A distributed robust optimization scheduling method, device and equipment for a hydropower station and a medium

Abstract

This invention discloses a method, apparatus, equipment, and medium for the sub-Bruker optimal scheduling of hydropower stations, relating to the field of hydropower station scheduling technology. It includes: determining a fuzzy set of inflow error based on the historical estimated and actual historical inflow of at least one hydropower station; constructing a sub-Bruker optimal scheduling model for the hydropower station based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error; reconstructing the sub-Bruker optimal scheduling model based on conditional value-at-risk theory to obtain a reconstructed optimal scheduling model; and determining the target output, target head, and target power generation flow of each hydropower station based on the reconstructed optimal scheduling model and the target flow estimated value of at least one hydropower station. This solution reduces the water wastage of hydropower stations and improves their power generation and operational stability by determining the fuzzy set of inflow error and establishing a sub-Bruker optimal scheduling model.

Description

Technical Field

[0001] This invention relates to the field of hydropower station dispatching technology, and in particular to a method, apparatus, equipment and medium for optimized dispatching of hydropower stations using a distributed bar system. Background Technology

[0002] Hydropower, as a renewable energy source, plays a vital role in optimizing power grid resource allocation, and the optimized scheduling of hydropower stations is a crucial issue in power system dispatching. The inflow of water to hydropower stations is uncertain, making their power generation capacity susceptible to fluctuations. Currently, stochastic optimization and robust optimization methods are commonly used for hydropower station scheduling optimization. However, these methods suffer from drawbacks such as conservative reservoir capacity scheduling, leading to large water wastage and consequently lower power generation, as well as operational risks. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, and medium for optimized scheduling of hydropower stations to improve power generation and operational stability.

[0004] In a first aspect, the present invention provides a method for optimal scheduling of hydropower stations using a distributed bar system, comprising:

[0005] Based on the historical inflow estimates and actual historical inflow values ​​of at least one hydropower station, determine the fuzzy set of inflow error.

[0006] Based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error, a sub-Blu-shaped optimal scheduling model for the hydropower station is constructed.

[0007] Based on the conditional value at risk theory, the sub-bulk optimal scheduling model of the hydropower station is reconstructed to obtain the reconstructed optimal scheduling model.

[0008] Based on the reconstructed and optimized scheduling model and the estimated inflow of the target hydropower station, the target output, target head, and target power generation flow of the target hydropower station are determined.

[0009] Secondly, the present invention also provides a hydropower station split-rod optimization scheduling device, comprising:

[0010] The fuzzy set determination module is used to determine the fuzzy set of inflow error based on the estimated historical inflow and the actual historical inflow of at least one hydropower station.

[0011] The model building module is used to construct a hydropower station split-bar optimal scheduling model based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error.

[0012] The reconstructed model determination module is used to reconstruct the hydropower station's sub-bar optimal scheduling model based on the conditional value at risk theory, so as to obtain a reconstructed optimal scheduling model.

[0013] Based on the reconstructed and optimized scheduling model and the estimated inflow of the target hydropower station, the target output, target head, and target power generation flow of the target hydropower station are determined.

[0014] Thirdly, embodiments of the present invention also provide an electronic device, comprising:

[0015] At least one processor; and

[0016] A memory that is communicatively connected to at least one processor; wherein

[0017] The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to execute the hydropower station split-bar optimization scheduling method provided in any embodiment of the present invention.

[0018] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute and implement the hydropower station split-rod optimization scheduling method of any embodiment of the present invention.

[0019] This invention, in its embodiments, determines a fuzzy set of inflow error based on the historical inflow estimates and actual historical inflow values ​​of at least one hydropower station; it then constructs a pluripotentiary optimal scheduling model for the hydropower station based on the historical output, historical head, and historical power generation flow of the at least one hydropower station, as well as the fuzzy set of inflow error; based on conditional value-at-risk theory, the pluripotentiary optimal scheduling model is reconstructed to obtain a reconstructed optimal scheduling model; finally, based on the reconstructed optimal scheduling model and the target flow estimates of at least one hydropower station, the target output, target head, and target power generation flow of each hydropower station are determined. This technical solution, by determining the fuzzy set of inflow error based on historical inflow and establishing a pluripotentiary optimal scheduling model, reduces the water wastage of hydropower stations and improves their power generation and operational stability.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a hydropower station's distributed bar optimization scheduling method according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a hydropower station's distributed bar optimization scheduling method according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a flowchart of a hydropower station's distributed bar optimization scheduling method according to Embodiment 3 of the present invention;

[0025] Figure 4 This is a flowchart of a hydropower station's distributed bar optimization scheduling method according to Embodiment 4 of the present invention;

[0026] Figure 5 This is a structural diagram of a hydropower station's distributed bar optimization scheduling device according to Embodiment 5 of the present invention;

[0027] Figure 6 This is a schematic diagram of the electronic equipment of a hydropower station's distributed bar optimization scheduling device according to Embodiment Six of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This is a flowchart of a hydropower station's sub-bar optimization scheduling method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of optimizing the scheduling of hydropower stations. The method can be executed by a hydropower station's sub-bar optimization scheduling device, which can be implemented in hardware and / or software and specifically configured in electronic devices, such as servers.

[0032] like Figure 1 As shown, the method includes:

[0033] S101. Determine the fuzzy set of inflow error based on the estimated historical inflow and actual historical inflow of at least one hydropower station.

[0034] In this embodiment, the estimated historical inflow can be the estimated inflow during each scheduling period of each scheduling cycle in the history of the hydropower station; the actual historical inflow can be the actual inflow during each scheduling period of each scheduling cycle in the history of the hydropower station; the fuzzy set of inflow error can be the fuzzy set of the probability distribution of inflow error; wherein, a scheduling cycle can include a preset number of scheduling periods; the inflow error can be the difference between the estimated historical inflow and the corresponding actual historical inflow.

[0035] It should be noted that the scheduling time period and the preset number of scheduling time periods within a scheduling cycle can be set independently by technical personnel based on actual needs or practical experience, and this invention does not impose any limitations. For example, a scheduling cycle can be 1 day, the corresponding preset number can be 24, and a scheduling time period can be 1 hour.

[0036] S102. Based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error, construct a distributed bar optimization scheduling model for the hydropower station.

[0037] In this embodiment, historical output can be the output during each scheduling period of each scheduling cycle in the history of the hydropower station; historical head can be the head during each scheduling period of each scheduling cycle in the history of the hydropower station; historical power generation flow can be the power generation flow during each scheduling period of each scheduling cycle in the history of the hydropower station; the hydropower station's distributed bar optimization scheduling model can be a mathematical model for determining the optimal scheduling parameters of the hydropower station; wherein, the scheduling parameters include, but are not limited to, hydropower station output, hydropower station head, and hydropower station power generation flow.

[0038] S103. Based on the conditional risk value theory, the hydropower station split-bar optimal scheduling model is reconstructed to obtain the reconstructed optimal scheduling model.

[0039] In this embodiment, the reconstructed optimal scheduling model can be a mathematical model that facilitates the determination of the optimal scheduling parameters for the hydropower station. Specifically, based on the conditional value at risk theory, the hydropower station's sub-bar optimal scheduling model is reconstructed, and the reconstructed sub-bar optimal scheduling model is used as the reconstructed optimal scheduling model.

[0040] S104. Based on the reconfiguration optimization scheduling model and the target flow estimate of at least one hydropower station, determine the target output, target head, and target power generation flow of each hydropower station.

[0041] In this embodiment, the target flow estimate may include, but is not limited to, the inflow estimate of the primary hydropower station and the inflow estimate of other hydropower stations within each scheduling time period of the target scheduling cycle. The target scheduling cycle can be the next scheduling cycle after the current scheduling cycle of the hydropower station. The target output can be the output of each hydropower station within each scheduling time period of the target scheduling cycle; the target head can be the head of each hydropower station within each scheduling time period of the target scheduling cycle; and the target power generation flow can be the power generation flow of the target hydropower station within each scheduling time period of the target scheduling cycle. The preset quantities can be set independently by technical personnel based on actual needs or practical experience, and this invention does not limit this. For example, if the scheduling cycle is 1 day and the scheduling time period is 1 hour, the power output, head, and power generation flow of each hydropower station in each hour of the next 24 hours can be determined based on the reconstructed and optimized scheduling model and the inflow estimate of at least one hydropower station within each hour of the next 24 hours.

[0042] In one specific implementation, the product of the power output and the power conversion coefficient of each hydropower station within a scheduling period is taken as the power generation of the corresponding hydropower station within that scheduling period; the function for determining the sum of the power generation of all hydropower stations within each scheduling period is taken as the objective function for reconstructing the optimized scheduling model; for example, the objective function can be determined by the following formula:

[0043]

[0044] Where f represents the objective function; T represents the number of scheduling time periods in the objective scheduling cycle; t represents the sequence number of the scheduling time period of the hydropower station in a scheduling cycle; N represents the number of hydropower stations; i represents the sequence number of the hydropower station; P i,t Δp represents the output of hydropower station i during the dispatch time period t; Δp represents the output-to-power conversion coefficient.

[0045] The output of each hydropower station when the objective function is at its maximum value during each scheduling period of the target scheduling cycle is taken as the target output; the head of each hydropower station when the objective function is at its maximum value during each scheduling period of the target scheduling cycle is taken as the target head; and the power generation flow of each hydropower station when the objective function is at its maximum value during each scheduling period of the target scheduling cycle is taken as the target power generation flow.

[0046] In one specific embodiment, hydropower stations may face the risk of exceeding limits, meaning that the maximum reservoir capacity, minimum reservoir capacity, or reservoir water level may exceed the designated operating range. The hydropower station's distributed bar optimization scheduling method according to this invention can maintain a certain reservoir capacity to cope with uncertain inflow rates, thereby reducing the risk of exceeding limits.

[0047] This invention, in its embodiments, determines a fuzzy set of inflow error based on the historical inflow estimates and actual historical inflow values ​​of at least one hydropower station; it then constructs a sub-Bruker optimal scheduling model for the hydropower station based on the historical output, historical head, and historical power generation flow of the at least one hydropower station, as well as the fuzzy set of inflow error; based on conditional value-at-risk theory, the sub-Bruker optimal scheduling model is reconstructed to obtain a reconstructed optimal scheduling model; and finally, based on the reconstructed optimal scheduling model and the inflow estimate of the target hydropower station, the target output, target head, and target power generation flow of the target hydropower station are determined. This technical solution, by determining the fuzzy set of inflow error based on historical inflow and establishing a sub-Bruker optimal scheduling model, reduces the water wastage of the hydropower station and improves its power generation and operational stability.

[0048] Example 2

[0049] Figure 2 This is a flowchart of a hydropower station's sub-bar optimization scheduling method provided in Embodiment 2 of the present invention. Based on the technical solution of the above embodiments, the present invention optimizes and improves the determination operation of the fuzzy set of inflow error.

[0050] Furthermore, the step of "determining the fuzzy set of inflow error based on the estimated and actual historical inflow of at least one hydropower station" is refined to "determining the total historical inflow error of at least one hydropower station based on the estimated and actual historical inflow of at least one hydropower station; determining the expected error distribution constraint, expected variance distribution constraint, expected error value constraint, and expected variance constraint based on the expected and variance of the total historical inflow error of at least one hydropower station; and determining the fuzzy set of inflow error based on the expected error distribution constraint, expected variance distribution constraint, expected error constraint, and expected variance constraint," thus improving the operation of determining the fuzzy set of inflow error.

[0051] It should be noted that for any parts not described in detail in the embodiments of the present invention, please refer to the description in the foregoing embodiments.

[0052] like Figure 2 The method shown includes:

[0053] S201. Based on the estimated historical inflow and actual historical inflow of at least one hydropower station, determine the total historical inflow error of at least one hydropower station.

[0054] In this embodiment, the total historical inflow error may include the error between the estimated historical inflow of each hydropower station and the corresponding actual historical inflow.

[0055] Specifically, for a hydropower station, the difference between the estimated historical inflow and the actual historical inflow of each hydropower station is determined, and the difference between the inflows of each hydropower station is taken as the historical inflow error of the hydropower station; the historical inflow errors of each hydropower station are taken as the total historical inflow error.

[0056] S202. Based on the expected value and variance of the total historical inflow error of at least one hydropower station, determine the expected constraint of error distribution, the expected constraint of variance distribution, the expected constraint of error, and the constraint of error variance.

[0057] In this embodiment, the expected error distribution constraint can be used to constrain the expected value of the inbound flow error when it follows a probability distribution; the expected variance distribution constraint can be used to constrain the expected value of the variance of the inbound flow error when it follows a probability distribution; the expected error constraint is used to constrain the upper and lower limits of the inbound flow error; and the variance error constraint is used to constrain the upper and lower limits of the variance of the inbound flow error.

[0058] Specifically, the expected value of the inbound flow error when it follows a probability distribution is taken as the expected value of the inbound flow error, and used as the constraint condition for the expected error distribution. For example, the constraint condition for the expected error distribution can be determined using the following formula:

[0059]

[0060] Where ξ represents the inbound flow error; μ represents the expected value of the inbound flow error; This represents the probability distribution of the inbound flow error ξ. It indicates that it follows a probability distribution The expected value at that time.

[0061] The expected value of the variance of the inbound flow error following a probability distribution is defined as the variance of the inbound flow error, and this is taken as the variance distribution expectation constraint. For example, the variance distribution expectation constraint can be determined using the following formula:

[0062]

[0063] Where ξ represents the inbound flow error; μ represents the expected value of the inbound flow error; This represents the probability distribution of the inbound flow error ξ. It indicates that it follows a probability distribution Expected value at time; σ 2 This represents the variance of the inbound flow rate error.

[0064] The expected value of the inbound flow error is defined as not exceeding the upper limit and not falling below the lower limit, which is determined as the expected error constraint. For example, the expected error constraint can be determined using the following formula:

[0065]

[0066] Where μ represents the expected value of the inbound flow error; μ represents the lower limit of the expected value of the inbound flow error; This represents the upper limit of the expected value of the inbound flow error.

[0067] The variance of the inbound flow error is defined as not exceeding the upper limit of variance and not less than the lower limit of variance, which is set as the error variance constraint condition. For example, the expected error constraint condition can be determined using the following formula:

[0068]

[0069] Where, σ 2 σ represents the expected value of the inbound flow error; 2 This represents the lower limit of the variance of the inbound flow rate error; This represents the upper limit of the variance of the inbound flow rate error.

[0070] It should be noted that the upper and lower limits of the expected value of the inbound flow error, as well as the upper and lower limits of the variance of the inbound flow error, can be set independently by technical personnel based on actual needs or practical experience, and this invention does not impose any limitations on this.

[0071] S203. Determine the fuzzy set of inbound flow error based on the expected value probability constraint, variance probability constraint, expected value constraint, and variance constraint.

[0072] Specifically, the probability distribution of the inbound flow error that satisfies the expected value probability constraint, the variance probability constraint, and the expected value constraint and variance constraint is determined as the fuzzy set of inbound flow error. For example, the fuzzy set of inbound flow error can be expressed by the following formula:

[0073]

[0074] in, Represents the fuzzy set of inbound flow error; Represents the probability distribution of uncertain flow; This represents the set of all probability distributions of the inbound flow error ξ; It indicates that it follows a probability distribution The expected value at time; μ is the expected value of the inflow rate estimate; σ is the expected value of the error in the inflow rate estimate. 2 denoted as the variance of the estimated inflow rate; μ represents the lower bound of the expected value of the inflow rate error. σ represents the upper limit of the expected value of the inbound flow error; 2 This represents the lower limit of the variance of the inbound flow rate error; This represents the upper limit of the variance of the inbound flow rate error.

[0075] S204. Based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error, construct a sub-Blu-ray optimal scheduling model for the hydropower station.

[0076] S205. Based on the conditional risk value theory, the hydropower station split-bar optimal scheduling model is reconstructed to obtain the reconstructed optimal scheduling model.

[0077] S206. Based on the reconfiguration optimization scheduling model and the target flow estimate of the target hydropower station, determine the target output, target head, and target power generation flow of the target hydropower station.

[0078] This invention, in its embodiments, determines the total historical inflow error of at least one hydropower station by using its estimated and actual historical inflow values. Based on the expected value and variance of this total historical inflow error, it determines constraint conditions for error distribution expectation, variance distribution expectation, expected error value, and error variance. Finally, based on these constraint conditions, a fuzzy set of inflow error is determined. This technical solution, by determining the total historical inflow error and then using its expected value and variance to determine the fuzzy set of inflow error, makes the probability distribution of the fuzzy set of inflow error closer to the probability distribution of the actual inflow error, thereby improving the accuracy of the hydropower station's distributed bar optimization scheduling model determined based on the fuzzy set of inflow error.

[0079] Example 3

[0080] Figure 3 This is a flowchart of a hydropower station's distributed bar optimal scheduling method provided in Embodiment 3 of the present invention. Based on the technical solutions of the above embodiments, the present invention optimizes and improves the determination operation of the hydropower station's distributed bar optimal scheduling model.

[0081] Furthermore, the step of "constructing a hydropower station sub-bar optimal scheduling model based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error" is refined into "determining the hydropower station output function based on the historical output, historical head, and historical power generation flow of at least one hydropower station; determining the reservoir capacity sub-bar opportunity constraint based on the fuzzy set of inflow error and reservoir capacity constraints; and constructing a sub-bar optimal scheduling model based on the hydropower station output function and the reservoir capacity sub-bar opportunity constraint," in order to improve the determination operation of the sub-bar optimal scheduling model.

[0082] It should be noted that for any parts not described in detail in the embodiments of the present invention, please refer to the description in the foregoing embodiments.

[0083] like Figure 3 The method shown includes:

[0084] S301. Determine the fuzzy set of inflow error based on the estimated historical inflow and actual historical inflow of at least one hydropower station.

[0085] S302. Determine the power output function of at least one hydropower station based on its historical power output, historical head, and historical power generation flow.

[0086] In this embodiment, the power output function of the hydropower station can be used to determine the power output of the corresponding hydropower station based on the head and power generation flow of the hydropower station.

[0087] Optionally, the power output function of a hydropower station is determined based on the historical power output, historical head, and historical power generation flow of at least one hydropower station, including: fitting the historical power output based on the historical head and historical power generation flow of at least one hydropower station, and using the fitted result function as the power output function of the hydropower station.

[0088] In this embodiment, the fitting result function is the result function obtained by fitting the historical output.

[0089] Specifically, based on the historical head and historical power generation flow of at least one hydropower station, a curve is fitted to the corresponding historical output to obtain a fitting result function with historical output as the dependent variable and historical head and historical power generation flow as independent variables. This fitting result function is then used as the hydropower station's output function. It should be noted that the type of fitting in this invention is not limited; for example, it can be linear fitting or quadratic fitting.

[0090] For example, based on the historical head and historical power generation flow of at least one hydropower station, a second fitting is performed on the historical power output to obtain the fitting result function, which is then used as the power output function of the hydropower station. The power output function of the hydropower station can then be expressed by the following formula:

[0091]

[0092] Among them, P i,t The output of hydropower station i during the dispatch time period t; h i,t Let q be the head of hydropower station i during the dispatching time period t; i,t Let c be the power generation flow of hydropower station i during the dispatching time period t; 0,i c 1,i c 2,i c 3,i c 4,i and c 5,i These are the coefficients of hydropower station i determined during the fitting operation.

[0093] It is understandable that by adopting the above technical solution, the historical output is fitted based on the historical head and historical power generation flow of at least one hydropower station to obtain the hydropower station output function. This ensures that the hydropower station output function can fully describe the functional relationship between head, power generation flow and output, and meet the operating characteristics of the hydropower station unit, thereby improving the accuracy of the hydropower station output determined based on the hydropower station output function.

[0094] S303. Determine the storage capacity distribution opportunity constraint based on the fuzzy set of inbound flow error and storage capacity constraints.

[0095] In this embodiment, the reservoir capacity constraint can be used to constrain the reservoir capacity of the hydropower station; the Kurufbau bar chance constraint can be used to constrain the probability of the reservoir capacity constraint being met, so as to constrain the reservoir capacity of the hydropower station.

[0096] Specifically, the reservoir capacity constraint is defined as the reservoir capacity not exceeding the upper limit of the corresponding hydropower station's capacity and not being less than the lower limit of the corresponding hydropower station's capacity. For example, the reservoir capacity constraint can be determined using the following formula:

[0097]

[0098] Where, r i,t The reservoir capacity of hydropower station i during the scheduling time period t; r i This represents the upper limit of the reservoir capacity of hydropower station i; This represents the lower limit of the reservoir capacity of hydropower station i.

[0099] When the probability distribution of inbound flow error belongs to the fuzzy set of inbound flow error, the lower bound of the storage capacity constraint being no less than the corresponding confidence level is used as the Bruker chance constraint for storage capacity distribution; where the confidence level is the difference between 1 and the risk coefficient. For example, the Bruker chance constraint for storage capacity distribution can be determined using the following formula:

[0100]

[0101] Where, r i,t The reservoir capacity of hydropower station i during the scheduling time period t; r i This represents the upper limit of the reservoir capacity of hydropower station i; ε represents the lower limit of the reservoir capacity of hydropower station i. i Indicates the risk coefficient; This represents the probability distribution of inbound flow error; denoted as the fuzzy set of inbound flow error; Pr represents the probability that the storage capacity constraint condition is met; inf represents the infimum.

[0102] It should be noted that the upper limit of storage capacity, the lower limit of storage capacity, and the risk coefficient can be set independently by technical personnel based on actual needs or practical experience, and this invention does not impose any restrictions on this.

[0103] S304. Based on the power output function of the hydropower station and the opportunity constraints of the reservoir capacity distribution bar, construct a distribution bar optimal scheduling model.

[0104] Specifically, a mathematical model is constructed based on the power output function of the hydropower station and the opportunity constraints of the reservoir capacity distribution bar, and this model serves as the optimal scheduling model for the distribution bar.

[0105] S305. Based on the conditional risk value theory, the hydropower station split-bar optimal scheduling model is reconstructed to obtain the reconstructed optimal scheduling model.

[0106] S306. Based on the reconfiguration optimization scheduling model and the target flow estimate of the target hydropower station, determine the target output, target head, and target power generation flow of the target hydropower station.

[0107] This invention, in its embodiments, determines the hydropower station's output function based on the historical output, historical head, and historical power generation flow of at least one hydropower station; it determines the reservoir capacity distribution bar opportunity constraint based on the fuzzy set of inflow error and reservoir capacity constraints; and it constructs a distribution bar optimal scheduling model based on the hydropower station's output function and the reservoir capacity distribution bar opportunity constraint. The technical solution of this invention, by determining the hydropower station's processing function and the reservoir capacity distribution bar opportunity constraint, constructs a more flexible distribution bar optimal scheduling model. Furthermore, based on the reconstructed model of the distribution bar optimal scheduling model, the hydropower station is optimally scheduled, reducing the hydropower station's water wastage and improving its power generation and operational stability.

[0108] Example 4

[0109] Figure 4 This is a flowchart of a hydropower station's sub-buffing optimization scheduling method provided in Embodiment 4 of the present invention. Based on the technical solutions of the above embodiments, the present invention optimizes and improves the determination operation of the reconstructed optimization scheduling model.

[0110] Furthermore, the statement "based on the conditional value at risk theory, the sub-Bru bar optimal scheduling model is reconstructed to obtain the reconstructed optimal scheduling model" is refined to "based on the conditional value at risk theory, the capacity sub-Bru bar opportunity constraint is reconstructed into the capacity second-order cone programming constraint; the capacity sub-Bru bar opportunity constraint in the sub-Bru bar optimal scheduling model is replaced with the capacity second-order cone programming constraint to obtain the reconstructed optimal scheduling model," in order to improve the determination operation of the reconstructed optimal scheduling model.

[0111] It should be noted that for any parts not described in detail in the embodiments of the present invention, please refer to the description in the foregoing embodiments.

[0112] like Figure 4 The method shown includes:

[0113] S401. Determine the fuzzy set of inflow error based on the estimated historical inflow and actual historical inflow of at least one hydropower station.

[0114] S402. Based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error, construct a sub-Blu-ray optimal scheduling model for the hydropower station.

[0115] Optionally, the hydropower station's distributed bar optimization scheduling model includes at least one of the following: flow constraints, hydropower station output constraints, water balance constraints, and initial and final reservoir capacity constraints corresponding to the water balance constraints, or a head determination function and the head constraints, upstream water level determination function, and downstream water level determination function corresponding to the head function.

[0116] In this embodiment, the flow constraints may include water discharge flow constraints and power generation flow constraints, used to constrain the water discharge flow and power generation flow of the hydropower station. The existence of water discharge flow is used as a water discharge flow constraint; the power generation flow constraint condition is that the power generation flow is not greater than the upper limit value and not less than the lower limit value of the corresponding hydropower station's power generation flow. For example, the flow constraints can be determined using the following formula:

[0117]

[0118] Among them, s i,t q represents the water discharge of hydropower station i during the scheduling time period t; i,t q represents the power generation flow of hydropower station i during the scheduling time period t; i This represents the upper limit of the power generation flow of hydropower station i; This represents the lower limit of the power generation flow of hydropower station i.

[0119] Hydropower station output constraints are used to limit the output of hydropower stations. Outputs that are not greater than the upper limit and not less than the lower limit of the corresponding hydropower station's output are used as output constraints. For example, the hydropower station output constraint can be determined using the following formula:

[0120]

[0121] Among them, P i,t P represents the output of hydropower station i during the dispatch time period t; i This represents the lower limit of the output of hydropower station i; This represents the lower limit of the output of hydropower station i.

[0122] Water balance constraints are applied to the inflow, power generation, and discharge flows of a hydropower station within adjacent scheduling periods. For a scheduling period, the following steps are taken: First, determine the first flow difference between the inflow and power generation flows; second, determine the second flow difference between the first flow difference and the discharge flows; third, determine the first flow product of the second flow difference and the flow-volume conversion coefficient; fourth, determine the sum of the power generation and discharge flows of adjacent hydropower stations before the water flow delay in the scheduling period; fifth, determine the second flow product of this sum and the flow-volume conversion coefficient; sixth, sum the second flow products of all upstream hydropower stations to obtain a product sum; seventh, sum the reservoir capacity, the first flow product, and the product sum for the scheduling period, and this sum equals the reservoir capacity for the next scheduling cycle of the hydropower station, serving as the water balance constraint. For example, the water balance constraint condition can be expressed by the following formula:

[0123]

[0124] Where, r i,t w represents the reservoir capacity of hydropower station i during the scheduling period t; i,t q represents the estimated inflow rate of hydropower station i during the scheduling period t; i,t s represents the power generation flow of hydropower station i during time period t; i,t H represents the discharge flow of hydropower station i during the dispatch time period t; Δt represents the flow-volume conversion coefficient; i Let be the set of upstream power stations of hydropower station i; τ is the water flow delay of adjacent hydropower stations; This represents the power generation flow of hydropower station j, which is adjacent to hydropower station i, before the water flow delay τ during the scheduling time period t. Let represent the water discharge of hydropower station j adjacent to hydropower station i before the water flow delay τ during the scheduling time period t.

[0125] The initial and final reservoir capacity constraints are used to constrain the initial and final reservoir capacity values ​​of a hydropower station within a scheduling cycle. The initial reservoir capacity value for the first scheduling period of a scheduling cycle is a preset initial reservoir capacity value, and the final reservoir capacity value for the last scheduling period of a scheduling cycle is a preset final reservoir capacity value, which serves as the initial and final reservoir capacity constraints. The preset initial and final reservoir capacity values ​​can be set independently by technical personnel based on actual needs or practical experience; this invention does not impose any limitations on this. For example, the initial and final reservoir capacity constraints can be determined using the following formula:

[0126] r i,0 =r 0 i,0 ,r i,T =r 0 i,T ;

[0127] Where, r i,0This represents the initial reservoir capacity of hydropower station i during a scheduling cycle; r 0 i,0 This indicates the initial value of the preset storage capacity; r i,T This represents the final reservoir capacity of hydropower station i during a scheduling cycle; r 0 i,T This indicates the preset storage capacity limit.

[0128] The head determination function is used to determine the head of a hydropower station. It is a function that determines the head of the hydropower station during a specific scheduling period based on the difference between the upstream and downstream water levels. For example, the head determination function can be expressed by the following formula:

[0129] h i,t =z u i,t -z d i,t ;

[0130] Among them, h i,t The z represents the head of hydropower station i during the scheduling time period t; u i,t This represents the upstream water level of hydropower station i during the scheduling period t; z d i,t This represents the downstream water level of hydropower station i during the scheduling period t.

[0131] Head constraints can be used to constrain the head of a hydropower station. A head not exceeding the upper limit and not less than the lower limit of the head for the corresponding hydropower station is used as a head constraint. The upper and lower head limits can be set independently by technical personnel based on actual needs or practical experience; this invention does not impose such limitations. For example, the head constraint can be determined using the following formula:

[0132]

[0133] Among them, h i,t The head of hydropower station i during the scheduling period t is represented by h. i This represents the lower limit of the head of hydropower station i; This represents the lower limit of the head of hydropower station i.

[0134] The upstream water level determination function can be used to determine the upstream water level of a hydropower station based on its reservoir capacity during the scheduling period. The upstream water level determination function is obtained by curve fitting based on the reservoir capacity and upstream water level of the hydropower station's historical scheduling cycles. For example, the upstream water level determination function can be expressed by the following formula:

[0135]

[0136] in, This represents the upstream water level of hydropower station i during the scheduling period t; r i,t This represents the reservoir capacity of hydropower station i during the scheduling time period t.

[0137] The downstream water level determination function can be used to determine the downstream water level of a hydropower station based on its power generation and discharge flows during a scheduled period. This can be achieved by curve fitting, using historical data on power generation, discharge flows, and downstream water levels from different scheduling periods. For example, the upstream water level determination function can be expressed by the following formula:

[0138]

[0139] in, q represents the upstream water level of hydropower station i during the scheduling period t; i,t s represents the reservoir power generation flow of hydropower station i during the scheduling time period t; i,t This represents the water discharge of hydropower station i during the scheduling time period t.

[0140] It is understandable that by adopting the above technical solution, the hydropower station's distributed bar optimization scheduling model can be constrained based on at least one of the following: flow constraints, hydropower station output constraints, water balance constraints and the initial and final reservoir capacity constraints corresponding to the water balance constraints, or head determination function and the head constraints corresponding to the head function, upstream water level determination function and downstream water level determination function. This can improve the accuracy of the target output, target head and target flow determined by the reconstructed optimization scheduling model.

[0141] S403. Based on the conditional risk value theory, the capacity distribution bar opportunity constraint is reconstructed into a second-order cone programming constraint for capacity.

[0142] Specifically, for the storage capacity constraint in the opportunity constraint of storage capacity allocation, the upper limit constraint, which states that the storage capacity cannot exceed the upper limit, can be adjusted into an inequality constraint for uncertain variables. For example, the adjusted upper limit constraint can be expressed by the following formula:

[0143]

[0144]

[0145]

[0146] in, This represents an estimated value of natural water inflow. Describes the function of decision variables. This represents the coefficient function of the uncertain variable.

[0147] For the storage capacity constraint condition where the storage capacity is not less than the lower limit value, the lower limit constraint condition after being adjusted to an inequality constraint form with uncertain variables is similar to the upper limit constraint condition mentioned above, and will not be elaborated here.

[0148] Based on the adjusted upper and lower capacity constraints, the adjusted capacity allocation opportunity constraints are determined. For example, the adjusted capacity allocation opportunity constraints can be expressed by the following formula:

[0149]

[0150] Based on the conditional value risk theory, the storage capacity partitioning opportunity constraint based on the fuzzy set of inbound flow error is reconstructed into a storage capacity second-order cone programming constraint. For example, the storage capacity second-order cone programming constraint can be expressed by the following formula:

[0151]

[0152] Among them, γ1, γ2, v, z, θ, β and κ are auxiliary variables.

[0153] S404. Replace the capacity distribution opportunity constraint in the distribution bar optimization scheduling model with the capacity second-order cone programming constraint to obtain the reconstructed optimization scheduling model.

[0154] Specifically, the capacity distribution opportunity constraint in the distribution-bar optimization scheduling model is replaced with the capacity second-order cone programming constraint, and the replaced distribution-bar optimization scheduling model is used as the reconstructed optimization scheduling model.

[0155] S405. Based on the reconfiguration optimization scheduling model and the target flow estimate of the target hydropower station, determine the target output, target head, and target power generation flow of the target hydropower station.

[0156] This invention, based on conditional value at risk (VaR) theory, reconstructs the reservoir capacity partial Brussels bar opportunity constraint into a reservoir capacity second-order cone programming constraint; it then replaces the reservoir capacity partial Brussels bar opportunity constraint in the partial Brussels bar optimal scheduling model with the reservoir capacity second-order cone programming constraint, thus obtaining a reconstructed optimal scheduling model. This technical solution improves the ease of determining the target output, target head, and target power generation flow of each hydropower station based on the reconstructed optimal scheduling model by reconstructing the reservoir capacity partial Brussels bar opportunity constraint into a reservoir capacity second-order cone programming constraint.

[0157] Example 5

[0158] Figure 5This is a structural diagram of a hydropower station's sub-bar optimization scheduling device provided in Embodiment 5 of the present invention. This embodiment is applicable to the situation of optimizing the scheduling of hydropower stations. The sub-bar optimization scheduling device of the hydropower station can be implemented in hardware and / or software and specifically configured in electronic devices, such as servers.

[0159] like Figure 5 The hydropower station shown is a fuzzy set optimization scheduling device, which includes a fuzzy set determination module 501, a model building module 502, a reconstructed model determination module 503, and a target output determination module 504. Among them,

[0160] The fuzzy set determination module 501 is used to determine the fuzzy set of inflow error based on the estimated historical inflow and the actual historical inflow of at least one hydropower station.

[0161] The model building module 502 is used to build a hydropower station split-bar optimal scheduling model based on the historical output, historical head and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error.

[0162] The reconstructed model determination module 503 is used to reconstruct the hydropower station's sub-bar optimal scheduling model based on the conditional risk value theory, so as to obtain the reconstructed optimal scheduling model.

[0163] The target output determination module 504 is used to determine the target output, target head, and target power generation flow of each hydropower station based on the reconfiguration optimization scheduling model and the target flow estimate of at least one hydropower station.

[0164] This invention employs a fuzzy set determination module to determine the fuzzy set of inflow error based on the historical inflow estimates and actual historical inflow values ​​of at least one hydropower station; a model building module to construct a pluripotentiary optimal scheduling model for the hydropower station based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error; a reconstructed model determination module to reconstruct the pluripotentiary optimal scheduling model based on conditional value at risk theory, resulting in a reconstructed optimal scheduling model; and a target output determination module to determine the target output, target head, and target power generation flow of each hydropower station based on the reconstructed optimal scheduling model and the target flow estimates of at least one hydropower station. This technical solution, by determining the fuzzy set of inflow error based on historical inflow and establishing a pluripotentiary optimal scheduling model, reduces the water wastage of hydropower stations and improves their power generation and operational stability.

[0165] Optionally, the fuzzy set determination module 501 includes:

[0166] The flow error determination unit is used to determine the total historical inflow error of at least one hydropower station based on the estimated historical inflow and the actual historical inflow of at least one hydropower station.

[0167] The flow error determination unit is used to determine the expected constraint conditions of error distribution, expected constraint conditions of variance distribution, expected constraint conditions of error value, and constraint conditions of error variance based on the expected value and variance of the total historical inflow error of at least one hydropower station.

[0168] The flow error determination unit is used to determine the fuzzy set of inbound flow error based on the expected constraint of error distribution, the expected constraint of variance distribution, the expected constraint of error value, and the constraint of error variance.

[0169] Optional, model building module 502 includes:

[0170] The output function determination unit is used to determine the output function of a hydropower station based on the historical output, historical head, and historical power generation flow of at least one hydropower station.

[0171] The opportunity constraint determination unit is used to determine the opportunity constraint of the storage capacity distribution based on the fuzzy set of the inbound flow error and the storage capacity constraint conditions.

[0172] The scheduling model construction unit is used to construct the sub-bar optimal scheduling model based on the power output function of the hydropower station and the sub-bar opportunity constraints of the reservoir capacity.

[0173] Optional, the output function determination element includes:

[0174] The output function determination sub-unit is used to fit the historical output based on the historical head and historical power generation flow of at least one hydropower station, and the fitted result function is used as the output function of the hydropower station.

[0175] Optionally, the reconstructed model determination module 503 includes:

[0176] The storage capacity constraint reconstruction unit is used to reconstruct the storage capacity split bar opportunity constraint into the storage capacity second-order cone programming constraint based on the conditional value at risk theory.

[0177] The reconstructed model determination unit is used to replace the capacity distribution opportunity constraint in the distribution bar optimization scheduling model with the capacity second-order cone programming constraint to obtain the reconstructed optimization scheduling model.

[0178] Optionally, the hydropower station's distributed bar optimal dispatching device includes at least one of the following in the distributed bar chance constraints:

[0179] Flow constraints, hydropower station output constraints, water balance constraints, and the initial and final reservoir capacity constraints corresponding to the water balance constraints, or head determination functions and the head constraints, upstream water level determination functions, and downstream water level determination functions corresponding to the head functions.

[0180] The above-mentioned hydropower station's sub-bar optimal scheduling device can execute the hydropower station's sub-bar optimal scheduling method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the sub-bar optimal scheduling method of each hydropower station.

[0181] Example 6

[0182] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0183] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0184] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0185] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the distributed bar optimization scheduling method for hydroelectric power plants.

[0186] In some embodiments, the hydropower station's distributed bar optimization scheduling method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the hydropower station's distributed bar optimization scheduling method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the hydropower station's distributed bar optimization scheduling method by any other suitable means (e.g., by means of firmware).

[0187] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0188] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0189] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0190] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0191] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0192] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0193] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0194] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for optimal scheduling of hydropower stations using distributed bar scheduling, characterized in that, include: Based on the historical inflow estimates and actual historical inflow values ​​of at least one hydropower station, determine the fuzzy set of inflow error. Based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error, a sub-Blu-shaped optimal scheduling model for the hydropower station is constructed. Based on the conditional value at risk theory, the sub-bulk optimal scheduling model of the hydropower station is reconstructed to obtain the reconstructed optimal scheduling model. Based on the reconstructed optimization scheduling model and the target flow estimate of at least one hydropower station, determine the target output, target head and target power generation flow of each hydropower station. The step of determining the fuzzy set of inflow error based on the estimated historical inflow of at least one hydropower station and the actual historical inflow includes: Based on the estimated historical inflow and actual historical inflow of at least one hydropower station, determine the total historical inflow error of at least one hydropower station. Based on the expected value and variance of the total historical inflow error of the at least one hydropower station, determine the expected constraint of error distribution, the expected constraint of variance distribution, the expected value constraint of error, and the variance constraint of error. Based on the error distribution expectation constraint, the variance distribution expectation constraint, the error expectation value constraint, and the error variance constraint, determine the fuzzy set of inbound flow error; The step of constructing a hydropower station distributed bar optimization scheduling model based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error, includes: Determine the power output function of at least one hydropower station based on its historical power output, historical head, and historical power generation flow. Based on the fuzzy set of inbound flow error and the storage capacity constraint, determine the storage capacity distribution opportunity constraint. Based on the power output function of the hydropower station and the opportunity constraints of the reservoir capacity distribution bar, the optimal scheduling model of the distribution bar is constructed. The reconstructed optimal scheduling model of the hydropower station based on conditional value at risk theory includes: Based on the conditional risk value theory, the capacity distribution bar opportunity constraint is reconstructed into a second-order cone programming constraint for capacity. By replacing the capacity distribution opportunity constraint in the distribution bar optimization scheduling model with the capacity second-order cone programming constraint, the reconstructed optimization scheduling model is obtained.

2. The method according to claim 1, characterized in that, The process of determining the power output function of at least one hydropower station based on its historical power output, historical head, and historical power generation flow includes: Based on the historical head and historical power generation flow of at least one hydropower station, the historical power output is fitted, and the fitted result function is used as the power output function of the hydropower station.

3. The method according to claim 1, characterized in that, The hydropower station distributed bar optimization scheduling model includes at least one of the following: Flow constraints, hydropower station output constraints, water balance constraints, and the initial and final reservoir capacity constraints corresponding to the water balance constraints, or head determination functions and the head constraints, upstream water level determination functions, and downstream water level determination functions corresponding to the head functions.

4. A distributed bar-based optimized dispatching device for a hydropower station, characterized in that, include: The fuzzy set determination module is used to determine the fuzzy set of inflow error based on the estimated historical inflow and the actual historical inflow of at least one hydropower station. The model building module is used to construct a hydropower station split-bar optimal scheduling model based on the historical output, historical head, and historical power generation flow of at least one hydropower station, as well as the fuzzy set of inflow error. The reconstructed model determination module is used to reconstruct the hydropower station's sub-bar optimal scheduling model based on the conditional value at risk theory, so as to obtain a reconstructed optimal scheduling model. The target output determination module is used to determine the target output, target head, and target power generation flow of each hydropower station based on the reconstructed optimization scheduling model and the target flow estimate of at least one hydropower station. The fuzzy set determination module includes: The flow error determination unit is used to determine the total historical inflow error of at least one hydropower station based on the estimated historical inflow and the actual historical inflow of at least one hydropower station. The flow error determination unit is used to determine the expected constraint conditions of error distribution, expected constraint conditions of variance distribution, expected constraint conditions of error value, and constraint conditions of error variance based on the expected value and variance of the total historical inflow error of at least one hydropower station. The flow error determination unit is used to determine the fuzzy set of inbound flow error based on the error distribution expectation constraint, the variance distribution expectation constraint, the error expectation value constraint, and the error variance constraint. The model building module includes: The output function determination unit is used to determine the output function of a hydropower station based on the historical output, historical head, and historical power generation flow of at least one hydropower station. The opportunity constraint determination unit is used to determine the opportunity constraint of the storage capacity distribution based on the fuzzy set of the inbound flow error and the storage capacity constraint conditions. The scheduling model construction unit is used to construct the sub-bar optimal scheduling model based on the power output function of the hydropower station and the sub-bar opportunity constraints of the reservoir capacity. The reconstruction model determination module includes: The storage capacity constraint reconstruction unit is used to reconstruct the storage capacity split bar opportunity constraint into the storage capacity second-order cone programming constraint based on the conditional value at risk theory. The reconstructed model determination unit is used to replace the capacity distribution opportunity constraint in the distribution bar optimization scheduling model with the capacity second-order cone programming constraint to obtain the reconstructed optimization scheduling model.

5. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the hydropower station's distributed bar optimization scheduling method according to any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the sub-bar optimal scheduling method for the hydropower station according to any one of claims 1-3.

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