Optimal dispatching method, device and computer equipment for wind-solar-hydro combined power generation

Abstract

The disclosure provides a wind-solar-hydro combined power generation optimization scheduling method and device and computer equipment, the method comprising: obtaining wind power historical data, wind power prediction data, photovoltaic historical data and photovoltaic prediction data of a wind-solar-hydro complementary energy base, constructing a wind-solar power deviation probability distribution model according to the wind power historical data and the photovoltaic historical data, the wind-solar power deviation probability distribution model being used to indicate wind power prediction deviation data and photovoltaic prediction deviation data, determining a sum value of the wind power prediction data, the photovoltaic prediction data, the wind power prediction deviation data and the photovoltaic prediction deviation data as a target random variable, constructing an optimization scheduling model based on the target random variable, and solving the decision variable by using an optimization algorithm, thereby, the uncertainty of wind power and photovoltaic output prediction can be accurately described based on the wind-solar power deviation probability distribution model, so as to ensure the reliability of the scheduling process and effectively improve the scheduling effect of the wind-solar-hydro combined power generation.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of multi-energy complementary power generation, and in particular to a wind-solar-hydro combined power generation optimization scheduling method and device and computer equipment. BACKGROUND

[0002] In order to cope with environmental pollution, climate change, energy depletion crisis and other problems, the process of renewable energy development and utilization and energy structure transformation has obviously accelerated, so that the traditional power system mainly based on fossil energy and hydropower is transformed into a new type of power system mainly based on renewable energy such as wind power, photovoltaic and hydropower. However, wind power and photovoltaic power generation have randomness, volatility and intermittency, and large-scale grid connection of new energy power generation leads to frequent curtailment of wind and light. The wind-solar-hydro complementary energy base is based on existing or planned hydropower bases, and appropriate capacity of wind power and photovoltaic power is configured in the base, and the wind-solar-hydro power is bundled and sent out by using the external sending channel of the hydropower base, so as to realize the complementary development and operation of wind-solar-hydro.

[0003] In related technologies, when the wind-solar-hydro complementary energy base is scheduled for power generation, the output scene is usually used to represent the uncertainty of wind power and photovoltaic power output prediction.

[0004] In this way, the description accuracy of the uncertainty of wind power and photovoltaic power output prediction is low in the scheduling process, which affects the scheduling effect of wind-solar-hydro combined power generation. SUMMARY

[0005] The present disclosure aims to at least solve one of the technical problems in the related art to some extent.

[0006] To this end, the purpose of the present disclosure is to provide a wind-solar-hydro combined power generation optimization scheduling method, device, computer equipment and storage medium, which can accurately describe the uncertainty of wind power and photovoltaic power output prediction based on a wind-solar power deviation probability distribution model, so as to ensure the reliability of the scheduling process and effectively improve the scheduling effect of wind-solar-hydro combined power generation.

[0007] The wind-solar-hydro combined power generation optimization scheduling method provided by the first aspect of the present disclosure comprises:

[0008] Obtain wind power historical data, wind power prediction data, photovoltaic historical data, and photovoltaic prediction data of a wind-solar-hydro complementary energy base;

[0009] According to the wind power historical data and the photovoltaic historical data, a wind-solar power deviation probability distribution model is constructed, which is used to indicate wind power prediction deviation data corresponding to the wind power prediction data and photovoltaic prediction deviation data corresponding to the photovoltaic prediction data;

[0010] determine a sum value of the wind power prediction data, the photovoltaic prediction data, the wind power prediction deviation data and the photovoltaic prediction deviation data as a target random variable;

[0011] construct an optimal scheduling model based on the target random variable, wherein the optimal scheduling model comprises a target function, a decision variable and a constraint condition, the target function is used to indicate an optimization target of the optimal scheduling model, the decision variable comprises an output of a hydroelectric generating set, and the constraint condition is used to constrain the decision variable;

[0012] solve the decision variable in the optimal scheduling model by using an optimization algorithm.

[0013] The wind-solar-hydro joint power generation optimal scheduling method provided in the first aspect embodiment of the present disclosure comprises the following steps: obtaining wind power historical data, wind power prediction data, photovoltaic historical data and photovoltaic prediction data of a wind-solar-hydro complementary energy base; constructing a wind-solar power deviation probability distribution model according to the wind power historical data and the photovoltaic historical data, wherein the wind-solar power deviation probability distribution model is used to indicate wind power prediction deviation data corresponding to the wind power prediction data and photovoltaic prediction deviation data corresponding to the photovoltaic prediction data; determining a sum value of the wind power prediction data, the photovoltaic prediction data, the wind power prediction deviation data and the photovoltaic prediction deviation data as a target random variable; constructing an optimal scheduling model based on the target random variable, wherein the optimal scheduling model comprises a target function, a decision variable and a constraint condition, the target function is used to indicate an optimization target of the optimal scheduling model, the decision variable comprises an output of a hydroelectric generating set, and the constraint condition is used to constrain the decision variable; and solving the decision variable in the optimal scheduling model by using an optimization algorithm. Thus, the wind-solar power deviation probability distribution model can be used to accurately describe the uncertainty of wind power and photovoltaic output prediction, thereby ensuring the reliability of the scheduling process and effectively improving the scheduling effect of wind-solar-hydro joint power generation.

[0014] The wind-solar-hydro joint power generation optimal scheduling device provided in the second aspect embodiment of the present disclosure comprises:

[0015] an obtaining module, configured to obtain wind power historical data, wind power prediction data, photovoltaic historical data and photovoltaic prediction data of a wind-solar-hydro complementary energy base;

[0016] a first model constructing module, configured to construct a wind-solar power deviation probability distribution model according to the wind power historical data and the photovoltaic historical data, wherein the wind-solar power deviation probability distribution model is used to indicate wind power prediction deviation data corresponding to the wind power prediction data and photovoltaic prediction deviation data corresponding to the photovoltaic prediction data;

[0017] a determining module, configured to determine a sum value of the wind power prediction data, the photovoltaic prediction data, the wind power prediction deviation data and the photovoltaic prediction deviation data as a target random variable;

[0018] The second model construction module is used to construct an optimization scheduling model based on the target random variable. The optimization scheduling model includes an objective function, decision variables, and constraints. The objective function is used to indicate the optimization objective of the optimization scheduling model. The decision variables include the output of the hydropower unit. The constraints are used to constrain the decision variables.

[0019] The processing module is used to solve the decision variables in the optimization scheduling model using an optimization algorithm.

[0020] The optimized scheduling device for wind-solar-hydro combined power generation proposed in the second aspect of this disclosure acquires historical wind power data, wind power forecast data, historical photovoltaic data, and photovoltaic forecast data from wind-solar-hydro complementary energy bases. Based on the historical wind power data and photovoltaic data, a wind-solar power deviation probability distribution model is constructed. This model indicates the wind power forecast deviation data corresponding to the wind power forecast data and the photovoltaic forecast deviation data corresponding to the photovoltaic forecast data. The sum of the wind power forecast data, photovoltaic forecast data, wind power forecast deviation data, and photovoltaic forecast deviation data is determined as the target random variable. An optimized scheduling model is constructed based on the target random variable. The optimized scheduling model includes an objective function, decision variables, and constraints. The objective function indicates the optimization objective of the optimized scheduling model, the decision variables include the output of the hydropower units, and the constraints constrain the decision variables. An optimization algorithm is used to solve the decision variables in the optimized scheduling model. Thus, the uncertainty of wind power and photovoltaic output forecasts can be accurately described based on the wind-solar power deviation probability distribution model, thereby ensuring the reliability of the scheduling process and effectively improving the scheduling effect of wind-solar-hydro combined power generation.

[0021] The computer device proposed in the third aspect of this disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the optimized scheduling method for combined wind, solar, and hydropower generation as proposed in the first aspect of this disclosure.

[0022] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements an optimized scheduling method for combined wind, solar, and hydropower generation as proposed in the first aspect of this disclosure.

[0023] The fifth aspect of this disclosure provides a computer program product that, when executed by a processor, performs an optimized scheduling method for combined wind, solar, and hydropower generation as proposed in the first aspect of this disclosure.

[0024] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0026] Figure 1 This is a flowchart illustrating an optimized scheduling method for combined wind, solar, and hydropower generation proposed in an embodiment of this disclosure.

[0027] Figure 2 This is a flowchart illustrating an optimized scheduling method for combined wind, solar, and hydropower generation proposed in another embodiment of this disclosure;

[0028] Figure 3 This is a flowchart of a method for optimizing day-ahead power generation plans for wind-solar-hydro complementary energy bases that takes into account uncertainty, based on the present disclosure;

[0029] Figure 4 This is a schematic diagram of the structure of an optimized scheduling device for combined wind, solar and hydropower generation proposed in an embodiment of this disclosure;

[0030] Figure 5 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0031] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0032] Figure 1 This is a flowchart illustrating an optimized scheduling method for combined wind, solar, and hydropower generation proposed in one embodiment of this disclosure.

[0033] It should be noted that the execution subject of the wind-solar-hydro combined power generation optimization scheduling method in this embodiment is the wind-solar-hydro combined power generation optimization scheduling device. This device can be implemented by software and / or hardware. The device can be configured in a computer device, which may include, but is not limited to, a terminal, a server, etc. For example, the terminal may be a mobile phone, a handheld computer, etc.

[0034] like Figure 1 As shown, the optimized scheduling method for combined wind, solar, and hydropower generation includes:

[0035] S101: Obtain historical wind power data, wind power forecast data, historical photovoltaic data, and photovoltaic forecast data for wind-solar-hydro complementary energy bases.

[0036] Among them, the wind-solar-hydro complementary energy base relies on existing or planned hydropower bases, configures wind power and photovoltaic power of appropriate capacity within the base, and uses the external transmission channels of the hydropower base to transmit wind, solar and hydropower in a bundled manner, so as to realize the complementary development and operation of wind, solar and hydropower.

[0037] Historical wind power data can refer to historical data related to wind power in wind-solar-hydro hybrid energy bases, such as wind power output data. Forecasted wind power data refers to data obtained by predicting the wind power output of wind-solar-hydro hybrid energy bases for a future period.

[0038] Historical photovoltaic data can refer to historical data related to photovoltaic power generation in wind-solar-hydro complementary energy bases, such as photovoltaic power data. Photovoltaic forecast data, on the other hand, refers to data obtained by predicting the photovoltaic power of wind-solar-hydro complementary energy bases for a future period.

[0039] Optionally, in some embodiments, historical wind power data includes historical wind power prediction data and historical wind power actual data. Historical wind power prediction data includes historical predicted wind power resource data and historical predicted wind power corresponding to the predicted wind power resource data. Historical wind power actual data includes historical actual wind power resource data corresponding to the historical predicted wind power resource data and historical actual wind power corresponding to the historical predicted wind power.

[0040] Historical wind power resource data refers to data obtained by predicting wind power resources at a specific point in the past. Historical actual wind power resource data, on the other hand, indicates the actual wind power resources possessed by a wind-solar-hydro hybrid energy base at the predicted point in time.

[0041] Historical predicted wind power refers to the data obtained by predicting wind power based on historical predicted wind power resource data. Historical actual wind power refers to the actual wind power of the wind-solar-hydro hybrid energy base at the aforementioned predicted time point.

[0042] Optionally, in some embodiments, the photovoltaic historical data includes photovoltaic historical forecast data and photovoltaic historical actual data. The photovoltaic historical forecast data includes historical forecast photovoltaic resource data and historical forecast photovoltaic power corresponding to the forecast photovoltaic resource data. The photovoltaic historical actual data includes historical actual photovoltaic resource data corresponding to the historical forecast photovoltaic resource data and historical actual photovoltaic power corresponding to the historical forecast photovoltaic power.

[0043] Historical forecast photovoltaic resource data refers to data obtained by predicting photovoltaic resources at a specific point in the past. Historical actual photovoltaic resource data, on the other hand, indicates the actual photovoltaic resources possessed by the wind-solar-hydro hybrid energy base at the aforementioned forecast point in time.

[0044] Historically predicted photovoltaic power refers to the data obtained by predicting photovoltaic power based on historically predicted photovoltaic resource data. Historically actual photovoltaic power refers to the actual photovoltaic power of the wind-solar-hydro hybrid energy base at the aforementioned prediction time point.

[0045] In other words, in this embodiment of the present disclosure, when acquiring historical wind power data and historical photovoltaic data, not only the corresponding power data is acquired, but also the corresponding resource data is acquired. Therefore, the indicative effect of the acquired historical wind power data and historical photovoltaic data in the subsequent construction of the wind and solar power deviation probability distribution model can be effectively improved.

[0046] S102: Based on historical wind power data and historical photovoltaic data, construct a wind and solar power deviation probability distribution model. The wind and solar power deviation probability distribution model is used to indicate the wind power prediction deviation data corresponding to the wind power prediction data and the photovoltaic prediction deviation data corresponding to the photovoltaic prediction data.

[0047] Among them, the wind and solar power deviation probability distribution model refers to the model that can be used to describe the uncertainty of wind and solar power prediction for the aforementioned wind-solar-hydro complementary energy base.

[0048] Among them, wind power prediction deviation data can include the possible error range of predicting wind power, and the probability of each error value within the error range.

[0049] Among them, photovoltaic prediction deviation data can include the possible error range of predicting photovoltaic power, and the probability of each error value within the error range.

[0050] Optionally, in some embodiments, when constructing a wind and solar power deviation probability distribution model based on historical wind power data and historical solar power data, a first probabilistic statistical characteristic of the wind power prediction deviation can be determined based on multiple historical predicted wind power and the corresponding historical actual wind power. A second probabilistic statistical characteristic of the solar power prediction deviation can be determined based on multiple historical predicted solar power and the corresponding historical actual solar power. Based on the first and second probabilistic statistical characteristics, a generalized logical distribution model of wind and solar power deviation can be constructed as the wind and solar power deviation probability distribution model. Thus, the specificity of the wind and solar power deviation probability distribution model construction process can be effectively improved based on the first and second probabilistic statistical characteristics, thereby effectively improving the accuracy of the obtained wind and solar power deviation probability distribution model in describing wind and solar power deviation.

[0051] Among these, the probabilistic statistical characteristics can be features obtained by statistically analyzing the deviation data of wind power prediction. The first probabilistic statistical characteristic refers to the probabilistic statistical characteristics corresponding to the wind power prediction deviation. The second probabilistic statistical characteristic refers to the probabilistic statistical characteristics corresponding to the photovoltaic power prediction deviation.

[0052] S103: Determine the sum of wind power forecast data, photovoltaic forecast data, wind power forecast deviation data, and photovoltaic forecast deviation data as the target random variable.

[0053] It is understood that in the embodiments of this disclosure, both wind power prediction deviation data and photovoltaic prediction deviation data are random variables. However, too many random variables may affect the efficiency of model solving. Therefore, in the embodiments of this disclosure, when the sum of wind power prediction data, photovoltaic prediction data, wind power prediction deviation data and photovoltaic prediction deviation data is determined as the target random variable, the number of random variables in the subsequent optimization scheduling model can be reduced, thus ensuring the efficiency of model solving.

[0054] In this embodiment of the disclosure, when calculating the sum of wind power prediction deviation data and photovoltaic prediction deviation data, since both are random variables, the convolution operation method can be used to calculate the sum of wind power prediction deviation data and photovoltaic prediction deviation data.

[0055] Optionally, in some embodiments, the wind power forecast data includes wind power forecasts for multiple preset time intervals, and the photovoltaic forecast data includes photovoltaic power forecasts for multiple preset time intervals. This allows the wind power forecast data and photovoltaic forecast data to be adapted to personalized wind power and photovoltaic resources, thereby effectively improving the accuracy and practicality of the wind power forecast data and photovoltaic forecast data.

[0056] It is understood that the wind power forecast data and photovoltaic forecast data obtained in the embodiments of this disclosure may be forecast data for a relatively long period of time in the future, such as the next day. Wind power resources and photovoltaic resources may change over time. Therefore, dividing the next day into multiple preset time intervals, such as 1 hour, can effectively improve the forecast accuracy of wind power forecast data and photovoltaic forecast data.

[0057] S104: Construct an optimal scheduling model based on objective random variables. The optimal scheduling model includes an objective function, decision variables, and constraints. The objective function indicates the optimization objective of the optimal scheduling model. The decision variables include the output of the hydropower units. The constraints are used to constrain the decision variables.

[0058] Among them, the optimization scheduling model can refer to a model that solves for decision variables based on the objective function and constraints.

[0059] In this embodiment of the disclosure, when an optimized scheduling model is constructed based on the target random variable, the objective function and constraints can be flexibly configured according to the requirements of the application scenario, so as to effectively improve the flexibility of optimized scheduling.

[0060] S105: Solve the decision variables in the optimization scheduling model using an optimization algorithm.

[0061] Among them, optimization algorithms refer to stochastic optimization algorithms proposed by imitating the behavior of social animals. Examples include ant colony optimization algorithm, particle swarm optimization algorithm, bacterial foraging algorithm, firefly algorithm, and artificial fish swarm algorithm.

[0062] In this embodiment of the disclosure, when selecting an optimization algorithm to solve the decision variables in the optimization scheduling model, a single-objective particle swarm algorithm or a multi-objective particle swarm algorithm can be used to solve the problem based on the number of objective functions in the optimization scheduling model, and there is no limitation on this.

[0063] In this embodiment, historical wind power data, wind power forecast data, historical photovoltaic data, and photovoltaic forecast data of the wind-solar-hydro complementary energy base are acquired. Based on the historical wind power data and photovoltaic data, a wind-solar power deviation probability distribution model is constructed. This model indicates the wind power forecast deviation data corresponding to the wind power forecast data and the photovoltaic forecast deviation data corresponding to the photovoltaic forecast data. The sum of the wind power forecast data, photovoltaic forecast data, wind power forecast deviation data, and photovoltaic forecast deviation data is determined as the target random variable. An optimization scheduling model is constructed based on the target random variable. The optimization scheduling model includes an objective function, decision variables, and constraints. The objective function indicates the optimization objective of the optimization scheduling model, the decision variables include the output of the hydropower units, and the constraints constrain the decision variables. An optimization algorithm is used to solve the decision variables in the optimization scheduling model. Thus, the uncertainty of wind power and photovoltaic output forecasts can be accurately described based on the wind-solar-hydro power deviation probability distribution model, thereby ensuring the reliability of the scheduling process and effectively improving the scheduling effect of wind-solar-hydro combined power generation.

[0064] Figure 2 This is a flowchart illustrating an optimized scheduling method for combined wind, solar, and hydropower generation proposed in another embodiment of this disclosure.

[0065] like Figure 2 As shown, the optimized scheduling method for combined wind, solar, and hydropower generation includes:

[0066] S201: Obtain historical wind power data, wind power forecast data, historical photovoltaic data, and photovoltaic forecast data for wind-solar-hydro complementary energy bases.

[0067] S202: Based on historical wind power data and historical photovoltaic data, construct a wind and solar power deviation probability distribution model. The wind and solar power deviation probability distribution model is used to indicate the wind power prediction deviation data corresponding to the wind power prediction data and the photovoltaic prediction deviation data corresponding to the photovoltaic prediction data.

[0068] S203: Determine the sum of wind power forecast data, photovoltaic forecast data, wind power forecast deviation data, and photovoltaic forecast deviation data as the target random variable.

[0069] The descriptions of S201-S203 can be found in the above embodiments, and will not be repeated here.

[0070] S204: Obtain the operating parameters of the wind-solar-hydro complementary energy base, including the installed capacity and upper and lower limits of power generation corresponding to wind power, photovoltaic power and hydropower respectively.

[0071] Among them, installed power generation capacity refers to the capacity of power generation equipment that has been officially installed and put into use, including normal operating capacity, emergency reserve capacity and maintenance reserve capacity, and the unit of calculation is kilowatt.

[0072] In this context, "output" refers to the energy output of a corresponding generator unit (wind turbine, photovoltaic unit, or hydroelectric unit) per unit of time, also known as the power output of a power plant. For example, the average output of a power plant over 24 hours is called the daily average output. The integral of the output over time within a day is the daily power generation of the power plant.

[0073] Among them, the upper and lower limits of output information can be used to describe the upper and lower limits of output for wind power, photovoltaic power, and hydropower.

[0074] It is understood that the operating parameters of wind-solar-hydro complementary energy bases may differ in different application scenarios, and the operating parameters may affect the optimization scheduling process of wind-solar-hydro combined power generation. Therefore, in this embodiment of the disclosure, when the operating parameters of the wind-solar-hydro complementary energy base are obtained, reliable reference information can be provided for the subsequent construction of the optimization scheduling model, which can effectively improve the reliability of the construction process of the optimization scheduling model.

[0075] S205: Obtain the power generation attribute information of hydropower stations in the wind-solar-hydro complementary energy base.

[0076] In this embodiment of the disclosure, the wind-solar-hydro complementary energy base may include one or more hydropower stations, and there is no limitation thereto.

[0077] Among them, power generation attribute information refers to the relevant attribute information that affects the power generation function of the corresponding hydropower station, such as the characteristic water level, flow rate, characteristic curve, hydraulic connection of cascade power stations, and the initial state boundary conditions of the hydropower station, etc., and there are no restrictions on this.

[0078] S206: Construct an optimal scheduling model based on the installed power generation capacity, upper and lower limits of output, power generation attribute information, and target random variables.

[0079] In other words, in this embodiment of the present disclosure, after determining the sum of wind power forecast data, photovoltaic forecast data, wind power forecast deviation data, and photovoltaic forecast deviation data as the target random variable, the operating parameters of the wind-solar-hydro complementary energy base can be obtained. The operating parameters include the installed capacity and upper and lower limits of power output corresponding to wind power, photovoltaic power, and hydropower, respectively. The power generation attribute information of the hydropower station in the wind-solar-hydro complementary energy base is obtained. Based on the installed capacity, upper and lower limits of power output, power generation attribute information, and the target random variable, an optimized scheduling model is constructed. Thus, relevant information in the wind-solar-hydro complementary energy base can be effectively combined in the process of constructing the optimized scheduling model, thereby effectively improving the adaptability of the obtained optimized scheduling model to the wind-solar-hydro complementary energy base and improving the applicability of the obtained optimized scheduling model.

[0080] S207: Determine the operational characteristics of each objective function and constraint in the optimization scheduling model.

[0081] It is understood that the optimization scheduling model in this embodiment is constructed based on the target random variable, which contains integral operations. Therefore, the objective function and constraints may also contain integral operations. In this embodiment, when determining the operational characteristics of each objective function and constraint in the optimization scheduling model, the object to be processed containing integral operations can be accurately and quickly determined from multiple objective functions and constraints.

[0082] Optionally, in some embodiments, the objective function includes: a first objective function and a second objective function, wherein the first objective function is used to indicate the optimal scheduling objective of the optimal scheduling model for the total power generation of wind, solar and hydro, and the second objective function is used to indicate the optimal scheduling objective of the optimal scheduling model for the expected load shedding of wind, solar and hydro complementary energy bases and the curtailment of wind and solar power.

[0083] The first objective function can be expressed as a mathematical expression that maximizes the total power generation of wind, solar, and hydropower in a wind-solar-hydro complementary energy base. The output of wind, solar, and hydropower in the wind-solar-hydro complementary energy base at different times can be used as parameters in this first objective function.

[0084] The second objective function can be expressed as a mathematical expression that minimizes the expected load shedding and curtailment of wind and solar power in a wind-solar-hydro complementary energy base. This second objective function can use parameters such as wind and solar power deviation, maximum hydropower output, minimum hydropower output, and the hydropower output adjustment capability at different times, without imposing any restrictions.

[0085] Optionally, in some embodiments, the constraints include conventional constraints, inflexibility probability constraints, and extreme failure probability constraints.

[0086] Among them, conventional constraints can refer to some common constraints when optimizing the scheduling of wind-solar-hydro complementary energy bases, such as water balance constraints, water level and flow constraints, hydropower utilization constraints, hydropower station output constraints, and variable non-negativity constraints, etc., and there are no restrictions on them.

[0087] Among them, the insufficient flexibility probability constraint refers to the constraint condition configured for the insufficient flexibility of system adjustment, which can be used to avoid system loss of control due to insufficient flexibility of system adjustment during the optimization scheduling process.

[0088] Among them, the extreme failure probability constraint condition refers to the constraint condition configured for the load loss and wind power and photovoltaic curtailment probability of wind-solar-hydro complementary energy bases.

[0089] Optionally, in some embodiments, the insufficiency probability constraint includes raising the insufficiency probability constraint and lowering the insufficiency probability constraint, and the extreme failure probability constraint includes extreme load shedding probability constraint and extreme power curtailment probability constraint.

[0090] In other words, the optimized scheduling model constructed in this embodiment takes the total power generation of wind, solar and hydro as the optimized scheduling target and conventional constraints as constraints. It also considers the risks and extreme probability of damage caused by the load loss of wind, solar and hydro complementary energy bases and the expected curtailment of wind and solar power and insufficient flexibility of day-ahead power generation plans. This avoids the shortcomings of commonly used multi-scenario stochastic optimization methods that rely on scenario selection and are difficult to consider extreme damage. It effectively avoids the impact of prediction uncertainty on the day-ahead power generation plans of wind, solar and hydro complementary energy bases and provides support for the short-term scheduling operation of multi-energy complementarity.

[0091] S208: Based on the operational characteristics, determine the object to be processed from multiple objective functions and constraints, wherein the object to be processed includes integral operations.

[0092] The object to be processed can refer to the object of integration operations in multiple objective functions and constraints.

[0093] S209: Process the object to be processed based on numerical integration to obtain an updated optimized scheduling model.

[0094] In this embodiment of the disclosure, when processing the object to be processed based on numerical integration, the complex trapezoidal integral method can be used to calculate the probability distribution and its inverse function in the objective function and constraints.

[0095] S210: Solve the decision variables in the updated optimized scheduling model using an optimization algorithm.

[0096] In other words, in this embodiment of the present disclosure, after constructing the optimized scheduling model, the operational characteristics of each objective function and constraint in the optimized scheduling model can be determined. Based on the operational characteristics, the object to be processed is determined from multiple objective functions and constraints. The object to be processed includes integral operations. The object to be processed is processed based on numerical integration to obtain the updated optimized scheduling model. The decision variables in the updated optimized scheduling model are solved using an optimization algorithm. Thus, the objectives and constraints of the optimized scheduling model corresponding to the wind-solar-hydro complementary energy base can be processed based on numerical integration, thereby realizing the solution of complex stochastic optimization problems.

[0097] In this embodiment, by acquiring the operating parameters of the wind-solar-hydro complementary energy base, including the installed capacity and upper and lower limits of power output corresponding to wind power, photovoltaic power, and hydropower respectively, and acquiring the power generation attribute information of the hydropower station in the wind-solar-hydro complementary energy base, an optimized scheduling model is constructed based on the installed capacity, upper and lower limits of power output, power generation attribute information, and target random variables. This effectively combines relevant information from the wind-solar-hydro complementary energy base during the construction of the optimized scheduling model, thereby effectively improving the adaptability and applicability of the obtained optimized scheduling model to the wind-solar-hydro complementary energy base. By determining the operational characteristics of each objective function and constraint condition in the optimized scheduling model, and based on these characteristics, the object to be processed is determined from multiple objective functions and constraints. The object to be processed includes integral operations. The object to be processed is processed based on numerical integration to obtain an updated optimized scheduling model. An optimization algorithm is then used to solve the decision variables in the updated optimized scheduling model. Thus, the objectives and constraints of the optimized scheduling model corresponding to the wind-solar-hydro complementary energy base can be processed based on numerical integration, realizing the solution of complex stochastic optimization problems.

[0098] Figure 3 This is a flowchart of a method for optimizing day-ahead power generation plans for wind-solar-hydro complementary energy bases that takes into account uncertainty, based on the present disclosure, which includes the following steps:

[0099] Step 1: Obtain the operating parameters of each power source in the wind-solar-hydro complementary energy base; obtain historical and predicted data on wind and solar power output.

[0100] Step 2: To address the uncertainties in wind and solar power prediction, a generalized logistic distribution (GLO-V) is introduced to construct a wind and solar prediction bias distribution model.

[0101] Step 3: Establish a day-ahead stochastic optimization scheduling model for the wind-solar-hydro complementary energy base considering uncertainties (i.e., the optimization scheduling model in the above embodiments). The objectives are to maximize the total power generation of wind, photovoltaic and hydropower in the wind-solar-hydro energy base (i.e., the first objective function in the above embodiments) and minimize the expected load shedding and wind and photovoltaic curtailment (i.e., the second objective function in the above embodiments). The model considers conventional optimization model constraints such as water balance constraints, water level and flow constraints, hydropower utilization constraints, hydropower station output constraints and variable non-negativity constraints. It also introduces probabilistic constraints such as the risk of insufficient flexibility in the day-ahead power generation plan of the wind-solar-hydro energy base and the probability of extreme damage (i.e., the probability constraints of insufficient flexibility and the probability constraints of extreme damage in the above embodiments).

[0102] Step 4: For the probability distribution and its inverse function in the day-ahead stochastic optimization scheduling model of the wind-solar-hydro complementary energy base established in Step 3, a method for processing the objective function and constraint conditions using complex trapezoidal integrals is proposed.

[0103] Step 5: Use the multi-objective particle swarm optimization (MOPSO) algorithm to solve the day-ahead stochastic optimization scheduling model of the wind-solar-hydro complementary energy base, and formulate a day-ahead flexible power generation plan for the wind-solar-hydro complementary energy base considering uncertainties.

[0104] In step 1, the wind-solar-hydro complementary energy base is a bundled power system consisting of wind power, photovoltaic power station clusters and river basin (cross-basin) hydropower station clusters.

[0105] The specific operating parameters of wind power, photovoltaic power, and hydropower stations include the installed capacity and upper and lower limits of each power source; the characteristic water level, flow rate, characteristic curve of the hydropower station, the hydraulic connection of the cascade power stations, and the initial state boundary conditions of the hydropower station.

[0106] Historical data for wind and solar power specifically includes long-term actual and predicted wind speed, wind direction, air pressure, wind power, solar irradiance, temperature, and solar power.

[0107] The forecast data for wind power and solar power are the hourly forecast power for wind power and solar power for the following day.

[0108] The specific process of step 2 is as follows:

[0109] Based on long-term historical data of wind and solar power, the probabilistic statistical characteristics of prediction deviations for wind and solar power are analyzed, and a generalized logistic distribution model of wind and solar power deviations is constructed. Its probability distribution function, inverse probability distribution function, and probability density function are as follows:

[0110] (1)

[0111] (2)

[0112] (3)

[0113] In the formula, and Let Cdf be the cumulative distribution (Cdf) and probability density (Pdf) functions of the GLO-V distribution; It is the inverse function of the cumulative distribution of the GLO-V distribution; For position parameters, ; For scale parameters, ; This is the skewness parameter (coefficient). .

[0114] By superimposing the wind power and solar power prediction data (hourly wind power and solar power prediction for the next day) obtained in step 1 with the wind and solar power deviation probability distribution model, the confidence interval of the predicted wind and solar power value for the next day and its corresponding probability value can be obtained.

[0115] To minimize the number of random variables in the subsequent stochastic optimization scheduling model and ensure model solution efficiency, wind power and photovoltaic power and their prediction deviations can be added together and input as a single random variable into the scheduling model.

[0116] Since it involves the addition of two random variables, wind power prediction bias and photovoltaic prediction bias, convolution operation is required. The calculation formula is as follows:

[0117] (4)

[0118] In the formula, This represents the total deviation between the predicted wind power and photovoltaic power values. This represents the deviation in the predicted wind power output. This represents the deviation in the predicted photovoltaic power.

[0119] The specific process of step 3 is as follows:

[0120] Objective 1: The mathematical expression for maximizing the total power generation of wind, solar, and hydropower in a wind-solar-hydro complementary energy base is as follows:

[0121] (5)

[0122] In the formula, , and These represent wind power, solar power, and hydropower in a wind-solar-hydro complementary energy base. Efforts made at all times; The time scale of the optimization model is taken as... ; This represents the total number of time periods in the scheduling period.

[0123] Objective 2: The mathematical expression for minimizing the expected load shedding and wind and solar curtailment of the wind-solar-hydro complementary energy base is as follows:

[0124] (6)

[0125] In the formula, and They represent time periods respectively. The calculation method for the expectation of insufficient flexibility in adjusting the upward and downward adjustments of wind-solar-hydro complementary energy bases is as follows:

[0126] (7)

[0127] (8)

[0128] In the formula, For wind and solar power deviation, Positive or negative, It is negatively biased; Then it is The deviation distribution of wind power and photovoltaic power output over time periods; and The system flexibility deficit is represented by the following calculation method:

[0129] (9)

[0130] In the formula, and This indicates taking the absolute value and the minimum value. and They are respectively The calculation method for the flexible supply capacity of hydropower during different time periods, including upward and downward adjustments, is as follows:

[0131] (10)

[0132] (11)

[0133] In the formula, and These are the maximum and minimum output of hydropower, respectively. and Indicates hydropower The ability to climb uphill and downhill during different time periods.

[0134] In the stochastic optimization scheduling model, the risk of insufficient flexibility in the day-ahead power generation plan of the wind-solar-hydro complementary energy base and the equiprobability constraint of ultimate destruction are:

[0135] (12)

[0136] In the formula, and For time period The probability of insufficient flexibility in system adjustments (both upward and downward); and The upper limit is allowed for the probability of insufficient flexibility; and They represent time periods respectively. The extreme shortage of system upswing and downswing flexibility is addressed because the impact of load shedding during power system operation is far greater than that of power curtailment; therefore, the probability of extreme load shedding is taken as 1. The extreme probability of power curtailment is taken as 1%; The inverse function representing the cumulative distribution of wind power and photovoltaic deviations; and These represent the maximum possible extreme deficits for both upward and downward adjustments to system flexibility.

[0137] In addition, the stochastic optimization scheduling model also includes conventional constraints such as water balance constraints, water level and flow constraints, hydropower utilization constraints, hydropower station output constraints, and variable non-negativity constraints, as follows:

[0138] (1) Power system balance constraints

[0139] (13)

[0140] In the formula, This indicates that the wind-solar-hydro complementary energy base is in The burden that is constantly borne.

[0141] (2) Power system reserve capacity constraints

[0142] (14)

[0143] In the formula, and These represent the hydropower in the system. Reserve capacity at any given time; and These are the backup needs for wind, solar and hydropower complementary energy bases.

[0144] (3) Water balance constraints

[0145] (15)

[0146] In the formula, and Cascade reservoirs exist Storage capacity at the beginning and end of the time period; Cascade reservoirs exist Average inbound flow rate within the time period.

[0147] (4) Available water quantity constraints

[0148] (16)

[0149] In the formula, Cascade reservoirs exist Average discharge flow during the time period; Indicates cascade reservoirs The total volume of water released during the scheduling period; This refers to the collection of all cascade reservoirs within the energy base.

[0150] (5) Hydraulic connection between cascade power stations

[0151] (17)

[0152] In the formula, Indicates the first A series of reservoirs Inbound traffic within a given time period; Indicates the first A series of reservoirs Outbound flow during the time period Then it is the first The reservoir and the first The water flow stagnation time between the reservoirs; Indicates the first A series of reservoirs Inflow within a time period.

[0153] (6) Reservoir water level and flow constraints

[0154] (18)

[0155] In the formula, For reservoir exist Water level at the beginning of the period; and Reservoirs The upper and lower limits of the water level are determined by the fact that the water level and the reservoir capacity are in one-to-one correspondence, so the capacity constraint and the water level constraint are equivalent and do not need to be considered repeatedly. and Reservoirs Upper and lower limits of the discharge flow rate.

[0156] (7) Constraints on hydropower utilization

[0157] (19)

[0158] In the formula, , and These are the cascade power stations of the system. exist Power output, power generation diversion volume, and water head during different time periods; Indicates cascade hydropower stations The output coefficient of the generator set.

[0159] (8) Output constraints of wind, solar and hydropower stations

[0160] (20)

[0161] In the formula, and Indicates cascade hydropower stations The upper and lower limits of output; and Indicates the upper and lower limits of the output of a photovoltaic power station; and This indicates the upper and lower limits of the wind power station's output.

[0162] (9) Variable non-negativity constraint: Since the relevant variables of the wind-solar-hydro hybrid complementary energy base scheduling and operation in actual production cannot have negative values, all variables in the above model are greater than 0.

[0163] The specific process of step 4 is as follows:

[0164] The stochastic optimization scheduling model for the wind-solar-hydro complementary energy base constructed in step 3 involves the calculation of probability distributions and integrals. The distributions and integrals in the model also have feedback relationships with other state variables and decision variables, making the equation structure of the model's objective and constraints complex and difficult to handle using analytical algorithms. Therefore, the complex trapezoidal integral method is used to calculate the probability distributions and their inverse functions in the objective function and constraints. The calculation formula is as follows:

[0165] (twenty one)

[0166] In the formula, The result of a definite integral has upper and lower limits of integration. ; These are discrete points on the integration interval; is the discrete number of the integration interval; Let the step size of the discrete interval be denoted by the following formula:

[0167] (twenty two)

[0168] The specific process of step 5 is as follows:

[0169] ① Initialize the MOPSO algorithm parameters and randomly generate an initial particle cluster that satisfies the constraints;

[0170] ② Update the particle swarm evolution rate and iteratively calculate the next generation of particle clusters;

[0171] ③ Calculate the fitness of each particle in the particle swarm based on the model's objective function;

[0172] ④ Determine the particle with the best fitness in the particle cluster and update the pBest particle;

[0173] ⑤ Compare the particle with the best fitness in the particle cluster with the globally optimal particle, and update the gBest particle;

[0174] ⑥ Repeat steps ② to ⑤. When the iteration termination condition set during algorithm initialization is met, the loop ends, and the final optimization result is output. Develop a day-ahead flexible power generation plan for a wind-solar-hydro complementary energy base that considers uncertainties.

[0175] Figure 4This is a schematic diagram of the structure of an optimized scheduling device for combined wind, solar and hydropower generation proposed in one embodiment of this disclosure.

[0176] like Figure 4 As shown, the optimized dispatching device 40 for combined wind, solar, and hydropower generation includes:

[0177] The acquisition module 401 is used to acquire historical wind power data, wind power forecast data, historical photovoltaic data, and photovoltaic forecast data of the wind-solar-hydro complementary energy base.

[0178] The first model construction module 402 is used to construct a wind and solar power deviation probability distribution model based on historical wind power data and historical photovoltaic data. The wind and solar power deviation probability distribution model is used to indicate the wind power prediction deviation data corresponding to the wind power prediction data and the photovoltaic prediction deviation data corresponding to the photovoltaic prediction data.

[0179] Module 403 is used to determine the sum of wind power prediction data, photovoltaic prediction data, wind power prediction deviation data and photovoltaic prediction deviation data as the target random variable;

[0180] The second model construction module 404 is used to construct an optimization scheduling model based on the objective random variables. The optimization scheduling model includes an objective function, decision variables, and constraints. The objective function is used to indicate the optimization objective of the optimization scheduling model. The decision variables include the output of the hydropower unit, and the constraints are used to constrain the decision variables.

[0181] The processing module 405 is used to solve the decision variables in the optimization scheduling model using an optimization algorithm.

[0182] It should be noted that the aforementioned explanation of the optimized scheduling method for wind, solar and hydropower combined generation also applies to the optimized scheduling device for wind, solar and hydropower combined generation in this embodiment, and will not be repeated here.

[0183] In this embodiment, historical wind power data, wind power forecast data, historical photovoltaic data, and photovoltaic forecast data of the wind-solar-hydro complementary energy base are acquired. Based on the historical wind power data and photovoltaic data, a wind-solar power deviation probability distribution model is constructed. This model indicates the wind power forecast deviation data corresponding to the wind power forecast data and the photovoltaic forecast deviation data corresponding to the photovoltaic forecast data. The sum of the wind power forecast data, photovoltaic forecast data, wind power forecast deviation data, and photovoltaic forecast deviation data is determined as the target random variable. An optimization scheduling model is constructed based on the target random variable. The optimization scheduling model includes an objective function, decision variables, and constraints. The objective function indicates the optimization objective of the optimization scheduling model, the decision variables include the output of the hydropower units, and the constraints constrain the decision variables. An optimization algorithm is used to solve the decision variables in the optimization scheduling model. Thus, the uncertainty of wind power and photovoltaic output forecasts can be accurately described based on the wind-solar-hydro power deviation probability distribution model, thereby ensuring the reliability of the scheduling process and effectively improving the scheduling effect of wind-solar-hydro combined power generation.

[0184] Figure 5 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Figure 5 The computer device 12 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0185] like Figure 5 As shown, the computer device 12 is represented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0186] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0187] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0188] Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 5 Not shown; usually referred to as a "hard drive".

[0189] although Figure 5 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0190] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0191] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0192] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the optimized scheduling method for combined wind, solar and hydropower generation mentioned in the foregoing embodiments.

[0193] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the optimized scheduling method for combined wind, solar and hydropower generation as proposed in the foregoing embodiments of this disclosure.

[0194] To implement the above embodiments, this disclosure also proposes a computer program product, which, when executed by an instruction processor, performs an optimized scheduling method for combined wind, solar and hydropower generation as proposed in the foregoing embodiments of this disclosure.

[0195] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0196] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

[0197] It should be noted that in the description of this disclosure, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0198] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0199] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0200] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0201] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0202] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0203] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0204] Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. An optimized scheduling method for combined wind, solar, and hydropower generation, characterized in that, include: Obtain historical wind power data, wind power forecast data, historical photovoltaic data, and photovoltaic forecast data for wind-solar-hydro complementary energy bases; Based on the historical wind power data and the historical photovoltaic data, a first probabilistic statistical characteristic of the wind power prediction deviation and a second probabilistic statistical characteristic of the photovoltaic power prediction deviation are determined respectively. A wind and solar power deviation probability distribution model is constructed based on the first and second probabilistic statistical characteristics. The wind and solar power deviation probability distribution model is a generalized logical distribution model, which is used to indicate the wind power prediction deviation data corresponding to the wind power prediction data and the photovoltaic prediction deviation data corresponding to the photovoltaic prediction data. The sum of the wind power forecast data, the photovoltaic forecast data, the wind power forecast deviation data, and the photovoltaic forecast deviation data is determined as the target random variable; wherein, the sum of the wind power forecast deviation data and the photovoltaic forecast deviation data is determined by convolution operation; An optimization scheduling model is constructed based on the target random variable. The optimization scheduling model includes an objective function, decision variables, and constraints. The objective function indicates the optimization objective of the optimization scheduling model. The objective function includes a first objective function and a second objective function. The first objective function indicates maximizing the total power generation of wind, solar, and hydropower. The second objective function indicates minimizing the expected load shedding and curtailment of wind and solar power at the wind-solar-hydro complementary energy base. The decision variable includes the output of hydropower units. The constraints are used to constrain the decision variables. The constraints include conventional constraints, insufficient flexibility probability constraints, and extreme failure probability constraints. The decision variables in the optimized scheduling model are solved using an optimization algorithm.

2. The method as described in claim 1, characterized in that, in, The historical wind power data includes historical wind power forecast data and historical wind power actual data. The historical wind power forecast data includes historical forecast wind power resource data and historical forecast wind power corresponding to the forecast wind power resource data. The historical wind power actual data includes historical actual wind power resource data corresponding to the historical forecast wind power resource data and historical actual wind power corresponding to the historical forecast wind power. The photovoltaic historical data includes photovoltaic historical forecast data and photovoltaic historical actual data. The photovoltaic historical forecast data includes historical forecast photovoltaic resource data and historical forecast photovoltaic power corresponding to the forecast photovoltaic resource data. The photovoltaic historical actual data includes historical actual photovoltaic resource data corresponding to the historical forecast photovoltaic resource data and historical actual photovoltaic power corresponding to the historical forecast photovoltaic power.

3. The method as described in claim 2, characterized in that, The step involves determining a first probabilistic statistical characteristic of wind power prediction deviation and a second probabilistic statistical characteristic of photovoltaic power prediction deviation based on the historical wind power data and the historical photovoltaic data, respectively, and constructing a wind and solar power deviation probability distribution model based on the first and second probabilistic statistical characteristics, including: Based on the multiple historical predicted wind power and the historical actual wind power corresponding to each historical predicted wind power, a first probabilistic statistical characteristic of the wind power prediction deviation is determined. A second probabilistic statistical characteristic of photovoltaic power prediction deviation is determined based on multiple historical predicted photovoltaic power and the historical actual photovoltaic power corresponding to each historical predicted photovoltaic power. Based on the first probability statistical characteristic and the second probability statistical characteristic, a generalized logical distribution model of wind and solar power deviation is constructed as the probability distribution model of wind and solar power deviation.

4. The method as described in claim 1, characterized in that, in, The wind power forecast data includes wind power forecasts for multiple preset time intervals; The photovoltaic forecast data includes photovoltaic forecast power for multiple preset time intervals.

5. The method as described in claim 1, characterized in that, The construction of the optimized scheduling model based on the target random variable includes: Obtain the operating parameters of the wind-solar-hydro complementary energy base, wherein the operating parameters include the power generation capacity and upper and lower limits of wind power, photovoltaic power and hydropower respectively; Obtain the power generation attribute information of the hydropower station in the wind-solar-hydro complementary energy base; The optimized scheduling model is constructed based on the installed power generation capacity, the upper and lower limits of power output, the power generation attribute information, and the target random variable.

6. The method as described in claim 1, characterized in that, in, The insufficient flexibility probability constraint includes both increasing the insufficient flexibility probability constraint and decreasing the insufficient flexibility probability constraint. The extreme failure probability constraint includes the extreme load loss probability constraint and the extreme power curtailment probability constraint.

7. The method as described in claim 1, characterized in that, Before solving for the decision variables in the optimized scheduling model using the optimization algorithm, the method further includes: Determine the operational characteristics of each objective function and constraint condition in the optimized scheduling model; Based on the operational characteristics, an object to be processed is determined from multiple objective functions and constraints, wherein the object to be processed includes integration operations; The object to be processed is processed based on numerical integration to obtain an updated optimized scheduling model; The step of solving for the decision variables in the optimization scheduling model using an optimization algorithm includes: The optimization algorithm is used to solve for the decision variables in the updated optimized scheduling model.

8. An optimized scheduling device for combined wind, solar, and hydropower generation, characterized in that, include: The acquisition module is used to acquire historical wind power data, wind power forecast data, historical photovoltaic data, and photovoltaic forecast data of the wind-solar-hydro complementary energy base. The first model construction module is used to determine a first probabilistic statistical characteristic of wind power prediction deviation and a second probabilistic statistical characteristic of photovoltaic power prediction deviation based on the historical wind power data and the historical photovoltaic data, respectively, and to construct a wind and solar power deviation probability distribution model based on the first and second probabilistic statistical characteristics; wherein, the wind and solar power deviation probability distribution model is a generalized logical distribution model, and the wind and solar power deviation probability distribution model is used to indicate the wind power prediction deviation data corresponding to the wind power prediction data and the photovoltaic prediction deviation data corresponding to the photovoltaic prediction data; The determination module is used to determine the sum of the wind power prediction data, the photovoltaic prediction data, the wind power prediction deviation data, and the photovoltaic prediction deviation data as a target random variable; wherein, the sum of the wind power prediction deviation data and the photovoltaic prediction deviation data is determined by convolution operation; The second model construction module is used to construct an optimization scheduling model based on the target random variable. The optimization scheduling model includes an objective function, decision variables, and constraints. The objective function indicates the optimization objective of the optimization scheduling model. The objective function includes a first objective function and a second objective function. The first objective function indicates maximizing the total power generation of wind, solar, and hydropower. The second objective function indicates minimizing the expected load shedding and curtailment of wind and solar power at the wind-solar-hydro complementary energy base. The decision variable includes the output of hydropower units. The constraints are used to constrain the decision variables. The constraints include conventional constraints, insufficient flexibility probability constraints, and extreme failure probability constraints. The processing module is used to solve the decision variables in the optimization scheduling model using an optimization algorithm.

9. A computer device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-7.

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