Real-time rolling dispatch method for water-wind complementary system with wind and light uncertainty coupling
By acquiring real-time deviations in hydropower, wind power, and solar power output, adaptively adjusting the prediction frequency and time period, constructing an uncertainty range, and matching multiple adjustment strategies, the problem of low adjustment efficiency in traditional hydropower-wind-solar complementary system scheduling methods is solved, achieving more efficient dynamic adjustment and stable operation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BAOZHUSI HYDROPOWER PLANT OF HUADIAN SICHUAN POWER GENERATION CO LTD
- Filing Date
- 2026-04-16
- Publication Date
- 2026-07-07
AI Technical Summary
Traditional hydro-wind-solar hybrid systems have difficulty in timely and dynamic adjustment, leading to problems such as power imbalance, wind and solar curtailment, or reduced regulation efficiency.
By acquiring the actual power output of water, wind and solar power and the model-predicted power output in real time, the power output prediction deviation is constructed, the prediction frequency and prediction period length are adaptively adjusted, the normal fluctuation range and the extreme fluctuation range are divided, and the energy storage regulation, hydropower regulation or flexible load joint correction strategy is matched to achieve dynamic adjustment and real-time correction.
It improves the regulation efficiency of the hydro-wind-solar hybrid system, enhances the system's ability to cope with wind and solar fluctuations, reduces wind and solar curtailment, and improves operational stability and economy.
Smart Images
Figure CN122026537B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system strategy optimization, and in particular to a real-time correction and rolling scheduling method for hydro-wind-solar hybrid systems with coupled wind and solar uncertainties. Background Technology
[0002] With the rapid development of new energy power generation technologies, wind power and photovoltaic power generation are increasingly accounting for a larger share of the power system. However, wind and photovoltaic power generation are greatly affected by natural factors such as wind speed and solar radiation intensity, resulting in significant randomness, fluctuations, and uncertainties in their output. This poses a considerable challenge to the safe and stable operation of the power system. To improve the capacity for new energy absorption, hydropower-wind-solar hybrid systems that integrate hydropower, wind power, and photovoltaic power generation are gradually being widely adopted.
[0003] However, in actual operation, the scheduling methods of traditional hydro-wind-solar hybrid systems mostly rely on fixed forecast periods and day-ahead scheduling plans. When there is a large deviation between the actual wind and solar power output and the forecast values, existing scheduling methods often struggle to make timely dynamic adjustments, easily leading to problems such as power imbalance, wind and solar curtailment, or reduced regulation efficiency. Therefore, this invention provides a real-time correction and rolling scheduling method for hydro-wind-solar hybrid systems that couples wind and solar uncertainties to solve the above problems. Summary of the Invention
[0004] This invention provides a real-time correction and rolling scheduling method for water-wind-solar hybrid systems with coupled wind and solar uncertainties, which solves the technical problem of low regulation efficiency in existing scheduling methods for water-wind-solar hybrid systems and achieves the technical effect of improving the regulation efficiency of water-wind-solar hybrid systems.
[0005] In a first aspect, the present invention provides a real-time correction and rolling scheduling method for a hydro-wind-solar hybrid system coupled with wind and solar uncertainties, comprising:
[0006] Obtain the actual power output of water, wind and solar power during the target period and the model-predicted power output, and determine the power output prediction deviation during the target period based on the actual power output and the model-predicted power output.
[0007] Based on the output prediction deviation, determine the model prediction frequency and the length of the next prediction period adjacent to the target period.
[0008] Based on the model's predicted frequency, the uncertainty range of water, wind, and solar power output for the next prediction period is constructed, and the normal fluctuation range and the extreme fluctuation range are obtained.
[0009] Based on the length of the next forecast period, corrective strategies are matched for both normal and extreme fluctuation ranges. These corrective strategies include hydropower regulation correction, energy storage regulation correction, or flexible load joint correction.
[0010] When entering the next forecast period, based on the actual output at each moment of the next forecast period, the target fluctuation range corresponding to that moment is determined from the uncertainty range of hydropower, wind power and solar power output, and the correction strategy of the target fluctuation range corresponding to that moment is used as the correction rolling scheduling strategy for that moment.
[0011] Furthermore, based on the actual power output and the model-predicted power output, the power output prediction deviation for the target period is determined, including:
[0012]
[0013] in, The output prediction deviation for the target time period. For the actual effort during the target period, It contributes to the model prediction for the target time period.
[0014] Furthermore, based on the output prediction deviation, the model prediction frequency is determined, including:
[0015]
[0016] in, For the model to predict frequencies, Set a minimum value for the model's prediction frequency. Set a maximum value for the model's prediction frequency. This is a preset empirical deviation threshold.
[0017] Furthermore, based on the output prediction deviation, the length of the next prediction period adjacent to the target period is determined, including:
[0018]
[0019] in, The length of the next forecast period. To be the minimum allowed time period length, The maximum allowed time period length, This is a preset empirical deviation threshold.
[0020] Furthermore, based on the model's prediction frequency, the uncertainty range of hydropower, wind power, and solar power output for the next prediction period is constructed, and the normal fluctuation range and the extreme fluctuation range are obtained, including:
[0021] Extract prediction error data with the same frequency as the model's predictions from historical data to construct an error sample set, including:
[0022]
[0023] in, To predict the frequency in historical data The historical prediction error sample set, For the first Historical output prediction bias Given the sample size; based on the error sample set, determine the mean error and the standard deviation;
[0024] Based on the standard deviation, the uncertainty interval for hydropower, wind power, and solar power output in the next forecast period is constructed, including:
[0025]
[0026] in, , , The lower boundary of the uncertainty range for water, wind, and solar energy output. The upper boundary of the uncertainty range for water, wind, and solar energy contributions. The model predicts the average output for the next forecast period. To pre-set the confidence coefficient, For the predicted frequency is The standard deviation of historical output prediction bias;
[0027] Divide the fluctuation range into normal fluctuation range and extreme fluctuation range, including:
[0028] .
[0029] Furthermore, based on the error sample set, the mean error and the standard deviation are determined, including:
[0030]
[0031]
[0032] in, For the predicted frequency is Historical average output prediction deviation, For the predicted frequency is The standard deviation of the historical output prediction bias.
[0033] Furthermore, based on the length of the next forecast period, correction strategies are matched for both normal and extreme volatility ranges, including:
[0034] Set the response time, including:
[0035]
[0036] in, For the response time of the energy storage system, The response time of the hydroelectric generator unit. The response time of the flexible load;
[0037] For the normal fluctuation range, if Then, the energy storage regulation and correction will be matched;
[0038] like Then, it will match the water and electricity regulation and correction;
[0039] like Then, the water and electricity regulation correction and flexible load joint correction are matched;
[0040] For extreme fluctuation ranges, if Then, the energy storage regulation correction and hydropower regulation correction are matched;
[0041] like Then, the energy storage regulation correction and hydropower regulation correction are matched;
[0042] like Then, it is matched with energy storage regulation and correction, hydropower regulation and correction, and flexible load joint correction.
[0043] Furthermore, a correction strategy matching function is generated, including:
[0044]
[0045] in, As a corrective strategy, For matching functions, The length of the next forecast period. This refers to either the normal fluctuation range or the extreme fluctuation range.
[0046] Furthermore, including:
[0047] Energy storage regulation and correction:
[0048] When the actual output is lower than the corresponding model prediction output, the energy storage system discharges.
[0049] When the actual output is higher than the corresponding model prediction, the energy storage system charges.
[0050] Water and electricity regulation and correction:
[0051] When the actual output is lower than the corresponding model prediction output, the output of the hydropower unit is increased;
[0052] When the actual output is higher than the corresponding model prediction, the output of the hydropower unit is reduced.
[0053] Furthermore, including:
[0054] Flexible load combined correction:
[0055] When the actual output is lower than the corresponding model prediction output, the adjustable load will be reduced or the power consumption will be delayed.
[0056] When the actual output exceeds the corresponding model prediction output, the adjustable load is guided to increase power consumption or perform a transferable power consumption task.
[0057] One or more technical solutions provided in this invention have at least the following technical effects or advantages:
[0058] This invention proposes a real-time correction and rolling scheduling method for hydro-wind-solar hybrid systems that couples wind and solar uncertainties. This method addresses the strong randomness and volatility of wind and solar power output by dynamically adjusting scheduling strategies and correcting deviations in real time. The invention calculates the deviation between actual and predicted output, adaptively adjusting the model's prediction frequency and the length of the next prediction period. This allows the prediction update speed to be dynamically optimized according to changes in system operating status. Simultaneously, it utilizes historical prediction errors to construct uncertainty intervals for hydro-wind-solar output, dividing output fluctuations into normal and extreme fluctuation intervals, thus more accurately characterizing the uncertainty features of wind and solar output. Based on this, according to the prediction period length and the response characteristics of different regulatory resources, corresponding correction strategies are matched for different fluctuation intervals. Through the coordinated regulation of multiple resources such as hydropower, energy storage, and flexible loads, rapid compensation for power deviations is achieved. Compared to traditional scheduling methods that rely on fixed prediction cycles and single regulation methods, this invention improves prediction adaptability and scheduling flexibility, enhances the system's ability to cope with wind and solar fluctuations, reduces wind and solar curtailment, and improves the stability and economy of hydro-wind-solar hybrid systems.
[0059] This invention constructs an uncertainty interval for hydropower, wind power, and solar power output in the next prediction period based on the model's prediction frequency. This ensures that the uncertainty characterization remains consistent with the current prediction timescale, thereby improving the accuracy and adaptability of the interval estimation. Since different prediction frequencies correspond to different prediction timescales, their prediction error distribution characteristics also differ. Higher prediction frequencies generally mean shorter prediction timescales and smaller error fluctuations, while lower prediction frequencies result in relatively larger error fluctuation ranges. By extracting historical prediction error samples consistent with the current prediction frequency and constructing an uncertainty interval, the power output fluctuation range under current prediction conditions can be more realistically reflected. The interval width can adaptively adjust with changes in the prediction frequency, thereby improving the accuracy of identifying normal and extreme fluctuations. This provides a more reliable basis for subsequent selection of correction strategies such as energy storage regulation, hydropower regulation, or flexible load joint regulation.
[0060] This invention improves the response capability when the prediction frequency and prediction period length are simultaneously and adaptively adjusted, while maintaining stable operation when the prediction deviation is small, thereby enhancing the overall efficiency of real-time correction and rolling scheduling of the hydro-wind-solar hybrid system. Attached Figure Description
[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0062] Figure 1 A flowchart illustrating the real-time correction and rolling scheduling method for a hydro-wind-solar hybrid system with coupled wind-solar uncertainties provided by this invention. Detailed Implementation
[0063] This invention provides a real-time correction and rolling scheduling method for water-wind-solar hybrid systems with coupled wind and solar uncertainties, thereby solving the technical problem of low adjustment efficiency in existing scheduling methods for water-wind-solar hybrid systems.
[0064] The technical solution of this invention is to solve the above-mentioned technical problems, and the overall idea is as follows:
[0065] A real-time correction and rolling scheduling method for a hydro-wind-solar complementary system with coupled wind and solar uncertainties includes: acquiring the actual power output and model-predicted power output of hydropower, wind power, and solar power during a target time period, and determining the power output prediction deviation for the target time period based on the actual power output and model-predicted power output; determining the model prediction frequency and the duration of the next prediction period adjacent to the target time period based on the power output prediction deviation; constructing the hydropower, wind power, and solar power output uncertainty interval for the next prediction period based on the model prediction frequency, and obtaining the normal fluctuation interval and the extreme fluctuation interval; matching correction strategies for the normal fluctuation interval and the extreme fluctuation interval based on the duration of the next prediction period, the correction strategies include hydropower regulation correction, energy storage regulation correction, or flexible load joint correction; when entering the next prediction period, determining the target fluctuation interval corresponding to that moment from the hydropower, wind power, and solar power output uncertainty interval based on the actual power output at each moment of the next prediction period, and using the correction strategy of the target fluctuation interval corresponding to that moment as the correction and rolling scheduling strategy for that moment.
[0066] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.
[0067] First, it should be clarified that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0068] This invention provides, for example Figure 1The real-time correction and rolling scheduling method for a hydro-wind-solar hybrid system with coupled wind-solar uncertainties, as shown, includes steps S11-S15:
[0069] Step S11: Obtain the actual power output of water, wind and solar power during the target period and the model-predicted power output, and determine the power output prediction deviation for the target period based on the actual power output and the model-predicted power output.
[0070] Based on the actual power output and the model-predicted power output, determine the power output prediction deviation for the target time period, including:
[0071]
[0072] in, The output prediction deviation for the target time period. For the actual effort during the target period, It contributes to the model prediction for the target time period.
[0073] Step S11 is used to evaluate the accuracy of the power output prediction of the hydro-wind-solar hybrid system during the target time period.
[0074] Specifically, the system first obtains the actual output (actual power generation data) of hydropower, wind power and photovoltaic power during the target period through the system operation monitoring platform, and at the same time obtains the model prediction output given by the prediction model for the target period.
[0075] when When the value is positive, it indicates that the actual output of the system is higher than the predicted value, suggesting that the prediction has been underestimated.
[0076] when When the value is negative, it indicates that the actual output of the system is lower than the predicted value, suggesting that the prediction has been overestimated.
[0077] By calculating and analyzing the prediction deviation, the degree of difference between the current prediction result and the actual operating status can be quantified.
[0078] Step S12: Based on the output prediction deviation, determine the model prediction frequency and the duration of the next prediction period adjacent to the target period.
[0079] Based on the output prediction deviation, determine the model prediction frequency, including:
[0080]
[0081] in, For the model to predict frequencies, Set a minimum value for the model's prediction frequency. Set a maximum value for the model's prediction frequency. This is a preset empirical deviation threshold.
[0082] Based on the output prediction deviation, determine the length of the next prediction period adjacent to the target period, including:
[0083]
[0084] in, The length of the next forecast period. To be the minimum allowed time period length, The maximum allowed time period length, This is a preset empirical deviation threshold.
[0085] Step S12 is mainly used to adaptively adjust the operating parameters of the prediction model according to the output prediction deviation of the target time period, so as to improve the real-time performance and accuracy of the rolling scheduling of the hydro-wind-solar hybrid system.
[0086] Specifically, after obtaining the output prediction deviation for the target period, the degree of deviation between the current prediction result and the actual operating state is determined by analyzing the absolute value of the prediction deviation.
[0087] When the prediction deviation is small, it indicates that the current prediction result is close to the actual power output, and the system is operating relatively stably. In this case, a low prediction update frequency can be maintained. However, when the prediction deviation gradually increases, it indicates that the fluctuation of wind and solar power output is increasing or the prediction error is expanding. It is necessary to increase the update speed of the prediction model in order to capture power output changes more promptly. The formula for determining the model prediction frequency makes the prediction frequency gradually increase as the prediction deviation increases, thereby achieving adaptive updating of the prediction model.
[0088] While determining the prediction frequency, it is also necessary to dynamically determine the length of the next prediction period adjacent to the target period based on the prediction deviation, so as to ensure that the time resolution of the rolling scheduling can match the system operating status.
[0089] When the prediction deviation is small, a longer prediction period is used to reduce the scheduling update frequency and computational burden; when the prediction deviation increases, the prediction period is shortened and the rolling scheduling update speed is increased.
[0090] By simultaneously and adaptively adjusting the prediction frequency and prediction period length, the response capability can be improved when the prediction deviation is large, and the operation can be kept stable when the prediction deviation is small, thereby improving the overall efficiency of real-time correction and rolling scheduling of the hydro-wind-solar hybrid system.
[0091] Step S13: Based on the model prediction frequency, construct the uncertainty range of water, wind and solar power output for the next prediction period, and obtain the normal fluctuation range and the extreme fluctuation range.
[0092] Based on the model's prediction frequency, the uncertainty range for hydropower, wind power, and solar power output in the next prediction period is constructed, and the normal fluctuation range and the extreme fluctuation range are obtained, including:
[0093] Extract prediction error data with the same frequency as the model's predictions from historical data to construct an error sample set, including:
[0094]
[0095] in, To predict the frequency in historical data The historical prediction error sample set, For the first Historical output prediction bias Given the sample size; based on the error sample set, determine the mean error and the standard deviation;
[0096] Based on the error sample set, determine the error mean and standard deviation, including:
[0097]
[0098]
[0099] in, For the predicted frequency is Historical average output prediction deviation, For the predicted frequency is The standard deviation of the historical output prediction bias.
[0100] Based on the standard deviation, the uncertainty interval for hydropower, wind power, and solar power output in the next forecast period is constructed, including:
[0101]
[0102] in, , , The lower boundary of the uncertainty range for water, wind, and solar energy output. The upper boundary of the uncertainty range for water, wind, and solar energy contributions. The model predicts the average output for the next forecast period. To pre-set the confidence coefficient, For the predicted frequency is The standard deviation of historical output prediction bias;
[0103] Divide the fluctuation range into normal fluctuation range and extreme fluctuation range, including:
[0104] .
[0105] Step S13 is mainly used to quantify the possible fluctuation range of the combined output of hydropower, wind power and photovoltaic power in the next prediction period after determining the model prediction frequency, so as to provide a decision basis for subsequent correction scheduling strategies.
[0106] Specifically, historical prediction error data under the same prediction frequency as the current model are extracted from historical operating data, and an error sample set is constructed.
[0107] Subsequently, the statistical characteristics of the prediction error are calculated based on this set of error samples, reflecting the average deviation of the prediction error at this prediction frequency.
[0108] Based on this, the mean of the model's predicted power output for the next forecast period is used as the center, and the uncertainty range of water, wind and solar power output for the next forecast period is constructed by combining the confidence coefficient and the standard deviation of the error.
[0109] When the actual output of the system is within this range, it is determined to be a normal fluctuation range, indicating that the changes in wind and solar power output are still within an acceptable normal fluctuation range; when the actual output exceeds this range, it is determined to be an extreme fluctuation range, indicating that the system has a large prediction deviation or abnormal fluctuation.
[0110] This invention constructs an uncertainty interval for hydropower, wind power, and solar power output in the next prediction period based on the model's prediction frequency. This ensures that the uncertainty characterization remains consistent with the current prediction timescale, thereby improving the accuracy and adaptability of the interval estimation. Since different prediction frequencies correspond to different prediction timescales, their prediction error distribution characteristics also differ. Higher prediction frequencies generally mean shorter prediction timescales and smaller error fluctuations, while lower prediction frequencies result in relatively larger error fluctuation ranges. By extracting historical prediction error samples consistent with the current prediction frequency and constructing an uncertainty interval, the power output fluctuation range under current prediction conditions can be more realistically reflected. The interval width can adaptively adjust with changes in the prediction frequency, thereby improving the accuracy of identifying normal and extreme fluctuations. This provides a more reliable basis for subsequent selection of correction strategies such as energy storage regulation, hydropower regulation, or flexible load joint regulation.
[0111] Step S14: Based on the length of the next forecast period, match the correction strategies for the normal fluctuation range and the extreme fluctuation range. The correction strategies include hydropower regulation correction, energy storage regulation correction, or flexible load joint correction.
[0112] Specifically, it includes:
[0113] Set the response time, including:
[0114]
[0115] in, For the response time of the energy storage system, The response time of the hydroelectric generator unit. The response time of the flexible load;
[0116] For the normal fluctuation range, if Then, the energy storage regulation and correction will be matched;
[0117] like Then, it will match the water and electricity regulation and correction;
[0118] like Then, the water and electricity regulation correction and flexible load joint correction are matched;
[0119] For extreme fluctuation ranges, if Then, the energy storage regulation correction and hydropower regulation correction are matched;
[0120] like Then, the energy storage regulation correction and hydropower regulation correction are matched;
[0121] like Then, it is matched with energy storage regulation and correction, hydropower regulation and correction, and flexible load joint correction.
[0122] Generate a correction strategy matching function, including:
[0123]
[0124] in, As a corrective strategy, For matching functions, The length of the next forecast period. This refers to either the normal fluctuation range or the extreme fluctuation range.
[0125] Step S14 is mainly used to rationally select the correction and adjustment resources suitable for the next prediction period based on the time scale and intensity characteristics of the fluctuation of water, wind and solar power output, so as to realize the rapid and effective adjustment of the water, wind and solar complementary system.
[0126] Specifically, the response time relationship is first set according to the response characteristics of different adjustment resources in the system.
[0127] Then, the specific correction strategy is determined by combining the length of the next forecast period and the fluctuation range type corresponding to the uncertainty range of water, wind and solar power output.
[0128] When the system is in a normal fluctuation range, if the forecast period is short, it means that the system needs to respond quickly. Therefore, energy storage regulation and correction should be used first.
[0129] like This indicates that the fluctuation has a certain degree of persistence, so the correction is mainly achieved by adjusting the output of the hydropower units;
[0130] like Therefore, flexible load coordination regulation is introduced on the basis of hydropower regulation to enhance the system's continuous regulation capability.
[0131] When the system is in an extreme fluctuation range, due to the large output deviation, it is necessary to increase the system's regulation intensity. Therefore, a multi-resource coordinated regulation approach is adopted at all time scales.
[0132] when or At that time, energy storage and hydropower are used for combined regulation;
[0133] when At the same time, energy storage, hydropower, and flexible loads are simultaneously mobilized for joint correction.
[0134] Finally, by constructing a correction strategy matching function, the optimal correction strategy can be automatically matched according to the predicted time period length and the type of fluctuation interval. This enables flexible allocation of adjustment resources under different time scales and fluctuation intensities, thereby improving the real-time performance and stability of the rolling scheduling of the hydro-wind-solar hybrid system.
[0135] The following explanation covers energy storage regulation and correction, hydropower regulation and correction, and flexible load joint correction.
[0136] Energy storage regulation and correction:
[0137] When the actual output is lower than the corresponding model prediction output, the energy storage system discharges.
[0138] When the actual output is higher than the corresponding model prediction, the energy storage system charges.
[0139] Water and electricity regulation and correction:
[0140] When the actual output is lower than the corresponding model prediction output, the output of the hydropower unit is increased;
[0141] When the actual output is higher than the corresponding model prediction, the output of the hydropower unit is reduced.
[0142] Flexible load combined correction:
[0143] When the actual output is lower than the corresponding model prediction output, the adjustable load will be reduced or the power consumption will be delayed.
[0144] When the actual output exceeds the corresponding model prediction output, the adjustable load is guided to increase power consumption or perform a transferable power consumption task.
[0145] Step S15: When entering the next prediction period, based on the actual output at each moment of the next prediction period, determine the target fluctuation range corresponding to that moment from the uncertainty range of water, wind and solar output, and use the correction strategy of the target fluctuation range corresponding to that moment as the correction rolling scheduling strategy for that moment.
[0146] Step S15 is mainly used to perform real-time correction and rolling scheduling during actual operation to ensure that the hydro-wind-solar hybrid system can dynamically adjust the regulation strategy according to real-time output changes.
[0147] Once the system enters the next prediction period, it continuously acquires the actual power output data of hydropower, wind power, and photovoltaic power at each moment within the prediction period, and compares it with the uncertainty range of hydropower, wind power, and photovoltaic power output constructed in step S13, thereby determining whether the actual power output at that moment is in the normal fluctuation range or the extreme fluctuation range.
[0148] If the actual output is within the uncertainty range, it is determined to be a normal fluctuation range; if the actual output exceeds the range, it is determined to be an extreme fluctuation range.
[0149] Based on the established correction strategy matching relationship in step S14, the correction strategy corresponding to the target fluctuation range is retrieved and executed as the current rolling scheduling strategy. The system power deviation is compensated through corresponding energy storage regulation, hydropower unit output adjustment or flexible load joint regulation.
[0150] Through the above methods, the output changes at each moment during the prediction period can be monitored in real time and dynamically decided, so that the correction scheduling strategy can be continuously updated according to the actual operating status, thereby realizing the real-time adaptive rolling scheduling of the hydro-wind-solar hybrid system under uncertain conditions, and improving system stability and regulation efficiency.
[0151] In summary, the real-time correction and rolling scheduling method for hydro-wind-solar hybrid systems proposed in this invention addresses the challenges of the strong randomness and volatility in wind and solar power output, enabling dynamic adjustment and real-time correction of scheduling strategies. This invention calculates the deviation between actual and predicted output, adaptively adjusting the model's prediction frequency and the length of the next prediction period, allowing the prediction update speed to dynamically optimize with changes in system operating status. Simultaneously, it utilizes historical prediction errors to construct uncertainty intervals for hydro-wind-solar output, dividing output fluctuations into normal and extreme fluctuation intervals, thus more accurately characterizing the uncertainty features of wind and solar output. Based on this, according to the prediction period length and the response characteristics of different regulatory resources, corresponding correction strategies are matched for different fluctuation intervals. Furthermore, through the coordinated regulation of multiple resources such as hydropower, energy storage, and flexible loads, rapid compensation for power deviations is achieved. Compared to traditional scheduling methods relying on fixed prediction cycles and single regulation methods, this invention improves prediction adaptability and scheduling flexibility, enhances the system's ability to cope with wind and solar fluctuations, reduces wind and solar curtailment, and improves the stability and economy of hydro-wind-solar hybrid system operation.
[0152] This invention constructs an uncertainty interval for hydropower, wind power, and solar power output in the next prediction period based on the model's prediction frequency. This ensures that the uncertainty characterization remains consistent with the current prediction timescale, thereby improving the accuracy and adaptability of the interval estimation. Since different prediction frequencies correspond to different prediction timescales, their prediction error distribution characteristics also differ. Higher prediction frequencies generally mean shorter prediction timescales and smaller error fluctuations, while lower prediction frequencies result in relatively larger error fluctuation ranges. By extracting historical prediction error samples consistent with the current prediction frequency and constructing an uncertainty interval, the power output fluctuation range under current prediction conditions can be more realistically reflected. The interval width can adaptively adjust with changes in the prediction frequency, thereby improving the accuracy of identifying normal and extreme fluctuations. This provides a more reliable basis for subsequent selection of correction strategies such as energy storage regulation, hydropower regulation, or flexible load joint regulation.
[0153] This invention improves the response capability when the prediction frequency and prediction period length are simultaneously and adaptively adjusted, while maintaining stable operation when the prediction deviation is small, thereby enhancing the overall efficiency of real-time correction and rolling scheduling of the hydro-wind-solar hybrid system.
[0154] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.
[0155] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.
Claims
1. A real-time correction and rolling scheduling method for a hydro-wind-solar hybrid system with coupled wind and solar uncertainties, characterized in that, include: The actual power output and model-predicted power output of water, wind and solar power during the target period are obtained, and the power output prediction deviation during the target period is determined based on the actual power output and the model-predicted power output. Based on the output prediction deviation, determine the model prediction frequency and the length of the next prediction period adjacent to the target time period; include: in, The length of the next forecast period. To be the minimum allowed time period length, The maximum allowed time period length, To preset an empirical deviation threshold, The output prediction deviation for the target time period; Based on the model's predicted frequency, the uncertainty range of hydropower, wind power, and solar power output for the next prediction period is constructed, and the normal fluctuation range and the extreme fluctuation range are obtained; including: To construct an error sample set, prediction error data with the same prediction frequency as the model are obtained from historical data, including: in, To predict the frequency in historical data The historical prediction error sample set, For the first Historical output prediction bias Given the sample size; based on the error sample set, determine the mean error and the standard deviation; Based on the standard deviation, the uncertainty interval for hydropower, wind power, and solar power output in the next forecast period is constructed, including: in, , , The lower boundary of the uncertainty range for water, wind, and solar energy output. The upper boundary of the uncertainty range for water, wind, and solar energy contributions. The model predicts the average output for the next forecast period. To pre-set the confidence coefficient, For the predicted frequency is The standard deviation of historical output prediction bias; Divide the fluctuation range into normal fluctuation range and extreme fluctuation range, including: ； Based on the length of the next forecast period, correction strategies are matched for the normal fluctuation range and the extreme fluctuation range. These correction strategies include hydropower regulation correction, energy storage regulation correction, or flexible load combined correction. Set the response time, including: in, For the response time of the energy storage system, The response time of the hydroelectric generator unit. The response time of the flexible load; For the normal fluctuation range, if Then, the energy storage regulation and correction will be matched; like Then, the water and electricity regulation and correction will be matched; like Then, the water and electricity regulation correction and flexible load joint correction are matched; For extreme fluctuation ranges, if Then, the energy storage regulation correction and hydropower regulation correction are matched; like Then, the energy storage regulation correction and hydropower regulation correction are matched; like Then, it is matched with energy storage regulation correction, hydropower regulation correction and flexible load joint correction; When entering the next forecast period, based on the actual output at each moment of the next forecast period, the target fluctuation range corresponding to that moment is determined from the uncertainty range of hydropower, wind power and solar power output, and the correction strategy of the target fluctuation range corresponding to that moment is used as the correction rolling scheduling strategy for that moment.
2. The real-time correction and rolling scheduling method for a hydro-wind-solar hybrid system with coupled wind and solar uncertainties as described in claim 1, characterized in that, Based on the actual power output and the model-predicted power output, the power output prediction deviation for the target time period is determined, including: in, The output prediction deviation for the target time period. For the actual effort during the target period, It contributes to the model prediction for the target time period.
3. The real-time correction and rolling scheduling method for a hydro-wind-solar hybrid system with coupled wind and solar uncertainties as described in claim 2, characterized in that, Based on the output prediction deviation, the model prediction frequency is determined, including: in, For the model to predict frequencies, Set a minimum value for the model's prediction frequency. Set a maximum value for the model's prediction frequency. This is a preset empirical deviation threshold.
4. The real-time correction and rolling scheduling method for a hydro-wind-solar hybrid system with coupled wind and solar uncertainties as described in claim 1, characterized in that, Based on the error sample set, determine the error mean and standard deviation, including: in, For the predicted frequency is Historical average output prediction deviation, For the predicted frequency is The standard deviation of the historical output prediction bias.
5. The real-time correction and rolling scheduling method for a hydro-wind-solar hybrid system with coupled wind and solar uncertainties as described in claim 1, characterized in that, Generate a correction strategy matching function, including: in, As a corrective strategy, For matching functions, The length of the next forecast period. This refers to either the normal fluctuation range or the extreme fluctuation range.
6. The real-time correction and rolling scheduling method for a hydro-wind-solar hybrid system with coupled wind and solar uncertainties as described in claim 1, characterized in that, include: Energy storage regulation and correction: When the actual output is lower than the corresponding model prediction output, the energy storage system discharges. When the actual output is higher than the corresponding model prediction, the energy storage system charges. Water and electricity regulation and correction: When the actual output is lower than the corresponding model prediction output, the output of the hydropower unit is increased; When the actual output is higher than the corresponding model prediction, the output of the hydropower unit is reduced.
7. The real-time correction and rolling scheduling method for a hydro-wind-solar hybrid system with coupled wind and solar uncertainties as described in claim 1, characterized in that, include: Flexible load combined correction: When the actual output is lower than the corresponding model prediction output, the adjustable load will be reduced or the power consumption will be delayed. When the actual output exceeds the corresponding model prediction output, the adjustable load is guided to increase power consumption or perform a transferable power consumption task.