Water-wind-solar complementary energy scheduling method, device, equipment and storage medium

Abstract

The invention relates to a water-wind-solar complementary energy scheduling method, device and equipment and a storage medium. The method comprises the following steps: generating a scene tree according to historical water, wind and light resource data, and determining a probability corresponding to each scene; a multi-energy complementary stochastic optimization scheduling model is determined based on a multi-stage stochastic programming method, the stochastic optimization scheduling model takes the maximum total operation benefit as an objective function, and the total operation benefit is determined according to the probability of each scene, the energy storage at the end of the scheduling period of the hydropower station, and the average output and abandoned water energy of the water-wind-light complementary power generation system; based on an augmented Lagrange method and a diagonal quadratic approximation method, the target function is converted into a plurality of sub-target functions, and one sub-target function corresponds to a multi-stage planning problem of one scene; and solving each sub-objective function according to an optimization algorithm to obtain a target scheduling strategy. By adopting the method, the wind-solar complementary optimization scheduling strategy under the uncertain condition can be effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of energy scheduling, and particularly to a water-wind-solar complementary energy scheduling method, device, equipment, and storage medium. Background Art

[0002] Water-wind-solar energy has certain output complementary characteristics. Relying on the regulation capabilities and transmission channels of existing and incremental large and medium-sized hydropower stations and pumped storage power stations, it has the foundation and advantages for the "integration of water, wind, and solar" development and application. However, the water-wind-solar power generation system, that is, an energy system combining hydropower, wind power, and solar photovoltaic power generation, is extremely closely related to meteorological conditions, and due to its dependence on the variability of natural conditions, it exhibits extremely strong uncertainty. Therefore, conducting research on the optimal scheduling of basin water-wind-solar complementarity and exploring effective ways to achieve the allocation and scheduling of water-wind-solar integrated resources under uncertain conditions are beneficial to improving the comprehensive development economy of renewable energy and the utilization rate of transmission channels, and realizing the efficient development and utilization of clean energy.

[0003] Therefore, the optimal scheduling of basin water-wind-solar complementarity under uncertain conditions is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a water-wind-solar complementary energy scheduling method, device, equipment, and storage medium that can accurately determine the optimal scheduling strategy of basin water-wind-solar complementarity under uncertain conditions.

[0005] In a first aspect, the present application provides a water-wind-solar complementary energy scheduling method, including:

[0006] Generating a scenario tree based on historical water-wind-solar resource data and determining the probability corresponding to each scenario;

[0007] Determining a multi-energy complementary stochastic optimization scheduling model based on the multi-stage stochastic programming method. The stochastic optimization scheduling model takes the maximum total operating benefit as the objective function, and the total operating benefit is determined according to the probability of each scenario, the end-of-period storage energy of the hydropower station, the average output of the water-wind-solar complementary power generation system, and the abandoned water energy;

[0008] Solving the objective function based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the target scheduling strategy.

[0009] In one embodiment, the constraint conditions of the multi-energy complementary stochastic optimization scheduling model include: the piecewise fitting power generation function of the hydropower station, the water balance equation, the flow relationship, the upper and lower limits of the power output of the power station, the upper and lower limits of the reservoir storage capacity, the upper and lower limits of the downstream discharge flow, and the unexpected constraint.

[0010] In one embodiment, solving the objective function based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the target scheduling strategy includes:

[0011] Relax the unexpected constraints according to the augmented Lagrangian method to add a penalty function to the objective function, where the penalty function includes a quadratic term; approximate the quadratic term of the penalty function according to the diagonal quadratic approximation method, and convert the objective function into multiple sub-objective functions, where one sub-objective function corresponds to the multi-stage planning problem of one scenario; solve each sub-objective function according to the optimization algorithm to obtain the target scheduling strategy.

[0012] In one embodiment, generating a scenario tree according to historical water, wind, and light resource data includes:

[0013] Obtain a historical water, wind, and light resource sequence, where the historical water, wind, and light resource sequence includes multiple groups of historical water, wind, and light resource data; divide each historical water, wind, and light resource data into multiple stages according to a preset time period, and each stage corresponds to one layer in the scenario tree; determine the scenario tree according to the historical water, wind, and light resource data in each stage, and the path from the leaf node to the root node in the scenario tree represents a complete scenario sequence.

[0014] In one embodiment, determining the scenario tree according to the historical water, wind, and light resource data in each stage includes:

[0015] Obtain a preset scenario tree structure, randomly assign values from the historical water, wind, and light resource data in each stage to each node in the preset scenario tree structure to determine an initial scenario tree; iteratively update the initial scenario tree based on a preset update strategy until a preset iteration condition is met, and determine the scenario tree.

[0016] In one embodiment, determining the probability corresponding to each scenario includes:

[0017] Calculate the distances between each scenario sequence in the scenario tree and each historical water, wind, and light resource data to determine a distance matrix, where the element in the i-th row and j-th column of the distance matrix represents the distance between the i-th scenario sequence and the j-th group of historical water, wind, and light resource data; for the i-th scenario, use the minimum element value in the i-th row of the distance matrix as the scenario distance value of the i-th scenario; for each scenario, determine the probability corresponding to the scenario according to the scenario distance value.

[0018] In one embodiment, determining the probability corresponding to the scenario according to the scenario distance value includes:

[0019] Determine a target scenario distance value according to the sum of the scenario distance value and a preset constant; perform normalization processing on the reciprocal of the target scenario distance value to determine the probability corresponding to the scenario.

[0020] In a second aspect, the present application also provides a water, wind, and light complementary energy scheduling device, including:

[0021] The first generation module is used to generate a scenario tree based on historical water, wind and solar resource data and determine the probability corresponding to each scenario;

[0022] The second generation module is used to determine a multi - energy complementary stochastic optimization scheduling model based on the multi - stage stochastic programming method. The stochastic optimization scheduling model takes the maximum total operating benefit as the objective function, and the total operating benefit is determined according to the probabilities of each scenario, the end - of - period energy storage of the hydropower station, the average output of the water - wind - solar complementary power generation system, and the wasted water energy;

[0023] The determination module is used to solve the objective function based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the target scheduling strategy.

[0024] In one embodiment, the constraint conditions of the multi - energy complementary stochastic optimization scheduling model include: the piece - wise fitting power generation function of the hydropower station, the water balance equation, the flow relationship, the upper and lower limits of the power output of the power station, the upper and lower limits of the reservoir storage capacity, the upper and lower limits of the downstream discharge flow, and the unexpected constraint.

[0025] In one embodiment, the determination module is specifically used to relax the unexpected constraint according to the augmented Lagrangian method to add a penalty function to the objective function, and the penalty function includes a quadratic term; approximate the quadratic term of the penalty function according to the diagonal quadratic approximation method and convert the objective function into multiple sub - objective functions, where one sub - objective function corresponds to the multi - stage planning problem of one scenario; solve each sub - objective function according to the optimization algorithm to obtain the target scheduling strategy.

[0026] In one embodiment, the first generation module is specifically used to obtain a historical water, wind and solar resource sequence, and the historical water, wind and solar resource sequence includes multiple groups of historical water, wind and solar resource data; divide each historical water, wind and solar resource data into multiple stages according to a preset time period, and each stage corresponds to one layer in the scenario tree; determine the scenario tree according to the historical water, wind and solar resource data in each stage, and the path from the leaf node to the root node in the scenario tree represents a complete scenario sequence.

[0027] In one embodiment, the first generation module is specifically used to obtain a preset scenario tree structure, randomly assign values to each node in the preset scenario tree structure from the historical water, wind and solar resource data of each stage to determine an initial scenario tree; iteratively update the initial scenario tree based on a preset update strategy until the preset iteration condition is met, and then determine the scenario tree.

[0028] In one embodiment, the first generation module is specifically configured to calculate the distances between each scenario sequence in the scenario tree and each piece of historical water-wind-solar resource data, determine a distance matrix, where the element in the i-th row and j-th column of the distance matrix represents the distance between the i-th scenario sequence and the j-th set of historical water-wind-solar resource data; for the i-th scenario, use the minimum element value in the i-th row of the distance matrix as the scenario distance value of the i-th scenario; for each scenario, determine the probability corresponding to the scenario according to the scenario distance value.

[0029] In one embodiment, the first generation module is specifically configured to determine a target scenario distance value based on the sum of the scenario distance value and a preset constant; perform a normalization process on the reciprocal of the target scenario distance value to determine the probability corresponding to the scenario.

[0030] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the method described in any one of the first aspects above is implemented.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described in any one of the first aspects above is implemented.

[0032] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method described in any one of the first aspects above is implemented.

[0033] The above water-wind-solar complementary energy scheduling method, device, equipment, and storage medium can generate a scenario tree according to historical water-wind-solar resource data and determine the probability corresponding to each scenario; determine a multi-energy complementary stochastic optimization scheduling model based on the multi-stage stochastic programming method, where the stochastic optimization scheduling model takes the maximum total operating benefit as the objective function, and the total operating benefit is determined according to the probabilities of each scenario, the end-of-period storage energy of the hydropower station, the average output of the water-wind-solar complementary power generation system, and the discarded water energy; solve the objective function based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the target scheduling strategy. In this way, when determining the optimization scheduling model, the multi-stage stochastic programming method is used to reasonably consider different scenarios and the uncertainty of each scenario, and at the same time, the augmented Lagrangian method and the diagonal quadratic approximation method are used to convert the objective function in the optimization scheduling model into multiple sub-objective functions for separate solution, avoiding the correlation of the original objective function in solving the scheduling strategies under different scenarios, effectively solving the water-wind-solar complementary optimization scheduling strategy under uncertain conditions, and accelerating the solution speed of the water-wind-solar complementary stochastic optimization model under uncertain conditions, being able to better guide the operation of the water-wind-solar power generation system, helping to formulate effective scheduling strategies and emergency plans, improving the adaptability and response speed of the system, and thus ensuring a continuous and stable power supply. Brief Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0035] Figure 1 It is a schematic flowchart of the water-wind-solar complementary energy scheduling method in one embodiment;

[0036] Figure 2 It is a schematic flowchart of the steps for solving the objective function in one embodiment;

[0037] Figure 3 It is a schematic flowchart of the steps for generating the scenario tree in one embodiment;

[0038] Figure 4 It is a schematic flowchart of the steps for determining the scenario tree according to the historical water-wind-solar resource data in each stage in one embodiment;

[0039] Figure 5 It is a schematic diagram of the preset scenario tree structure in one embodiment;

[0040] Figure 6 It is a schematic flowchart of the steps for determining the probability corresponding to each scenario in one embodiment;

[0041] Figure 7 It is a schematic flowchart of the steps for determining the probability corresponding to each scenario in another embodiment;

[0042] Figure 8 It is a schematic flowchart of the water-wind-solar complementary energy scheduling method in another embodiment;

[0043] Figure 9 It is a schematic diagram of the change curve of the maximum error of the unexpected constraint with the number of iterations in one embodiment;

[0044] Figure 10 It is a structural block diagram of the water-wind-solar complementary energy scheduling device in one embodiment;

[0045] Figure 11 It is an internal structure diagram of a computer device in one embodiment; Detailed Embodiments

[0046] In order to make the objectives, technical solutions and advantages of the present application clearer, the following further describes the present application in detail with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] The basis for the integration of renewable energy in river basins is the mutual complementarity of the characteristics of power sources. Under the new situation of building a new power system, there has been a consensus that the functional orientation of hydropower has gradually shifted from mainly focusing on electricity generation to equally emphasizing electricity generation and capacity support. The output of wind power and photovoltaic power generation is highly random and cannot provide reliable capacity support for the power system. New energy has gradually shifted from being an incremental supplement to the main body of energy and power consumption. On the other hand, the development costs of wind power and photovoltaic power generation have been continuously decreasing, and their low-cost advantages can suppress the on-grid electricity price of hydropower, achieving the maximization of comprehensive benefits. Under the new situation of building a new power system, large-scale, high proportion, market-oriented, and high-quality have become the new characteristics and requirements for the development of renewable energy. The main river basins in China, such as the Jinsha River, Lancang River, Yalong River, Dadu River, and Yellow River, are rich in hydropower resources and have a good development foundation. At the same time, new energy resources such as wind and light are also very rich, possessing natural advantages for the integrated development of water, wind, and light. The integrated development of renewable energy mainly based on water, wind, and light integrates and develops the rich hydropower resources in the river basin with the concentrated wind and light resources in its surrounding areas in space. Through integrated and large-scale development, it realizes complementary advantages, improves the consumption and storage capacity of renewable energy, and provides almost 100% clean electricity for the power grid. It is the only way for the high-quality leapfrog development of renewable energy in the new era.

[0048] River basins in China, such as the Jinsha River, Yalong River, Dadu River, Wujiang River, Lancang River, and Yarlung Zangbo River, are rich in hydropower resources, as well as suitable wind and solar energy resources. The water, wind, and light energy have certain complementary output characteristics. Relying on the regulation capabilities and transmission channels of existing and incremental large and medium-sized hydropower stations and pumped storage power stations, they have the foundation and advantages for the development and application of "integration of water, wind, and light". However, the water, wind, and light power generation system, that is, the energy system combining hydropower generation, wind power generation, and solar photovoltaic power generation, is extremely closely related to meteorological conditions. And due to its dependence on the variability of natural conditions, it shows extremely strong uncertainty. Therefore, conducting research on the complementary optimal scheduling of water, wind, and light in river basins and exploring effective ways to achieve the integrated resource allocation and scheduling of water, wind, and light under uncertain conditions is conducive to improving the economic efficiency of the comprehensive development of renewable energy and the utilization rate of transmission channels, enhancing the development scale, competitiveness, and development quality of water, wind, and light integration, accelerating the process of large-scale and high-proportion development of renewable energy, and realizing the efficient development and utilization of clean energy.

[0049] Therefore, the complementary optimal scheduling of water, wind, and light in river basins under uncertain conditions is a technical problem that urgently needs to be solved.

[0050] In view of this, the present application provides a water-wind-solar complementary energy scheduling method that can effectively solve the optimal scheduling strategy of water-wind-solar complementary under uncertain conditions. The water-wind-solar complementary energy scheduling method provided by the embodiments of the present application may have an execution subject which is a water-wind-solar complementary energy scheduling device. The water-wind-solar complementary energy scheduling device can be implemented by software, hardware, or a combination of software and hardware. It can be embedded in the processor of a computer device in hardware form or be independent of it, or stored in the memory of a computer device in software form. In the following method embodiments, the execution subject is taken as a computer device for illustration. The computer device can be a server or a desktop computer. The embodiments of the present application do not limit the specific type of the computer device.

[0051] In an exemplary embodiment, as Figure 1 shown, a water-wind-solar complementary energy scheduling method is provided, including the following steps 101 to 103. Among them:

[0052] Step 101, generate a scenario tree according to historical water-wind-solar resource data and determine the probability corresponding to each scenario.

[0053] Optionally, the scenario tree can be a method based on a tree structure to describe and model the possible states and their evolution processes of various random variables in the water-wind-solar energy system at different time periods, presenting the uncertainties faced in water-wind-solar energy scheduling in an intuitive and systematic manner.

[0054] Optionally, the components of the scenario tree may include nodes and branches. The nodes include root nodes, internal nodes, and leaf nodes. The root node usually represents the initial moment of the scheduling period and contains various information about the system in the initial state, such as the initial water level of the reservoir, the initial operating state of the wind and solar power plants, etc. The internal nodes represent the intermediate moments and states during the scheduling process. Each internal node corresponds to a specific time period and a certain combination of values of random variables within that time period. For example, at a certain internal node, it may represent the state at the t-th time period where the wind power output is within a certain range, the photovoltaic power output is a certain value, and the reservoir inflow is a specific value. The leaf nodes can correspond to various possible final states at the end of the scheduling period and contain all relevant information about the system at the end of the entire scheduling process, such as the final water level of the reservoir, the wind and solar power generation, and the total system power generation. The branches are used to connect different nodes and can represent the process of transitioning from one state in a time period to another state in the next time period.

[0055] Optionally, the historical water, wind, and solar resource data may include historical water resource data, historical wind energy resource data, and historical solar energy resource data. The historical water resource data may include hydrometeorological data, runoff data, and water resource volume data over the years. For example, precipitation data, evaporation data, river runoff data, surface runoff, and groundwater runoff data, etc. The historical wind energy resource data may include wind speed data, wind direction data, and wind power density data over the years, etc. The historical solar energy resource data may include solar radiation data, sunshine duration data, and solar altitude angle and azimuth angle data over the years, etc.

[0056] In a possible implementation manner, generating a scenario tree based on the historical water, wind, and solar resource data may be achieved by dividing the entire time range into several time periods according to a preset time span, and then discretizing the random variables according to a preset discretization method in each time period. Among them, the preset discretization method may be the equal-probability interval method. Finally, starting from the initial stage, the discretization results of each time period are used as nodes and connected by branches to form a scenario tree.

[0057] Exemplarily, there are 3 discrete states in the first time period , in the second time period, state may have 2 subsequent states , state may have 3 subsequent states etc., and so on, gradually constructing a complete scenario tree.

[0058] In a possible implementation manner, when determining the probability of each scenario in the scenario tree, it can be calculated according to the discretization method of the random variable and the original distribution. For example, in the equal-probability interval method, the probability of each discrete interval is equal, and by calculating the product of the probabilities of the discrete intervals passed by each scenario, the probability of that scenario is obtained.

[0059] Exemplarily, in a two-time-period scenario tree, the probability of the discrete state in the first time period is , and the probability from to in the second time period is , then the probability of scenario is .

[0060] Step 102, determining a multi-energy complementary stochastic optimization scheduling model based on the multi-stage stochastic programming method.

[0061] Among them, the stochastic optimization scheduling model takes the maximum total operating benefit as the objective function, and the total operating benefit is determined according to the probability of each scenario, the end-of-period storage energy of the hydropower station, the average output of the water-wind-solar complementary power generation system, and the wasted water energy.

[0062] Optionally, the problem of complementary scheduling of water, wind, and solar energy can be divided into multiple time stages, and each stage faces different decision-making and uncertainty factors. For example, taking time intervals such as hours, days, or months, the scheduling period is divided into multiple stages. In each stage, decision variables such as the output of hydropower units, wind turbines, and photovoltaic units need to be determined based on information such as water conditions, wind conditions, light conditions, and load demands at that time.

[0063] Optionally, when determining the multi-energy complementary stochastic optimization scheduling model, factors such as the randomness of the output of water, wind, and solar energy and the uncertainty of load demand can be considered, and these uncertain factors can be described by introducing random variables.

[0064] Optionally, the expression of the objective function of the multi-energy complementary stochastic optimization scheduling model can be as follows:

[0065]

[0066] where S is the number of scenarios, M is the number of months, N is the number of hydropower stations, is the probability of scenario s, is the average output of the water, wind, and solar complementary power generation system, is the number of hours in a time period, is the end-of-period energy storage of the hydropower station, is the wasted water energy, are different benefit coefficients.

[0067] It should be noted that in the average output of the water, wind, and solar complementary power generation system, the power generation output of the hydropower station is mainly analyzed, and the power generation output of wind and solar can be preset.

[0068] Optionally, the constraint conditions of the multi-energy complementary stochastic optimization scheduling model include: the piecewise fitting power generation function of the hydropower station, the water balance equation, the flow relationship, the upper and lower limits of the power station output, the upper and lower limits of the reservoir storage capacity, the upper and lower limits of the downstream discharge flow, and the unexpected constraints.

[0069] Optionally, the expression of the piecewise fitting power generation function of the hydropower station is as follows:

[0070]

[0071] where, represents the power generation function, , where H is the total number of segments of the power generation function, represents the coefficient of the power generation function, which is obtained by fitting historical data, respectively represent the reservoir storage capacities of the upstream and downstream, represents the power generation flow. It can be understood that the superscript i in the above expression represents the i-th scenario, and the subscript n represents the n-th hydropower station. The same will not be elaborated later.

[0072] Optionally, the expression of the water balance equation can be as follows:

[0073]

[0074] Wherein, represents the reservoir storage at time t, represents the reservoir storage at time t-1, Mn represents the total number of upstream reservoirs, represents the downstream discharge of the upstream reservoir, represents the reservoir runoff inflow.

[0075] Optionally, the expression of the flow relationship is as follows:

[0076]

[0077] Wherein, represents the reservoir downstream discharge, represents the spillage flow.

[0078] Optionally, the upper and lower limits of the power station output can be expressed by the following formula: , wherein, and represent the minimum and maximum outputs respectively.

[0079] Optionally, the upper and lower limits of the reservoir storage can be expressed by the following formula: , wherein, and represent the minimum and maximum reservoir storages respectively.

[0080] Optionally, the upper and lower limits of the downstream discharge can be expressed by the following formula: , wherein, and represent the minimum and maximum downstream discharges respectively.

[0081] Optionally, the non-expected constraint can be expressed by the following formula: , wherein, represents the decision vector corresponding to scenario i in the scenario tree, represents the decision vector corresponding to scenario in the scenario tree, scenarios i and belong to the set of scenarios of the shared node , and SH(t) is the maximum number of periods with shared same scenario tree nodes during the scheduling period.

[0082] Step 103, solve the objective function based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the objective scheduling strategy.

[0083] Optionally, the augmented Lagrangian method can be used to solve the constrained optimization problem, which combines the ideas of the Lagrange multiplier method and the penalty function method. By introducing Lagrange multipliers and penalty terms, an augmented Lagrangian function is constructed based on the objective function.

[0084] Optionally, the augmented Lagrangian function can be quadratically approximated based on the diagonal quadratic approximation method, converting the augmented Lagrangian function into multiple sub-objective functions, and then solving the multiple sub-objective functions separately to obtain the target scheduling strategy.

[0085] The above water-wind-solar complementary energy scheduling method can generate a scenario tree based on historical water-wind-solar resource data and determine the probability corresponding to each scenario; determine a multi-energy complementary stochastic optimization scheduling model based on the multi-stage stochastic programming method, where the stochastic optimization scheduling model takes the maximum total operating benefit as the objective function, and the total operating benefit is determined according to the probability of each scenario, the end-of-period storage energy of the hydropower station, the average output of the water-wind-solar complementary power generation system, and the abandoned water energy; solve the objective function based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the target scheduling strategy. In this way, when determining the optimization scheduling model, the multi-stage stochastic programming method reasonably considers different scenarios and the uncertainties of each scenario. At the same time, through the augmented Lagrangian method and the diagonal quadratic approximation method, the objective function in the optimization scheduling model is converted into multiple sub-objective functions for separate solution, avoiding the correlation of the original objective function in solving the scheduling strategies under different scenarios, effectively solving the water-wind-solar complementary optimization scheduling strategy under uncertain conditions, and accelerating the solution speed of the water-wind-solar complementary stochastic optimization model under uncertain conditions, being able to better guide the operation of the water-wind-solar power generation system, contributing to formulating effective scheduling strategies and emergency plans, improving the adaptability and response speed of the system, and thus ensuring continuous and stable power supply.

[0086] In an exemplary embodiment, as Figure 2 shown, optionally, solving the objective function based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the target scheduling strategy includes the following steps 201 to 203. Among them:

[0087] Step 201, relax the non-expected constraints according to the augmented Lagrangian method to add a penalty function to the objective function.

[0088] Among them, the penalty function includes a quadratic term.

[0089] Optionally, the non-expected constraints make the decisions in different scenarios interrelated and inseparable. The general representation of the objective function in step 102 can be as follows:

[0090]

[0091] Among them, Represents the probability of the i-th scenario, Represents the revenue function of the hydropower, wind power and photovoltaic power generation system.

[0092] The constraints in step 102 are generally expressed as follows:

[0093]

[0094]

[0095] Among them, Represents the physical constraint under the condition of scenario i, I represents the total number of scenarios, Is the decision vector corresponding to scenario i, Represents the scenario in the scenario tree Corresponding decision vector, scenario i and scenario Belong to the scenario set of the shared node.

[0096] Optionally, the augmented Lagrangian method is used to relax the non-anticipativity constraint and punish the violation of the constraint. The violation of the non-anticipativity constraint is punished in the objective function to obtain the augmented Lagrangian objective function, as follows:

[0097]

[0098] Among them, Represents Corresponding Lagrangian multiplier vector; And Represents the decision vector; Represents the vector And the vector Inner product of; Represents the vector And Euclidean distance of, Represents the penalty parameter; if the original model has a solution, the augmented Lagrangian method is finitely convergent.

[0099] Step 202, approximate the quadratic term of the penalty function according to the diagonal quadratic approximation method and convert the objective function into multiple sub-objective functions.

[0100] Among them, one sub-objective function corresponds to the multi-stage planning problem of one scenario.

[0101] Optionally, according to the diagonal quadratic approximation method, the quadratic term Of the penalty function can be expanded and approximated as:

[0102]

[0103] Optionally, the local approximation of the inner product can be expressed as:

[0104]

[0105] Among them, and represent the estimated value of the decision vector and The approximation method assumes that the decision vector belongs to the neighborhood of the reference point Substituting the inner product approximation into the quadratic term gives:

[0106]

[0107] Among them, represents the scenario with the same past and current states as scenario i corresponding decision, that is, the decision sequence of scenario i and scenario before time t is the same.

[0108] Optionally, the augmented Lagrangian objective function can be written as:

[0109]

[0110] Among them, represents corresponding Lagrange multiplier vector.

[0111] Optionally, the above augmented Lagrangian objective function can be decomposed into sub-objective functions, and the sub-objective functions are represented as follows:

[0112]

[0113] Step 203, solve each sub-objective function according to the optimization algorithm to obtain the target scheduling strategy.

[0114] Optionally, each sub-objective function can be solved separately according to the optimization algorithm to obtain the target scheduling strategy under each scenario. The optimization algorithm can be the gradient descent method, conjugate gradient method, genetic algorithm, particle swarm optimization algorithm, ant colony algorithm, etc., and the embodiments of the present application do not limit this.

[0115] The above relaxes the unexpected constraints according to the augmented Lagrangian method to add a penalty function to the objective function, where the penalty function includes a quadratic term; approximates the quadratic term of the penalty function according to the diagonal quadratic approximation method, and converts the objective function into multiple sub-objective functions, where one sub-objective function corresponds to the multi-stage planning problem of one scenario; solves each sub-objective function according to the optimization algorithm to obtain the target scheduling strategy, which can convert the objective function into multiple independent sub-objective functions, avoid the correlation of the original objective function in solving the scheduling strategies under different scenarios, and improve the solution speed of the water-wind-solar complementary stochastic optimization model under uncertain conditions.

[0116] In an exemplary embodiment, as Figure 3 shown, optionally, generating a scenario tree according to historical water-wind-solar resource data includes the following steps 301 to 303. Among them:

[0117] Step 301, obtain the historical water-wind-solar resource sequence.

[0118] Among them, the historical water-wind-solar resource sequence includes multiple groups of historical water-wind-solar resource data.

[0119] Optionally, there is no limitation on the method of obtaining the historical water-wind-solar resource sequence, which can be directly obtained from a database, or obtained from departments such as meteorological departments, water conservancy departments, and hydrological monitoring stations.

[0120] Optionally, the historical water-wind-solar resource sequence is expressed as , , where is the number of historical sequences. For example, when is 20,

[0121] can represent the water-wind-solar resource data in the past 1 to 20 years.

[0122] Optionally, consists of , , where

[0123] represents the number of time series periods. For example, when T is 12, each historical water-wind-solar resource data can be divided into 12 months, that is, the number of periods within the scheduling cycle.

[0124] Step 303, determine the scenario tree according to each historical water-wind-solar resource data in each stage.

[0125] Among them, the path from the leaf node to the root node in the scenario tree represents a complete scenario sequence.In a possible implementation, the historical hydropower, wind, and solar resource data for each stage can be clustered according to a preset clustering algorithm, and each cluster represents a node; then, the scenario tree can be determined based on the clustering results for each stage.

[0126] In another possible implementation, as Figure 4 shown, optionally, determining the scenario tree based on the historical hydropower, wind, and solar resource data for each stage includes the following steps 401 to 402, where:

[0127] Step 401: Obtain a preset scenario tree structure, randomly assign values from the historical hydropower, wind, and solar resource data for each stage to each node in the preset scenario tree structure to determine the initial scenario tree.

[0128] Optionally, the hydropower, wind, and solar energy resource scenario is represented as , , where represents the total number of scenarios. Similarly, the scenario is composed of , , where represents the number of time series periods.

[0129] Optionally, as Figure 5 shown, a possible preset scenario tree structure is provided. Values can be randomly assigned from the historical hydropower, wind, and solar resource data for each stage to each node in the preset scenario tree structure, i.e., .

[0130] Optionally, is in vector form and can include , where N represents the total number of energy resources. Similarly, is also in vector form .

[0131] Optionally, as Figure 5 shown, it can be understood that at t = 1, the values of the root nodes in the scenario tree , ,..., are all the same. At t = 2, and ,..., are the same, and ,..., are the same.

[0132] Step 402: Iteratively update the initial scenario tree based on a preset update strategy until the preset iteration condition is met, and then determine the scenario tree.

[0133] Optionally, the preset update strategy may include a greedy algorithm or a genetic algorithm. The target scenario is determined according to the greedy algorithm or the genetic algorithm, and the node in the scenario tree is replaced according to the target scenario. The target scenario may be the scenario closest to the historical water-wind-light resource sequence. The embodiments of the present application do not limit the process of determining the target scenario according to the preset update strategy.

[0134] Optionally, the distance between the scenario sequence and the historical water-wind-light resource sequence may be represented by the Euclidean distance or the Manhattan distance. The embodiments of the present application do not limit this.

[0135] Exemplarily, calculating the distance between the scenario sequence and the historical water-wind-light resource sequence through the Euclidean distance can be represented by the following formula:

[0136]

[0137] where represents the preset time period importance coefficient.

[0138] Optionally, the deviation between the scenario sequence and the historical water-wind-light resource sequence can be represented by the following formula:

[0139]

[0140]

[0141]

[0142]

[0143] where is an array determined after sorting according to the distance between the scenario sequence and the historical water-wind-light resource sequence, represents the historical water-wind-light resource sequence, represents the number of iterations. As the number of iterations gradually increases, the deviation between the scenario sequence and the historical water-wind-light resource sequence gradually decreases. It can be seen that as the number of iterations ranges from 0 to , the deviation gradually shrinks, and the value of the scenario tree gradually converges to the historical water-wind-light resource sequence. Exemplarily, the parameter values of the above formula are respectively .

[0144] Optionally, the preset iteration condition includes that the number of iterations reaches a preset number, or the average distance between all scenario sequences in the scenario tree and the historical actual sequence is less than a set threshold.

[0145] The above-mentioned acquisition of the historical water, wind, and light resource sequence, where the historical water, wind, and light resource sequence includes multiple groups of historical water, wind, and light resource data; each historical water, wind, and light resource data is divided into multiple stages according to a preset time period, and each stage corresponds to one layer in the scenario tree; the scenario tree is determined according to the historical water, wind, and light resource data in each stage. The path from the leaf node to the root node in the scenario tree represents a complete scenario sequence. In this way, a scenario tree approximating the actual historical water, wind, and light resource data can be obtained, and the uncertainty of water, wind, and light energy can be quantified through this scenario tree.

[0146] In an exemplary embodiment, as Figure 6 shown, optionally, determining the probability corresponding to each scenario includes the following steps 601 to step 602. Wherein:

[0147] Step 601, calculate the distance between each scenario sequence in the scenario tree and each historical water, wind, and light resource data, and determine the distance matrix.

[0148] Wherein, the element in the i-th row and j-th column of the distance matrix represents the distance between the i-th scenario sequence and the j-th group of historical water, wind, and light resource data.

[0149] Optionally, after determining the scenario tree, the distance matrix can be determined according to each scenario sequence in the scenario tree and each historical water, wind, and light resource data. The calculation method of the distance is the same as that in step 402 above, and this application embodiment will not elaborate on it here.

[0150] Step 602, for the i-th scenario, use the minimum element value in the i-th row of the distance matrix as the scenario distance value of the i-th scenario.

[0151] Optionally, the scenario distance value of the i-th scenario can be represented by the minimum distance between the scenario sequence and the historical water, wind, and light resource sequence, that is , that is, the minimum element value in the i-th row of the distance matrix is used as the scenario distance value of this scenario.

[0152] Step 603, for each scenario, determine the probability corresponding to the scenario according to the scenario distance value.

[0153] Optionally, when determining the probability of each scenario, the scenario distance value of this scenario can be determined first, and then the probability of this scenario can be determined according to this scenario distance value.

[0154] In a possible implementation manner, to calculate the probability of a scenario, normalization can be performed according to the reciprocal of the scenario distance value corresponding to this scenario, and the normalized result is used as the probability of this scenario.

[0155] In another possible implementation manner, optionally, as Figure 7As shown, determining the probability corresponding to the scenario according to the scenario distance value includes the following steps 701 to 702. Wherein:

[0156] Step 701, determine the target scenario distance value according to the sum of the scenario distance value and a preset constant.

[0157] Step 702, perform normalization processing on the reciprocal of the target scenario distance value to determine the probability corresponding to the scenario.

[0158] Optionally, the scenario distance value corresponding to the scenario can be added to the preset constant to determine the target scenario distance value, and then normalization processing is performed according to the reciprocal of the target scenario distance value to determine the probability corresponding to the scenario, which can be represented by the following formula:

[0159]

[0160] Wherein, A is the preset constant, represents the probability of the i-th scenario. Adding the scenario distance value to the preset constant can ensure that the probability of the "super scenario" very close to the historical sequence will not be too large.

[0161] The above calculates the distances between each scenario sequence in the scenario tree and each historical water-wind-light resource data, determines the distance matrix. For the i-th scenario, the minimum element value in the i-th row of the distance matrix is used as the scenario distance value of the i-th scenario. For each scenario, according to the scenario distance value, the probability corresponding to the scenario is determined. In this way, the similarity between the scenario and the historical situation can be intuitively considered, the scenarios with high matching degrees with the historical data can be highlighted, and the calculation process is relatively simple.

[0162] As an alternative implementation manner, as Figure 8 shown, the water-wind-light complementary energy scheduling method provided in the embodiment of the present application may include the following specific steps:

[0163] Step 801, obtain the historical water-wind-light resource sequence.

[0164] Wherein, the historical water-wind-light resource sequence includes multiple groups of historical water-wind-light resource data.

[0165] Step 802, divide each historical water-wind-light resource data into multiple stages according to a preset time period, and each stage corresponds to one layer in the scenario tree.

[0166] Step 803, obtain the preset scenario tree structure, randomly take values from the historical water-wind-light resource data of each stage to assign values to each node in the preset scenario tree structure, and determine the initial scenario tree;

[0167] Step 804, iteratively update the initial scenario tree based on a preset update strategy until the preset iteration condition is met, and determine the scenario tree.

[0168] Among them, the path from the leaf node to the root node in the scenario tree represents a complete scenario sequence.

[0169] Step 805: Calculate the distances between each scenario sequence in the scenario tree and each historical hydro-wind-solar resource data, and determine the distance matrix.

[0170] Among them, the element in the i-th row and j-th column of the distance matrix represents the distance between the i-th scenario sequence and the j-th group of historical hydro-wind-solar resource data;

[0171] Step 806: For the i-th scenario, use the minimum element value in the i-th row of the distance matrix as the scenario distance value of the i-th scenario.

[0172] Step 807: For each scenario, determine the target scenario distance value according to the sum of the scenario distance value and a preset constant.

[0173] Step 808: For each scenario, perform normalization processing on the reciprocal of the target scenario distance value to determine the probability corresponding to the scenario.

[0174] Step 809: Determine the multi-energy complementary stochastic optimization scheduling model based on the multi-stage stochastic programming method.

[0175] Among them, the stochastic optimization scheduling model takes the maximum total operating benefit as the objective function, and the total operating benefit is determined according to the probability of each scenario, the end-of-period storage energy of the hydropower station, the average output of the hydro-wind-solar complementary power generation system, and the wasted water energy; the constraint conditions of the multi-energy complementary stochastic optimization scheduling model include: the piecewise fitting power generation function of the hydropower station, the water balance equation, the flow relationship, the upper and lower limits of the power output of the power station, the upper and lower limits of the reservoir storage capacity, the upper and lower limits of the downstream discharge flow, and the unexpected constraint.

[0176] Step 810: Relax the unexpected constraint according to the augmented Lagrangian method to add a penalty function to the objective function.

[0177] Among them, the penalty function includes a quadratic term.

[0178] Step 811: Approximate the quadratic term of the penalty function according to the diagonal quadratic approximation method, and convert the objective function into multiple sub-objective functions.

[0179] Among them, one sub-objective function corresponds to the multi-stage planning problem of one scenario.

[0180] Step 812: Solve each sub-objective function according to the optimization algorithm to obtain the target scheduling strategy.

[0181] Exemplarily, taking a cascade hydropower station in a certain basin and the surrounding wind and solar power stations as an example, a hydro-wind-solar complementary stochastic optimization scheduling model is established, and the diagonal quadratic approximation algorithm is used to solve the model, and the penalty parameters are respectively set to , , corresponding to , the performance of the diagonal quadratic approximation algorithm is shown in Table 1, and the variation of the maximum error of the unexpected constraint with the number of iterations is as shown in Figure 9 , where 901 is the curve of change when the penalty parameter is , 902 is the curve of change when the penalty parameter is , and 903 is the curve of change when the penalty parameter is . It can be seen from the figure that as the number of iterations increases, the maximum error of the unexpected constraint decreases significantly. The method provided by the embodiments of the present application can effectively solve the optimal scheduling strategy of water-wind-solar complementary under uncertain conditions.

[0182] Table 1

[0183]

[0184] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps does not have a strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or alternately with at least a part of other steps or steps in other steps.

[0185] Based on the same inventive concept, the embodiments of the present application also provide a water-wind-solar complementary energy scheduling device for implementing the above-mentioned water-wind-solar complementary energy scheduling method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the water-wind-solar complementary energy scheduling device provided below can refer to the limitations on the water-wind-solar complementary energy scheduling method in the above text, and will not be repeated here.

[0186] In an exemplary embodiment, as shown in Figure 10 , a water-wind-solar complementary energy scheduling device 1000 is provided, including: a first generation module 1001, a second generation module 1002, and a determination module 1003, where:

[0187] The first generation module 1001 is configured to generate a scenario tree according to historical water-wind-solar resource data and determine the probability corresponding to each scenario;

[0188] The second generation module 1002 is configured to determine a multi - energy complementary stochastic optimization scheduling model based on a multi - stage stochastic programming method. The stochastic optimization scheduling model takes the maximum total operating benefit as the objective function, and the total operating benefit is determined according to the probabilities of each scenario, the end - of - period storage energy of the hydropower station, the average output of the water - wind - solar complementary power generation system, and the wasted water energy.

[0189] The determination module 1003 is configured to solve the objective function based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the target scheduling strategy.

[0190] In one embodiment, the constraint conditions of the multi - energy complementary stochastic optimization scheduling model include: the piece - wise fitting power generation function of the hydropower station, the water balance equation, the flow relationship, the upper and lower limits of the power output of the power station, the upper and lower limits of the reservoir storage capacity, the upper and lower limits of the downstream discharge flow, and the unexpected constraint.

[0191] In one embodiment, the determination module 1003 is specifically configured to relax the unexpected constraint according to the augmented Lagrangian method to add a penalty function to the objective function, where the penalty function includes a quadratic term; approximate the quadratic term of the penalty function according to the diagonal quadratic approximation method and convert the objective function into multiple sub - objective functions, where one sub - objective function corresponds to the multi - stage planning problem of one scenario; solve each sub - objective function according to the optimization algorithm to obtain the target scheduling strategy.

[0192] In one embodiment, the first generation module 1001 is specifically configured to obtain a historical water - wind - solar resource sequence, where the historical water - wind - solar resource sequence includes multiple groups of historical water - wind - solar resource data; divide each historical water - wind - solar resource data into multiple stages according to a preset time period, and each stage corresponds to one layer in the scenario tree; determine the scenario tree according to the historical water - wind - solar resource data in each stage, and the path from the leaf node to the root node in the scenario tree represents a complete scenario sequence.

[0193] In one embodiment, the first generation module 1001 is specifically configured to obtain a preset scenario tree structure, randomly assign values to each node in the preset scenario tree structure from the historical water - wind - solar resource data of each stage to determine an initial scenario tree; iteratively update the initial scenario tree based on a preset update strategy until a preset iteration condition is met, and then determine the scenario tree.

[0194] In one embodiment, the first generation module 1001 is specifically configured to calculate the distances between each scenario sequence in the scenario tree and each historical water - wind - solar resource data to determine a distance matrix, where the element in the i - th row and j - th column of the distance matrix represents the distance between the i - th scenario sequence and the j - th group of historical water - wind - solar resource data; for the i - th scenario, use the minimum element value in the i - th row of the distance matrix as the scenario distance value of the i - th scenario; for each scenario, determine the probability corresponding to the scenario according to the scenario distance value.

[0195] In one embodiment, the first generation module 1001 is specifically configured to determine a target scene distance value according to the sum of the scene distance value and a preset constant; perform a normalization process on the reciprocal of the target scene distance value to determine the probability corresponding to the scene.

[0196] Each module in the above-mentioned water-wind-solar complementary energy scheduling method device can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0197] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 11 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a water-wind-solar complementary energy scheduling method.

[0198] Those skilled in the art can understand that Figure 11 the structure shown in

[0199] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0200] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps described in any one of the above method embodiments.

[0201] In one embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the steps described in any of the above method embodiments.

[0202] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0203] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in the present application.

[0204] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for dispatching water-wind-solar complementary energy, characterized in that: The method comprises: Generate a scenario tree based on historical water, wind and light resource data, and determine the probability corresponding to each scenario; Determine a multi-energy complementary stochastic optimization scheduling model based on a multi-stage stochastic programming method, wherein the stochastic optimization scheduling model takes the maximum total operating benefit as the objective function, and the total operating benefit is determined according to the probability of each of the scenarios, the energy storage at the end of the scheduling period of the hydropower station, the average output of the water-wind-solar complementary power generation system, and the abandoned water energy; The objective function is solved based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the target scheduling strategy.

2. The method according to claim 1, characterized in that: The constraints of the multi-energy complementary random optimization scheduling model include: segmented fitting power generation function of the hydropower station, water balance equation, flow relationship, upper and lower limits of power station output, upper and lower limits of reservoir storage capacity, upper and lower limits of downstream flow, and unexpected constraints.

3. The method according to claim 2, characterized in that The objective function is solved based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the target scheduling strategy, including: Relaxing the unexpected constraint according to an augmented Lagrangian method to add a penalty function to the objective function, wherein the penalty function includes a quadratic term; Approximating the quadratic term of the penalty function according to the diagonal quadratic approximation method, and converting the objective function into a plurality of sub-objective functions, wherein one of the sub-objective functions corresponds to a multi-stage planning problem of a scenario; The sub-objective functions are solved according to the optimization algorithm to obtain the target scheduling strategy.

4. The method according to claim 1, characterized in that The generating of the scene tree according to the historical water, scenery and light resource data includes: Acquire a historical water, wind and light resource sequence, wherein the historical water, wind and light resource sequence includes multiple groups of historical water, wind and light resource data; Dividing the historical water, wind and light resource data into multiple stages according to preset time periods, each stage corresponding to a layer in the scene tree; The scene tree is determined according to the historical water, scenery and light resource data in each stage, and the path from the leaf node to the root node in the scene tree represents a complete scene sequence.

5. The method according to claim 4, characterized in that Determining the scene tree according to the historical water, scenery and light resource data in each stage includes: Obtain a preset scene tree structure, randomly select values ​​from the historical water, wind and light resource data of each stage to assign values ​​to each node in the preset scene tree structure, and determine an initial scene tree; The initial scene tree is iteratively updated based on a preset update strategy until a preset iteration condition is met, thereby determining the scene tree.

6. The method according to claim 4, characterized in that Determining the probability corresponding to each scenario includes: Calculate the distance between each of the scene sequences in the scene tree and each of the historical water, wind and light resource data to determine a distance matrix, wherein the element in the i-th row and j-th column in the distance matrix represents the distance between the i-th scene sequence and the j-th group of historical water, wind and light resource data; For the i-th scene, the minimum element value of the i-th row of the distance matrix is ​​used as the scene distance value of the i-th scene; For each of the scenes, a probability corresponding to the scene is determined according to the scene distance value.

7. The method according to claim 6, characterized in that The determining, according to the scene distance value, the probability corresponding to the scene includes: Determine a target scene distance value according to the sum of the scene distance value and a preset constant; The reciprocal of the target scene distance value is normalized to determine the probability corresponding to the scene.

8. A water-wind-solar complementary energy dispatching device, characterized in that: The device comprises: The first generation module is used to generate a scenario tree based on historical water, wind and light resource data, and determine the probability corresponding to each scenario; The second generation module is used to determine a multi-energy complementary random optimization scheduling model based on a multi-stage random programming method. The random optimization scheduling model takes the maximum total operating benefit as the objective function. The total operating benefit is determined according to the probability of each of the scenarios, the energy storage at the end of the scheduling period of the hydropower station, the average output of the water-wind-solar complementary power generation system, and the abandoned water energy; The determination module is used to solve the objective function based on the augmented Lagrangian method and the diagonal quadratic approximation method to obtain the target scheduling strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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