A short-term scheduling method and device for a cascade water and wind power sight mixed pumping and storage complementary system

Abstract

This invention relates to the field of hydropower, wind power, and solar power complementary renewable energy development technology, and discloses a short-term dispatching method and apparatus for a cascade hydropower, wind power, and solar power hybrid pumped storage complementary system. It aims to address the problems of poor accuracy and practicality in existing methods. The scheme mainly includes: generating combined wind and solar power output scenarios by dividing the system seasonally and using kernel density estimation, Markov Monte Carlo sampling, and a two-dimensional Copula function; generating runoff scenarios by weighted partitioning using cumulative frequency curves; and constructing short-term dispatching scenarios by combining these scenarios. A multi-objective short-term optimization model with power balance and constraints is constructed with the goal of minimizing wind and solar curtailment rates and load shedding rates. The boundary conditions of each scenario are input into the model, and a multi-objective algorithm is used to solve the Pareto solution set. The optimal solution is then selected using entropy-weighted TOPSIS, and the output process of the optimal solution is output as the dispatching plan. This invention improves the accuracy and practicality of the short-term dispatching scheme for cascade hydropower, wind power, and solar power hybrid pumped storage complementary systems.

Description

Technical Field

[0001] This invention relates to the field of renewable energy complementary development technology, specifically to a short-term dispatching method and apparatus for a cascade hydro-wind-solar hybrid pumped storage complementary system. Background Technology

[0002] A cascade hydropower-wind-solar hybrid pumped storage complementary system refers to a power generation system that, based on a cascade hydropower station group, integrates with surrounding wind farms, photovoltaic power stations, and hybrid pumped storage power stations to achieve complementary power output from multiple energy sources through joint scheduling and operation. The hybrid pumped storage power station involves adding reversible pump-turbines at suitable locations within the cascade hydropower stations, transforming conventional cascade reservoirs into new power stations with pumped water storage functions. When renewable energy is abundant, the hybrid pumped storage power station uses surplus electricity to pump water from downstream reservoirs to upstream reservoirs, storing energy as potential energy. When power is scarce, the hybrid pumped storage power station releases water to generate electricity, releasing the stored energy, thus achieving bidirectional regulation of wind and solar energy fluctuations. The addition of the hybrid pumped storage power station adds a pumping and power generation energy storage cycle to the system's conventional hydropower regulation, upgrading the system from a single-stage regulation by conventional hydropower to a dual flexible regulation mode combining conventional hydropower regulation and pumped storage energy storage regulation.

[0003] The addition of hybrid pumped-storage power stations presents new challenges to the short-term dispatching of cascade hydropower-wind-solar hybrid pumped-storage complementary systems. The pumping-power generation cycle of the hybrid pumped-storage power stations alters the system's power balance mechanism, and the transition between pumping and power generation modes is subject to strict power constraints, water volume conversion constraints, and mutually exclusive operating mode constraints, significantly increasing the complexity of system dispatching. Simultaneously, the randomness and volatility of wind and solar power output after large-scale integration introduce greater uncertainty into short-term system dispatching. How to fully utilize the bidirectional regulation capabilities of hybrid pumped-storage while satisfying the complex hydraulic connections of the cascade power stations and the operational constraints of the hybrid pumped-storage power stations, thereby maximizing the absorption of wind and solar energy and improving the reliability of system power supply, is a pressing issue that needs to be addressed in the short-term dispatching of cascade hydropower-wind-solar hybrid pumped-storage complementary systems.

[0004] Currently, for short-term scheduling of cascade hydropower, wind power, and solar power systems, existing methods typically generate typical daily wind and solar power output scenarios based on historical statistical data. These models aim to maximize renewable energy absorption or minimize system operating costs, establishing and solving short-term optimization scheduling models. Some existing technologies also consider the uncertainty of runoff inflow, simulating scheduling by setting typical scenarios such as high and low water levels.

[0005] However, the aforementioned existing technologies still have the following shortcomings:

[0006] First, regarding scene generation, existing methods mostly generate wind and solar power output scenes independently when processing wind and solar power output scenes, failing to fully consider the inherent correlation between wind and solar power output. Wind power output is higher at night, while solar power output is only during the day, and the two have significant temporal complementary characteristics within the day. Generating scenes independently disrupts this correlation structure, resulting in physically unreasonable scene combinations that are difficult to accurately reflect the true distribution characteristics of combined wind and solar power output. In addition, existing methods typically use simple scenario assumptions to generate runoff scenes, failing to fully utilize the distribution information of historical runoff data, making it difficult to generate statistically representative runoff scenes.

[0007] Secondly, in terms of model construction, existing scheduling models do not adequately characterize the detailed operational constraints of hybrid pumped storage power stations. Hybrid pumped storage power stations include two operating modes: power generation and pumping. The power-flow conversion efficiency relationship, output limit constraints, and mutually exclusive constraints between the two modes are often simplified in existing models, affecting the accuracy and practicality of the scheduling results. Summary of the Invention

[0008] This invention aims to address the problem of poor accuracy and practicality of existing short-term scheduling methods for cascade hydropower-wind-solar hybrid pumped storage complementary systems, and proposes a short-term scheduling method and device for such systems.

[0009] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0010] In a first aspect, the present invention provides a short-term scheduling method for a cascade hydropower-wind-solar hybrid pumped-storage complementary system, the method comprising:

[0011] Hourly wind and solar power output data and daily runoff data for the target study area within a predetermined historical timeframe are obtained. Based on the wind and solar power output data, the distribution is fitted using kernel density estimation after seasonal division. Markov Monte Carlo sampling is used to generate intraday wind and solar power output scenarios for each season, and a set of combined wind and solar power output scenarios for each season is generated using a two-dimensional Copula function. Fuzzy C-means clustering is used to filter typical combined wind and solar power output scenarios for each season. Based on the daily runoff data, cumulative frequency curves are fitted after seasonal division. The cumulative frequency curves are divided into multiple intervals according to a predetermined quantile. The area-weighted average of the frequency curves of each interval and the coordinate axes is used to calculate the typical runoff scenarios for each season. The typical runoff scenarios and typical combined wind and solar power output scenarios for each season are combined to construct short-term scheduling scenarios for each season.

[0012] Engineering data of a cascade hydropower-wind-solar hybrid pumped storage complementary system is obtained. Based on the engineering data, the corresponding engineering constraints are determined. With minimizing wind and solar curtailment rates and minimizing load shedding rates as optimization objectives, a multi-objective short-term optimization scheduling model for cascade hydropower-wind-solar hybrid pumped storage complementary system is constructed. The multi-objective short-term optimization scheduling model includes power balance equations and engineering constraints.

[0013] For each short-term scheduling scenario, the corresponding runoff data and wind and solar power output data are used as boundary conditions to input the multi-objective short-term optimization scheduling model. The multi-objective optimization algorithm is used to solve the multi-objective short-term optimization scheduling model after inputting boundary conditions to obtain the Pareto solution set corresponding to each short-term scheduling scenario. The optimal solution corresponding to each short-term scheduling scenario is selected from the Pareto solution set using the entropy weight-TOPSIS method. The power output process corresponding to the optimal solution of each short-term scheduling scenario is used as the scheduling plan for that short-term scheduling scenario.

[0014] Based on the scheduling plans under each short-term scheduling scenario, the optimal output process and optimal planned output curve of the cascade hydropower-wind-solar hybrid pumped storage complementary system under each short-term scheduling scenario are output.

[0015] Furthermore, the mathematical expression of the power balance equation is:

[0016] ;

[0017] ;

[0018] in, express The first hydropower station in The total effort exerted during the period. Indicates the first The first hydropower station in Efforts during a specific time period; Indicates that wind power is in the first Efforts during a specific time period; Indicates the photovoltaic power station in the first Efforts during a specific time period; Indicates the hybrid pumped storage power station in the first Net output during the period; This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. Load demand during different time periods; This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. The amount of renewable energy power curtailed during a given period; Indicates the hybrid pumped storage power station in the first Power output during specific time periods; Indicates the hybrid pumped storage power station in the first Power output during pumping operations at different times.

[0019] Furthermore, the engineering constraints include hydraulic connection constraints of the cascade power stations, reservoir capacity constraints of the cascade power stations, and flow constraints of the cascade power stations;

[0020] The hydraulic connection constraints of the cascade hydropower stations are as follows:

[0021] ;

[0022] The reservoir capacity constraint of the cascade hydropower stations is:

[0023] ;

[0024] The flow constraint of the cascade hydropower stations is:

[0025] ;

[0026] in, Indicates the downstream power station in the first Inbound flow during a specific time period Indicates the upstream power station in the Power generation flow during the period Indicates the upstream power station in the Water discharge rate during the period Indicates the upstream power station in the Inflow rate within a time period Indicates the hybrid pumped storage power station in the first Net flow during the period Indicates the hybrid pumped storage power station in the first The state variable for power generation during a given time period is 1 when power generation is in operation, and 0 otherwise. Indicates the hybrid pumped storage power station in the first Power generation flow rate under time-of-use power generation conditions Indicates the hybrid pumped storage power station in the first The state variable for the pumping operation during a given time period is set to 1 when pumping is in operation and 0 otherwise. Indicates the hybrid pumped storage power station in the first Pumping flow rate under time-limited pumping conditions Indicates the upstream power station in the The reservoir capacity at the end of the period Indicates the upstream power station in the The reservoir capacity at the end of the period Indicates time interval, Indicates the downstream power station in the first The reservoir capacity at the end of the period Indicates the downstream power station in the first Reservoir capacity at the end of the period Indicates the downstream power station in the first Power generation flow during the period Indicates the downstream power station in the first Water discharge rate during the period , These represent the upstream power stations at the [number]th [year]. Minimum and maximum storage capacity allowed for a given time period. , These represent the downstream power plants at the [number]th [year]. Minimum and maximum storage capacity allowed for a given time period. , These represent the upstream power stations at the [number]th [year]. The minimum and maximum allowable discharge flow rates for each time period. , These represent the hybrid pumped storage power stations in the [number]th [year]. The minimum and maximum allowable discharge flow rates for each time period. , These represent the downstream power plants at the [number]th [year]. The minimum and maximum allowable discharge flow rates for each time period.

[0027] Furthermore, the engineering constraints also include constraints on the relationship between the water level and reservoir capacity of the cascade power stations, as well as constraints on the relationship between the tailrace water level and the outflow of the hydropower units;

[0028] The water level-reservoir capacity relationship constraint of the cascade hydropower stations is as follows:

[0029] ;

[0030] The constraint on the relationship between the tailrace water level and the downstream flow rate of the hydropower unit is as follows:

[0031] ;

[0032] in, , , These represent the upstream power stations at the [number]th [year]. Reservoir water level at the end of the time period, minimum allowable water level and maximum allowable water level during the dispatch cycle. This represents a function relating the water level and reservoir capacity of an upstream power station. Indicates the upstream power station in the Reservoir capacity at the end of the period , , These represent the downstream power plants at the [number]th [year]. Reservoir water level at the end of the period, minimum allowable water level and maximum allowable water level during the dispatch cycle, The function representing the relationship between water level and reservoir capacity of the downstream power station. Indicates the downstream power station in the first Reservoir capacity at the end of the period Indicates the upstream power station in the Downstream tailwater level at the end of the period This represents a function relating the tailrace level of the upstream power station to its discharge flow rate. Indicates the upstream power station in the Power generation flow during the period Indicates the upstream power station in the Water discharge rate during the period Indicates the downstream power station in the first Downstream tailwater level at the end of the period This represents the function relating the tailrace level of the downstream power station to the discharge flow rate. Indicates the downstream power station in the first Power generation flow during the period Indicates the downstream power station in the first The amount of water discharged during a given time period.

[0033] Furthermore, the engineering constraints also include constraints on the output characteristics of the hydropower unit, constraints on the head of the hydropower unit, constraints on the output limit of the hydropower unit, and constraints on the flow rate of the hydropower unit.

[0034] The output characteristic constraint of the hydropower unit is:

[0035] ;

[0036] The head constraint of the hydropower unit is:

[0037] ;

[0038] The output limit constraint of the hydropower unit is:

[0039] ;

[0040] The flow constraint of the hydropower unit is:

[0041] ;

[0042] in, Indicates the generating units within the upstream power station In the Efforts during a specific time period Indicates the generating units within the upstream power station The output characteristic function, Indicates the generating units within the upstream power station In the Power generation flow during the period Indicates the generating units within the upstream power station In the The head of water generated during the power generation period, Indicates the generating units within the downstream power plant In the Efforts during a specific time period Indicates the generating units within the downstream power plant The output characteristic function, Indicates the generating units within the downstream power plant In the Power generation flow during the period Indicates the generating units within the downstream power plant In the The head of water generated during the power generation period, Indicates the upstream power station in the The reservoir water level at the end of the period, Indicates the upstream power station in the The reservoir water level at the end of the period, Indicates the upstream power station in the Downstream tailwater level at the end of the period Indicates head loss, Indicates the downstream power station in the first The reservoir water level at the end of the period, Indicates the downstream power station in the first The reservoir water level at the end of the period, Indicates the downstream power station in the first Downstream tailwater level at the end of the period , These represent the generating units within the upstream power station. The minimum and maximum hydroelectric heads, , These represent the generating units within the downstream power plant. The minimum and maximum hydroelectric heads, , These represent the generating units within the upstream and downstream power plants, respectively. In the Start and stop status variables for a given time period. , These represent the generating units within the upstream power station. Minimum and maximum technical output, , These represent the generating units within the downstream power plant. Minimum and maximum technical output, , These represent the generating units within the upstream power station. The minimum and maximum water diversion flow rates, , These represent the generating units within the downstream power plant. The minimum and maximum water diversion flow rates.

[0043] Furthermore, the engineering constraints also include power-flow conversion efficiency constraints of pumped storage units, output limit constraints of pumped storage units, and mutual exclusion constraints between power generation and pumping states.

[0044] The power-flow conversion efficiency constraint of the pumped storage unit is:

[0045] ;

[0046] The output limit constraint of the pumped storage unit is:

[0047] ;

[0048] The mutual exclusion constraint between power generation and pumping states is as follows:

[0049] ;

[0050] in, Indicates pumped storage unit In the Power generation flow rate under time-of-use power generation conditions Indicates pumped storage unit Power conversion coefficient under power generation conditions Indicates pumped storage unit In the Power output during peak hours This indicates the density of water. Indicates pumped storage unit Power generation efficiency, Indicates the average head. Indicates pumped storage unit In the Pumping flow rate under time-limited pumping conditions Indicates pumped storage unit Power conversion coefficient under pumping conditions Indicates pumped storage unit In the Power output during pumping operation (period). Indicates pumped storage unit Pumping efficiency , These represent pumped-storage units. In the State variables for power generation and pumping operations during different time periods. , These represent pumped-storage units. The lower and upper limits of power generation under power generation conditions. , These represent pumped-storage units. The lower and upper limits of power under pumping conditions.

[0051] Furthermore, in the optimization objective, the mathematical expression for minimizing the wind and solar curtailment rate is:

[0052] ;

[0053] ;

[0054] ;

[0055] The mathematical expression for minimizing the load failure rate is:

[0056] ;

[0057] ;

[0058] ;

[0059] in, This represents the objective function value that minimizes the wind and solar curtailment rate. This represents the total number of time periods within the scheduling cycle. This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. The rate of curtailment of renewable energy during a given period This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. Power deviation over time period Indicates that wind power is in the first Efforts during a specific time period Indicates the photovoltaic power station in the first Efforts during a specific time period Indicates the first The first hydropower station in Efforts during a specific time period Indicates the hybrid pumped storage power station in the first Net output during the period This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. Load demand during different time periods This represents the objective function value that minimizes the load failure rate. This indicates the total number of hydroelectric power stations. This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. The rate of load loss during a given period.

[0060] Furthermore, when using a multi-objective optimization algorithm to solve the multi-objective short-term optimization scheduling model, the convergence of the algorithm is determined based on the hypervolume index. The algorithm is considered to have converged when the hypervolume index no longer increases within a consecutive number of iterations.

[0061] Furthermore, in the scenario construction, the cumulative frequency curve is divided into three intervals, and the preset quantiles are the 10th quantile and the 90th quantile, respectively. The cumulative frequency curve of each season is divided into low runoff interval, medium runoff interval and high runoff interval according to the 10th quantile and the 90th quantile. The area enclosed by the frequency curve and the coordinate axis in each interval is weighted and averaged to calculate the low runoff scenario, medium runoff scenario and high runoff scenario corresponding to each season.

[0062] Secondly, the present invention provides a short-term scheduling device for a cascade hydropower-wind-solar hybrid pumped storage complementary system, used to implement the short-term scheduling method for the cascade hydropower-wind-solar hybrid pumped storage complementary system as described in the first aspect, the device comprising:

[0063] The scenario construction module is used to acquire hourly wind and solar power output data and daily runoff data of the target study area within a preset historical time period. Based on the wind and solar power output data, the distribution of wind and solar power output is fitted using kernel density estimation after seasonal division. Markov Monte Carlo sampling is used to generate intraday wind and solar power output scenarios for each season, and a set of combined wind and solar power output scenarios for each season is generated using a two-dimensional Copula function. Fuzzy C-means clustering is used to filter typical combined wind and solar power output scenarios for each season. Based on the daily runoff data, the cumulative frequency curve is fitted after seasonal division. The cumulative frequency curve is divided into multiple intervals according to a preset quantile. The area-weighted average of the frequency curves of each interval and the coordinate axis is used to calculate the typical runoff scenarios for each season. The typical runoff scenarios and typical combined wind and solar power output scenarios for each season are combined to construct short-term scheduling scenarios for each season.

[0064] The model building module is used to acquire engineering data of the cascade hydropower-wind-solar hybrid pumped storage complementary system, determine the corresponding engineering constraints based on the engineering data, and construct a multi-objective short-term optimization scheduling model for the cascade hydropower-wind-solar hybrid pumped storage complementary system with the optimization objectives of minimizing wind curtailment rate and minimizing load shedding rate. The multi-objective short-term optimization scheduling model includes power balance equations and engineering constraints.

[0065] The simulation and solution module is used to input the corresponding runoff data and wind and solar power output data as boundary conditions into the multi-objective short-term optimization scheduling model for each short-term scheduling scenario; the multi-objective optimization algorithm is used to solve the multi-objective short-term optimization scheduling model after inputting boundary conditions to obtain the Pareto solution set corresponding to each short-term scheduling scenario; the optimal solution corresponding to each short-term scheduling scenario is selected from the Pareto solution set using the entropy weight-TOPSIS method; and the power output process corresponding to the optimal solution of each short-term scheduling scenario is used as the scheduling plan for that short-term scheduling scenario.

[0066] The output module is used to output the optimal output process and optimal planned output curve of the cascade hydropower-wind-solar hybrid pumped storage complementary system under each short-term scheduling scenario, based on the scheduling plan under each short-term scheduling scenario.

[0067] The beneficial effects of this invention are as follows: The short-term scheduling method and device for a cascade hydropower-wind-solar hybrid pumped storage complementary system provided by this invention, by dividing the daily runoff data by season and fitting the wind and solar power output distribution using kernel density estimation, generating intraday wind and solar power output scenarios for each season using Markov Monte Carlo sampling, generating a set of combined wind and solar power output scenarios for each season using a two-dimensional Copula function, and using fuzzy C-means clustering to screen typical combined wind and solar power output scenarios, fully preserves the correlation structure between wind power and photovoltaic power output, enabling the generated scenario combinations to accurately reflect the true distribution characteristics of combined wind and solar power output; by dividing the daily runoff data by season and fitting it with cumulative frequency curves, dividing the cumulative frequency curves into multiple intervals according to preset quantiles, and calculating the typical runoff field using the area-weighted average enclosed by the frequency curves of each interval and the coordinate axes. The system fully utilizes the distribution information of historical runoff data to generate statistically representative runoff scenarios. By combining typical runoff scenarios with typical wind and solar power combined output scenarios, short-term scheduling scenarios for each season are constructed, comprehensively covering typical operating conditions under different combinations of water inflow and wind and solar resource conditions. By constructing a multi-objective short-term optimization scheduling model that includes power balance equations and engineering constraints, with the optimization objectives of minimizing wind and solar curtailment rates and minimizing load shedding rates, a Pareto solution set is obtained using a multi-objective optimization algorithm, and the optimal solution is selected using the entropy weight-TOPSIS method, realizing scientific decision-making on the multi-objective optimization results. Finally, the optimal output process and optimal planned output curve for each scenario are output, improving the accuracy and practicality of the short-term scheduling scheme of the cascade water-wind-solar hybrid pumped storage complementary system. Attached Figure Description

[0068] Figure 1 A flowchart illustrating the short-term scheduling method for a cascade hydropower-wind-solar hybrid pumped storage complementary system provided in this embodiment;

[0069] Figure 2 The following are schematic diagrams of typical wind and solar power output scenarios and typical runoff scenarios provided for the embodiments; wherein, (a) is a schematic diagram of wind and solar power output distribution in low power output scenario in spring, (b) is a schematic diagram of wind and solar power output distribution in typical power output scenario in spring, (c) is a schematic diagram of wind and solar power output process in high power output scenario in spring, and (d) is a schematic diagram of typical runoff scenarios in spring. The numbers in the figures represent the runoff volume corresponding to each runoff scenario.

[0070] Figure 3 A schematic diagram of the system's output process under a typical spring scheduling scenario is provided for this embodiment.

[0071] Figure 4A schematic diagram of the short-term dispatching device for a cascade hydropower-wind-solar hybrid pumped storage complementary system provided in the embodiment. Detailed Implementation

[0072] The existing technology suffers from problems such as not considering the correlation between wind and solar power output when generating wind and solar scenarios and not accurately depicting the operational constraints of hybrid pumped storage power stations, resulting in poor accuracy of short-term dispatch schemes.

[0073] Based on this, the technical solution of this invention is proposed. In this invention, historical wind and solar power output data are first divided by season, and the distribution of wind and solar power output is fitted using kernel density estimation. Markov Monte Carlo sampling is used to generate intraday wind and solar power output scenarios for each season. A two-dimensional Copula function is used to generate a set of combined wind and solar power output scenarios, and fuzzy C-means clustering is used to screen typical combined wind and solar power output scenarios. Simultaneously, daily runoff data is divided by season, and after fitting with a cumulative frequency curve, a weighted average is calculated based on preset quantiles to obtain typical runoff scenarios for each season. Typical runoff scenarios are combined with typical combined wind and solar power output scenarios to construct short-term scheduling scenarios for each season. Then, engineering data is obtained to determine engineering constraints. With the objectives of minimizing wind and solar curtailment rates and minimizing load shedding rates, a multi-objective short-term optimization scheduling model containing power balance equations and engineering constraints is constructed. The runoff and wind and solar data corresponding to each scenario are then used as boundary conditions input into the model. A multi-objective optimization algorithm is used to solve for the Pareto solution set, and the entropy-weighted TOPSIS method is used to screen the optimal solution for each scenario. The power output process of this solution is then used as the scheduling plan. Finally, the optimal output process and optimal planned output curve for each scenario are output.

[0074] The technical solutions in this embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0075] Figure 1 A flowchart illustrating a short-term scheduling method for a cascade hydropower-wind-solar hybrid pumped-storage complementary system is shown. Please refer to [link / reference]. Figure 1 The method includes the following steps:

[0076] Step 1: Constructing a short-term scheduling scenario:

[0077] Hourly wind and solar power output data and daily runoff data for the target study area within a predetermined historical timeframe are obtained. Based on the wind and solar power output data, the distribution is fitted using kernel density estimation after seasonal division. Markov Monte Carlo sampling is used to generate intraday wind and solar power output scenarios for each season, and a set of combined wind and solar power output scenarios for each season is generated using a two-dimensional Copula function. Fuzzy C-means clustering is used to filter typical combined wind and solar power output scenarios for each season. Based on the daily runoff data, cumulative frequency curves are fitted after seasonal division. The cumulative frequency curves are divided into multiple intervals according to a predetermined quantile. The area-weighted average of the frequency curves of each interval and the coordinate axes is used to calculate the typical runoff scenarios for each season. The typical runoff scenarios and typical combined wind and solar power output scenarios for each season are combined to construct short-term scheduling scenarios for each season.

[0078] Specifically, wind and solar power output data (including wind power and solar power output) and daily runoff data are derived from historical statistical data of the target study area. Considering the significant seasonal differences in wind and solar power output, and the intraday temporal complementarity between wind power and solar power output, this step is processed separately for the four seasons of spring, summer, autumn, and winter.

[0079] For each season, kernel density estimation is used to fit the probability density distributions of wind power and solar power from historical wind and solar power output data. This method does not require pre-setting the distribution form and can adapt to complex distribution characteristics such as multi-peak and skewed distributions of wind and solar power output. Then, Markov Monte Carlo sampling is used to generate a large number of intraday 24-hour wind and solar power output scenarios from the fitted distributions. Based on this, a two-dimensional Copula function is used to couple the marginal distributions of wind power and solar power output, generating a set of wind and solar combined power output scenarios that simultaneously satisfy their respective distribution characteristics and their correlation. After generating a large number of scenarios, fuzzy C-means clustering is used to classify the scenarios into typical categories, and the cluster center of each category is the typical wind and solar combined power output scenario of that category. In this embodiment, three typical wind and solar combined power output scenarios—abundant, average, and dry—are selected for each season.

[0080] For runoff scenarios, after seasonal division, cumulative frequency curves are used to fit the daily runoff data for each season. The preset quantiles are the 10th and 90th quantiles, and the multiple intervals are three intervals. The cumulative frequency curves for each season are divided into low runoff intervals, medium runoff intervals, and high runoff intervals according to the 10th and 90th quantiles, representing three runoff year types: dry year, normal year, and wet year, respectively. Each interval is further divided into several partitions, and the area enclosed by the frequency curves and coordinate axes in each interval is weighted and averaged to calculate the low runoff scenario, medium runoff scenario, and high runoff scenario corresponding to each season.

[0081] Finally, typical runoff scenarios for each season are combined with typical wind-solar combined output scenarios. Each season's runoff scenario (low, medium, high) is paired with a wind-solar output scenario (abundant, average, low), resulting in nine runoff-wind-solar combined scenarios per season, for a total of 36 short-term scheduling scenarios across all four seasons. Since this method focuses on short-term scheduling, runoff scenarios are defined by daily runoff volume, and wind-solar combined output scenarios are defined by a 24-hour daily sequence. Each combination is treated as a complete scheduling scenario for that season, effectively covering all typical operating conditions under different combinations of inflow and wind-solar resource conditions.

[0082] Step 2: Construction of a multi-objective short-term optimization scheduling model:

[0083] Engineering data of a cascade hydropower-wind-solar hybrid pumped storage complementary system is obtained. Based on the engineering data, the corresponding engineering constraints are determined. With the optimization objectives of minimizing wind and solar curtailment rates and minimizing load shedding rates, a multi-objective short-term optimization scheduling model for the cascade hydropower-wind-solar hybrid pumped storage complementary system is constructed. The multi-objective short-term optimization scheduling model includes power balance equations and engineering constraints.

[0084] Specifically, the first step is to obtain engineering data for the cascade hydropower-wind-solar hybrid pumped-storage complementary system. This data includes the installed capacity of each hydropower station in the cascade, reservoir characteristic parameters (normal water level, dead water level, total reservoir capacity, regulating capacity, etc.), unit power characteristic curves, the installed capacity of the hybrid pumped-storage power station, the operating characteristic parameters of reversible units (power generation efficiency, pumping efficiency, upper and lower limits of power generation / pumping operation), and basic data such as the installed capacity of the wind farm and photovoltaic power station. Based on this engineering data, the engineering constraints that the system must meet during the scheduling cycle are determined.

[0085] In this embodiment, the engineering constraints include hydraulic connection constraints of cascade power stations, reservoir capacity constraints of cascade power stations, flow constraints of cascade power stations, water level-reservoir capacity relationship constraints of cascade power stations, tailrace water level and downstream flow relationship constraints of hydropower units, power output characteristic constraints of hydropower units, head constraints of hydropower units, power output limitation constraints of hydropower units, flow constraints of hydropower units, power-flow conversion efficiency constraints of pumped storage units, power output limitation constraints of pumped storage units, and mutual exclusion constraints between power generation and pumping states.

[0086] In this embodiment, the hydraulic connection constraints of the cascade hydropower stations are as follows:

[0087] ;

[0088] The reservoir capacity constraint of the cascade hydropower stations is:

[0089] ;

[0090] The flow constraint of the cascade hydropower stations is:

[0091] ;

[0092] in, Indicates the downstream power station in the first Inbound flow during a specific time period Indicates the upstream power station in the Power generation flow during the period Indicates the upstream power station in the Water discharge rate during the period Indicates the upstream power station in the Inflow rate within a time period Indicates the hybrid pumped storage power station in the first Net flow during the period Indicates the hybrid pumped storage power station in the first The state variable for power generation during a given time period is 1 when power generation is in operation, and 0 otherwise. Indicates the hybrid pumped storage power station in the first Power generation flow rate under time-of-use power generation conditions Indicates the hybrid pumped storage power station in the first The state variable for the pumping operation during a given time period is set to 1 when pumping is in operation and 0 otherwise. Indicates the hybrid pumped storage power station in the first Pumping flow rate under time-limited pumping conditions Indicates the upstream power station in the Reservoir capacity at the end of the period Indicates the upstream power station in the Reservoir capacity at the end of the period Indicates time interval, Indicates the downstream power station in the first Reservoir capacity at the end of the period Indicates the downstream power station in the first Reservoir capacity at the end of the period Indicates the downstream power station in the first Power generation flow during the period Indicates the downstream power station in the first Water discharge rate during the period , These represent the upstream power stations at the [number]th [year]. Minimum and maximum storage capacity allowed for a given time period. , These represent the downstream power plants at the [number]th [year]. Minimum and maximum storage capacity allowed for a given time period. , These represent the upstream power stations at the [number]th [year]. The minimum and maximum allowable discharge flow rates for each time period. , These respectively represent the hybrid pumped storage power station in the 19th century. The minimum and maximum allowable discharge flow rates for each time period. , These represent the downstream power plants at the [number]th [year]. The minimum and maximum allowable discharge flow rates for each time period.

[0093] It is understandable that the hydraulic connection constraints of a cascade hydropower station describe the water transfer relationship between the upstream and downstream power stations in a cascade. The downstream power station... The inflow rate during the specified time period is equal to the upstream power station's flow rate during the specified time period. The power generation flow, water discharge flow, and inflow of the interval during the period are compared with those of the mixed pumped storage power station in the first period. The sum of net flows over the time period. The mixed pumped storage power station in the [number]th [period]. The net flow rate for a given period is equal to the difference between the power generation flow rate under power generation mode and the pumping flow rate under pumping mode, and the product of the state variables under power generation mode and pumping mode is zero, to ensure that the hybrid pumped storage power station cannot be in both power generation and pumping modes simultaneously. This constraint accurately characterizes the water quantity coupling relationship between the hybrid pumped storage power station and the cascade reservoirs.

[0094] The reservoir capacity constraint of cascade hydropower stations describes the time-period update process of reservoir capacity. The upstream hydropower station in the... The reservoir capacity at the end of the period is equal to the first period. The reservoir capacity at the end of the time period, plus the sum of the inflow and the net flow of the mixed pumped storage, minus the difference between the power generation flow and the water discharge flow, is multiplied by the time interval; the downstream power station in the... The reservoir capacity at the end of the period is equal to the first period. The reservoir capacity at the end of the time period is calculated by adding the inflow to the reservoir and subtracting the difference between the power generation flow and the water discharge flow, then multiplying by the time interval. Simultaneously, the reservoir capacity of each power station during any scheduling period must fall between the minimum and maximum allowable capacity.

[0095] The flow constraints of cascade hydropower stations define the upper and lower limits of flow for each station. The upstream power station... The sum of the power generation flow and the water discharge flow during a given period must be between the minimum and maximum allowable discharge flow; for hybrid pumped storage power stations in the [period]... The power generation flow rate during the specified time period must be between the minimum and maximum allowable discharge flow rates; the downstream power plant in the specified time period... The sum of the power generation flow and the water discharge flow during a given period must be between the minimum and maximum allowable discharge flow.

[0096] In this embodiment, the water level-reservoir capacity relationship constraint of the cascade hydropower stations is as follows:

[0097] ;

[0098] The constraint on the relationship between the tailrace water level and the downstream flow rate of the hydropower unit is as follows:

[0099] ;

[0100] in, , , These represent the upstream power stations at the [number]th [year]. Reservoir water level at the end of the time period, minimum allowable water level and maximum allowable water level during the dispatch cycle. This represents a function relating the water level and reservoir capacity of an upstream power station. Indicates the upstream power station in the Reservoir capacity at the end of the period , , These represent the downstream power plants at the [number]th [year]. Reservoir water level at the end of the time period, minimum allowable water level and maximum allowable water level during the dispatch cycle. The function representing the relationship between water level and reservoir capacity of the downstream power station. Indicates the downstream power station in the first Reservoir capacity at the end of the period Indicates the upstream power station in the The downstream tailwater level at the end of the period, This represents a function relating the tailrace level of the upstream power station to its discharge flow. Indicates the upstream power station in the Power generation flow during the period Indicates the upstream power station in the The amount of water discharged during a given period Indicates the downstream power station in the first The downstream tailwater level at the end of the period, This represents the function relating the tailrace level of the downstream power station to the discharge flow rate. Indicates the downstream power station in the first Power generation flow during the period Indicates the downstream power station in the first The amount of water discharged during a given time period.

[0101] It is understandable that the water level-reservoir capacity relationship constraint of the cascade hydropower stations establishes a functional mapping between reservoir water level and reservoir capacity. The upstream hydropower station... The reservoir water level at the end of the period is determined by the upstream power station's water level-reservoir capacity relationship function based on its reservoir capacity. The downstream power station's water level at the end of the period is determined by the upstream power station's water level-reservoir capacity relationship function based on its reservoir capacity. The reservoir water level at the end of the period is determined by the water level-reservoir capacity relationship function of the downstream power station based on its reservoir capacity, and the water level of each power station must be between the minimum and maximum water levels allowed within the scheduling cycle.

[0102] The constraint on the relationship between the tailrace level and the discharge flow rate of the hydroelectric generating unit establishes a functional mapping between the tailrace level and the discharge flow rate. The upstream power station... The downstream tailwater level at the end of the time period is determined by the relationship function between the tailwater level and the discharge flow of the upstream power station, based on the sum of its power generation flow and the water discharge. The downstream power station... The downstream tailwater level at the end of the period is determined by the relationship function between the tailwater level and the discharge flow of the downstream power station, based on the sum of its power generation flow and the discharge flow.

[0103] In this embodiment, the output characteristics constraint of the hydropower unit is as follows:

[0104] ;

[0105] The head constraint of the hydropower unit is:

[0106] ;

[0107] The output limit constraint of the hydropower unit is:

[0108] ;

[0109] The flow constraint of the hydropower unit is:

[0110] ;

[0111] in, Indicates the generating units within the upstream power station In the Efforts during a specific time period Indicates the generating units within the upstream power station The output characteristic function, Indicates the generating units within the upstream power station In the Power generation flow during the period Indicates the generating units within the upstream power station In the The head of water used for power generation during different time periods, Indicates the generating units within the downstream power plant In the Efforts during a specific time period Indicates the generating units within the downstream power plant The output characteristic function, Indicates the generating units within the downstream power plant In the Power generation flow during the period Indicates the generating units within the downstream power plant In the The head of water used for power generation during different time periods, Indicates the upstream power station in the The reservoir water level at the end of the period, Indicates the upstream power station in the The reservoir water level at the end of the period, Indicates the upstream power station in the The downstream tailwater level at the end of the period, Indicates head loss, Indicates the downstream power station in the first The reservoir water level at the end of the period, Indicates the downstream power station in the first The reservoir water level at the end of the period, Indicates the downstream power station in the first The downstream tailwater level at the end of the period, , These represent the generating units within the upstream power station. The minimum and maximum hydroelectric heads, , These represent the generating units within the downstream power plant. The minimum and maximum hydroelectric heads, , These represent the generating units within the upstream and downstream power plants, respectively. In the Start and stop status variables for a given time period. , These represent the generating units within the upstream power station. Minimum and maximum technical output, , These represent the generating units within the downstream power plant. Minimum and maximum technical output, , These represent the generating units within the upstream power station. The minimum and maximum water diversion flow rates, , These represent the generating units within the downstream power plant. The minimum and maximum water diversion flow rates.

[0112] It is understandable that the output characteristic constraints of hydropower units describe the functional relationship between unit output and power generation flow rate and water head. Upstream power station units... In the The power output during the period is generated by the units within the upstream power station. The output characteristic function is determined based on its power generation flow rate and net head, and the downstream power station units In the The power output during a given period is generated by the units within the downstream power station. The output characteristic function is determined based on its power generation flow rate and net head.

[0113] The head constraint for hydroelectric generating units specifies the calculation method and upper and lower limits for the net head of the generating unit. (Upstream power station units) In the The net head of power generation during the period is equal to that of the upstream power station in the first period. End of period and the first The average reservoir water level at the end of the period minus the downstream tailrace level and head loss; downstream power station units In the The net head of power generation during the period is equal to that of the downstream power station in the first period. End of period and the first The average reservoir water level at the end of the period minus the downstream tailwater level and head loss; the head of each generating unit must be between the minimum generating head and the maximum generating head.

[0114] Hydropower unit output limit constraints regulations for upstream power station units In the The output during a given period must be between the unit's start-up / shutdown status variable multiplied by the minimum technical output and the maximum technical output, within the downstream power plant units. Similarly, the flow constraint rules for hydropower units stipulate that the units in the upstream power station... In the The power generation flow rate during a given period must be between the unit start-up / shutdown status variable multiplied by the minimum and maximum water diversion flow rates, and the downstream power station units... Similarly, the above start-stop status variables are set to 1 to represent unit startup and 0 to represent unit shutdown, thus characterizing the unit's start-up and shutdown status.

[0115] In this embodiment, the power-to-flow conversion efficiency constraint of the pumped-storage unit is:

[0116] ;

[0117] The output limit constraint of the pumped storage unit is:

[0118] ;

[0119] The mutual exclusion constraint between power generation and pumping states is as follows:

[0120] ;

[0121] in, Indicates pumped storage unit In the Power generation flow rate under time-of-use power generation conditions Indicates pumped storage unit Power conversion coefficient under power generation conditions Indicates pumped storage unit In the Power output during peak hours This indicates the density of water. Indicates pumped storage unit Power generation efficiency, Indicates the average head. Indicates pumped storage unit In the Pumping flow rate under time-limited pumping conditions Indicates pumped storage unit Power conversion coefficient under pumping conditions Indicates pumped storage unit In the Power output during periodic pumping operations. Indicates pumped storage unit Pumping efficiency. , These represent pumped-storage units. In the State variables for power generation and pumping operations during different time periods. , These represent pumped-storage units. The lower and upper limits of power generation under power generation conditions. , These represent pumped-storage units. The lower and upper limits of power under pumping conditions.

[0122] It is understandable that the power-flow conversion efficiency constraint of pumped-storage units describes the conversion relationship between power and flow rate of pumped-storage units. In the The power generation flow rate under the time-limited power generation condition is equal to the power conversion coefficient of the power generation condition multiplied by the power generation capacity under the power generation condition. This coefficient is determined by the water density, power generation efficiency, and average head. (Pumped-storage unit) In the The pumping flow rate under the time-period pumping condition is equal to the pumping power conversion coefficient multiplied by the pumping power. This coefficient is determined by the pumping efficiency, water density, and average head.

[0123] Pumped storage unit output limit constraints regulations for pumped storage units In the The power output during the power generation period must fall between the power generation state variable multiplied by the lower and upper limits of the power output for the power generation state, and the same applies to the power output during the pumping state. Mutually exclusive constraints between power generation and pumping states apply to pumped-storage units. During any given scheduling period, the sum of the power generation status variable and the pumping status variable is between 0 and 1, ensuring that the unit can only be in one of the following states at the same time: power generation, pumping, or shutdown.

[0124] Based on the aforementioned engineering constraints, a multi-objective optimization scheduling model for cascade hydropower, wind power, solar power, and pumped storage is constructed with the optimization objectives of minimizing wind and solar curtailment rates and minimizing load shedding rates. This multi-objective optimization scheduling model includes the power balance equation and all the aforementioned engineering constraints.

[0125] In this embodiment, the mathematical expression of the power balance equation is:

[0126] ;

[0127] ;

[0128] in, express The first hydropower station in The total effort exerted during the period. Indicates the first The first hydropower station in Efforts during a specific time period; Indicates that wind power is in the first Efforts during a specific time period; Indicates the photovoltaic power station in the first Efforts during a specific time period; Indicates the hybrid pumped storage power station in the first Net output during the period; This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. Load demand during different time periods; This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. The amount of renewable energy power curtailed during a given period; Indicates the hybrid pumped storage power station in the first Power output during the specified time period; Indicates the hybrid pumped storage power station in the first Power output during pumping operations at different times.

[0129] It can be understood that the mathematical expression of the electricity balance equation is: The first hydropower station in The total output during the time period plus wind power in the first Output during the period, photovoltaic power station in the first The output during the period and the hybrid pumped storage power station in the first The net output during the period is equal to that of the cascade hydropower-wind-solar hybrid pumped storage complementary system on the [number]th [period]. Load demand during the period minus the system's load demand during the period The amount of renewable energy curtailed during the specified time period. Among them, the hybrid pumped storage power station... The net output of the pumped-storage power station in the first period is equal to that of the pumped-storage power station The equation represents the power output during the power generation phase minus the power output during the pumping phase. This equation comprehensively reflects the synergistic effect of cascade hydropower, wind power, photovoltaic power, and hybrid pumped storage power stations in the power balance.

[0130] In this embodiment, the mathematical expression for minimizing the wind and solar curtailment rate in the optimization objective is:

[0131] ;

[0132] ;

[0133] ;

[0134] The mathematical expression for minimizing the load failure rate is:

[0135] ;

[0136] ;

[0137] ;

[0138] in, This represents the objective function value that minimizes the wind and solar curtailment rate. This represents the total number of time periods within the scheduling cycle. This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. The rate of curtailment of renewable energy during a given period This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. Power deviation over time period Indicates that wind power is in the first Efforts during a specific time period Indicates the photovoltaic power station in the first Efforts during a specific time period Indicates the first The first hydropower station in Efforts during a specific time period Indicates the hybrid pumped storage power station in the first Net output during the period This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. Load demand during different time periods This represents the objective function value that minimizes the load failure rate. This indicates the total number of hydroelectric power stations. This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. Load loss rate during a given period.

[0139] Specifically, the objective function for minimizing wind and solar curtailment rates is to minimize the cumulative sum of wind and solar curtailment rates for each time period within the scheduling cycle. The wind and solar curtailment rate for each time period is equal to the ratio of the system power deviation to the sum of wind and solar power output. This power deviation is the positive deviation of the load demand minus the total system output (including hydropower, wind power, solar power, and the net output of hybrid pumped storage), reflecting the system's absorption level of wind and solar energy. The objective function for minimizing load shedding rates is to minimize the average load shedding rate of each hydropower station for each time period. The load shedding rate for each time period is equal to the ratio of the power deviation to the load demand. This power deviation is the positive deviation of the system output minus the load demand, reflecting the system's power supply reliability. These two objectives characterize the system's scheduling performance from the dimensions of renewable energy absorption and power supply reliability, respectively. A multi-objective optimization approach is used to achieve the optimal trade-off between these two conflicting objectives in the scheduling scheme.

[0140] Step 3: Multi-scenario scheduling simulation and optimal solution selection:

[0141] For each short-term scheduling scenario, the corresponding runoff data and wind and solar power output data are input as boundary conditions into the multi-objective short-term optimization scheduling model. The multi-objective optimization algorithm is used to solve the multi-objective short-term optimization scheduling model after inputting the boundary conditions to obtain the Pareto solution set corresponding to each short-term scheduling scenario. The optimal solution corresponding to each short-term scheduling scenario is selected from the Pareto solution set using the entropy weight-TOPSIS method. The power output process corresponding to the optimal solution of each short-term scheduling scenario is used as the scheduling plan for that short-term scheduling scenario.

[0142] Specifically, since the boundary conditions for runoff inflow and wind and solar power output differ under different scheduling scenarios, the optimal scheduling scheme of the system will also change accordingly. Therefore, it is necessary to simulate and solve for each short-term scheduling scenario separately. Runoff data, as the inflow boundary of cascade hydropower stations, determines the amount of water available in each period; wind and solar power output data, as the upper boundary of wind and solar power output, determines the maximum available power of new energy sources in each period. After inputting the runoff data and wind and solar power output data corresponding to each scenario into the model, the system scheduling can be optimized under the corresponding boundary conditions.

[0143] In solving the multi-objective optimization problem, since the multi-objective short-term optimization scheduling model contains two conflicting optimization objectives: minimizing wind and solar curtailment rates and minimizing load shedding rates, which cannot be simultaneously optimized, a multi-objective optimization algorithm is used to solve for its non-dominated solution set. In this embodiment, NSGA-II (Non-dominated sorting genetic algorithm II) is used. This algorithm, through non-dominated sorting and crowding distance calculation, can efficiently approximate the Pareto front and obtain a uniformly distributed Pareto solution set. Convergence is judged using a hypervolume index; the algorithm is considered convergent when the hypervolume index no longer increases within a consecutive number of iterations, thus obtaining the Pareto solution set corresponding to each short-term scheduling scenario.

[0144] For optimal solution selection, the entropy-weighted TOPSIS method is used to select the optimal solutions for each short-term scheduling scenario from the Pareto solution set. The objective function values ​​of each solution in the Pareto solution set are used to construct a decision matrix, and the objective weight of each objective is calculated using the entropy-weighted method: the entropy-weighted method determines the weight by calculating the information entropy of each objective function value; the smaller the information entropy, the greater the degree of variation of the objective in the solution set, the richer the information contained, and the greater the weight should be assigned. Then, the TOPSIS method is used to calculate the distance between each solution and the positive and negative ideal solutions, and the optimal solution is determined based on the criterion of maximum relative proximity. The output process corresponding to the optimal solution of each short-term scheduling scenario is used as the scheduling plan for that scenario.

[0145] Step 4: Output the optimal output result:

[0146] Based on the scheduling plans under each short-term scheduling scenario, the optimal output process and optimal planned output curve of the cascade hydropower-wind-solar hybrid pumped storage complementary system under each short-term scheduling scenario are output.

[0147] Specifically, this step outputs the optimal dispatch plans for each scenario obtained in step 3 in the form of tables and curves. The output process refers to the sequence of output values ​​for each power source in each time period, including the time-by-time output of each hydropower station in the cascade, the time-by-time net output of the mixed pumped storage power station (with generation and pumping conditions indicated separately), the time-by-time output of wind farms and photovoltaic power stations, and the comparison data between the total system output and load demand. The optimal planned output curve is a visual curve plotted with time as the horizontal axis and output as the vertical axis, displaying the hourly output change trend of each power source in the same coordinate system, as well as key performance indicators such as the system's wind and solar curtailment rate and load shedding rate. For typical scenarios in each season, the corresponding 24-hour optimal output process and optimal planned output curve are output, facilitating dispatchers' intuitive understanding of the optimal dispatch scheme and system operating status under each scenario, and providing a basis for decision-making in actual dispatch operations.

[0148] In summary, the short-term scheduling method for a cascade hydropower-wind-solar hybrid pumped-storage complementary system provided in this embodiment, by employing kernel density estimation to fit the wind and solar power output distribution, Markov Monte Carlo sampling to generate intraday wind and solar power output scenarios, and a two-dimensional Copula function to generate combined wind and solar power output scenarios, and using fuzzy C-means clustering to screen typical scenarios, fully preserves the correlation structure between wind and solar power output, making the generated scenario combinations physically more reasonable and accurately reflecting the true distribution characteristics of combined wind and solar power output; by generating typical runoff scenarios through cumulative frequency curve partitioning and area-weighted averaging, it fully utilizes the distribution information of historical runoff data to generate statistically representative runoff scenarios; and by combining typical runoff scenarios with typical combined wind and solar power output scenarios to construct a four-season short-term scheduling scenario, it achieves comprehensive... The system comprehensively covers typical operating conditions under different combinations of inflow and wind / solar resource conditions. By constructing a multi-objective short-term optimization scheduling model that includes detailed operating constraints of the hybrid pumped-storage power station, it meticulously characterizes the operating characteristics of the hybrid pumped-storage power station under power generation and pumping conditions, making the scheduling results more consistent with the actual operating conditions of the hybrid pumped-storage system. By using a hypervolume index to judge convergence, it avoids problems of insufficient convergence or computational redundancy caused by subjectively setting the number of iterations. By using the entropy-weighted TOPSIS method to select the optimal solution corresponding to each scenario from the Pareto solution set, it achieves scientific decision-making on the multi-objective optimization results, and finally outputs the optimal power output process and optimal planned power output curve for each scenario, providing a reliable decision-making basis for the short-term scheduling of the cascade hydropower-wind-solar hybrid pumped-storage complementary system.

[0149] The effectiveness of the short-term dispatching method for the cascade hydropower-wind-solar hybrid pumped storage complementary system provided in this embodiment is analyzed below, taking the Lianghekou hybrid pumped storage power station in the Yalong River basin as an example. This hybrid pumped storage base includes two hydropower stations, one hybrid pumped storage power station, and surrounding supporting wind and solar power stations. The installed capacities of the upstream and downstream Lianghekou and Yagen I hydropower stations are 3 million kilowatts and 300,000 kilowatts respectively, the installed capacity of the Lianghekou hybrid pumped storage power station is 1.2 million kilowatts, and the total installed capacity of wind and solar power is 7.9 million kilowatts.

[0150] When applying the short-term scheduling method for the cascade hydropower-wind-solar hybrid pumped storage complementary system provided in this embodiment, typical wind and solar combined output scenarios and typical runoff scenarios for each season are obtained through short-term scheduling scenario construction, multi-objective short-term optimization scheduling model construction, and multi-scenario scheduling simulation solution. Taking spring as an example, as follows... Figure 2 As shown, Figure 2This study presents the distribution characteristics of wind and solar power output under three scenarios in spring: low output, typical output, and high output, as well as the corresponding typical daily runoff flow. The results show that wind and solar energy exhibit significant intraday regularity in their distribution. Solar power output begins between 7:00 and 8:00 AM, peaking between 12:00 and 2:00 PM, while wind power peaks between 2:00 and 4:00 PM. The average daily output under the high wind and solar power output scenario in spring reaches 2.384 million kW, while the average daily output under the low output scenario is 1.936 million kW. Regarding runoff, the average daily flow under the high runoff scenario in a normal spring year is 811... The average daily flow in the low runoff scenario during spring is 175. .

[0151] By solving the problem using a multi-objective optimization algorithm and selecting the optimal solution using the entropy-weighted TOPSIS method, the power output of the scheduling system under typical wind and solar power output and typical runoff combinations in each season was obtained. Taking spring as an example, ... Figure 3 As shown, Figure 3 The results showcase the power output of the dispatching system under typical spring wind and solar power output and typical runoff combinations. The results indicate that, under typical spring scenarios, the daily peak power output of the cascade hydropower-wind-solar hybrid pumped storage system all exceeds 4 million kW. When wind and solar power output decreases at night, the Lianghekou cascade hydropower station and the hybrid pumped storage station undertake the system's peak-shaving tasks; during the midday peak of wind and solar power, the Lianghekou station reduces its output, and the hybrid pumped storage station utilizes excess wind and solar power for pumping, effectively improving the system's renewable energy absorption capacity. The load shedding rate under typical spring scenarios is below 1%, and the wind and solar curtailment rate is below 8%.

[0152] The above results demonstrate that the short-term scheduling method for the cascade hydropower-wind-solar hybrid pumped storage complementary system proposed in this embodiment can fully utilize the bidirectional regulation capability of the hybrid pumped storage system while meeting the complex hydraulic connections of the cascade power stations and the operational constraints of the hybrid pumped storage power stations. This effectively improves the wind and solar energy absorption level and ensures the power supply reliability of the system, providing a scientific decision-making basis for the short-term scheduling of the cascade hydropower-wind-solar hybrid pumped storage complementary system.

[0153] Based on the above technical solution, this embodiment also proposes a short-term scheduling device for a cascade hydropower-wind-solar hybrid pumped storage complementary system, used to implement the short-term scheduling method for the cascade hydropower-wind-solar hybrid pumped storage complementary system as described in the embodiment. Please refer to [link to relevant documentation]. Figure 4 The device includes:

[0154] The scenario construction module is used to acquire hourly wind and solar power output data and daily runoff data of the target study area within a preset historical time period. Based on the wind and solar power output data, the distribution of wind and solar power output is fitted using kernel density estimation after seasonal division. Markov Monte Carlo sampling is used to generate intraday wind and solar power output scenarios for each season, and a set of combined wind and solar power output scenarios for each season is generated using a two-dimensional Copula function. Fuzzy C-means clustering is used to filter typical combined wind and solar power output scenarios for each season. Based on the daily runoff data, the cumulative frequency curve is fitted after seasonal division. The cumulative frequency curve is divided into multiple intervals according to a preset quantile. The area-weighted average of the frequency curves of each interval and the coordinate axis is used to calculate the typical runoff scenarios for each season. The typical runoff scenarios and typical combined wind and solar power output scenarios for each season are combined to construct short-term scheduling scenarios for each season.

[0155] The model building module is used to acquire engineering data of the cascade hydropower-wind-solar hybrid pumped storage complementary system, determine the corresponding engineering constraints based on the engineering data, and construct a multi-objective short-term optimization scheduling model for the cascade hydropower-wind-solar hybrid pumped storage complementary system with the optimization objectives of minimizing wind curtailment rate and minimizing load shedding rate. The multi-objective short-term optimization scheduling model includes power balance equations and engineering constraints.

[0156] The simulation and solution module is used to input the corresponding runoff data and wind and solar power output data as boundary conditions into the multi-objective short-term optimization scheduling model for each short-term scheduling scenario; the multi-objective optimization algorithm is used to solve the multi-objective short-term optimization scheduling model after inputting boundary conditions to obtain the Pareto solution set corresponding to each short-term scheduling scenario; the optimal solution corresponding to each short-term scheduling scenario is selected from the Pareto solution set using the entropy weight-TOPSIS method; and the power output process corresponding to the optimal solution of each short-term scheduling scenario is used as the scheduling plan for that short-term scheduling scenario.

[0157] The output module is used to output the optimal output process and optimal planned output curve of the cascade hydropower-wind-solar hybrid pumped storage complementary system under each short-term scheduling scenario, based on the scheduling plan under each short-term scheduling scenario.

[0158] It is understood that the short-term scheduling device for the cascade hydropower-wind-solar hybrid pumped storage complementary system described in this embodiment is a device for implementing the short-term scheduling method of the cascade hydropower-wind-solar hybrid pumped storage complementary system described in the embodiment. As the device disclosed in the embodiment corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant parts, please refer to the description of the method. It will not be repeated here.

Claims

1. A short-term scheduling method for a cascade hydropower-wind-solar hybrid pumped storage complementary system, characterized in that, The method includes: Hourly wind and solar power output data and daily runoff data for the target study area within a predetermined historical timeframe are obtained. Based on the wind and solar power output data, the distribution is fitted using kernel density estimation after seasonal division. Markov Monte Carlo sampling is used to generate intraday wind and solar power output scenarios for each season, and a set of combined wind and solar power output scenarios for each season is generated using a two-dimensional Copula function. Fuzzy C-means clustering is used to filter typical combined wind and solar power output scenarios for each season. Based on the daily runoff data, cumulative frequency curves are fitted after seasonal division. The cumulative frequency curves are divided into multiple intervals according to a predetermined quantile. The area-weighted average of the frequency curves of each interval and the coordinate axes is used to calculate the typical runoff scenarios for each season. The typical runoff scenarios and typical combined wind and solar power output scenarios for each season are combined to construct short-term scheduling scenarios for each season. Engineering data of a cascade hydropower-wind-solar hybrid pumped storage complementary system is obtained. Based on the engineering data, the corresponding engineering constraints are determined. With minimizing wind and solar curtailment rates and minimizing load shedding rates as optimization objectives, a multi-objective short-term optimization scheduling model for cascade hydropower-wind-solar hybrid pumped storage complementary system is constructed. The multi-objective short-term optimization scheduling model includes power balance equations and engineering constraints. For each short-term scheduling scenario, the corresponding runoff data and wind and solar power output data are used as boundary conditions to input the multi-objective short-term optimization scheduling model. The multi-objective optimization algorithm is used to solve the multi-objective short-term optimization scheduling model after inputting boundary conditions to obtain the Pareto solution set corresponding to each short-term scheduling scenario. The optimal solution corresponding to each short-term scheduling scenario is selected from the Pareto solution set using the entropy weight-TOPSIS method. The power output process corresponding to the optimal solution of each short-term scheduling scenario is used as the scheduling plan for that short-term scheduling scenario. Based on the scheduling plans under each short-term scheduling scenario, the optimal output process and optimal planned output curve of the cascade hydropower-wind-solar hybrid pumped storage complementary system under each short-term scheduling scenario are output.

2. The short-term scheduling method for the cascade hydropower-wind-solar hybrid pumped storage complementary system according to claim 1, characterized in that, The mathematical expression for the power balance equation is: ； ； in, express The first hydropower station in The total effort exerted during the period. Indicates the first The first hydropower station in Efforts during a specific time period; Indicates that wind power is in the first Efforts during a specific time period; Indicates the photovoltaic power station in the first Efforts during a specific time period; Indicates the hybrid pumped storage power station in the first Net output during the period; This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. Load demand during different time periods; This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. The amount of renewable energy power curtailed during a given period; Indicates the hybrid pumped storage power station in the first Power output during specific time periods; Indicates the hybrid pumped storage power station in the first Power output during pumping operations at different times.

3. The short-term scheduling method for the cascade hydropower-wind-solar hybrid pumped storage complementary system according to claim 1, characterized in that, The engineering constraints include hydraulic connection constraints of cascade power stations, reservoir capacity constraints of cascade power stations, and flow constraints of cascade power stations. The hydraulic connection constraints of the cascade hydropower stations are as follows: ； The reservoir capacity constraint of the cascade hydropower stations is: ； The flow constraint of the cascade hydropower stations is: ； in, Indicates the downstream power station in the first Inbound flow during a specific time period Indicates the upstream power station in the Power generation flow during the period Indicates the upstream power station in the The amount of water discharged during a given period Indicates the upstream power station in the Inflow rate within a time period Indicates the hybrid pumped storage power station in the first Net flow during the period Indicates the hybrid pumped storage power station in the first The state variable for power generation during a given time period is 1 when power generation is in operation, and 0 otherwise. Indicates the hybrid pumped storage power station in the first Power generation flow rate under time-of-use power generation conditions Indicates the hybrid pumped storage power station in the first The state variable for the pumping operation during a given time period is set to 1 when pumping is in operation and 0 otherwise. Indicates the hybrid pumped storage power station in the first Pumping flow rate under time-limited pumping conditions Indicates the upstream power station in the The reservoir capacity at the end of the period Indicates the upstream power station in the Reservoir capacity at the end of the period Indicates time interval, Indicates the downstream power station in the first The reservoir capacity at the end of the period Indicates the downstream power station in the first The reservoir capacity at the end of the period Indicates the downstream power station in the first Power generation flow during the period Indicates the downstream power station in the first Water discharge rate during the period , These represent the upstream power stations at the [number]th [year]. Minimum and maximum storage capacity allowed for a given time period. , These represent the downstream power plants at the [number]th [year]. Minimum and maximum storage capacity allowed for a given time period. , These represent the upstream power stations at the [number]th [year]. The minimum and maximum allowable discharge flow rates for each time period. , These respectively represent the hybrid pumped storage power station in the 19th century. The minimum and maximum allowable discharge flow rates for each time period. , These represent the downstream power plants at the [number]th [year]. The minimum and maximum allowable discharge flow rates for each time period.

4. The short-term scheduling method for the cascade hydropower-wind-solar hybrid pumped storage complementary system according to claim 1, characterized in that, The engineering constraints also include constraints on the relationship between the water level and reservoir capacity of the cascade power stations, as well as constraints on the relationship between the tailrace water level and the outflow of the hydropower units. The water level-reservoir capacity relationship constraint of the cascade hydropower stations is as follows: ； The constraint on the relationship between the tailrace water level and the downstream flow rate of the hydropower unit is as follows: ； in, , , These represent the upstream power stations at the [number]th [year]. Reservoir water level at the end of the time period, minimum allowable water level and maximum allowable water level during the dispatch cycle. This represents a function relating the water level and reservoir capacity of an upstream power station. Indicates the upstream power station in the The reservoir capacity at the end of the period , , These represent the downstream power plants at the [number]th [year]. Reservoir water level at the end of the time period, minimum allowable water level and maximum allowable water level during the dispatch cycle. A function representing the relationship between water level and reservoir capacity at downstream power stations. Indicates the downstream power station in the first The reservoir capacity at the end of the period Indicates the upstream power station in the Downstream tailwater level at the end of the period This represents a function relating the tailrace level of the upstream power station to its discharge flow. Indicates the upstream power station in the Power generation flow during the period Indicates the upstream power station in the Water discharge rate during the period Indicates the downstream power station in the first Downstream tailwater level at the end of the period This represents the function relating the tailrace level of the downstream power station to the discharge flow rate. Indicates the downstream power station in the first Power generation flow during the period Indicates the downstream power station in the first The amount of water discharged during a given time period.

5. The short-term scheduling method for a cascade hydropower-wind-solar hybrid pumped storage complementary system according to claim 1, characterized in that, The engineering constraints also include hydropower unit output characteristic constraints, hydropower unit head constraints, hydropower unit output limitation constraints, and hydropower unit flow constraints. The output characteristic constraint of the hydropower unit is: ； The head constraint of the hydropower unit is: ； The output limit constraint of the hydropower unit is: ； The flow constraint of the hydropower unit is: ； in, Indicates the generating units within the upstream power station In the Efforts during a specific time period Indicates the generating units within the upstream power station The output characteristic function, Indicates the generating units within the upstream power station In the Power generation flow during the period Indicates the generating units within the upstream power station In the The head of water generated during the power generation period, Indicates the generating units within the downstream power plant In the Efforts during a specific time period Indicates the generating units within the downstream power plant The output characteristic function, Indicates the generating units within the downstream power plant In the Power generation flow during the period Indicates the generating units within the downstream power plant In the The head of water used for power generation during different time periods, Indicates the upstream power station in the The reservoir water level at the end of the period, Indicates the upstream power station in the The reservoir water level at the end of the period, Indicates the upstream power station in the Downstream tailwater level at the end of the period Indicates head loss, Indicates the downstream power station in the first The reservoir water level at the end of the period, Indicates the downstream power station in the first The reservoir water level at the end of the period, Indicates the downstream power station in the first Downstream tailwater level at the end of the period , These represent the generating units within the upstream power station. The minimum and maximum hydroelectric heads, , These represent the generating units within the downstream power plant. The minimum and maximum hydroelectric heads, , These represent the generating units within the upstream and downstream power plants, respectively. In the Start and stop status variables for a given time period. , These represent the generating units within the upstream power station. Minimum and maximum technical output, , These represent the generating units within the downstream power plant. Minimum and maximum technical output, , These represent the generating units within the upstream power station. The minimum and maximum water diversion flow rates, , These represent the generating units within the downstream power plant. The minimum and maximum water diversion flow rates.

6. The short-term scheduling method for a cascade hydropower-wind-solar hybrid pumped storage complementary system according to claim 1, characterized in that, The engineering constraints also include power-flow conversion efficiency constraints of pumped storage units, output limit constraints of pumped storage units, and mutual exclusion constraints between power generation and pumping states. The power-flow conversion efficiency constraint of the pumped storage unit is: ； The output limit constraint of the pumped storage unit is: ； The mutual exclusion constraint between power generation and pumping states is as follows: ； in, Indicates pumped storage unit In the Power generation flow rate under time-of-use power generation conditions Indicates pumped storage unit Power conversion coefficient under power generation conditions, Indicates pumped storage unit In the Power output during peak hours This indicates the density of water. Indicates pumped storage unit Power generation efficiency, Indicates the average head. Indicates pumped storage unit In the Pumping flow rate under time-limited pumping conditions Indicates pumped storage unit Power conversion coefficient under pumping conditions Indicates pumped storage unit In the Power output during pumping operation (period). Indicates pumped storage unit Pumping efficiency , These represent pumped-storage units. In the State variables for power generation and pumping operations during different time periods. , These represent pumped-storage units. The lower and upper limits of power generation under power generation conditions. , These represent pumped-storage units. The lower and upper limits of power under pumping conditions.

7. The short-term scheduling method for a cascade hydropower-wind-solar hybrid pumped storage complementary system according to claim 1, characterized in that, In the optimization objective, the mathematical expression for minimizing the wind and solar curtailment rate is: ； ； ； The mathematical expression for minimizing the load failure rate is: ； ； ； in, This represents the objective function value that minimizes the wind and solar curtailment rate. This represents the total number of time periods within the scheduling cycle. This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. The rate of curtailment of renewable energy during a given period This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. Power deviation over time period Indicates that wind power is in the first Efforts during a specific time period Indicates the photovoltaic power station in the first Efforts during a specific time period Indicates the first The first hydropower station in Efforts during a specific time period Indicates the hybrid pumped storage power station in the first Net output during the period This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. Load demand during different time periods This represents the objective function value that minimizes the load failure rate. This indicates the total number of hydroelectric power stations. This indicates that the cascade hydropower-wind-solar hybrid pumped storage system is in its first stage. The rate of load loss during a given period.

8. The short-term scheduling method for a cascade hydropower-wind-solar hybrid pumped storage complementary system according to claim 1, characterized in that, When solving the multi-objective short-term optimization scheduling model using a multi-objective optimization algorithm, the convergence of the algorithm is determined based on the hypervolume index. The algorithm is considered to have converged when the hypervolume index no longer increases within a consecutive number of iterations.

9. The short-term scheduling method for a cascade hydropower-wind-solar hybrid pumped storage complementary system according to claim 1, characterized in that, In the scenario construction, the cumulative frequency curve is divided into three intervals, and the preset quantiles are the 10th quantile and the 90th quantile. The cumulative frequency curve of each season is divided into low runoff interval, medium runoff interval and high runoff interval according to the 10th quantile and the 90th quantile. The area enclosed by the frequency curve and the coordinate axis in each interval is weighted and averaged to calculate the low runoff scenario, medium runoff scenario and high runoff scenario corresponding to each season.

10. A short-term dispatching device for a cascade hydropower-wind-solar hybrid pumped storage complementary system, characterized in that, A method for short-term scheduling of a cascade hydropower-wind-solar hybrid pumped storage complementary system as described in any one of claims 1 to 9, the apparatus comprising: The scenario construction module is used to acquire hourly wind and solar power output data and daily runoff data of the target study area within a preset historical time period. Based on the wind and solar power output data, the distribution of wind and solar power output is fitted using kernel density estimation after seasonal division. Markov Monte Carlo sampling is used to generate intraday wind and solar power output scenarios for each season, and a set of combined wind and solar power output scenarios for each season is generated using a two-dimensional Copula function. Fuzzy C-means clustering is used to filter typical combined wind and solar power output scenarios for each season. Based on the daily runoff data, the cumulative frequency curve is fitted after seasonal division. The cumulative frequency curve is divided into multiple intervals according to a preset quantile. The area-weighted average of the frequency curves of each interval and the coordinate axis is used to calculate the typical runoff scenarios for each season. The typical runoff scenarios and typical combined wind and solar power output scenarios for each season are combined to construct short-term scheduling scenarios for each season. The model building module is used to acquire engineering data of the cascade hydropower-wind-solar hybrid pumped storage complementary system, determine the corresponding engineering constraints based on the engineering data, and construct a multi-objective short-term optimization scheduling model for the cascade hydropower-wind-solar hybrid pumped storage complementary system with the optimization objectives of minimizing wind curtailment rate and minimizing load shedding rate. The multi-objective short-term optimization scheduling model includes power balance equations and engineering constraints. The simulation and solution module is used to input the corresponding runoff data and wind and solar power output data as boundary conditions into the multi-objective short-term optimization scheduling model for each short-term scheduling scenario; the multi-objective optimization algorithm is used to solve the multi-objective short-term optimization scheduling model after inputting boundary conditions to obtain the Pareto solution set corresponding to each short-term scheduling scenario; the optimal solution corresponding to each short-term scheduling scenario is selected from the Pareto solution set using the entropy weight-TOPSIS method; and the power output process corresponding to the optimal solution of each short-term scheduling scenario is used as the scheduling plan for that short-term scheduling scenario. The output module is used to output the optimal output process and optimal planned output curve of the cascade hydropower-wind-solar hybrid pumped storage complementary system under each short-term scheduling scenario, based on the scheduling plan under each short-term scheduling scenario.

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