Cascade hydropower segmented response method adaptive to new energy short-term power prediction deviation

Abstract

This invention relates to a segmented response method for cascade hydropower to adapt to short-term power forecast deviations in renewable energy sources, belonging to the field of multi-energy complementary dispatching technology in power systems. First, a set of wind and solar power output scenarios is generated, a segmented regulation model is constructed, the boundaries of the forecast deviation interval are determined, and a segmented linear mapping relationship is established between the cascade hydropower regulation output and the renewable energy forecast deviation. Second, a short-term optimal dispatching model for hydropower-wind-solar complementarity is established, with the objective of maximizing day-ahead power generation plan revenue, and joint optimization and solution are performed. Third, a short-term complementary dispatching evaluation index system for hydropower-wind-solar complementarity is constructed. Finally, based on the renewable energy forecast deviation, the output adjustment of each hydropower station is calculated using the segmented linear mapping relationship, dynamically adjusting the actual output of the hydropower stations. This invention dynamically adjusts the response boundary and regulation strategy based on the distribution characteristics of renewable energy forecast deviations, improving the adaptability of cascade hydropower to renewable energy uncertainties and the robustness of day-ahead plan operation while ensuring system economic benefits.

Description

Technical Field

[0001] This invention belongs to the field of multi-energy complementary dispatching of power systems, and relates to a cascade hydropower segmented response method that adapts to short-term power prediction deviations of new energy sources, and particularly to a cascade hydropower segmented response method that adapts to short-term power prediction deviations of new energy sources with high penetration rates. Background Technology

[0002] In recent years, new energy sources, represented by wind power and solar power, have been connected to the grid on a large scale, and the penetration rate of new energy sources has continued to increase. The power system is gradually transforming towards a structure dominated by renewable energy. However, the output of wind power and solar power has obvious randomness, intermittency, and volatility, and its prediction deviation is characterized by large amplitude and high frequency on an intraday scale. As the proportion of new energy sources increases, the impact of such uncertainties on system power balance and safe operation becomes increasingly significant.

[0003] Limited by factors such as installed capacity and cost, current flexible resources such as energy storage are insufficient to support the high-proportion consumption of large-scale new energy sources. Therefore, multi-energy complementary power generation systems have become an important way to improve system flexibility. Among these, cascade hydropower, as a key regulating resource, plays a crucial role in smoothing out fluctuations in new energy sources and maintaining the system's power balance. In actual operation, the system typically formulates a day-ahead power generation plan and relies on cascade hydropower for intraday regulation to address new energy forecast deviations. During the day-ahead phase, cascade hydropower needs to coordinate inter-period water allocation and reservoir capacity constraints while reserving adjustment space for intraday uncertainties, resulting in scheduling decisions with significant spatiotemporal coupling characteristics.

[0004] Existing multi-energy complementary scheduling methods are mostly based on fixed-proportion regulation strategies. For example, Chinese invention patent CN118944190A proposes allocating peak-shaving and frequency regulation tasks to each power station by setting a fixed regulation coefficient to smooth out fluctuations in wind and solar power output. However, short-term forecast deviations for wind and solar power typically exhibit nonlinear random distribution characteristics. Fixed-proportion methods struggle to reflect differentiated regulation needs under different deviation amplitudes and time periods, easily leading to unreasonable configuration of cascade hydropower regulation capacity. This can result in insufficient regulation or excessive consumption of long-term hydropower regulation capacity during intraday operation, affecting system economy and operational safety.

[0005] To address the above issues, this invention proposes a segmented response method for cascade hydropower that adapts to short-term power forecast deviations in new energy sources. This method combines the distribution characteristics of new energy forecast deviations with parameterized description and optimization of cascade hydropower regulation during the day-ahead phase. Application testing was conducted using the Beipanjiang hydropower-wind-solar integrated base as an engineering example. The results show that this invention can effectively respond to new energy forecast deviations while ensuring system power generation revenue, fully releasing the flexible regulation potential of cascade hydropower across multiple time scales. Summary of the Invention

[0006] To address the problems of existing technologies, this invention provides a segmented response method for cascade hydropower that adapts to short-term power forecasting deviations in new energy sources. Specifically, it is a short-term optimized scheduling method for cascade hydropower and new energy sources under conditions of high-penetration renewable energy access. This invention constructs a set of uncertain power output scenarios for high-penetration new energy sources and establishes a segmented response mapping relationship between the regulated output of cascade hydropower and the forecasting deviations of new energy sources. This effectively solves the problem of difficulty in accurately characterizing flexibility requirements caused by new energy forecasting deviations, achieving refined matching of cascade hydropower output adjustments to new energy forecasting deviations, and improving the executability of day-ahead planning and the economic efficiency of system operation.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A segmented response method for cascade hydropower that adapts to short-term power prediction deviations in new energy sources, wherein the cascade hydropower segmented response method can complete short-term complementary and optimized scheduling of hydropower, wind power, and solar power, specifically includes the following steps:

[0009] The first step is to obtain the basic data required for system scheduling;

[0010] The basic data includes the installed capacity parameters and regulating reservoir capacity parameters of the cascade hydropower stations, historical actual wind and solar power output data and day-ahead wind and solar power output forecast data of the integrated hydropower, wind and solar power base, time-of-use electricity price data, water level and reservoir capacity relationship curves of the cascade hydropower stations, water consumption rate characteristic curves of hydropower units, and capacity constraint limit data of external power transmission channels.

[0011] The second step is to extract statistical features from the historical actual power output data and the day-ahead predicted power output data of wind and solar power in the basic data obtained in the first step, and generate a set of wind and solar power output prediction deviation scenarios to characterize the uncertainty of intraday power output under high penetration new energy conditions.

[0012] The third step involves constructing a segmented regulation model based on the wind and solar power output prediction deviation scenario set obtained in the second step. This model characterizes the flexibility response capability of each hydropower station, dynamically determining the boundaries of the prediction deviation intervals, and establishing a segmented linear mapping relationship between the cascade hydropower regulation output and the new energy prediction deviations. It also determines the adjustment amount of each cascade hydropower station relative to the day-ahead planned output under different scenarios and time periods, and sets corresponding segmented mapping parameters within each interval. The specific process of constructing the segmented regulation model is as follows:

[0013] Step 3.1, the method for calculating the flexibility demand for new energy sources is as follows:

[0014] (1);

[0015] In the formula: Indicates the scene number; Indicates the scheduling period number; Representing a scene middle The need for flexibility in time slots; Indicates wind power Forecast power output for the specified time period, in MW; Indicates photovoltaic Forecast power output for the specified time period, in MW; Representing a scene China Wind Power Output per time period, in MW; Representing a scene China Photovoltaic Output over a given period, measured in MW.

[0016] Step 3.2: Based on the distribution characteristics of the concentrated flexibility demand in the wind and solar power output prediction deviation scenario, calculate the segmentation boundary points of the positive and negative flexibility demand intervals, divide the flexibility demand into multiple dynamic intervals, and determine the dynamic boundary of the flexibility demand as follows:

[0017] (2);

[0018] (3);

[0019] In the formula: The number of segments indicating the positive flexibility requirement; The segment number indicating the positive flexibility requirement; The number of segments indicating negative flexibility requirements; The segment number indicating negative flexibility requirements; This indicates a positive demand for flexibility. Dynamic boundary value at the location, in MW; This indicates negative flexibility requirements. The dynamic boundary value at the specified location, in MW.

[0020] Step 3.3: Within different demand boundary intervals, establish a piecewise linear mapping relationship between the regulating output of cascade hydropower stations and the prediction deviation of new energy sources, as shown below:

[0021] (4);

[0022] (5);

[0023] (6);

[0024] In the formula: Indicates the hydroelectric power station number; Indicates hydroelectric power station In the scene middle Output adjustment for different time periods, in MW; , They represent hydroelectric power stations. In the part The slope and intercept of the time interval are the response parameters to be optimized; , They represent hydroelectric power stations. In the part The slope and intercept of the time interval are the response parameters to be optimized; This indicates a positive demand for flexibility. Dynamic boundary value at the location, in MW; This indicates negative flexibility requirements. The dynamic boundary value at the specified location, in MW.

[0025] Step 3.4: Based on the piecewise linear mapping relationship obtained in Step 3.3, determine the adjustment amount of each cascade hydropower station relative to the planned daily output under different scenarios and time periods:

[0026] (7);

[0027] (8);

[0028] In the formula: Indicates hydroelectric power station exist Planned output for the day of the period, in MW; Indicates hydroelectric power station In the scene middle Power output after participating in the response during the specified time period, in MW; This indicates the number of hydroelectric power stations.

[0029] The fourth step is to establish a short-term optimization scheduling model for hydropower-wind-solar hybrid power generation based on dynamic boundary segmented regulation, with the goal of maximizing the day-ahead power generation plan revenue. The day-ahead planned output of the cascade hydropower stations and the segmented mapping parameters are used as decision variables for joint optimization.

[0030] The objective function of the short-term optimization scheduling model for hydro-wind-solar hybrid systems is as follows:

[0031] (9);

[0032] in, Represent the objective function of the model; express Electricity price for the specified time period, in yuan; express The duration of the time period, in seconds; This indicates the total number of time periods simulated by the model; This indicates the number of cascade hydropower stations. Indicates the number of the cascade hydropower station; Indicates wind power Predicted output for a given time period; Indicates photovoltaic Forecast power output for the specified time period, in MW; Indicates hydroelectric power station exist The planned output for the day of the period. The objective function shown in formula (9) is the formula for calculating the planned output for the day, and the planned output for the day is obtained by solving it.

[0033] The constraints of the short-term optimal scheduling model for hydropower-wind-solar hybrid systems include transmission channel capacity constraints, water balance equations, reservoir capacity constraints, outflow constraints, power generation flow constraints, hydropower station output constraints, ramp constraints, hydropower station output function, water level and reservoir capacity constraints, and initial and final water level constraints, as detailed below:

[0034] The power transmission channel capacity constraint:

[0035] (10);

[0036] In the formula: Indicates the time period of the integrated water, wind and solar power base The capacity of the power transmission channel, in MW.

[0037] The water balance equation is as follows:

[0038] (11);

[0039] (12);

[0040] In the formula: Indicates hydroelectric power station Time period Storage capacity, unit m 3 ; Indicates hydroelectric power station Time period Storage capacity, unit m 3 ; Indicates hydroelectric power station Time period Interval flow rate, in m 3 / s; Indicates hydroelectric power station Time period Outbound flow rate, in m³ 3 / s; Indicates hydroelectric power station upstream hydropower station period Outbound flow rate, in m³ 3 / s; Indicates hydroelectric power station Time period Power generation flow, in m³ 3 / s; Indicates hydroelectric power station Time period The discharge flow rate, in meters 3 / s.

[0041] The storage capacity constraint:

[0042] (13);

[0043] In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of the storage capacity, in meters. 3 .

[0044] The outbound flow constraint:

[0045] (14);

[0046] In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of outbound flow rate, in meters. 3 / s.

[0047] The power generation flow constraint:

[0048] (15);

[0049] In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of power generation flow, in m³ 3 / s.

[0050] The power output constraints of the hydropower station are as follows:

[0051] (16);

[0052] In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of output, in MW.

[0053] The climbing constraint:

[0054] (17);

[0055] In the formula: Indicates hydroelectric power station Permissible output range, in MW; Indicates hydroelectric power station exist Output per time period, unit: MW.

[0056] The power output function of the hydropower station:

[0057] (18);

[0058] In the formula: Indicates hydroelectric power station Water consumption rate, in m 3 / kWh.

[0059] The water level and reservoir capacity constraints:

[0060] (19);

[0061] In the formula: Indicates hydroelectric power station Time period Water level, in meters; This indicates the relationship between water level and reservoir capacity at a hydropower station.

[0062] The initial and final water level constraints:

[0063] (20);

[0064] In the formula: , Indicates hydroelectric power station The initial and final water level settings, in meters (m); Indicates hydroelectric power station The coefficient; Indicates hydroelectric power station The initial water level, in meters (m). Indicates hydroelectric power station The final water level.

[0065] The fifth step, to verify and evaluate the actual scheduling effect of the short-term optimal scheduling model for hydro-wind-solar integration constructed in the fourth step within a closed loop, independently constructs a short-term complementary scheduling evaluation index system for hydro-wind-solar integration. This system includes day-ahead planned revenue, expected positive deviation power generation, expected negative deviation power generation, intraday operating revenue, cumulative deviation time, positive deviation power generation, and negative deviation power generation. Among these, day-ahead planned revenue, expected positive deviation power generation, and expected negative deviation power generation are used to evaluate the rationality of the day-ahead power generation plan of the integrated hydro-wind-solar base. Intraday operating revenue, cumulative deviation time, positive deviation power generation, and negative deviation power generation are used to evaluate the intraday execution effect. Specifically:

[0066] Step 5.1 introduces the expected positive deviation in power generation to evaluate the downward adjustment capability of the short-term optimal scheduling model for hydropower-wind-solar hybrid systems; the smaller the deviation, the greater the downward adjustment capability of hydropower.

[0067] (twenty one);

[0068] (twenty two);

[0069] in, Representing a scene The probability of; S represents the scene number, and S represents the total number of scenes; This represents the expected positive deviation in electricity consumption, expressed in MWh.

[0070] Step 5.2 introduces the negative deviation power expectation to evaluate the upward adjustment capability of the hydropower-wind-solar hybrid short-term optimal scheduling model; the smaller the deviation, the greater the hydropower upward adjustment capability.

[0071] (twenty three);

[0072] (twenty four);

[0073] Step 5.3 introduces intraday operational revenue measurement to assess the intraday power generation efficiency of the hydro-wind-solar hybrid short-term optimal scheduling model:

[0074] (25);

[0075] in, This represents the model's actual intraday profit, expressed in yuan. Indicates wind power Actual output during the time period, in MW; Indicates photovoltaic Actual output during the time period, in MW; Indicates hydroelectric power station exist Actual output during the time period, in MW.

[0076] Step 5.4 introduces the cumulative deviation time to measure the cumulative time during which the actual daily output of the hydro-wind-solar hybrid short-term optimization scheduling model does not equal the planned output of the previous day:

[0077] (26);

[0078] (27);

[0079] in, This indicates that the model measures an indicator variable that shows the actual output during the day is not equal to the planned output the day before. The model's planned output is indicated by the unit MW.

[0080] Step 6: Solve the short-term optimal scheduling model for water-wind-solar hybrid systems constructed in Step 4;

[0081] To verify the effectiveness of the short-term optimal scheduling model for hydro-wind-solar hybrid systems, this invention sets the number of segments for positive flexibility requirements. Number of segments with negative flexibility requirements The Gurobi optimization solver was used to solve the short-term optimal scheduling model for hydro-wind-solar hybrid power generation, obtaining the optimal day-ahead generation plan and the corresponding piecewise response parameters. The piecewise response parameters include those representing the hydropower station. In the part Slope of the time period , indicating a hydroelectric power station In the part Intercept of time period , indicating a hydroelectric power station In the part Slope of the time period , indicating a hydroelectric power station In the part Intercept of time period ;

[0082] Step 7: During the intraday operation phase, based on the real-time new energy forecast deviation, the output adjustment of each hydropower station is calculated using the piecewise linear mapping relationship determined in Step 3, and the actual output of the hydropower stations is dynamically adjusted accordingly. The variables include those representing the hydropower stations. exist The planned output for the current period , indicating a hydroelectric power station In the scene middle Output adjustment during time period , indicating a hydroelectric power station In the scene middle Output after participating in the response during the time period .

[0083] The present invention has the following beneficial effects:

[0084] (1) By constructing a set of new energy prediction deviation scenarios and dynamically dividing the deviation interval, this invention can effectively characterize the distribution characteristics of prediction deviation in terms of amplitude and probability under the condition of high new energy penetration rate.

[0085] (2) In the third step, this invention establishes a piecewise linear mapping relationship between the output of cascade hydropower regulation and the prediction deviation of new energy sources, and configures corresponding slope and intercept parameters for different deviations. This mathematical mapping feature realizes differentiated regulation of hydropower regulation capacity within different deviation ranges, overcoming the problems of insufficient local regulation capacity or excessive waste of flexibility resources caused by the traditional fixed-proportion regulation method;

[0086] (3) Meanwhile, in the fourth step, the present invention constructs and jointly solves a short-term optimal scheduling model for hydropower-wind-solar complementary power generation by taking the segmented mapping parameters and the day-ahead power generation plan of the cascade hydropower as decision variables.

[0087] In summary, this invention ensures that the system, while maximizing the total day-ahead power generation revenue, allocates a reasonable and ample intraday adjustment space for the optimal piecewise function of each hydropower station. This improves the operability and robustness of static day-ahead planning in the face of random intraday scenarios, and provides an effective technical approach that balances revenue and flexibility for short-term complementary scheduling of hydropower, wind power, and solar power in river basins under conditions of high renewable energy penetration. Attached Figure Description

[0088] Figure 1 This is a diagram of wind power output.

[0089] Figure 2 This is a diagram of a photovoltaic power generation scenario;

[0090] Figure 3 This is a supply and demand mapping diagram with a segmentation of 1.

[0091] Figure 4 The supply and demand mapping diagram with a positive segmentation of 4 is used to represent the flexibility requirement. Figure 4 (a) in the diagram represents the supply and demand mapping relationship in the first paragraph below, where flexibility requirements are positive. Figure 4 (b) in the diagram represents the supply and demand mapping relationship in the second paragraph below, where flexibility requirements are positive. Figure 4 (c) in the diagram represents the supply and demand mapping relationship in the third paragraph below, where flexibility requirements are positive. Figure 4 (d) represents the flexibility requirement, which is shown in the supply and demand mapping diagram in the fourth paragraph below.

[0092] Figure 5 A supply and demand mapping diagram with 4 segments for a negative flexibility requirement; Figure 5 (a) in the diagram represents the supply and demand mapping relationship in the first paragraph when flexibility demand is negative; Figure 5 (b) in the diagram represents the supply and demand mapping relationship in the second paragraph when flexibility demand is negative; Figure 5 (c) in the diagram represents the supply and demand mapping relationship in the third paragraph when flexibility demand is negative; Figure 5 (d) in the diagram represents the supply and demand mapping relationship in the fourth paragraph, where the flexibility demand is negative.

[0093] Figure 6 The daytime power output plan for the Guangzhao Hydropower Station;

[0094] Figure 7 The planned output for the Mama Cliff Hydropower Station;

[0095] Figure 8 The planned output for the Dongqing Hydropower Station. Detailed Implementation

[0096] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0097] A segmented response method for cascade hydropower to adapt to short-term power forecasting deviations in new energy sources includes the following steps:

[0098] The first step is to obtain the basic data required for system scheduling;

[0099] The basic data includes: installed capacity parameters and regulating reservoir capacity parameters of the cascade hydropower stations, with a total installed capacity of 2478MW for the three hydropower stations; hourly historical actual wind and solar power output data and day-ahead wind and solar power output forecast data of the integrated hydropower, wind and solar power base collected on typical days in 2019, with a total installed capacity of 1180MW for the photovoltaic power station and 470MW for the wind power station; time-of-use electricity price data, water level-reservoir capacity relationship curves of the cascade hydropower stations, water consumption rate characteristic curves of hydropower units, and capacity constraint limit data of power transmission channels.

[0100] The second step is to extract statistical features from the historical actual power output data and the day-ahead predicted power output data of wind and solar power in the basic data obtained in the first step, and generate a set of wind and solar power output prediction deviation scenarios to characterize the uncertainty of intraday power output under high penetration new energy conditions. Figure 1 It is a wind power output scenario map, which shows the possible trajectories of wind power output within a day based on historical typical daily data; Figure 2 It is a photovoltaic power output scenario diagram, which shows the randomness of photovoltaic power output during the daytime and multiple possible power output trajectory scenarios;

[0101] The third step involves constructing a segmented regulation model based on the wind and solar power output prediction deviation scenario set obtained in the second step. This model characterizes the flexibility response capability of each hydropower station, dynamically determining the boundaries of the prediction deviation intervals, and establishing a segmented linear mapping relationship between the cascade hydropower regulation output and the new energy prediction deviations. It also determines the adjustment amount of each cascade hydropower station relative to the day-ahead planned output under different scenarios and time periods, and sets corresponding segmented mapping parameters within each interval. The specific process of constructing the segmented regulation model is as follows:

[0102] Step 3.1, the method for calculating the flexibility demand for new energy sources is as follows:

[0103] (28);

[0104] In the formula: Indicates the scene number; Indicates the scheduling period number; Representing a scene middle The need for flexibility in time slots; Indicates wind power Forecast power output for the specified time period, in MW; Indicates photovoltaic Forecast power output for the specified time period, in MW; Representing a scene China Wind Power Output per time period, in MW; Representing a scene China Photovoltaic Output over a given period, measured in MW.

[0105] Step 3.2: Based on the distribution characteristics of the concentrated flexibility demand in the wind and solar power output prediction deviation scenario, calculate the segmentation boundary points of the positive and negative flexibility demand intervals, divide the flexibility demand into multiple dynamic intervals, and determine the dynamic boundary of the flexibility demand as follows:

[0106] (29);

[0107] (30);

[0108] In the formula: The number of segments indicating the positive flexibility requirement; The segment number indicating the positive flexibility requirement; The number of segments indicating negative flexibility requirements; The segment number indicating negative flexibility requirements; This indicates a positive demand for flexibility. Dynamic boundary value at the location, in MW; This indicates negative flexibility requirements. The dynamic boundary value at the specified location, in MW.

[0109] Step 3.3: Within different demand boundary intervals, establish a piecewise linear mapping relationship between the regulating output of cascade hydropower stations and the prediction deviation of new energy sources, as shown below:

[0110] (31);

[0111] (32);

[0112] (33);

[0113] In the formula: Indicates the hydroelectric power station number; Indicates hydroelectric power station In the scene middle Output adjustment for different time periods, in MW; , They represent hydroelectric power stations. In the part The slope and intercept of the time interval are the response parameters to be optimized; , They represent hydroelectric power stations. In the part The slope and intercept of the time interval are the response parameters to be optimized; This indicates a positive demand for flexibility. Dynamic boundary value at the location, in MW; This indicates negative flexibility requirements. The dynamic boundary value at the specified location, in MW.

[0114] in Figure 3 This is a supply-demand mapping diagram with one segment, where the horizontal axis represents flexibility demand and the vertical axis represents the response ratio of cascade hydropower output adjustment. When the number of segments is one, this method degenerates into a single linear proportional adjustment strategy, adopting the same response ratio parameter across the entire deviation range; Figure 4 The supply-demand mapping diagram with a positive subdivision of 4 segments illustrates a refined response strategy based on 4 dynamic boundary points when the actual output of new energy exceeds the predicted output. Figure 4 (a) in the diagram represents the supply and demand mapping relationship in the first paragraph below, where flexibility requirements are positive. Figure 4 (b) in the diagram represents the supply and demand mapping relationship in the second paragraph below, where flexibility requirements are positive. Figure 4 (c) in the diagram represents the supply and demand mapping relationship in the third paragraph below, where flexibility requirements are positive. Figure 4 (d) represents the flexibility requirement, which is shown in the supply and demand mapping diagram in the fourth paragraph below. Figure 5The supply-demand mapping diagram with 4 segments under negative flexibility requirements illustrates the response strategy when the actual output of new energy is less than the predicted output: Figure 5 (a) in the diagram represents the supply and demand mapping relationship in the first paragraph when flexibility demand is negative; Figure 5 (b) in the diagram represents the supply and demand mapping relationship in the second paragraph when flexibility demand is negative; Figure 5 (c) in the diagram represents the supply and demand mapping relationship in the third paragraph when flexibility demand is negative; Figure 5 (d) in the diagram represents the supply and demand mapping relationship in the fourth paragraph, where the flexibility requirement is negative.

[0115] Step 3.4: Based on the piecewise linear mapping relationship obtained in Step 3.3, determine the adjustment amount of each cascade hydropower station relative to the planned daily output under different scenarios and time periods:

[0116] (34);

[0117] (35);

[0118] In the formula: Indicates hydroelectric power station exist Planned output for the day of the period, in MW; Indicates hydroelectric power station In the scene middle Power output after participating in the response during the specified time period, in MW; This indicates the number of hydroelectric power stations.

[0119] Fourth, based on the adjustment amount obtained in the third step, a short-term optimal scheduling model for hydro-wind-solar hybrid power generation is established based on dynamic boundary segmented regulation. With the objective of maximizing the day-ahead planned power generation revenue, the day-ahead planned output of the cascade hydropower stations and the segmented mapping parameters are used as decision variables for joint optimization. The objective function of the short-term optimal scheduling model for hydro-wind-solar hybrid power generation is as follows:

[0120] (36);

[0121] in, Represent the objective function of the model; express Electricity price for the specified time period, in yuan; express The duration of the time period, in seconds; This indicates the total number of time periods simulated by the model; This indicates the number of cascade hydropower stations. Indicates the number of the cascade hydropower station; Indicates wind power Predicted output for a given time period; Indicates photovoltaic Forecast power output for the specified time period, in MW; Indicates hydroelectric power station exist The daytime plan for the period will be implemented.

[0122] The constraints of the short-term optimal scheduling model for hydropower-wind-solar hybrid systems include transmission channel capacity constraints, water balance equations, reservoir capacity constraints, outflow constraints, power generation flow constraints, hydropower station output constraints, ramp constraints, hydropower station output function, water level and reservoir capacity constraints, and initial and final water level constraints, as detailed below:

[0123] 1) Transmission channel capacity constraints:

[0124] (37);

[0125] In the formula: Indicates the time period of the integrated water, wind and solar power base The capacity of the power transmission channel, in MW.

[0126] 2) Water balance equation:

[0127] (38);

[0128] (39);

[0129] In the formula: Indicates hydroelectric power station Time period Storage capacity, unit m 3 ; Indicates hydroelectric power station Time period Storage capacity, unit m 3 ; Indicates hydroelectric power station Time period Interval flow rate, in m 3 / s; Indicates hydroelectric power station Time period Outbound flow rate, in m³ 3 / s; Indicates hydroelectric power station upstream hydropower station period Outbound flow rate, in m³ 3 / s; Indicates hydroelectric power station Time period Power generation flow, in m³ 3 / s; Indicates hydroelectric power station Time period The discharge flow rate, in meters3 / s.

[0130] 3) Storage capacity constraints:

[0131] (40);

[0132] In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of the storage capacity, in meters. 3 .

[0133] 4) Outbound flow constraints:

[0134] (41);

[0135] In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of outbound flow rate, in meters. 3 / s.

[0136] 5) Power generation flow constraints:

[0137] (42);

[0138] In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of power generation flow, in m³ 3 / s.

[0139] 6) Power output constraints of hydropower stations:

[0140] (43);

[0141] In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of output, in MW.

[0142] 7) Climbing constraint:

[0143] (44);

[0144] In the formula: Indicates hydroelectric power station Permissible output range, in MW; Indicates hydroelectric power station exist Output per time period, unit: MW.

[0145] 8) Hydropower station output function:

[0146] (45);

[0147] In the formula: Indicates hydroelectric power station Water consumption rate, in m 3 / kWh.

[0148] 9) Water level and reservoir capacity constraints:

[0149] (46);

[0150] In the formula: Indicates hydroelectric power station Time period Water level, in meters; This indicates the relationship between water level and reservoir capacity at a hydropower station.

[0151] 10) Initial and final water level constraints:

[0152] (47);

[0153] In the formula: , Indicates hydroelectric power station The initial and final water level settings, in meters (m); Indicates hydroelectric power station The coefficient; Indicates hydroelectric power station The initial water level, in meters (m). Indicates hydroelectric power station The final water level.

[0154] The fifth step, to verify and evaluate the actual scheduling effect of the short-term optimal scheduling model for hydro-wind-solar integration constructed in the fourth step within a closed loop, independently constructs a short-term complementary scheduling evaluation index system for hydro-wind-solar integration. This system includes day-ahead planned revenue, expected positive deviation power generation, expected negative deviation power generation, intraday operating revenue, cumulative deviation time, positive deviation power generation, and negative deviation power generation. Among these, day-ahead planned revenue, expected positive deviation power generation, and expected negative deviation power generation are used to evaluate the rationality of the day-ahead power generation plan of the integrated hydro-wind-solar base. Intraday operating revenue, cumulative deviation time, positive deviation power generation, and negative deviation power generation are used to evaluate the intraday execution effect. Specifically:

[0155] Step 5.1 introduces the expected positive deviation in power generation to evaluate the downward adjustment capability of the short-term optimal scheduling model for hydropower-wind-solar hybrid systems; the smaller the deviation, the greater the downward adjustment capability of hydropower.

[0156] (48);

[0157] (49);

[0158] in, Representing a scene The probability of; S represents the scene number, and S represents the total number of scenes; This represents the expected positive deviation in electricity consumption, expressed in MWh.

[0159] Step 5.2 introduces the negative deviation power expectation to evaluate the upward adjustment capability of the hydropower-wind-solar hybrid short-term optimal scheduling model; the smaller the deviation, the greater the hydropower upward adjustment capability.

[0160] (50);

[0161] (51);

[0162] Step 5.3 introduces intraday operational revenue measurement to assess the intraday power generation efficiency of the hydro-wind-solar hybrid short-term optimal scheduling model:

[0163] (52);

[0164] in, This represents the model's actual intraday profit, expressed in yuan. Indicates wind power Actual output during the time period, in MW; Indicates photovoltaic Actual output during the time period, in MW; Indicates hydroelectric power station exist Actual output during the time period, in MW.

[0165] Step 5.4 introduces the cumulative deviation time to measure the cumulative time during which the actual daily output of the hydro-wind-solar hybrid short-term optimization scheduling model does not equal the planned output of the previous day:

[0166] (53);

[0167] (54);

[0168] in, This indicates that the model measures an indicator variable that shows the actual output during the day is not equal to the planned output the day before. The model's planned output is indicated by the unit MW.

[0169] Step 6: Solve the short-term optimal scheduling model for water-wind-solar hybrid systems constructed in Step 4;

[0170] To verify the effectiveness of the hydro-wind-solar hybrid short-term optimal scheduling model, this invention sets up a comparative model, a baseline case where both the number of positive and negative flexibility demand segments are 1, and a case where the number of segments is 4. The comparative model represents traditional deterministic scheduling. The Gurobi optimization solver is used to solve the hydro-wind-solar hybrid short-term optimal scheduling model to obtain the optimal day-ahead generation plan and the corresponding segmented response parameters. Figure 6 The day-ahead power plan for the Guangzhao Hydropower Station is shown, displaying the planned power output curve of the station over a 24-hour period. Figure 7 The daytime output plan for the Mama Cliff Hydropower Station is shown, displaying the planned output curve of the station over a 24-hour period. Figure 8 The day-ahead power output plan for the Dongqing Hydropower Station is shown, displaying the planned power output curve of the station over a 24-hour period. In contrast, the comparative model, aiming for maximum revenue, employs deterministic scheduling where "forecast values ​​are the same as planned values" during the day, and determines flexible supply based on ramp-up capability and installed capacity during the day. This results in the hydropower station operating at full capacity for most of the time, limiting its flexible supply capacity.

[0171] Referring to Table 1, the comparative model achieves higher theoretical returns during the day-ahead phase, but this result is based on the premise of high-intensity full-load generation and a lack of adjustment margin. The model does not fully consider the uncertainty of wind and solar power output in its decision-making, resulting in key cascade units operating at high load for extended periods, leading to a significant lack of system flexibility. In actual intraday operation, affected by fluctuations in renewable energy sources, the system's adjustment capacity is limited, making it difficult to respond promptly to power deviations, thus resulting in significant power curtailment and shortages. The expected positive and negative power deviations reach 439.87 MWh and 750.05 MWh, respectively, and there is a risk of power imbalance for 2 hours, posing high operating and assessment costs.

[0172] Table 1: Evaluation Indicators of Short-Term Complementarity between Water, Wind, and Solar Energy under Different Models

[0173]

[0174] In contrast, this invention, during model construction, jointly optimizes the cascade response parameters and output plan, ensuring system regulation capability while implementing day-ahead planning. When using the proposed model with 4 segments, the day-ahead planned revenue is 15.2561 million yuan, slightly lower than the comparative model. However, the model optimizes a highly efficient piecewise linear mapping relationship, achieving unbiased operation in various scenarios and within the day, greatly improving the executability and robustness of the plan. Combined with... Figure 6 , Figure 7 , Figure 8 It is evident that this mode experiences numerous periods of "zero output, full power generation, and reaching ramp-up constraints," making it difficult to cope with intraday fluctuations in renewable energy sources and posing a high risk of system imbalance. Combined with... Figure 3This demonstrates that the present invention achieves a good balance between the flexibility and economy of planned output: when the flexibility demand is positive, the system mainly relies on the Dongqing Hydropower Station, which has daily regulation capacity, to undertake the downward adjustment task, due to its large installed capacity and strong regulation capacity; when the demand is negative, the Guangzhao Hydropower Station, with the largest installed capacity, mainly undertakes the upward adjustment task; although the Mamaya Hydropower Station is a daily regulation hydropower station, its small installed capacity means it can only provide partial regulation capacity during certain periods. This allocation method fully utilizes the regulation characteristics of each power station, demonstrating strong comprehensive performance. Further comparison with the number of segments from 1 to 4 shows that even with fewer segments, better scheduling results than traditional methods can be achieved through parameter optimization. As the number of segments increases, the model can more accurately depict the nonlinear relationship between flexibility supply and demand; a higher number of segments is beneficial for simultaneously improving the planned benefits before the day and reducing intraday deviations.

[0175] In summary, this invention establishes a short-term optimized scheduling model for hydro-wind-solar integration that adapts to short-term power prediction deviations in high-penetration renewable energy sources. This model achieves precise mitigation of renewable energy fluctuations by cascade hydropower, solving the problem that traditional fixed-ratio allocation methods are ill-suited to complex spatiotemporal coupling constraints. The formulated scheduling plan balances economic efficiency with flexible adjustment capabilities, making it suitable for short-term scheduling of high-proportion renewable energy power systems and providing effective technical support for the operation of integrated hydro-wind-solar power bases.

[0176] The above embodiments are merely illustrative of the implementation methods of the present invention, but should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the protection scope of the present invention.

Claims

1. A segmented response method for cascade hydropower to adapt to short-term power prediction deviations of new energy sources, characterized in that, The cascade hydropower segmented response method can achieve short-term complementary and optimized scheduling of hydropower, wind power, and solar power, including the following steps: The first step is to acquire the basic data required for system scheduling. This basic data includes the installed capacity parameters and regulating reservoir capacity parameters of cascade hydropower stations, historical actual wind and solar power output data and day-ahead wind and solar power output forecast data of the integrated hydropower, wind and solar power base, time-of-use electricity price data, water level and reservoir capacity relationship curves of cascade hydropower stations, water consumption rate characteristic curves of hydropower units, and capacity constraint limit data of power transmission channels. The second step is to extract statistical features from the historical actual wind and solar power output data and day-ahead wind and solar power output forecast data in the basic data to generate a set of wind and solar power output forecast deviation scenarios to characterize the uncertainty of intraday power output under high penetration renewable energy conditions. The third step is to construct a segmented adjustment model to characterize the flexible response capability of each hydropower station based on the scenario set of wind and solar power output prediction deviations, dynamically determine the division boundary of the prediction deviation interval, establish a segmented linear mapping relationship between the cascade hydropower regulation output and the new energy prediction deviation, determine the adjustment amount of each cascade hydropower station relative to the day-ahead planned output under different scenarios and different time periods, and set the corresponding segmented mapping parameters in each interval. The fourth step is to establish a short-term optimization scheduling model for hydropower-wind-solar hybrid power generation based on the adjustment amount determined in the third step. With the goal of maximizing the day-ahead power generation plan revenue, the day-ahead planned output of the cascade hydropower stations and the segmented mapping parameters are used as decision variables for joint optimization. The objective function of the short-term optimal scheduling model for hydro-wind-solar hybrid systems is determined as follows: （9）； in, Represent the objective function of the model; express Electricity price for the specified time period, in yuan; express The duration of the time period, in seconds; This indicates the total number of time periods simulated by the model; This indicates the number of cascade hydropower stations. Indicates the number of the cascade hydropower station; Indicates wind power Predicted output for a given time period; Indicates photovoltaic Forecast power output for the specified time period, in MW; Indicates hydroelectric power station exist The planned output for the day of the period; solve formula (9) to obtain the planned revenue for the day of the period; Determine the constraints of the short-term optimal scheduling model for hydropower-wind-solar hybrid systems; The fifth step is to independently construct an evaluation index system for short-term complementary scheduling of water, wind and solar power in order to verify and evaluate the actual scheduling effect of the water, wind and solar power complementary scheduling model in a closed loop. Step 6: Solve the short-term optimal scheduling model for water-wind-solar hybrid systems constructed in Step 4; The seventh step, during the intraday operation phase, involves calculating the output adjustment of each hydropower station based on the real-time new energy forecast deviation using a piecewise linear mapping relationship, and dynamically adjusting the actual output of the hydropower station accordingly.

2. The cascade hydropower segmented response method for adapting to short-term power prediction deviations of new energy sources according to claim 1, characterized in that, The specific process of constructing the segmented adjustment model in the third step is as follows: Step 3.1, the method for calculating the flexibility demand for new energy sources is as follows: （1）； In the formula: Indicates the scene number; Indicates the number of the scheduling period; Representing a scene middle The need for flexibility in time slots; Indicates wind power Forecast power output for the specified time period, in MW; Indicates photovoltaic Forecast power output for the specified time period, in MW; Representing a scene China Wind Power Output per time period, in MW; Representing a scene China Photovoltaic Output per time period, in MW; Step 3.2: Based on the distribution characteristics of the concentrated flexibility demand in the wind and solar power output prediction deviation scenario, calculate the segmentation boundary points of the positive and negative flexibility demand intervals, divide the flexibility demand into multiple dynamic intervals, and determine the dynamic boundary of the flexibility demand as follows: （2）； （3）； In the formula: The number of segments indicating the positive flexibility requirement; The segment number indicating the positive flexibility requirement; The number of segments indicating negative flexibility requirements; The segment number indicating negative flexibility requirements; This indicates a positive demand for flexibility. Dynamic boundary value at the location, in MW; This indicates negative flexibility requirements. Dynamic boundary value at the location, in MW; Step 3.3: Within different demand boundary intervals, establish a piecewise linear mapping relationship between the regulating output of cascade hydropower stations and the prediction deviation of new energy sources, as shown below: （4）； （5）； （6）； In the formula: Indicates the hydroelectric power station number; Indicates hydroelectric power station In the scene middle Output adjustment for different time periods, in MW; , They represent hydroelectric power stations. In the part The slope and intercept of the time interval are the response parameters to be optimized; , They represent hydroelectric power stations. In the part The slope and intercept of the time interval are the response parameters to be optimized; This indicates a positive demand for flexibility. Dynamic boundary value at the location, in MW; This indicates negative flexibility requirements. Dynamic boundary value at the location, in MW; Step 3.4: Based on the piecewise linear mapping relationship obtained in Step 3.3, determine the adjustment amount of each cascade hydropower station relative to the planned daily output under different scenarios and time periods: （7）； （8）； In the formula: Indicates hydroelectric power station exist Planned output for the day of the period, in MW; Indicates hydroelectric power station In the scene middle Power output after participating in the response during the specified time period, in MW; This indicates the number of hydroelectric power stations.

3. The cascade hydropower segmented response method for adapting to short-term power prediction deviations of new energy sources according to claim 2, characterized in that, In the fourth step, the constraints of the short-term optimal scheduling model for hydropower-wind-solar hybrid systems include transmission channel capacity constraints, water balance equations, reservoir capacity constraints, outflow constraints, power generation flow constraints, hydropower station output constraints, ramp constraints, hydropower station output functions, water level and reservoir capacity constraints, and initial and final water level constraints; specifically as follows: The capacity constraint of the power transmission channel is: （10）； In the formula: Indicates the time period of the integrated water, wind and solar power base The capacity of the power transmission channel, in MW; The water balance equation is as follows: （11）； （12）； In the formula: Indicates hydroelectric power station Time period Storage capacity, unit m 3 ; Indicates hydroelectric power station Time period Storage capacity, unit m 3 ; Indicates hydroelectric power station Time period Interval flow rate, in m 3 / s; Indicates hydroelectric power station Time period Outbound flow rate, in m³ 3 / s; Indicates hydroelectric power station upstream hydropower station period Outbound flow rate, in m³ 3 / s; Indicates hydroelectric power station Time period Power generation flow, in m³ 3 / s; Indicates hydroelectric power station Time period The discharge flow rate, in meters 3 / s; The storage capacity constraint is: （13）； In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of the storage capacity, in meters. 3 ; The outbound flow constraint is: （14）； In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of outbound flow rate, in meters. 3 / s; The power generation flow constraint is: （15）； In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of power generation flow, in m³ 3 / s; The power output constraint of the hydropower station is: （16）； In the formula: , They represent hydroelectric power stations. Time period The lower and upper limits of output, in MW; Indicates hydroelectric power station exist The planned output for the current period; The climbing constraint is: （17）； In the formula: Indicates hydroelectric power station Permissible output range, in MW; Indicates hydroelectric power station exist Power output per time period, in MW; The power output function of the hydropower station is: （18）； In the formula: Indicates hydroelectric power station Water consumption rate, in m 3 / kWh; The water level and reservoir capacity constraints are as follows: （19）； In the formula: Indicates hydroelectric power station Time period Water level, in meters; This indicates the relationship between water level and reservoir capacity in a hydropower station. The initial and final water level constraints are as follows: （20）； In the formula: , Indicates hydroelectric power station The initial and final water level settings, in meters (m); Indicates hydroelectric power station The coefficient; Indicates hydroelectric power station The initial water level, in meters (m). Indicates hydroelectric power station The final water level.

4. The cascade hydropower segmented response method for adapting to short-term power prediction deviations of new energy sources according to claim 3, characterized in that, In the fifth step, the evaluation index system for short-term wind-solar complementary dispatch includes day-ahead planned revenue, expected positive deviation power generation, expected negative deviation power generation, intraday operational revenue, cumulative deviation time, positive deviation power generation, and negative deviation power generation. Among these, day-ahead planned revenue, expected positive deviation power generation, and expected negative deviation power generation are used to evaluate the rationality of the day-ahead power generation plan of the integrated wind-solar hydropower base; intraday operational revenue, cumulative deviation time, positive deviation power generation, and negative deviation power generation are used to evaluate the intraday execution effect. Details are as follows: Step 5.1 introduces the expected positive deviation in power generation to evaluate the downward adjustment capability of the short-term optimal scheduling model for hydropower-wind-solar hybrid systems; the smaller the deviation, the greater the downward adjustment capability of hydropower. （21）； （22）； in, Representing a scene The probability of; S represents the scene number, and S represents the total number of scenes; This represents the expected value of positive deviation electricity consumption, in MWh. Step 5.2 introduces the negative deviation power expectation to evaluate the upward adjustment capability of the hydropower-wind-solar hybrid short-term optimal scheduling model; the smaller the deviation, the greater the hydropower upward adjustment capability. （23）； （24）； Step 5.3 introduces intraday operational revenue measurement to assess the intraday power generation efficiency of the hydro-wind-solar hybrid short-term optimal scheduling model: （25）； in, This represents the model's actual intraday profit, expressed in yuan. Indicates wind power Actual output during the time period, in MW; Indicates photovoltaic Actual output during the time period, in MW; Indicates hydroelectric power station exist Actual output during the time period, in MW; Step 5.4 introduces the cumulative deviation time to measure the cumulative time during which the actual daily output of the hydro-wind-solar hybrid short-term optimization scheduling model does not equal the planned output of the previous day: （26）； （27）； in, This indicates that the model measures an indicator variable that shows the actual output during the day is not equal to the planned output the day before. The model's planned output is indicated by the unit MW.

5. The cascade hydropower segmented response method for adapting to short-term power prediction deviations of new energy sources according to claim 4, characterized in that, In the sixth step, to verify the effectiveness of the short-term optimal scheduling model for hydro-wind-solar hybrid systems, the number of positive flexibility demand segments is set respectively. Number of segments with negative flexibility requirements ; The Gurobi optimization solver was used to solve the short-term optimal scheduling model of hydro-wind-solar hybrid power generation to obtain the optimal day-ahead power generation plan and the corresponding piecewise response parameters.

6. The cascade hydropower segmented response method for adapting to short-term power prediction deviations of new energy sources according to claim 5, characterized in that, In the sixth step, the segmented response parameters include those representing the hydropower station. In the part Slope of the time period , indicating a hydroelectric power station In the part Intercept of time period , indicating a hydroelectric power station In the part Slope of the time period , indicating a hydroelectric power station In the part Intercept of time period .

7. The cascade hydropower segmented response method for adapting to short-term power prediction deviations of new energy sources according to claim 6, characterized in that, In the seventh step, the variables include those representing the hydropower station. exist The planned output for the current period , indicating a hydroelectric power station In the scene middle Output adjustment during time period , indicating a hydroelectric power station In the scene middle Output after participating in the response during the time period .

Images