Method for quantifying complementarity of renewable energy sources and related device

Abstract

The application discloses a renewable energy complementarity quantification method and related equipment. The method comprises the following steps: acquiring time series data corresponding to at least two types of renewable energy, and constructing a representation sequence of each renewable energy subsystem; based on the installed capacity proportion of each renewable energy subsystem, the representation sequences are combined to obtain a joint system representation sequence; for a preset duration scale, a ramp rate sequence is calculated, and a ramp strength corresponding to each ramp rate sequence is determined based on a preset norm order; and according to the joint system ramp strength and the reference ramp strength obtained by weighting the ramp strength of each renewable energy subsystem according to the installed capacity proportion, a renewable energy complementarity index is calculated. The method can solve the problem that the existing analysis method has weak dynamic sensitivity, poor cross-scale stability and poor physical intuitiveness when quantifying complementarity, and cannot directly reflect the reduction degree of joint output fluctuation relative to independent output fluctuation and the potential impact on system operation.

Description

Technical Field

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

[0002] As the penetration rate of renewable energy sources such as wind and solar power in the power system continues to increase, their intermittency and volatility pose challenges to the stable operation of the power grid. Existing indicators and methods for quantifying the complementarity of renewable energy sources include correlation coefficients, stability coefficients, and slope complementarity ratios. There are various existing methods for quantifying the complementarity of wind and solar power, such as the correlation coefficient (…). ), which only characterizes the synchronous or reverse relationship of output in the numerical direction, and cannot reflect the ramp-up magnitude and reduction of regulation pressure of combined wind power and photovoltaic output at different time scales; stability coefficient ( C_stab This only reflects the average stability improvement of resources on a daily scale, and cannot identify intraday fluctuations or seasonal upswing behavior, nor is it sensitive to extreme events; slope complementarity ( R_SL These methods focus only on the average absolute ramp rate between two adjacent sampling points, lacking the ability to characterize ramp rates across multiple time scales and extreme ramp rates. Furthermore, they deviate from the logic of capacity-weighted combination comparisons, making it difficult to stably and accurately quantify the real alleviating effect of wind-solar complementarity on grid ramp regulation needs. It should be noted that existing methods for quantifying renewable energy complementarity lack intuitive physical meaning and do not directly correspond to the regulation pressures actually concerned in grid operation (such as ramp regulation needs, reserve configuration, and flexibility constraints). At the same time, most of these indicators cannot be used to quantitatively calculate the optimal ratio between different renewable energy sources, meaning they cannot objectively solve for the reasonable allocation ratio of wind and solar installed capacity based on resource complementarity. This deficiency leads to insufficient engineering interpretability of the complementarity effect, making it difficult to support capacity optimization and grid planning decisions under multi-energy, multi-site conditions. 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] To address the shortcomings of existing analytical methods in quantifying complementarity, such as weak dynamic sensitivity, poor cross-scale stability, and poor physical intuitiveness, which prevent them from directly reflecting the reduction in combined power output fluctuations relative to individual independent power output fluctuations and their potential impact on system operation, this invention proposes a method for quantifying renewable energy complementarity. This method includes: Obtain time series data corresponding to at least two types of renewable energy, and construct a characterization sequence for each renewable energy subsystem based on the time series data; Based on the installed capacity ratio of each renewable energy subsystem, the characterization sequences are weighted and combined to obtain the joint system characterization sequence. For a preset duration scale, the ramp rate sequence of each renewable energy subsystem and the joint ramp rate sequence corresponding to the joint system characterization sequence are calculated respectively, and the ramp intensity corresponding to each ramp rate sequence is determined based on a preset norm order. Based on the benchmark ramp intensity obtained by weighting the ramp intensity of the combined system and the ramp intensity of each of the renewable energy subsystems according to their installed capacity ratio, a renewable energy complementarity index is calculated. The renewable energy complementarity index is used to characterize the degree of reduction in ramp intensity of the combined system relative to the independent operation of each subsystem.

[0005] Secondly, the present invention also proposes a renewable energy complementarity quantification device, comprising: An acquisition unit is used to acquire time series data corresponding to at least two types of renewable energy, and construct a characterization sequence of each renewable energy subsystem based on the time series data; A combination unit is used to weight and combine the characterization sequences based on the installed capacity ratio of each of the renewable energy subsystems to obtain a joint system characterization sequence. Computational unit, used for: For a preset duration scale, the ramp rate sequence of each renewable energy subsystem and the joint ramp rate sequence corresponding to the joint system characterization sequence are calculated respectively, and the ramp intensity corresponding to each ramp rate sequence is determined based on a preset norm order. Based on the benchmark ramp intensity obtained by weighting the ramp intensity of the combined system and the ramp intensity of each of the renewable energy subsystems according to their installed capacity ratio, a renewable energy complementarity index is calculated. The renewable energy complementarity index is used to characterize the degree of reduction in ramp intensity of the combined system relative to the independent operation of each subsystem.

[0006] 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 renewable energy complementarity quantification method as described in any of the first aspects above.

[0007] 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 renewable energy complementarity quantification method of any of the preceding claims in the first aspect.

[0008] In summary, the renewable energy complementarity quantification method proposed in this application obtains time-series data corresponding to at least two types of renewable energy, and constructs a characterization sequence for each renewable energy subsystem based on the time-series data. Based on the installed capacity ratio of each renewable energy subsystem, the characterization sequences are weighted and combined to obtain a joint system characterization sequence. For a preset duration scale, the ramp rate sequence for each renewable energy subsystem and the joint ramp rate sequence corresponding to the joint system characterization sequence are calculated, and the ramp intensity corresponding to each ramp rate sequence is determined based on a preset norm order. Based on the benchmark ramp intensity obtained by weighting the ramp intensity of the joint system with the ramp intensity of each renewable energy subsystem according to the installed capacity ratio, a renewable energy complementarity index is calculated. This index characterizes the degree of reduction in ramp intensity of the joint system relative to the independent operation of each subsystem. Traditional correlation coefficients only indicate the degree of synchronous or inverse changes between two sequences, but do not directly correspond to how much power the grid needs to adjust or how much reserve is prepared. This scheme directly starts from the ramp intensity, transforming complementarity into a quantification of the degree of reduction in regulation pressure, thus better aligning with the actual operational concerns of the power system. By using different step sizes corresponding to different duration scales, complementary characteristics on hourly, daily, and even longer time scales can be analyzed simultaneously, unlike some existing methods that can only describe statistical relationships on a single scale. By setting the norm order, targeted analysis of daily system regulation needs, uncertainty pressures caused by large fluctuations, and reserve requirements during extreme times can be achieved, thus moving complementarity assessment beyond mere average values. Since both the joint system characterization sequence and the baseline ramp-up intensity explicitly depend on the installed capacity ratio, this indicator can not only be used for evaluation but also for solving optimization schemes. This method can be used for operational assessment of existing projects as well as for planning and site selection of proposed projects; it can be applied to both wind and solar power, and can be extended to multiple wind farms, multiple solar farms, wind-solar-hydro-storage synergy, and even extended to other time-series variables that reflect regulation pressure, such as net load and prediction errors.

[0009] The renewable energy complementarity quantification 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

[0010] 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 1A schematic diagram of a method for quantifying the complementarity of renewable energy provided in this application embodiment; Figure 2 A schematic diagram of the daily power generation time series of wind power, photovoltaic power and combined system of four wind-solar hybrid power stations provided in the embodiments of this application; Figure 3 A schematic diagram comparing the monthly ramp intensity complementarity index (RCI) of four wind-solar hybrid power stations provided in this application embodiment; Figure 4 A schematic diagram of a renewable energy complementarity quantification device provided in this application embodiment; Figure 5 This is a schematic diagram of a renewable energy complementarity quantification electronic device provided in an embodiment of this application. Detailed Implementation

[0011] 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.

[0012] To address the shortcomings of existing analytical methods in quantifying complementarity, such as weak dynamic sensitivity, poor cross-scale stability, and limited physical intuition, which prevent them from directly reflecting the reduction in combined output fluctuations relative to individual independent output fluctuations and their potential impact on system operation, please refer to [link to relevant documentation]. Figure 1 This is a schematic diagram of a method for quantifying the complementarity of renewable energy provided in an embodiment of this application, which may specifically include steps S110 to S140.

[0013] S110, acquire time series data corresponding to at least two types of renewable energy, and construct a characterization sequence for each renewable energy subsystem based on the time series data.

[0014] S120, based on the installed capacity ratio of each of the renewable energy subsystems, the characterization sequences are weighted and combined to obtain the joint system characterization sequence.

[0015] S130, for a preset duration scale, calculate the ramp rate sequence of each of the renewable energy subsystems and the joint ramp rate sequence corresponding to the joint system characterization sequence, and determine the ramp intensity corresponding to each ramp rate sequence based on a preset norm order.

[0016] S140, based on the benchmark ramp intensity obtained by weighting the ramp intensity of the combined system and the ramp intensity of each of the renewable energy subsystems according to their installed capacity ratio, a renewable energy complementarity index is calculated. The renewable energy complementarity index is used to characterize the degree of reduction in ramp intensity of the combined system relative to the independent operation state of each subsystem.

[0017] It is understandable that the complementarity between renewable energy sources should not be judged solely by whether their power output curves show numerical differences, but rather by whether their combined operation can substantially reduce the rate of change in the system's net power output, thus reducing the grid's ramp-up pressure. In other words, this scheme transforms complementarity into a more engineering-significant problem: when wind and solar power are connected together at a certain installed capacity ratio, does the combined power output sequence change more smoothly over a given time scale than the capacity-weighted fluctuations when they operate independently? If it is smoother, it indicates that the rise or fall of one energy source has a time-dependent effect of offsetting, buffering, or weakening the change in the other, thereby reducing the burden on standby units, frequency regulation resources, or flexibility adjustment units. This degree of reduction can be defined as the ramp-up strength complementarity index (RCI), which has the advantages of intuitive physical meaning, strong applicability across time scales, ability to identify extreme fluctuation risks, and direct applicability for optimizing installed capacity ratios. Furthermore, this scheme does not directly perform a simple correlation judgment on the original power series. Instead, it first converts different energy sources into comparable representative series, such as capacity factor time series. Then, based on this unified representation, it constructs a joint system series. Next, it calculates the ramp rate for both individual energy sources and the joint system, and then uses a norm to condense a large number of discrete ramp rate samples over a period of time into a single ramp intensity value. Since the norm order can adjust the sensitivity to large fluctuation events, the same method can characterize average regulation pressure, large fluctuation risks, and even specifically extreme ramp risks. Finally, by comparing the joint system ramp intensity with a benchmark value obtained by weighting the independent ramp intensities of each subsystem according to their installed capacity proportion, the proportion of regulation pressure reduced by joint operation relative to independent operation can be obtained. The larger this proportion, the stronger the complementarity.

[0018] For example, the physical meanings and dimensions of raw data from different renewable energy sources often differ, making direct comparison impossible. For instance, wind power data might be hub-height wind speed, while photovoltaic data might be horizontal irradiance and ambient temperature. Directly combining wind speed and irradiance not only lacks physical consistency but also fails to directly correlate with power output fluctuations in the power system. Therefore, it's necessary to first convert various raw time series into sequences that uniformly characterize the available power output state on the generation side. The capacity factor is preferred as the characterization variable because it describes the ratio of actual output to theoretical maximum output, and its value has a uniform scale, eliminating the incomparability caused by differences in installed capacity and rated capacity of different energy sources. Furthermore, the wind power capacity factor originates from the mapping from wind speed to unit output power, while the photovoltaic capacity factor originates from the mapping from factors such as irradiance and module temperature to actual photovoltaic output. In other words, this step is not simply about reading the current success rate, but rather establishing a relationship between resource status and power output through resource-side data and equipment models. This ensures that the complementarity of subsequent calculations is not limited to existing power plants but can also be used in the planning, site selection, or resource assessment stages. Therefore, this method can use both measured data and reanalysis data like ERA5, giving it strong generalization ability. In implementation, the evaluation object can be determined first, such as a wind farm and a photovoltaic (PV) farm within a region, or multiple wind farms aggregated into a wind power system and multiple PV farms aggregated into a PV subsystem. Then, time-series data are collected or processed according to a uniform time resolution. If hourly analysis is used, hourly wind speed, hourly surface horizontal irradiance, and hourly ambient temperature data can be obtained. For the wind power system, based on the cut-in wind speed, rated wind speed, and cut-out wind speed of the selected wind turbine model, the hourly wind speed can be converted into hourly output power, and then divided by the rated output power to obtain the hourly wind power capacity factor sequence. For the PV subsystem, based on solar irradiance, module temperature correction coefficient, module rated operating parameters, and installation tilt angle, the hourly PV output power can be calculated, and then normalized to obtain the hourly PV capacity factor sequence. First, heterogeneous resource data such as wind speed, irradiance, and temperature are uniformly converted into comparable output characterization sequences, resolving the issue of inconsistent physical meanings of raw data across different energy types. Second, capacity factor normalization avoids the problem of distorted complementarity assessments caused by relying solely on installed capacity size. Third, it supports both measured operational data and reanalysis meteorological data, thus enabling its use in both operational assessments of existing projects and planning demonstrations of proposed projects. While RCI defaults to using the capacity factor as the characterization variable, it can also be extended to time-series variables such as net load and power prediction errors, demonstrating the inherent scalability of this characterization layer design.

[0019] For example, the power grid receives not abstract signals from the wind power and photovoltaic sequences, which exist independently, but rather the total net injected power formed by their combined actual installed capacity. In other words, complementarity should be examined within the context of the actual system combination, not simply by averaging the two sequences equally. The combined system capacity factor sequence is obtained by weighting the capacity factors of each component according to their installed capacity proportion. Essentially, this reflects the physical contribution of different subsystems to the overall system using capacity weights. Ignoring the installed capacity proportion leads to a common misjudgment: a system with a small but highly volatile installed capacity may be overestimated in terms of impact under equal weighting; conversely, a system with a large installed capacity may have its true dominant role underestimated. By using installed capacity proportion weighting, the combined system characterization sequence can more accurately reflect the actual output state of the overall system, laying the foundation for subsequent comparable comparison with the capacity-weighted benchmark ramp-up intensity. In other words, this step reflects the physical reality of the combined system and ensures that the numerator and denominator of the subsequent reduction ratio calculation have the same reference system. In implementation, the installed capacity of each subsystem can be calculated first. For example, if the installed wind power capacity is 300 MW and the installed photovoltaic capacity is 200 MW, the total installed capacity is 500 MW. The wind power capacity accounts for 0.6 and the photovoltaic capacity accounts for 0.4. Subsequently, the combined system capacity factor at the corresponding time can be obtained by multiplying the wind power capacity factor by 0.6 and then adding the photovoltaic capacity factor multiplied by 0.4. For example, in a certain hour, the wind power capacity factor is 0.50 and the photovoltaic capacity factor is 0.20, so the combined system capacity factor is 0.6 multiplied by 0.50, plus 0.4, multiplied by 0.20, resulting in 0.38. As another example, in another hour, the wind power capacity factor decreases to 0.30 while the photovoltaic capacity factor increases to 0.70, so the combined system capacity factor is 0.6 multiplied by 0.30, plus 0.4, multiplied by 0.70, resulting in 0.46. It is evident that although fluctuations in a single energy source are significant, changes in the combined system can be partially offset, which explains the subsequent ramp-up reduction. The same approach can be applied to more than two energy sources. For example, if wind, solar, and hydropower systems account for 0.4, 0.35, and 0.25% respectively, the combined system characterization sequence is the weighted sum of the installed capacity percentages of the three subsystems. This explicitly incorporates the capacity contribution of each subsystem in practical engineering into the complementarity assessment, making the combined system characterization results more consistent with real grid connection scenarios. Simultaneously, it allows for adjustable parameters in the complementarity analysis, meaning the installed capacity percentage itself can be used as an optimization variable in subsequent optimal allocation solutions.

[0020] For example, the real pressure on the power grid is not whether the power is high or low at a particular moment, but how fast and drastically the power changes over a period of time. Therefore, system fluctuations must be characterized from the perspective of the rate of change over time. This rate of change can be defined as the ramp rate, and different time scales, such as 1 hour, 3 hours, and 24 hours, can be corresponded to by different differential step sizes. The reason for combining a preset time scale is that different regulatory resources face different time domains of pressure. Thermal power units may focus more on hourly ramp rates, energy storage systems may focus more on minute-level ramp rates, and inter-day dispatch may focus more on inter-day changes. If only analyzed on a single time scale, one-sided complementary conclusions may be drawn. For example, a wind-solar combination may be highly complementary on an hourly scale, but not necessarily on a daily scale; and vice versa. It is precisely to solve this problem that this method is emphasized to have cross-time scale stability and a unified characterization capability. The ramp rate sequence alone is not enough, because many ramp rate samples will be obtained within a statistical window. These samples must be compressed into an index that can represent the overall regulatory pressure level, which is the ramp intensity. This compression can be achieved using norms. A smaller norm order emphasizes the overall average level; a larger norm order emphasizes a few significant ramp events; when the order becomes very large, it reflects almost only extreme maximum ramps. Therefore, by selecting different orders, we can observe daily average regulation pressure, large fluctuation risks, and extreme reserve demands separately. In implementation, we can first determine the assessment time resolution and duration scale. Assuming the original data is sampled hourly, the sampling interval is 1 hour. If the difference step size is 1, it corresponds to a 1-hour time scale; if the difference step size is 3, it corresponds to a 3-hour time scale; and if the difference step size is 24, it corresponds to a 24-hour time scale. For each time series, the difference between the current moment and the value after a certain lag is taken, and then divided by the corresponding duration scale, to obtain the ramp rate at that scale. For example, if a wind power capacity factor series is 0.60 at hour 10 and 0.45 at hour 11, then the ramp rate for that period at the 1-hour scale is negative, indicating a decrease in output. If the rate is 0.60 at the 10th hour and 0.30 at the 13th hour, then the ramp rate on a 3-hour timescale reflects the average rate of decline over a longer period. The same calculation is performed for photovoltaic and combined systems, yielding wind power ramp rate sequences, photovoltaic ramp rate sequences, and combined system ramp rate sequences, respectively. Subsequently, the ramp intensity is calculated for each ramp rate sequence according to a preset norm order. If the order is 1, the average absolute ramp intensity is obtained, suitable for measuring daily average adjustment needs; if the order is 2, large ramp samples are more significantly amplified, suitable for reflecting greater fluctuation risks; if the maximum value is used, it reflects the challenge to reserve capacity during extreme times.By using multi-timescale differencing, it is possible to simultaneously identify short-term rapid fluctuations and long-term gradual trends, avoiding information loss caused by comparing only adjacent sampling points; secondly, by adjusting the norm order, it is possible to conduct refined assessments for different grid operation concerns, rather than using a single statistic to summarize all scenarios; thirdly, since single energy sources and combined systems are calculated using completely consistent scales and calibers, subsequent comparisons are strictly comparable.

[0021] For example, complementarity is not simply a matter of intuition that the combination results in a smoother performance; rather, it requires a clear benchmark. This benchmark can be defined as the weighted sum of the independent ramp intensities of each subsystem according to their installed capacity proportions. The combined system ramp intensity reflects the total changing pressure actually seen by the grid after they are truly connected and operating together; the benchmark ramp intensity reflects the pressure that should have been seen if the time-series offsetting between the two were not considered, and only the independent pressures were summed according to their capacity contributions. Normalizing the difference between the two yields the reduction ratio, also known as the complementarity index RCI. This definition is more reasonable because it precisely limits complementarity to a reduction in the regulating pressure of the combined system, rather than an abstract statistical inverse correlation. Even if the two sequences are not statistically strongly correlated, if their combined ramp intensities do not substantially decrease, they cannot be considered highly complementary; conversely, even without a very significant negative correlation, as long as they effectively reduce the combined ramp intensities on the time scale of interest to the grid, they can be considered to have engineering-value complementarity. When RCI equals 0, it indicates that the combined ramp-up intensity equals the capacity-weighted benchmark, and no ramp-up reduction effect occurs. When RCI is greater than 0, it indicates positive complementarity. The closer to 1, the higher the reduction ratio. In implementation, three quantities are first obtained: the ramp-up intensity of the wind power subsystem, the ramp-up intensity of the photovoltaic subsystem, and the ramp-up intensity of the combined system. Then, the first two quantities are weighted according to their installed capacity share to obtain the benchmark ramp-up intensity. For example, if the wind power share is 0.6 and the wind power ramp-up intensity is 0.20, and the photovoltaic share is 0.4 and the photovoltaic ramp-up intensity is 0.15, then the benchmark ramp-up intensity is 0.6 multiplied by 0.20 plus 0.4 multiplied by 0.15, resulting in 0.18. If the combined system ramp-up intensity is calculated to be 0.13, then the complementarity index is the ratio of the difference between the benchmark 0.18 and the combined 0.13 to the benchmark 0.18, which is approximately 0.278. This value indicates that the combined operation reduced the climbing pressure by approximately 27.8% at this time scale and statistical caliber. If the combined system's climbing intensity is exactly 0.18, the complementarity index is 0, indicating that although the two systems operated together, there was no substantial relief from the climbing pressure. If the combined system's climbing intensity is actually higher, for example, reaching 0.19, it means that the volatility has not improved or has even worsened after the combination. In this case, the complementarity index may be near zero or even lower, reflecting that the combination scheme is not ideal. Thus, all the preceding intermediate calculations are transformed into an interpretable, comparable, and optimizable final quantitative result.First, it clearly expresses the reduction in regulation pressure in proportion, facilitating horizontal comparisons between different projects, regions, and installation schemes. Second, it makes the indicator naturally suitable for ratio optimization, because maximizing RCI is equivalent to maximizing the ramp reduction effect of the combined system relative to the benchmark. Third, it combines physical intuitiveness with statistical rigor, and can directly serve engineering decisions such as planning, scheduling, and backup configuration.

[0022] In summary, this scheme constructs a joint system characterization sequence based on a unified characterization sequence for each renewable energy subsystem and its installed capacity ratio. Furthermore, it extracts the ramp rate sequences for each subsystem and the joint system under a preset duration scale, and calculates the corresponding ramp intensity based on a preset norm order. Finally, it uses the reduction ratio of the joint system ramp intensity relative to the baseline ramp intensity obtained by weighting the independent ramp intensities of each subsystem by installed capacity ratio as a complementary indicator. This achieves a unified quantitative assessment of the smoothing effect of combined renewable energy output, the degree of mitigation of regulation pressure, and the ability to reduce extreme fluctuation risks. This scheme can accommodate complementary analyses under different time scales, risk profiles, and installed capacity structures, possessing clear physical meaning and strong engineering applicability.

[0023] In some examples, the characterization sequence is a capacity factor time series, and the joint system characterization sequence is obtained by linearly weighting the capacity factor time series of each renewable energy subsystem according to the corresponding installed capacity ratio.

[0024] In some examples, calculating the ramp rate sequence for each of the renewable energy subsystems and the joint ramp rate sequence corresponding to the joint system characterization sequence for a preset duration scale includes: The preset duration scale is determined based on the sampling time interval and the differential step size; According to the difference step size, the numerical values ​​of the representation sequence with an interval of the difference step size between adjacent time points are differentially calculated; The corresponding ramp rate sequence is obtained by normalizing the data using the preset duration scale.

[0025] In some examples, determining the climbing intensity corresponding to each climbing rate sequence based on a preset norm order includes: When the preset norm order is 1, the average absolute climbing intensity is determined based on the average absolute value of the climbing rate sequence. When the preset norm order is greater than 1 and is a finite value, the climbing intensity at the corresponding order is determined based on the norm of the climbing rate sequence at the preset norm order. When the preset norm order approaches infinity, the extreme climbing intensity is determined based on the maximum absolute value in the climbing rate sequence. Different norm orders correspond to different regulatory pressure representations, reflecting daily regulatory needs, risks of large fluctuations, and risks of extreme ramp-up events, respectively.

[0026] Understandably, the above scheme maps different types of regulation pressures faced by the power grid to different norm orders. A larger norm order makes it more sensitive to larger components in the sequence; in the case of ramp rate sequences, this means that different norm orders will assign different levels of attention to power changes of varying magnitudes. The same set of ramp rate samples does not have only one interpretation of intensity. Averaging the absolute values ​​of all samples yields the average absolute ramp intensity. This statistical approach ensures that each sample participates in the result formation in a relatively balanced manner, making it more suitable for describing the average regulation demand experienced by the power grid during most normal operating periods. If the norm order is further increased, larger ramp samples will be statistically amplified more prominently. This is because large values ​​increase rapidly with higher powers, thus occupying a higher weight in the overall metric. Therefore, when the norm order is greater than 1 and is a finite value, the obtained ramp intensity is no longer primarily determined by ordinary samples, but rather focuses more on larger fluctuation events.

[0027] For example, the pre-defined norm order can be selected based on the actual assessment objectives. If the focus is on the system's daily average regulatory pressure, the norm order is set to 1. In this case, the absolute value of the ramp rate at each moment in the ramp rate sequence is taken, and then its average level is calculated to obtain the average absolute ramp intensity. For instance, if 720 one-hour ramp rate samples are obtained within a month, the average of the absolute values ​​of these 720 samples is taken to obtain the average absolute ramp intensity of that month at the one-hour scale. Then, the average absolute ramp intensity of the joint system is compared with the capacity-weighted benchmark to obtain the complementarity index under the corresponding caliber. If the focus is on the risk of larger fluctuations, the norm order is set to 2, or other finite values ​​greater than 1 are taken. In this case, the absolute value of each ramp rate sample is first taken and raised to the corresponding order, then all samples are aggregated, and finally the corresponding order is restored. In this way, larger samples will be more prominently reflected in the statistics. For example, in two sets of samples, the typical hourly ramp rate is mostly around 0.05, with a few hours reaching 0.30. When using a norm order of 2, 0.30 contributes significantly more to the results than 0.05, thus more clearly reflecting whether there are moderately large ramps that pose a greater challenge to the system. If focusing on extreme events, the norm order is made very large, and the maximum absolute value in the ramp rate sequence can be directly taken as the extreme ramp intensity. For example, in a one-year series, if the largest 1-hour net drop is 0.42, this value reflects the most severe ramp impact that the system may bring to the power grid at that scale. In some implementations, the norm order can be pre-set to multiple candidate values, such as 1, 2, and extreme port diameter, outputting three types of ramp intensity and three types of RCI results for different application scenarios. This expands complementarity evaluation from a single smoothness assessment to a multi-level risk assessment. Traditional indicators often only provide an overall correlation or average smoothness. This approach, by using different norm orders, allows the same method to reflect average regulation demand, significant fluctuation risk, and extreme reserve demand separately. This enhances the engineering interpretability of complementary indicators. Power grid operation is not only concerned with average values ​​but also with moderate to severe fluctuations and extreme abrupt changes. This approach maps these different concerns to different norm orders, allowing the output to directly serve different operational departments. For example, dispatching departments may be more concerned with average or root-mean-square (RMS) figures, while safety verification departments may be more concerned with extreme cases. This improves the ability to identify extreme events. Existing methods generally suffer from insufficient identification of low-probability, high-impact ramp-up events, and higher-order norms and maximum values ​​are important means to address this problem.

[0028] In some examples, the preset duration scale includes multiple duration scales, the preset norm order includes multiple norm orders, and the method further includes: Calculate the renewable energy complementarity index under each duration scale and each norm order combination; The complementary indicators of each renewable energy source are weighted and aggregated according to preset weights to obtain a comprehensive complementary indicator. The preset weights are used to characterize the importance of ramp adjustment scenarios corresponding to different duration scales and different norm orders. The preset weights are determined based on at least one of the power system's adjustment cost coefficient for different ramp scenarios, reserve cost curve, and marginal cost of flexibility resources, so as to improve the adaptability of the comprehensive complementarity index to the grid adjustment needs.

[0029] Understandably, even with different risk assessment criteria, evaluating performance on only a single timescale can still lead to distortions. Existing methods lack a unified characterization of fluctuations and ramp-up characteristics across different timescales. This invention addresses this issue by combining multi-scale difference and Lp norm. The ramp-up pressure faced by the power grid is not confined to a single timescale. Ramp-up on short timescales primarily affects rapid adjustment resources, such as energy storage, automatic generation control, and rapid backup; ramp-up on longer timescales impacts unit combination, load shifting, and inter-period dispatching. Using only a single timescale, such as a 1-hour scale, may overestimate short-term complementarity and underestimate long-term gradual change risks; conversely, focusing solely on a daily scale may ignore rapid intraday fluctuations. Furthermore, different norm orders correspond to different risk assessment criteria. Therefore, to comprehensively evaluate complementarity, multiple timescales and multiple norm orders should be considered simultaneously, and these should be weighted and aggregated according to preset weights to form a comprehensive complementarity index.

[0030] For example, the weights can be further linked to the system regulation cost coefficient, reserve cost curve, and marginal cost of flexibility resources. The weights can be set based on the regulation cost coefficient of the system for different time scales of ramping, and this coefficient can be determined by planning experience parameters, reserve cost curves, or marginal costs of regulation resources. Different time scales and different fluctuation levels result in different economic costs to the power grid. Some ramping events, although small in magnitude, occur at time scales where system regulation capacity is strained, requiring high regulation costs; some fluctuations, although long in duration, can be absorbed by slow adjustments from conventional units, resulting in lower marginal costs. If the weights can reflect these cost differences, then the comprehensive complementarity index is not merely a statistical summary, but a summary in an operational economic sense. This allows the comprehensive complementarity index to more closely reflect the true economic costs of the system. Compared to simply assigning weights based on experience, determining weights based on regulation costs and marginal costs allows the evaluation results to truly reflect the value of complementarity.

[0031] In some examples, it also includes: The installed capacity ratio of at least one type of renewable energy is used as the variable to be optimized, and the renewable energy complementarity index or comprehensive complementarity index under different installed capacity ratio conditions are traversed or scanned. A target installed capacity ratio is determined to achieve the optimal ratio of the renewable energy complementarity index or comprehensive complementarity index, and the target installed capacity ratio is used as the optimized allocation result of each renewable energy source in the corresponding region.

[0032] It is understandable that the joint system characterization sequence is itself obtained by weighting the characterization sequences of each subsystem according to their installed capacity ratio. Therefore, the installed capacity ratio is not an externally fixed parameter, but a core variable directly embedded in the joint system construction and complementarity index calculation process. Changing the wind power ratio or the photovoltaic ratio will change the joint system capacity factor sequence, as well as the joint ramp rate sequence, joint ramp intensity, and the final RCI. In other words, the RCI is explicitly sensitive to the installed capacity ratio. Given this, the installed capacity ratio can be used as a variable to be optimized, and by traversing or scanning the RCI values ​​under different ratio conditions, the capacity ratio that maximizes the complementarity index can be found.

[0033] In some examples, the time series data includes at least two of the following: wind speed time series, solar irradiance time series, and temperature time series; The construction of characterization sequences for each renewable energy subsystem based on the time series data includes: Calculate the wind power capacity factor time series based on wind speed time series and preset wind turbine parameters; and / or, The photovoltaic capacity factor time series was calculated based on the solar irradiance time series, air temperature time series, and photovoltaic module temperature correction parameters.

[0034] Understandably, raw resource data such as wind speed, irradiance, and temperature cannot directly reflect the output state of a power generation system; they must be mapped to a capacity factor through energy conversion mechanisms. The capacity factor, the ratio of actual output to rated output, unifies the representation scale across different energy sources and equipment scales, making it highly suitable as input for a joint system representation sequence. For wind power, wind speed does not linearly correspond to power but is constrained by turbine characteristics such as cut-in wind speed, rated wind speed, and cut-out wind speed. Therefore, constructing the wind power capacity factor is essentially a conversion process from wind speed and turbine output power to the capacity factor. For photovoltaics, while irradiance is the dominant factor, actual output is also affected by module temperature, as increased module temperature reduces output efficiency. Therefore, constructing the photovoltaic capacity factor involves not only considering solar radiation but also incorporating ambient temperature and module temperature correction coefficients. This allows heterogeneous resource data to be uniformly converted into a standardized and comparable representation sequence. Wind speed, irradiance, and temperature, with their different dimensions and physical meanings, cannot be directly used for joint complementary analysis. After capacity factor conversion, different energy sources are mapped to a unified relative output scale, making the joint construction more reasonable. This enhances the physical accuracy of the complementarity evaluation results. Because the capacity factor construction process considers equipment factors such as unit characteristics and photovoltaic temperature correction, the subsequent ramp-up rate reflects not abstract resource changes, but rather changes in actual power output.

[0035] According to some specific implementations, the index used to quantify renewable energy complementarity can be expressed as the Ramping Intensity Complementarity Index (RCI). Complementarity can be defined as the reduction ratio of the output ramping intensity of a combined renewable energy system (such as wind and solar power) relative to the ramping intensity of each subsystem's independent output (a benchmark obtained by weighted summation over their respective installed capacities) at a given time scale. Taking a combined wind and solar power system as an example, its… RCI The calculation formula is as follows: (1) in, and These are time series data for wind power and solar power capacity factors, respectively. Wind power and solar power are weighted by installed capacity. (Wind power percentage, The joint capacity factor time series is obtained by linear superposition of the photovoltaic (PV) ratio; The difference step size (positive integer) corresponds to the time scale as follows: ( (sampling time interval); The order of the norm is used to characterize the climbing intensity under different statistical calibers; Indicated on the time scale Below, the norm order is The measure of climbing intensity; The range of values ​​is The larger the value, the higher the reduction ratio of the combined output to the climbing intensity of the benchmark, that is, the better the wind-solar complementary effect.

[0036] Capacity factor sequence of combined system (wind-solar combined) The capacity factor is obtained by weighting each component according to its installed capacity percentage: (2) For any sequence When selecting a time scale The rate of ascent The calculation formula is as follows: (3) in, Sampling time interval (hours); differential step size The corresponding duration scale is ; Characterization system in The rate of change of net output over the duration of the event.

[0037] Given a sequence of ramp rates Then, the climbing intensity can be defined. For this gradient rate sequence at order The norm of the sub-norm is calculated using the following formula: when hour, (4) when hour, (5) in, This represents the total number of sampling points for the time series. The number of samples from which the slope rate can be calculated; norm order. Used to control the degree of emphasis on large climb events: The corresponding average absolute ramp intensity represents the average power change demand for daily grid regulation. The corresponding root mean square ramp strength is more sensitive to larger fluctuations and can characterize the second-order risk of power grid uncertainty. The maximum ramp intensity corresponds to the time, which characterizes the grid's reserve capacity requirement when facing extreme power changes.

[0038] Based on the above definition of complementarity, the optimal installed capacity ratio of wind power and photovoltaic power in the region can be solved to maximize the complementary effect of wind and solar power output.

[0039] 1) Optimal allocation for a single time scale and a single order When focusing only on a single scale With a single order At that time, the optimal landscape ratio for a region can be defined. To make the complementarity index The largest weight value, that is: (6) Here, arg max represents the optimal solution that maximizes the objective function. This solution can be achieved using a grid scan: taking a step size... (e.g., 0.01 or less), let And calculate point by point. Take the value that maximizes the index. As the optimal ratio .

[0040] 2) Multi-timescale and multi-order comprehensive optimal allocation If multiple time scales and statistical orders are considered simultaneously, a comprehensive objective function can be constructed to balance the pressure of short-term rapid ascent with long-term slow ascent: (7) Accordingly, the optimal ratio under the comprehensive complementary effect is: (8) in, For each time scale order The weighting coefficients of the complementary indicators (satisfying) and This ensures that the overall indicator is a weighted average of the sub-indicators and is comparable. Adjusting the weights can highlight different emphases: for example, increasing the weight of indicators for short-duration climbs (e.g., 1 hour, 3 hours). This indicates a greater emphasis on reducing rapid climbs within short periods; and an increase in extreme climb metrics (such as maximum climb or 95% / 99% percentile climb). This indicates a greater focus on controlling extreme volatility risks and reserve constraints. In some embodiments, the adjustment cost coefficient for system ramp-up at different time scales can be used. To set weights, for example: (9) in, This can be determined by planning experience parameters, reserve cost curves, or adjustment resource marginal costs. Such allocation allows ramp-up scenarios with high adjustment costs (and significant challenges to the system) to have a greater weight in the comprehensive indicators.

[0041] Based on the above-mentioned indicator definitions and methods, the complementarity of wind power and photovoltaic output at a selected time scale can be calculated using the following steps. RCI : 1) Constructing wind and solar capacity factors ( CF Time series First, obtain hourly time-series data of wind speed at turbine height (e.g., 100m) from measured data or reanalysis datasets (e.g., ERA5). Horizontal irradiance of the ground and temperature data Secondly, based on the acquired data, the hourly wind energy capacity factor of the observation point or grid unit is calculated. and solar capacity factor .

[0042] Wind energy capacity factor The calculation formula is as follows: (10) in, This refers to the actual output power of the wind turbine. This refers to the rated output power of the wind turbine. , and These are the cut-in wind speeds (m / s) of the wind turbines. -1 ), cut off wind speed (ms) -1 ) and rated wind speed (m / s) -1 This invention uses the GW 140 / 3400 wind turbine (rated power 3.4MW) for wind energy capacity factor calculation. This model... Rated wind speed Cut-out wind speed is .

[0043] Solar capacity factor The calculation formula is as follows: (11) in, and These refer to the actual hourly output power and rated output power per unit land area, respectively. The amount of solar radiation received by the photovoltaic module ( ); This is a reference value for irradiance under standard test conditions (corresponding to a component operating temperature of 25°C). (and atmospheric quality AM 1.5 spectrum). This is the temperature modification coefficient for photovoltaic modules.

[0044] Photovoltaic module temperature correction factor The actual temperature of the photovoltaic panel ( The function is . The calculation formula is as follows: (12) (13) in, Represents the peak power temperature coefficient (-0.41% / °C); Ambient air temperature (°C); The rated operating temperature of the photovoltaic module's cells (set to 45°C in this invention, defined under the conditions of: ambient temperature 20°C and solar irradiance 0.8 kW m²) is... -2 And wind speed 1 m / s -1 ).

[0045] Solar radiation received by photovoltaic modules ( It can be decomposed into direct radiation ( ), scattered radiation ( ) and reflected radiation ( The calculation formula is as follows: (14) in, Direct radiation from a horizontal plane ( ) and the angle of incidence of the sun ( ) was calculated; Radiation can be scattered by the horizontal plane ( ) and its conversion factor ( Estimate, of which It mainly depends on the solar azimuth angle, solar zenith angle, sky brightness, and sky clarity; and Solar radiation is calculated using empirical piecewise equations. The equation is obtained through separation and quantization, utilizing the solar altitude angle and the incident shortwave radiation flux (W / m²) at the Earth's surface. -2 ) and the shortwave radiation flux incident at the top of the atmosphere (W m -2 To describe exist The proportion in; Install the tilt angle for the photovoltaic panels; This represents the ground reflectivity. For ordinary ground or grass, this value is usually around 0.2 by default.

[0046] 2) Calculate the complementary index of climbing intensity First, based on the assessment requirements for renewable energy complementarity, determine the assessment time window (e.g., day, month, year) and data time resolution. (e.g., minutes, hours), select the duration scale. With the order of climbing intensity (like And set the corresponding weights. (If calculating the composite index); specify the installed capacity weight. (or will) (as a variable to be optimized) Secondly, wind energy capacity factor is calculated based on meteorological data. (Formula 10) and solar capacity factor (Formulas 11-14); and according to the given... Calculate the combined output capacity factor sequence Formula (2); then, at each duration scale The following is a calculation of the ramp rate sequence for combined wind and solar power output. Formula (3); Finally, calculate the climbing intensity according to formulas (4) and (5). The combined output climbing intensity is compared with the capacity-weighted benchmark to calculate the climbing intensity complementarity index. Formula (1). If a landscape-sun ratio is to be calculated, then the output should be obtained by formula (6) or formula (7-9). Simultaneously output As a result of the energy complementarity assessment.

[0047] 3) Renewable Energy Complementarity Assessment Slope Intensity Complementary Index The range of values ​​is .when When the combined output's ramp intensity equals the capacity-weighted baseline, it indicates that no ramp reduction effect (no complementarity) occurs at this time scale and caliber; when When this occurs, it indicates the existence of a positive complementary effect, and the volatility of joint operation is reduced; The closer the value is to 1, the higher the fluctuation reduction ratio and the stronger the wind-solar complementarity. Theoretically, This indicates that complete complementarity has been achieved, meaning that the output fluctuations between individual energy sources completely cancel each other out, and the combined output power tends to be constant.

[0048] In summary, the key point of this invention is the definition of a novel method for renewable energy complementarity based on multi-timescale climbing intensity. RCI (Ramping Intensity Complementarity Index). This method quantifies the complementarity between different energy sources by calculating the ramping intensity of the capacity factors of renewable energy sources such as wind power and solar power over a selected period. The ramping intensity complementarity index (… RCI ) is a novel method for representing complementary data, similar to the correlation coefficient in statistics. ρ ), stability coefficient ( C_stab ) and slope complementarity ( R_SL Indicators such as ) but RCI This method exhibits clear system orientation and engineering interpretability, demonstrating strong dynamic sensitivity, cross-scale stability, and physical intuitiveness in quantifying complementarity. It directly reflects the degree to which combined output fluctuations are reduced relative to individual independent output fluctuations and their potential impact on system operation. This method is suitable for complementarity assessment of time-series data, particularly in the application of highly stochastic and volatile renewable energy sectors such as wind and solar power, including scenarios such as energy planning and site selection, capacity allocation optimization, grid dispatching, and risk assessment. Furthermore, this method can be extended to interdisciplinary fields such as meteorological process analysis, water resource scheduling, and economic variable complementarity assessment, demonstrating good versatility and practical value. The slope intensity complementarity index proposed in this invention (…) RCI In quantifying the complementarity of renewable energy, it has advantages such as stronger system orientation, better cross-scale stability, and support for ratio optimization. Compared with traditional statistical methods such as correlation coefficients (… ρ ), stability coefficient ( C_stab ) and slope complementarity ( R_SL Compared with indicators such as ), RCI This method directly reflects the most sensitive aspects of power system operation: ramp intensity and extreme steep change risks, while simultaneously characterizing the reduction of average, typical, and extreme power ramp pressure. It has broad practical value in numerous application scenarios, including renewable energy resource complementarity assessment, regional energy synergy, capacity allocation optimization, grid dispatching, and risk assessment. Furthermore, it is also relevant in interdisciplinary fields such as the complementarity of meteorological and hydrological variables and the correlation volatility analysis of economic and financial indicators. This broad applicability reflects the high originality and universal value of the invention's technical solution, and it is expected to have broad application prospects in power systems with a high proportion of new energy development and other fields requiring the assessment of the synergistic effects of multiple stochastic processes.

[0049] Based on some embodiments, taking the complementarity analysis of wind power and photovoltaic power generation as an example, four representative wind-solar hybrid projects in my country were selected as evaluation objects, including a wind-solar hybrid power station in region A, a wind-solar hybrid power station in region B, a wind-solar hybrid power station in region C, and a wind-solar hybrid power station in region D (see Table 1 for details), to verify the proposed slope intensity complementarity index (). RCI This study examines the scientific validity and applicability of wind and solar power in engineering practice, using the complementarity assessment and installed capacity optimization of wind and solar power as examples for empirical analysis. Using 2020 wind and solar power output data as input, it quantitatively compares the complementarity of each project under the current and optimal installed capacity structures, thereby verifying its effectiveness. RCI The application effects and engineering value of the indicators in real-world scenarios. Figure 2The daily power generation time series of wind power, photovoltaic (PV), and combined wind-solar power systems for the four power plants mentioned above in 2020 are presented under the current wind and solar installed capacity ratio. Wind power output exhibits significant random fluctuations, generally characterized by high-frequency, irregular fluctuations, accompanied by frequent sharp rises and falls in extreme values. In contrast, PV output shows obvious periodicity and seasonality, exhibiting an annual variation pattern of high in summer and low in winter. After the superposition of the time-series characteristics of the two types of resources, the combined wind-solar power output still shows significant fluctuations under the current ratio. Relying solely on empirical ratios cannot achieve the optimal ramp-up reduction effect; therefore, it is necessary to introduce quantifiable indicators to evaluate and optimize complementarity.

[0050] Based on the climbing strength complementarity index proposed in this invention (On an hourly scale) Below, in summary (Mean absolute climbing diameter) and (Root Mean Square Climbing Diameter) Comprehensive Complementarity Measurement), comparing and evaluating the current installed capacity structure and optimal installed capacity structure of four typical wind-solar hybrid projects. Table 1 shows the geographical location (latitude, longitude), current wind power and photovoltaic installed capacity and ratio of each project ( The complementarity index under the current wind-solar ratio conditions was calculated. RCI And the optimal wind power ratio obtained by optimizing the solution through this invention ( The results show that the optimal wind-solar hybrid ratio for the wind-solar hybrid projects in region A is [missing information]. The optimal ratio for wind-solar hybrid projects is 0.43 in region B, 0.32 in region C, 0.56 in region D, and 0.64 in region D. Under these optimal ratios, each project... RCI All have improved compared to the current situation; among them, the wind-solar hybrid project in Area A has improved. RCI The coefficient for wind-solar hybrid projects in Region B increased from 0.174 to 0.208 (an increase of 19.54%). RCI The percentage of wind-solar hybrid projects in region C increased significantly from 0.057 to 0.177 (an increase of 210.53%), while the percentage of projects in region D increased slightly from 0.204 to 0.215 (an increase of 5.39%). In summary, the comparison results in Table 1 demonstrate that the method of this invention can provide the optimal wind power ratio at the site scale that matches resource endowment and installed capacity structure. and through RCI The quantitative improvement verifies its effectiveness in reducing the intensity of combined output climbing (enhanced complementarity), which can provide verifiable engineering basis for optimizing the wind and solar power plant ratio and planning decisions.

[0051] To further examine the variation patterns of complementarity across different months of the year, this embodiment uses a monthly scale as the statistical window, and analyzes the current wind-sun ratio. Optimal landscape ratio Calculating the monthly figures for 2020 under two scenarios RCI The result is as follows Figure 3 As shown. Under the current configuration ( Figure 3 a) All four wind-solar hybrid projects exhibited significant monthly fluctuations and seasonal differences, with the wind-solar hybrid project in area D experiencing its lowest point of the year in June. RCI =0.212), in September–November RCI The high level (0.273–0.277) indicates that the mismatch between autumn wind and solar timelines is more conducive to weakening joint fluctuations; the monthly fluctuations of wind-solar complementary projects in region C are relatively small. RCI The range is 0.179–0.225, with the lowest in June and the highest in February; the wind-solar hybrid project in region A saw its highest value in February. RCI The lowest (0.134) and highest (0.198) occurred in May, indicating that the complementary effect was weak in late winter and early spring, and strengthened around the beginning of summer; the overall complementary level of wind-solar complementary projects in region B was the lowest ( RCI The range is 0.037–0.089, and it weakened further in November and December, reflecting that the combined output under the current installed capacity structure still exhibits strong synchronous fluctuation characteristics. An optimal annual-scale weight is introduced. back( Figure 3 (b) Overall monthly complementarity increased, exhibiting quantifiable monthly response differences. Specifically, the current wind-solar ratio for wind-solar complementary projects in region B is 0.83, while the optimal wind-solar ratio... The two differ significantly, therefore the monthly increase is the most significant, in a single month. RCI The increase ranged from 0.067 to 0.151 (with a maximum increase of +0.151 in June and a further increase of +0.120 in December), indicating that the station can achieve continuous and stable complementary enhancement through ratio adjustments. The current wind-solar hybrid project in region A has a wind-solar ratio of 0.25, with the optimal ratio being... Monthly RCI The increase ranges from 0.002 to 0.065 (maximum +0.065 in September, +0.056 in October), indicating that this invention not only improves the complementarity intensity but also identifies the seasonal advantage of complementarity throughout the year. The current wind-solar hybrid projects in region D and region C have wind-solar hybrid projects with wind-solar hybrid ratios close to the optimal ratio (approximately 0.71→0.64; 0.67→0.56), therefore the overall monthly gain is small but mainly positive. Among them, compared to the current wind-solar hybrid ratio, the wind-solar hybrid projects in region D... RCIThe performance showed an improvement in most months of the year, with a slight decline in a few months. However, the monthly fluctuation range converged from 0.066 to 0.051, indicating that the optimal annual scale weight tends to boost the trough month and improve the annual balance. For the Luneng Haixi wind-solar hybrid project in Region C, the improvement was more significant in spring and summer (increased by +0.032 and +0.029 in May and June respectively), and the peak month for complementarity shifted, reflecting a certain seasonal compromise under the annual scale target. In summary, the RCI index and ratio optimization method proposed in this invention can achieve complementarity assessment and response analysis at multiple time scales from year to month, providing quantitative basis for the planning and site selection, installed capacity structure design, energy storage configuration, and grid dispatch strategy formulation of wind and solar projects. This helps to improve the operational stability and absorption capacity of high-proportion renewable energy systems.

[0052] Table 1. Basic Information and Optimization Results of Wind-Solar Ratio for Typical Wind-Solar Hybrid Projects In some cases, the above scheme assumes that the characterization sequences of each renewable energy subsystem at the same sampling time can be directly constructed and compared jointly. However, this assumption does not always hold true in the actual evolution of wind and solar resources. The basic model defines the joint system capacity factor sequence as a linear combination of the capacity factors of each component according to their installed capacity ratio, and calculates the ramp rate and ramp intensity on this combined sequence. This means that the basic model implicitly assumes that the complementary effects of wind power, photovoltaic, and other subsystems occur simultaneously. However, in real-world scenarios, there is often a situation that is not easily observed directly: the response times of different energy sources to the same meteorological process, the same geographical propagation process, or the same regional energy exchange process are not consistent. For example, cloud cover will first affect the irradiance input of the photovoltaic array, while local thermal changes, sea and land breeze propagation, valley wind development, or frontal passage may only be reflected in wind power output in several subsequent sampling periods. In other words, the two energy sources are not completely synchronously complementary, but rather there is delayed complementarity or phase-shifted complementarity. If the joint construction method of strict simultaneous alignment is still used, the compensatory effect of one energy source on another may be incorrectly cut off, causing the ramp-up intensity of the joint system to appear too high, thus underestimating the objectively existing complementary relationship. Based on this, some examples also include: setting multiple candidate time-shift quantities for the characterization sequence of at least one renewable energy subsystem, and performing time-shift processing on the characterization sequence based on each candidate time-shift quantity; For each candidate time-shifted ... The target time shift is determined based on the renewable energy complementarity index corresponding to each candidate time shift, and the renewable energy complementarity index corresponding to the target time shift is used as the quantification result of renewable energy complementarity after time shift compensation.

[0053] Understandably, before calculating the ramp-up intensity of the joint system, a candidate time-shift scan is performed on the characterization sequences of at least one renewable energy subsystem to find the time-series alignment that best reflects the ramp-up reduction effect. In other words, natural time alignment is no longer considered a fixed premise; instead, time-shift is used as a parameter to be identified in complementarity quantification. This approach does not pursue the maximum correlation coefficient in the traditional statistical sense; it still uses the reduction ratio of the joint system's ramp-up intensity relative to the capacity-weighted baseline ramp-up intensity as the criterion, but allows this reduction ratio to more realistically reflect the delay-complementary relationship under time-shift compensation. The basic RCI itself is defined around ramp-up reduction at different time scales, so time-shift compensation can be embedded within it, using the difference step size to correspond to the time scale and the norm order to determine the sensitivity to average or extreme fluctuations. Adding a time-shift dimension to this framework allows the joint system sequence to more closely approximate the true complementary propagation relationship under the same time scale and norm caliber.

[0054] For example, instead of directly fixing the time alignment between wind power and photovoltaic sequences, candidate time shifts are introduced within permissible limits to perform time shift scanning on the characterization sequences of at least one renewable energy subsystem. For different candidate time shifts, joint system characterization sequences are constructed, and the corresponding joint ramp-up intensity and complementarity index are calculated. Then, a target time shift is determined based on preset criteria, and the complementarity index corresponding to this target time shift is used as the compensated complementarity quantification result. This allows for the identification of delayed complementarity relationships caused by weather propagation, terrain differences, and energy conversion inertia, avoiding misjudging delayed complementarity as low complementarity. This is particularly effective for cross-regional wind and solar power bases, long-corridor distributed energy clusters, and areas with complex terrain. It improves the accuracy of complementarity assessment and provides a more targeted basis for cross-site coordinated scheduling and cross-regional power transmission configuration.

[0055] In some cases, considering the specific calculations, the ramp rate samples for each sampling period are processed using a uniform statistical standard by default. For example, given a time scale and norm order, the ramp rate samples throughout the entire assessment period are uniformly included in the norm calculation. While this approach can already characterize the combined system fluctuation reduction effect as a whole, it still implicitly assumes that the fluctuation samples in all periods are of approximately the same importance to the system. However, real power systems do not treat all periods equally. Even if there is a certain degree of combined output ramping during off-peak periods, the system may have sufficient adjustment margin to absorb it; while during evening peaks, peak load windows, transitional periods with concentrated unit ramping pressure, or critical windows where reserves are already tight, the same magnitude of renewable energy combined output fluctuations will bring greater risks and higher adjustment costs to system operation. In other words, ramp reduction in certain periods is particularly valuable to the system, while ramp reduction in other periods, although existing, has less practical operational significance. If the statistics are still weighted equally across all time periods, a hidden distortion may occur: a certain scheme may be highly complementary during ordinary periods, thus increasing the overall RCI, but it may not significantly reduce system pressure during critical load periods, ultimately leading to a misjudgment that the energy side evaluation is good but the system side value is average. Based on this, some examples also include: determining the ramp intensity corresponding to each ramp rate sequence based on a preset norm order, including: Obtain the load time series of the target area, identify key load periods based on the load time series, and assign a higher time weight to the ramp rate samples within the key load periods than to non-key load periods; Based on the time period weights, the ramp rate sequences of each renewable energy subsystem and the joint ramp rate sequence are weighted statistically to determine the corresponding weighted ramp intensity. Based on the benchmark weighted ramp intensity obtained by weighting the weighted ramp intensity of the joint system and the weighted ramp intensity of each renewable energy subsystem according to the installed capacity ratio, the renewable energy complementarity index for critical load periods is calculated.

[0056] Understandably, the assessment incorporates regional load time-series or net load risk weights, constructing time-varying weights based on the importance of each sampling period to system regulation pressure. When calculating ramp intensity, higher weights are assigned to ramp rate samples located within critical load windows, rapid net load increase windows, or reserve shortage windows. The final output is a weighted complementarity index oriented towards critical periods. While the evaluation object remains wind-solar combined output, the statistical caliber has been reshaped by the value of critical system periods, no longer treating all time samples equally. This more closely reflects the actual needs of the grid dispatch side, avoiding misjudgments where energy-side smoothing is good but system-side value is average. It is particularly suitable for peak-load supply assurance, renewable energy consumption, and flexible resource coordination planning.

[0057] For example, in implementation, firstly, in addition to the original required renewable energy characterization sequences such as wind power and photovoltaics, a load time series for the target area is obtained. The sampling frequency of this load time series is preferably consistent with the renewable energy characterization sequence, or consistent after resampling, to facilitate the subsequent establishment of time-period correspondences. For example, if both wind power and photovoltaic capacity factors are hourly data, then hourly load data for the same area is preferred. If wind and solar data are in 15-minute increments, then the load data is also correspondingly organized into 15-minute increments. In this way, each ramp rate sample can correspond to a specific load state. Key load periods are identified based on the load time series. Key load periods can be determined in several ways. One method is based on the absolute load level, i.e., periods where the load is above a preset threshold or above a certain high percentile are identified as key load periods. For example, periods where the daily load is in the top 20% are defined as key load periods. Another method is based on net load ramp pressure, i.e., not only considering the load level, but also whether the system is in a rapid net load change process during that period. In this case, evening peak hours, early morning load rise windows, and weekday load transition windows can be identified. Another approach is to incorporate scheduling experience rules into the identification process, such as pre-defining summer evening peaks, winter heating load peak periods, and holiday load recovery windows as critical windows. Regardless of the identification method, the essence is to clearly identify which periods of fluctuation the system is most concerned with. After identifying the critical load periods, weights are constructed for each period. The weight within critical load periods is higher than that within non-critical load periods. This weight can be in binary or continuous form. If a binary form is used, critical load periods are uniformly assigned a larger constant weight, while ordinary periods are assigned a smaller constant weight. If a continuous form is used, the weight can vary with load levels, net load ramp-up pressure, reserve tightness, or other operational indicators. For example, higher loads result in larger weights; or the closer the net load is to the adjustment limit, the larger the weight. In this way, subsequent ramp-up rate samples of the same magnitude will contribute more to the overall ramp-up intensity during critical periods and less during non-critical periods. After constructing the period weights, the ramp-up rate sequences for each subsystem and the combined system are calculated at a preset time scale. Then, when calculating the climbing intensity, instead of directly performing uniform norm statistics on the original climbing rate samples, a weighted climbing intensity is formed based on the weight of the time period to which the sample belongs. If the preset norm order corresponds to the average absolute climbing intensity, then the absolute climbing value of each sample is first multiplied by its time period weight before being aggregated. If the preset norm order is high, large climbing samples appearing in high-weight key time periods will be further amplified. Therefore, it can emphasize key time periods under the average caliber, as well as the extreme risks of key time periods under extreme calibers.After obtaining the weighted ramp intensity of each subsystem and the weighted ramp intensity of the combined system, the weighted ramp intensity of each subsystem, weighted by its installed capacity percentage, is used as the benchmark weighted ramp intensity. The reduction ratio of the combined system's weighted ramp intensity relative to this benchmark value is defined as the complementarity index for critical load periods. The result calculated in this way is not the overall RCI in the general sense, but rather the RCI weighted for critical load periods. This index numerically emphasizes whether the combined system truly reduces ramp pressure during the most stressful, sensitive, and valuable adjustment periods of the system. Ultimately, two types of results can be output simultaneously: one is the base RCI, used to illustrate the general complementarity over the overall period; the other is the critical load period weighted RCI, used to illustrate the complementary value during the most critical periods of the system. If the difference between the two is significant, it can reveal whether a combination scheme has structural problems that appear good overall but are not ideal during critical periods.

[0058] Please see Figure 4 One embodiment of the renewable energy complementarity quantification device in this application may include: Acquisition unit 21 is used to acquire time series data corresponding to at least two types of renewable energy, and construct a characterization sequence of each renewable energy subsystem based on the time series data; Combination unit 22 is used to weight and combine the characterization sequences based on the installed capacity ratio of each of the renewable energy subsystems to obtain a joint system characterization sequence. Calculation unit 23 is used for: For a preset duration scale, the ramp rate sequence of each renewable energy subsystem and the joint ramp rate sequence corresponding to the joint system characterization sequence are calculated respectively, and the ramp intensity corresponding to each ramp rate sequence is determined based on a preset norm order. Based on the benchmark ramp intensity obtained by weighting the ramp intensity of the combined system and the ramp intensity of each of the renewable energy subsystems according to their installed capacity ratio, a renewable energy complementarity index is calculated. The renewable energy complementarity index is used to characterize the degree of reduction in ramp intensity of the combined system relative to the independent operation of each subsystem.

[0059] 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-described methods for quantifying the complementarity of renewable energy.

[0060] Since the electronic device described in this embodiment is the device used to implement a renewable energy complementarity quantification 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.

[0061] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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 quantifying the complementarity of renewable energy in the corresponding embodiment.

[0068] 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)).

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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 quantifying the complementarity of renewable energy sources, characterized in that, include: Obtain time series data corresponding to at least two types of renewable energy, and construct a characterization sequence for each renewable energy subsystem based on the time series data; Based on the installed capacity ratio of each renewable energy subsystem, the characterization sequences are weighted and combined to obtain the joint system characterization sequence. For a preset duration scale, the ramp rate sequence of each renewable energy subsystem and the joint ramp rate sequence corresponding to the joint system characterization sequence are calculated respectively, and the ramp intensity corresponding to each ramp rate sequence is determined based on a preset norm order. Based on the benchmark ramp intensity obtained by weighting the ramp intensity of the combined system and the ramp intensity of each of the renewable energy subsystems according to their installed capacity ratio, a renewable energy complementarity index is calculated. The renewable energy complementarity index is used to characterize the degree of reduction in ramp intensity of the combined system relative to the independent operation of each subsystem.

2. The method as described in claim 1, characterized in that, The characterization sequence is a capacity factor time series, and the joint system characterization sequence is obtained by linearly weighting the capacity factor time series of each renewable energy subsystem according to the corresponding installed capacity ratio.

3. The method as described in claim 1, characterized in that, The step of calculating the ramp rate sequence for each of the renewable energy subsystems and the joint ramp rate sequence corresponding to the joint system characterization sequence for a preset duration scale includes: The preset duration scale is determined based on the sampling time interval and the differential step size; According to the difference step size, the numerical values ​​of the representation sequence with an interval of the difference step size between adjacent time points are differentially calculated; The corresponding ramp rate sequence is obtained by normalizing the data using the preset duration scale.

4. The method as described in claim 1, characterized in that, The step of determining the climbing intensity corresponding to each climbing rate sequence based on a preset norm order includes: When the preset norm order is 1, the average absolute climbing intensity is determined based on the average absolute value of the climbing rate sequence. When the preset norm order is greater than 1 and is a finite value, the climbing intensity at the corresponding order is determined based on the norm of the climbing rate sequence at the preset norm order. When the preset norm order approaches infinity, the extreme climbing intensity is determined based on the maximum absolute value in the climbing rate sequence. Different norm orders correspond to different regulatory pressure representations, reflecting daily regulatory needs, risks of large fluctuations, and risks of extreme ramp-up events, respectively.

5. The method as described in claim 1, characterized in that, The preset duration scale includes multiple duration scales, the preset norm order includes multiple norm orders, and the method further includes: Calculate the renewable energy complementarity index under each duration scale and each norm order combination; The complementary indicators of each renewable energy source are weighted and aggregated according to preset weights to obtain a comprehensive complementary indicator. The preset weights are used to characterize the importance of ramp adjustment scenarios corresponding to different duration scales and different norm orders. The preset weights are determined based on at least one of the power system's adjustment cost coefficient for different ramp scenarios, reserve cost curve, and marginal cost of flexibility resources, so as to improve the adaptability of the comprehensive complementarity index to the grid adjustment needs.

6. The method according to any one of claims 1-5, characterized in that, Also includes: The installed capacity ratio of at least one type of renewable energy is used as the variable to be optimized, and the renewable energy complementarity index or comprehensive complementarity index under different installed capacity ratio conditions are traversed or scanned. A target installed capacity ratio is determined to achieve the optimal ratio of the renewable energy complementarity index or comprehensive complementarity index, and the target installed capacity ratio is used as the optimized allocation result of each renewable energy source in the corresponding region.

7. The method according to any one of claims 1-5, characterized in that, The time series data includes at least two of the following: wind speed time series, solar irradiance time series, and temperature time series. The construction of characterization sequences for each renewable energy subsystem based on the time series data includes: Calculate the wind power capacity factor time series based on wind speed time series and preset wind turbine parameters; and / or, The photovoltaic capacity factor time series was calculated based on the solar irradiance time series, air temperature time series, and photovoltaic module temperature correction parameters.

8. A renewable energy complementarity quantification device, characterized in that, include: An acquisition unit is used to acquire time series data corresponding to at least two types of renewable energy, and construct a characterization sequence of each renewable energy subsystem based on the time series data; A combination unit is used to weight and combine the characterization sequences based on the installed capacity ratio of each of the renewable energy subsystems to obtain a joint system characterization sequence. Computational unit, used for: For a preset duration scale, the ramp rate sequence of each renewable energy subsystem and the joint ramp rate sequence corresponding to the joint system characterization sequence are calculated respectively, and the ramp intensity corresponding to each ramp rate sequence is determined based on a preset norm order. Based on the benchmark ramp intensity obtained by weighting the ramp intensity of the combined system and the ramp intensity of each of the renewable energy subsystems according to their installed capacity ratio, a renewable energy complementarity index is calculated. The renewable energy complementarity index is used to characterize the degree of reduction in ramp intensity of the combined system relative to the independent operation of each subsystem.

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 renewable energy complementarity quantification 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 renewable energy complementarity quantification method as described in any one of claims 1-7.

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