect.

[0048] To address the above problems, according to some embodiments, the method further includes: Obtain meteorological time-series data of the target area associated with the renewable energy source; 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. 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; 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. 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.

[0049] 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.

[0050] 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.

[0051] Please see Figure 4 One embodiment of the regional renewable energy complementarity assessment device in this application may include: Acquisition unit 21 is used to acquire meteorological time series data of the target area associated with the renewable energy source; 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; 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.

[0052] 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.

[0053] 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: Obtain meteorological time-series data of the target area associated with the renewable energy source; 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; 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.

[0054] 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.

[0055] 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: Obtain meteorological time-series data of the target area associated with the renewable energy source; 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; 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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... Figure 1 The process for assessing the regional renewable energy complementarity in the corresponding embodiment.

[0062] 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)).

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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, include: Obtain meteorological time-series data of the target area associated with the renewable energy source; 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; 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, respectively, in order 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 joint fluctuation characteristics of different renewable energy sources.

2. The method as described in claim 1, characterized in that, The assessment of the complementarity index of the first and second renewable energy sources in the target area based on the process line length includes: Based on formula Calculate the relative process line complementarity index of the primary and secondary renewable energy sources in the target region, where, The relative process line length of the first renewable energy power series is used to characterize the intensity of wind power fluctuations. The relative process line length of the second renewable energy power series is used to characterize the intensity of photovoltaic fluctuations. The relative process line length, obtained by combining the first and second renewable energy sources according to their weights, is used to characterize the fluctuation intensity of the combined output of the first and second renewable energy sources. and These are the weighting factors for the installed capacity of the first and second renewable energy sources, respectively.

3. The method as described in claim 2, characterized in that, 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.

4. The method as described in claim 3, characterized in that, The process line length Through formula Obtained through calculation; The rated power process line length Through formula Obtained through calculation; Combined power Represented as ; Combined rated power Represented as ; in, X It is a type of renewable energy. For time interval resolution, To evaluate the time scale of the smallest computational unit; The power difference between adjacent time periods. Rated power for renewable energy.

5. The method as described in claim 3, characterized in that, When the first renewable energy source is wind power and the second renewable energy source is solar energy, the meteorological time series data includes wind speed time series data, surface horizontal irradiance time series data, and temperature time series data; With a target timescale of 24 hours, the smooth path of zero solar output at night is eliminated to avoid underestimating the complementarity of renewable energy.

6. The method as described in claim 3, characterized in that, The process of eliminating stable solar power output paths at night when the target timescale is 24 hours includes: Given a target timescale of 24 hours, the process length of solar energy Through formula Calculated in, For time interval resolution, To evaluate the time scale of the smallest computational unit, The power difference between adjacent time periods. The rated power of the photovoltaic module, and the index is defined as follows: when the photovoltaic output is zero for consecutive moments. The corresponding incremental term becomes 0, and the corresponding rated process line length... Adjusted to .

7. The method according to any one of claims 2 to 6, characterized in that, Also includes: exist PLCI When =0, it indicates that there is no complementary effect between renewable energy sources; In 0< PLCI When the value is less than 1, it indicates that there is a complementary effect between renewable energy sources, and the closer the value is to 1, the better the effect. exist PLCI When <0, it indicates that renewable energy sources are negatively complementary.

8. A regional renewable energy complementarity assessment device, characterized in that, include: An acquisition unit is used to acquire meteorological time-series data of the target area associated with the renewable energy source; The calculation unit 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. The evaluation unit 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 over time at a target time scale, 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.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program stored in the memory, implements the steps of the regional renewable energy complementarity assessment method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the regional renewable energy complementarity assessment method as described in any one of claims 1-7.

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