A method for evaluating photovoltaic ground power supply guarantee capacity

Abstract

The application discloses a photovoltaic base power supply guarantee capacity day-ahead evaluation method, and belongs to the technical field of new energy power system operation. The method firstly constructs a quantitative evaluation index system containing three dimensions of absolute power supply guarantee capacity, relative power supply guarantee capacity and probability power supply guarantee capacity; then obtains power prediction results of photovoltaic power generation in the future day, combines multi-quantile probability prediction and low output event identification, and describes the randomness and uncertainty of photovoltaic output; further, statistical analysis is carried out on the load prediction time sequence, and the day-ahead load peak period is identified; finally, various evaluation indexes under different dimensions are calculated, including the proportion of photovoltaic power generation to theoretical power generation, the proportion of photovoltaic power generation in the load peak period, the proportion of low output events, the peak-valley matching degree of supply and demand, the proportion of photovoltaic power generation in the target power, the peak coverage rate and concentration degree under different quantiles and the like.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power base guarantee capacity assessment technology, and more specifically, to a day-ahead assessment method for the power supply guarantee capacity of a photovoltaic power base. Background Technology

[0002] Because photovoltaic (PV) power output is significantly affected by weather conditions, it exhibits strong randomness and volatility. When large-scale PV power plants are connected to the power system, especially under extreme weather conditions or sudden changes in cloud cover, problems such as voltage instability, frequency deviation, and reverse power flow can easily occur, posing challenges to the safe and stable operation of the power grid. Therefore, there is an urgent need for a reliable method for assessing day-ahead PV power supply capacity to provide information support for the coordinated and optimized operation of large-scale PV bases.

[0003] Currently, there is a lack of systematic research on the power supply capacity of large-scale photovoltaic (PV) bases. Existing studies mostly focus on PV power generation prediction, grid connection stability analysis, or economic evaluation, lacking a comprehensive quantitative evaluation method from the perspective of "power supply capacity." In particular, in terms of evaluation dimensions, they are generally limited to a single output level or average output characteristics, failing to fully consider the matching relationship between PV output and load demand, as well as the uncertainty of output fluctuations.

[0004] In conclusion, large-scale photovoltaic (PV) bases play a crucial role in the energy transition. However, considering the volatility and randomness of their power output, large-scale grid connection of PV power plants will pose certain challenges to grid operation. Currently, there is no systematic method for quantitatively assessing their power supply capacity, and the coordinated optimization operation of large-scale PV bases lacks effective information support. Furthermore, related research is insufficient in terms of the completeness and practicality of its assessment dimensions.

[0005] Therefore, we urgently need to design a day-ahead assessment method for the power supply guarantee capability of photovoltaic bases to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to solve the technical problems mentioned in the background section and to provide a day-ahead assessment method for the power supply guarantee capability of a photovoltaic power base, characterized by the following steps:

[0007] Step S10: Construct a quantitative evaluation index system for the power supply guarantee capability of the photovoltaic base. The index system covers three dimensions: absolute power supply guarantee capability, relative power supply guarantee capability, and probabilistic power supply guarantee capability, which are used to reflect the power supply guarantee level of the photovoltaic base under different operating conditions from multiple perspectives.

[0008] Step S20: Obtain the predicted power output of photovoltaic power generation in the next day, and quantify the randomness and uncertainty of photovoltaic power by combining multi-quantile probability prediction and low-output event identification.

[0009] Step S30: Based on the load forecast time series, identify the peak load period within the next day as the key assessment object for evaluating the photovoltaic power supply guarantee capability;

[0010] Step S40: Combine the power forecast results, load forecast results and peak period division to calculate various evaluation indicators and conduct a day-ahead assessment of the power supply guarantee capability of the photovoltaic base.

[0011] As a preferred technical solution of the present invention, step S20 includes:

[0012] The hourly or segmented output forecasts of photovoltaic power generation are obtained for the next 24 hours and discretized at 15-minute intervals to form 96 forecast points for the whole day.

[0013] Based on this, we further obtain the probability prediction results of photovoltaic power generation at multiple quantiles such as 10%, 20%, 30%, and 40%, which are used to characterize the upper and lower fluctuation range of photovoltaic output.

[0014] At the same time, identifying low-output events within the next day and marking situations where photovoltaic output is significantly lower than normal at a certain moment serves as an important input for assessing the risk to power supply security.

[0015] As a preferred technical solution of the present invention, step S30 includes:

[0016] By performing statistical analysis on the load forecast time series, the average load level and load fluctuation amplitude for the next day are calculated, and a sensitivity coefficient is introduced as an adjustment factor.

[0017] Based on the above adjustment factors, a peak threshold is set, and the time exceeding the peak threshold is identified as the peak load period. Then, the matching of photovoltaic output under peak demand is examined in detail.

[0018] As a preferred technical solution of the present invention, step S40 includes:

[0019] The proportion of photovoltaic power generation to the theoretical maximum power generation under the dimension of absolute power supply guarantee capability is calculated to reflect the utilization efficiency of photovoltaic installed capacity.

[0020] The proportion of photovoltaic power generation during peak load periods to daytime photovoltaic power generation is calculated under the dimension of absolute power supply guarantee capacity, which is used to measure the contribution of photovoltaic power to power supply during high demand periods.

[0021] The proportion of low-output events in the absolute power supply guarantee capacity is calculated to assess the risk level caused by photovoltaic power output fluctuations.

[0022] As a preferred technical solution of the present invention, step S40 further includes:

[0023] The proportion of photovoltaic power generation to load power during peak hours is calculated under the dimension of absolute power supply guarantee capacity, which is used to quantify the direct supporting role of photovoltaic output on peak load.

[0024] The proportion of the number of times when the extreme points of photovoltaic output and the extreme points of electricity load match under the dimension of relative power supply guarantee capacity is calculated to reflect the synchronicity of supply and demand peaks and valleys.

[0025] The proportion of photovoltaic power generation to the total power supply of all target objects at the power end is calculated under the dimension of relative power supply security, which is used to measure the energy supply ratio of photovoltaic output in the entire power supply system.

[0026] As a preferred technical solution of the present invention, step S40 further includes:

[0027] The calculation of peak load periods under the probabilistic power supply guarantee capability dimension, and the proportion of photovoltaic power generation to daytime photovoltaic power generation at different quantiles, are used to reflect the proportion of photovoltaic power generation during peak periods under different output probability scenarios.

[0028] As a preferred technical solution of the present invention, step S40 further includes:

[0029] The calculation of the proportion of photovoltaic power generation to load demand at different quantiles under the dimension of probabilistic power supply guarantee capability is used to assess the coverage of photovoltaic output to load demand under different probabilistic scenarios.

[0030] As a preferred technical solution of the present invention, step S40 further includes:

[0031] The photovoltaic power output concentration index at different quantiles under the probabilistic power supply guarantee capability dimension is used to reflect the distribution balance of photovoltaic power over time series and its potential impact on power system stability.

[0032] As a preferred technical solution of the present invention, the evaluation index system includes:

[0033] The absolute power supply guarantee capability indicator is used to reflect the photovoltaic system's ability to support installed capacity and peak periods.

[0034] The relative power supply guarantee capability dimension index is used to reflect the matching degree between photovoltaic output and load fluctuations and its contribution to the overall power structure;

[0035] The probabilistic power supply guarantee capability dimension index is used to reflect the power supply coverage and concentration of photovoltaics under multi-quantile prediction.

[0036] As a preferred technical solution of the present invention, the method can realize the matching and quantification of photovoltaic power generation and load demand through multi-dimensional and multi-index comprehensive evaluation, fully reflect the power supply guarantee capability of photovoltaic base under different conditions, and provide a scientific decision-making basis for grid operation scheduling and new energy consumption.

[0037] Beneficial effects:

[0038] (1) This invention proposes a day-ahead assessment method for the power supply guarantee capability of photovoltaic (PV) bases. For the first time, it constructs a multi-dimensional assessment system encompassing "absolute power supply guarantee capability, relative power supply guarantee capability, and probabilistic power supply guarantee." By comprehensively considering the PV base's own output level, its ability to support target load demand, and the probabilistic characteristics of output stability, it achieves a systematic quantitative assessment of the PV base's power supply capability on a day-ahead scale. This method not only reflects the expected level of PV output but also introduces probabilistic indicators to characterize the risk of insufficient power supply. It effectively overcomes the limitations of traditional assessments that only focus on average or peak output while ignoring power supply stability, significantly improving the assessment results' support for power system dispatch decisions.

[0039] (2) This invention innovatively introduces a coupled analysis mechanism of peak load period identification and photovoltaic output guarantee, dynamically identifying typical day-ahead peak load intervals and assessing the support capacity of photovoltaic output during these critical periods. By calculating core indicators such as power supply guarantee probability and output coverage during peak periods, it can accurately identify high-risk scenarios such as "overlap of photovoltaic output troughs and peak loads," providing a scientific basis for dispatching agencies to formulate energy storage dispatching, cross-regional support, or demand response strategies in advance, and significantly enhancing the safe operation level of the new power system under high-proportion renewable energy access. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart of an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0043] Example 1: Please refer to Figure 1This invention also provides an application scenario for a method for assessing the day-ahead power supply guarantee capability of a photovoltaic base. This embodiment mainly describes the situation of large-scale photovoltaic grid-connected operation in a typical regional power grid.

[0044] Step S10: Construct a quantitative evaluation index system for the power supply guarantee capability of photovoltaic bases. In this embodiment, the index system also includes three dimensions: absolute power supply guarantee capability, relative power supply guarantee capability, and probabilistic power supply guarantee capability. However, in application, the power grid dispatch center can adjust the weight of each dimension index according to operational needs to highlight the evaluation focus under different scenarios. For example, during peak summer power grid operation, the focus is on the power supply guarantee capability during peak load periods; during optimized operation for renewable energy consumption, the emphasis is placed on the concentration of photovoltaic output and performance under multi-quantile scenarios.

[0045] Step S20: Obtain the power forecast results for the photovoltaic power station for the next day. This embodiment assumes a photovoltaic power station capacity of 500MW. A numerical weather prediction model combined with historical operating data is used to predict the power output for the next day, obtaining a 15-minute output sequence with 96 points. Simultaneously, probability prediction curves are generated at four quantiles (10%, 20%, 30%, and 40%) to characterize the uncertainty range of photovoltaic power output and identify potential low-output events for the next day. For example, if the predicted output is below 50MW and persists for more than 30 minutes, it is identified as a low-output event and recorded.

[0046] Step S30: Identify peak periods in the load forecast time series. In this embodiment, the regional power grid load forecast results show that the daily peak electricity consumption is mainly concentrated in two periods: 10:30–12:00 and 18:00–20:00. By calculating the average load level and standard deviation, a peak threshold is obtained, and the times exceeding the threshold are identified as load peaks, providing a basis for subsequent photovoltaic capacity assessment.

[0047] Step S40: Calculate evaluation indicators. In this embodiment, the dispatch center quantifies the operational status of the photovoltaic base based on predicted data and peak period divisions, from three dimensions: absolute power supply guarantee capability, relative power supply guarantee capability, and probabilistic power supply guarantee capability.

[0048] Under the dimension of absolute power supply guarantee capacity, the calculation of the proportion of photovoltaic power generation to theoretical power generation shows that the installed capacity utilization rate is 82%; the calculation of the proportion of photovoltaic power generation during peak load periods to daytime photovoltaic power generation shows that it is 46%, indicating that photovoltaic power supply support during peak demand is limited; the calculation of the proportion of low output events is 12%, reflecting that the base has a large fluctuation risk in some periods.

[0049] In terms of relative power supply security, the photovoltaic power generation during peak load periods accounts for 38% of the load power during those periods, indicating that the coverage of peak loads by photovoltaics still needs to rely on conventional power sources for supplementation. The matching ratio between the photovoltaic output extreme point and the load extreme point is 0.27, indicating that the synchronicity between photovoltaic peak and valley fluctuations and load peak and valley fluctuations is weak. The photovoltaic power generation accounts for 22% of the total power of the target objects in the region, reflecting the contribution of photovoltaics to the overall power structure.

[0050] Under the dimension of probabilistic power supply guarantee capability, the peak-hour photovoltaic power generation ratio was calculated at the 10%, 20%, 30%, and 40% quantiles, respectively. The results show that the peak-hour photovoltaic coverage under different probabilistic scenarios is between 30% and 50%. At the same time, the proportion of photovoltaic power generation to load demand was calculated, which is 25% in the lowest scenario and can reach 55% in the highest scenario. Finally, the concentration index of photovoltaic power output was calculated, and the result is 0.61, indicating that the fluctuation distribution of photovoltaic power over time is relatively balanced.

[0051] Based on the above evaluation results, this embodiment can clearly depict the power supply guarantee capability of photovoltaic bases under different operating scenarios. It can reflect the installed capacity utilization efficiency, as well as reveal its support capability during peak load periods and its coverage level under probabilistic scenarios, providing a scientific reference for the operation and scheduling of regional power grids under high proportion of new energy conditions.

[0052] Example 2: Please refer to Figure 1 This invention provides a method for assessing the day-ahead power supply guarantee capability of a photovoltaic power base, comprising the following steps:

[0053] Step S10: Construct a quantitative characterization index system for the power supply guarantee capability of photovoltaic bases.

[0054] Step S20: Obtain the theoretical output, point prediction results, guaranteed power prediction results at each quantile, load prediction results, and low output event prediction results of the photovoltaic base.

[0055] Step S30: Identify peak periods in the load forecast time series.

[0056] Step S40: Calculate each indicator and conduct a day-ahead assessment of the power supply guarantee capability of the photovoltaic base.

[0057] Further, step S10 includes:

[0058] Step S101 proposes absolute power supply guarantee capability assessment indicators, including the proportion of photovoltaic power generation to theoretical power generation, the proportion of photovoltaic power generation during peak load periods to daytime photovoltaic power generation, and the proportion of low-output event times to daytime times.

[0059] Step S102 proposes relative power supply guarantee capability assessment indicators, including the proportion of photovoltaic power generation during peak load periods to the load power consumption during that period, the proportion of the number of times when the photovoltaic output extreme point matches the power load extreme point to the number of times during the day, and the proportion of photovoltaic power generation to the total power generation of all power sources supplying power to the target load at the power generation end.

[0060] Step S103 proposes probabilistic power supply guarantee capability assessment indicators, including the proportion of photovoltaic power generation at different quantiles during peak load periods to the daytime photovoltaic power generation, the proportion of photovoltaic power generation at different quantiles to the load demand during that period, and the degree of photovoltaic output concentration at different quantiles.

[0061] Further, step S20 includes:

[0062] Step S201: Obtain the theoretical predicted power output of the photovoltaic base for the next day. The time is divided into 15-minute intervals, and there are a total of 24 hours in the next day, with a total of 96 points.

[0063] Step S202: Obtain the photovoltaic power point prediction results for the photovoltaic base for the next day. The time is divided into 15-minute intervals, and there are a total of 24 hours in the next day, with a total of 96 points.

[0064] Step S203: Obtain the photovoltaic guaranteed power prediction results for the photovoltaic base at each quantile for the next day. The time resolution is 15 minutes, covering a total of 24 hours in the future, with a total of 96 data points. Power prediction results at quantiles of 10%, 20%, 30%, and 40% were selected.

[0065] Step S204: Obtain the load forecast results for the photovoltaic base for the next day. The time is divided into 15-minute intervals, and there are a total of 24 hours in the next day, with a total of 96 points.

[0066] Step S205: Obtain the prediction results of low photovoltaic output events for the photovoltaic base in the coming day. The time is divided into 15-minute intervals, and there are a total of 24 hours in the next day, with a total of 96 points.

[0067] Further, step S30 includes:

[0068] Step S301: Identify peak periods in the load forecast time series. Define daily baseline peak thresholds. Used to determine whether a certain moment is a peak:

[0069] (1)

[0070] (2)

[0071] (3)

[0072] In the formula, It is the average load for the next day. It is the standard deviation of the load for the next day; α This is the sensitivity coefficient, set to 1.5. Subsequently, it is determined whether each moment of the following day falls within a peak period, resulting in a peak period identifier sequence.

[0073] (4)

[0074] Further, step S40 includes:

[0075] Step S401: Calculate the "proportion of photovoltaic power generation to theoretical power generation" index under the dimension of absolute power supply guarantee capability. .

[0076] (5)

[0077] Step S402: Calculate the "proportion of photovoltaic power generation during peak load periods to daytime photovoltaic power generation" under the dimension of absolute power supply guarantee capability. .

[0078] (6)

[0079] In the formula It represents the multiplication sign.

[0080] Step S403: Calculate the "proportion of low-output event times to daytime times" index under the absolute power supply guarantee capability dimension. .

[0081] (7)

[0082] Step S404: Calculate the "proportion of photovoltaic power generation during peak load periods to the electricity consumption during those periods" under the dimension of relative power supply guarantee capability. .

[0083] (8)

[0084] Step S405: Calculate the "proportion of the number of times when the extreme points of photovoltaic output match the extreme points of electricity load to the number of times during the day" under the dimension of relative power supply guarantee capability. .

[0085] (9)

[0086] (10)

[0087] In the formula They represent Photovoltaic power point prediction results and load prediction results at any given time. They represent The photovoltaic power point prediction results and load prediction results at each time point; It is a judgment The variable is whether the moment is an extreme point of the change in photovoltaic power output and the change in load demand, and whether the change trend is consistent. If it is 1, it means that the change trend is consistent.

[0088] Step S406: Calculate the "proportion of photovoltaic power generation to all power generation at the generation end supplying the target load" index under the dimension of relative power supply guarantee capability. .

[0089] (11)

[0090] Step S407: Calculate the "proportion of photovoltaic power generation to daytime photovoltaic power generation at different quantiles during peak load periods" under the dimension of probabilistic power supply guarantee capability. .

[0091] (12)

[0092] Step S408: Calculate the "proportion of photovoltaic power generation to load demand at different quantiles" under the probabilistic power supply guarantee capability dimension. .

[0093] (13)

[0094] Step S409: Calculate the "concentration of photovoltaic output at different quantiles" index under the dimension of probabilistic power supply guarantee capability. .

[0095] (14)

[0096] In summary, the day-ahead assessment method for power supply guarantee capability of a photovoltaic base according to an embodiment of the present invention scientifically assesses the day-ahead power supply guarantee capability of a photovoltaic base from three dimensions: absolute power supply guarantee capability, relative power supply guarantee capability, and probabilistic power supply guarantee capability, providing information support for the coordinated and optimized operation of large-scale photovoltaic bases.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for day-ahead assessment of power supply security capability of a photovoltaic ground, characterized in that, The method comprises the following steps: Step S10, constructing a photovoltaic power station power supply guarantee capacity quantitative evaluation index system, the index system covering absolute power supply guarantee capacity, relative power supply guarantee capacity and probability power supply guarantee capacity three dimensions, for multi-angle reflecting photovoltaic power station in different operating conditions Power supply guarantee level; The step S10 comprises: Step S101, proposing absolute power supply guarantee capacity evaluation index, including photovoltaic power generation capacity proportion of theoretical power generation capacity, photovoltaic power generation capacity proportion of daytime photovoltaic power generation capacity in load peak period, low power event time proportion of daytime time; Step S102, proposing relative power supply guarantee capacity evaluation index, including photovoltaic power generation capacity proportion of load power consumption in load peak period, photovoltaic power output extreme point and electricity load extreme point matching time proportion of daytime time, photovoltaic power generation capacity proportion of all power generation capacity for target load object power supply; Step S103, proposing probability power supply guarantee capacity evaluation index, including photovoltaic power generation capacity proportion of daytime photovoltaic power generation capacity in different quantile number in load peak period, photovoltaic power generation capacity proportion of load demand in different quantile number, photovoltaic power output concentration degree in different quantile number; Step S20, obtaining photovoltaic power generation power prediction output in the future one day, combining multi-quantile probability prediction and low power event identification, quantifying randomness and uncertainty of photovoltaic power; Step S30, based on load prediction time sequence, identifying load peak period in the future one day as the key object of photovoltaic power supply guarantee capacity evaluation; Step S40, combining power prediction result, load prediction result and peak period division, calculating various evaluation indexes, and pre-evaluating photovoltaic power station power supply guarantee capacity.

2. The photovoltaic ground-based power supply guarantee capability day-ahead evaluation method according to claim 1, characterized in that, The step S20 comprises: Obtaining photovoltaic power generation hourly or segmented output prediction value in the future 24 hours, and discretizing according to 15-minute interval to form 96 prediction points in a day; On this basis, further obtain probability prediction results of photovoltaic power generation in 10%, 20%, 30%, 40% quantile numbers, for describing photovoltaic power output fluctuation interval; At the same time, identify low power event in the future one day, mark the situation that photovoltaic power output is significantly lower than the normal level at a certain time, as an important input of power supply guarantee capacity risk.

3. The photovoltaic ground-based power supply guarantee capability day-ahead evaluation method according to claim 1, characterized in that, The step S30 comprises: By statistical analysis on load prediction time sequence, calculating average load level and load fluctuation amplitude of the future one day, and introducing sensitivity coefficient as an adjustment factor; Based on the above adjustment factor, set the peak threshold, identify the time points exceeding the peak threshold as load peak period, and then focus on the matching of photovoltaic power output under peak demand.

4. The photovoltaic ground-based power supply guarantee capability day-ahead evaluation method according to claim 1, characterized in that, The step S40 comprises: Calculating photovoltaic power generation capacity proportion of theoretical maximum power generation capacity in absolute power supply guarantee capacity dimension, for reflecting photovoltaic installed capacity utilization efficiency; Calculating photovoltaic power generation capacity proportion of daytime photovoltaic power generation capacity in load peak period in absolute power supply guarantee capacity dimension, for measuring photovoltaic power supply contribution degree in high demand period; The proportion of the number of low-power events in the absolute power supply guarantee capability dimension to the total number of time points in a day is calculated, which is used to evaluate the risk level caused by photovoltaic power fluctuation.

5. The photovoltaic ground-based power supply guarantee capability day-ahead evaluation method according to claim 4, characterized in that, The step S40 further includes: The proportion of photovoltaic power generation in the load peak period in the absolute power supply guarantee capability dimension is calculated, which is used to quantify the direct support of photovoltaic output to peak load. The proportion of the number of time points at which the photovoltaic output extreme point matches the load extreme point in the relative power supply guarantee capability dimension to the total number of time points in a day is calculated, which is used to reflect the synchronization of supply and demand peaks and valleys. The proportion of photovoltaic power generation in the relative power supply guarantee capability dimension to the total target object power supply is calculated, which is used to measure the proportion of photovoltaic output in the entire power supply system.

6. The method of claim 1, wherein, The step S40 further includes: The proportion of photovoltaic power generation in the load peak period in the probability power supply guarantee capability dimension under different quantiles is calculated, which is used to reflect the proportion of photovoltaic power in the peak period under different output probability scenarios.

7. The photovoltaic ground-based power supply guarantee capability day-ahead evaluation method according to claim 6, characterized in that, The step S40 further includes: The proportion of photovoltaic power generation in the probability power supply guarantee capability dimension under different quantiles is calculated, which is used to evaluate the coverage of photovoltaic output to load demand under different probability scenarios.

8. The photovoltaic ground-based power supply guarantee capability day-ahead evaluation method according to claim 7, characterized in that, The step S40 further includes: The photovoltaic power concentration degree index under different quantiles in the probability power supply guarantee capability dimension is calculated, which is used to reflect the distribution balance of photovoltaic power in the time sequence and its potential impact on power system stability.

9. The photovoltaic ground-based power supply guarantee capability day-ahead assessment method according to claim 1, characterized in that, The evaluation index system includes: The absolute power supply guarantee capability dimension index is used to reflect the capacity of photovoltaic in terms of installed capacity utilization and peak period support. The relative power supply guarantee capability dimension index is used to reflect the matching degree of photovoltaic output and load fluctuation and its contribution to the overall power supply structure. The probability power supply guarantee capability dimension index is used to reflect the power supply coverage and concentration degree of photovoltaic under multi-quantile prediction.

10. The method of claim 1, wherein, The method can quantify the matching of photovoltaic power generation and load demand through multi-dimensional and multi-index comprehensive evaluation, comprehensively reflect the power supply guarantee capability of photovoltaic bases under different conditions, and provide scientific decision-making basis for power grid operation and dispatching and new energy consumption.

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