A Conditional Target Optimization Scheduling Method and System for Optical Water Cluster Time-Sharing and Grouping

Abstract

The present invention discloses a conditional target optimization scheduling method and system for optical water cluster time-sharing - grouping, and the method includes: constructing a first day - ahead prediction model and a first intra - day prediction model of photovoltaic power generation based on historical data of a photovoltaic power generation system and historical load data, and a second day - ahead prediction model and a second intra - day prediction model of load power consumption; constructing a day - ahead prediction scheduling optimization model for a basic group of hydropower stations based on the first day - ahead prediction model and the second day - ahead prediction model, and adopting a two - stage conditional optimization method to form a first scheduling plan for the basic group of hydropower stations; constructing an intra - day prediction scheduling optimization model for an adjustment group of hydropower stations based on the first intra - day prediction model and the second intra - day prediction model, and adopting a two - stage conditional optimization method to form a second scheduling plan for the adjustment group of hydropower stations. The overall energy utilization rate of the power generation cluster and the stability of power supply to the load are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of hydropower cluster control under distributed photovoltaic access, and particularly relates to a conditional target optimization scheduling method and system for time-sharing and grouping of a photovoltaic and hydropower cluster. Background Art

[0002] As an unstable power source type, the photovoltaic power generation system with randomness and volatility has increased its penetration rate in the power system, bringing huge challenges to the power supply and demand balance of the power system. Although the energy storage system with both power supply and load characteristics can effectively balance the contradiction between power supply and load demand, the economic, energy consumption, and safety issues of the energy storage system are always the key factors restricting its development. Utilizing existing controllable and stable power sources, such as small hydropower, to quickly and efficiently cooperate with the photovoltaic power generation system to achieve reliable power supply for power demand will be another effective means to promote the further application of the photovoltaic power generation system. Therefore, how to establish a collaborative control strategy for distributed hydropower clusters, improve the utilization rate of new energy, reduce the adverse impact of the uncertainty of the photovoltaic power generation system on the power grid transmission and distribution, and ensure the power consumption quality of the load has become a problem worthy of research.

[0003] In the actual application of current distributed hydropower and photovoltaic power generation systems, most distributed hydropower and photovoltaic clusters operate independently, lacking unified optimized scheduling management. This extensive operation mode may lead to the idle waste of some distributed resources or low power supply performance. Therefore, it is necessary to establish an optimized scheduling strategy and system for unified management of distributed resources. In existing research, some studies consider transforming small hydropower stations into micro-pumped storage systems, and then establishing an optimized model for improving photovoltaic accommodation and an intraday rolling optimization strategy. This method requires appropriate transformation of the hydropower station to make it have both power supply and load characteristics, ignoring the complementarity between hydropower and photovoltaic power generation and the ability to cooperate with photovoltaic power generation. Some studies have discovered the complementary characteristics between small hydropower stations and photovoltaic power generation, and calculated the output power of the hydropower station cluster based on the power system sending end surface, or achieved the collaborative control of small hydropower clusters by optimizing the complementary accommodation model of distributed water / photovoltaic power generation clusters. However, on the one hand, the relevant research ignores the differences between hydropower stations in the small hydropower cluster, especially the performance in response speed, regulation energy, and energy loss, which will result in low dynamic response and high energy consumption in collaborative control. On the other hand, the relevant research ignores the advantages shown by day-ahead and intraday forecasts in the prediction scheduling strategy of small hydropower clusters at different time scales. Summary of the Invention

[0004] The invention provides a conditional target optimization scheduling method and system for time-sharing and grouping of a photovoltaic and hydropower cluster, which is used to solve the technical problems of low dynamic response and high energy consumption in the collaborative control of small hydropower clusters.

[0005] In a first aspect, the present invention provides a conditional target optimization scheduling method for optical-hydro cluster time-sharing and grouping, including:

[0006] Using the technical parameters of each hydropower station in the hydropower station cluster within the distribution network, constructing a feature vector representing the operation ability of the hydropower station, and using a machine learning method to divide each hydropower station into a basic group hydropower station cluster and a regulating group hydropower station cluster;

[0007] Based on the historical data of the photovoltaic power generation system and the historical load data, constructing a first-day-ahead prediction model and a first-day-in prediction model for the photovoltaic power generation power, as well as a second-day-ahead prediction model and a second-day-in prediction model for the load power consumption;

[0008] According to the first-day-ahead prediction model and the second-day-ahead prediction model, constructing a day-ahead prediction scheduling optimization model for the basic group hydropower station cluster, and adopting a two-stage conditional optimization method to form a first scheduling plan for the basic group hydropower station cluster;

[0009] According to the first-day-in prediction model and the second-day-in prediction model, constructing an in-day prediction scheduling optimization model for the regulating group hydropower station cluster, and adopting a two-stage conditional optimization method to form a second scheduling plan for the regulating group hydropower station cluster.

[0010] In a second aspect, the present invention provides a conditional target optimization scheduling system for optical-hydro cluster time-sharing and grouping, including:

[0011] A division module configured to use the technical parameters of each hydropower station in the hydropower station cluster within the distribution network to construct a feature vector representing the operation ability of the hydropower station, and use a machine learning method to divide each hydropower station into a basic group hydropower station cluster and a regulating group hydropower station cluster;

[0012] A first construction module configured to construct a first-day-ahead prediction model and a first-day-in prediction model for the photovoltaic power generation power based on the historical data of the photovoltaic power generation system and the historical load data, as well as a second-day-ahead prediction model and a second-day-in prediction model for the load power consumption;

[0013] A second construction module configured to construct a day-ahead prediction scheduling optimization model for the basic group hydropower station cluster according to the first-day-ahead prediction model and the second-day-ahead prediction model, and adopt a two-stage conditional optimization method to form a first scheduling plan for the basic group hydropower station cluster;

[0014] A third construction module configured to construct an in-day prediction scheduling optimization model for the regulating group hydropower station cluster according to the first-day-in prediction model and the second-day-in prediction model, and adopt a two-stage conditional optimization method to form a second scheduling plan for the regulating group hydropower station cluster.

[0015] In a third aspect, an electronic device is provided, which includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the steps of the conditional target optimization scheduling method for time-sharing and grouping of the optical water cluster according to any embodiment of the present invention.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the program instructions are executed by a processor, the processor is caused to execute the steps of the conditional target optimization scheduling method for time-sharing and grouping of the optical water cluster according to any embodiment of the present invention.

[0017] The conditional target optimization scheduling method and system for time-sharing and grouping of the optical water cluster in this application consider the global and low time-resolution characteristics of day-ahead prediction and the accuracy and high time-resolution characteristics of intra-day rolling prediction, and establish a time-sharing and grouping scheduling strategy for the small hydropower station cluster. In addition, factors such as water consumption and switching of small hydropower are considered in the scheduling strategy, and a two-stage conditional target optimization method is established. While cooperating with photovoltaic power generation to supply load electricity, the operating performance of the small hydropower cluster is improved, which is beneficial to promoting the matching degree of the coordinated operation of the distributed small hydropower cluster and the distributed photovoltaic power generation system, and enhancing the overall energy utilization rate of the power generation cluster and the stability of power supply to the load. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a flowchart of a conditional target optimization scheduling method for time-sharing and grouping of an optical water cluster provided by an embodiment of the present invention;

[0020] Figure 2 It is a structural block diagram of a conditional target optimization scheduling system for time-sharing and grouping of an optical water cluster provided by an embodiment of the present invention;

[0021] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1 , which shows a flowchart of a conditional objective optimization scheduling method for optical - water cluster time - sharing and grouping in this application.

[0024] As Figure 1 shown, the conditional objective optimization scheduling method for optical - water cluster time - sharing and grouping specifically includes the following steps:

[0025] Step S101: Using the technical parameters of each hydropower station in the hydropower station cluster within the distribution network, construct a feature vector representing the operation ability of the hydropower station, and use machine learning methods to divide each hydropower station into a basic - group hydropower station cluster and a regulating - group hydropower station cluster.

[0026] In this step, define the indicators representing the regulation speed and regulation range of the hydropower station as the characteristic variables for hydropower station grouping, and use expert experience to define the importance of different characteristic variables. The indicators representing the regulation speed of the hydropower station include response speed, ramp rate, etc.; the indicators representing the regulation range of the hydropower station include installed capacity, rated water head, etc. In the feature vector, the features representing the regulation speed (such as response speed, ramp rate, etc.) are more important for grouping than the features representing the scale of the hydropower station (such as installed capacity, rated water head, etc.).

[0027] Based on the feature vectors of each hydropower station in the hydropower station cluster and the importance of the features, use the clustering method in machine learning to divide each hydropower station into a basic - group hydropower station cluster and a regulating - group hydropower station cluster. The basic - group hydropower station cluster is a hydropower station cluster with slow regulation speed and large regulation range; the regulating - group hydropower station cluster is a hydropower station cluster with fast regulation speed and small regulation range.

[0028] Step S102: Based on the historical data of the photovoltaic power generation system and the historical data of the load, construct a first - day - ahead prediction model and a first - day - within prediction model for the photovoltaic power generation power, and a second - day - ahead prediction model and a second - day - within prediction model for the load power consumption.

[0029] In this step, based on the hourly power generation data and meteorological data in the history of the photovoltaic power generation system, a first-day-ahead prediction model of the photovoltaic power generation is established by using machine learning methods. The output of the first-day-ahead prediction model is the power generation of the photovoltaic power generation system on the second day. The input of the first-day-ahead prediction model is the photovoltaic power generation and meteorological data on the same date as the second day in history, and the prediction error of the photovoltaic power generation system every day is statistically analyzed to form a predicted value of the minimum power generation of the photovoltaic power generation system on the second day;

[0030] Based on the hourly electrical power of the load in history, a second-day-ahead prediction model of the load electrical power is established by using a neural network. The output of the second-day-ahead prediction model is the electrical power consumption of the load on the second day. The input of the second-day-ahead prediction model is the load electrical power on the same date as the second day in history, and the prediction error of the load electrical power consumption every day is statistically analyzed to form a predicted value of the minimum electrical power consumption of the load;

[0031] Based on the minute-level power generation data and meteorological data in the history of the photovoltaic power generation, a first-day-within prediction model of the photovoltaic power generation is established by using an autoregressive prediction method. The output of the first-day-within prediction model is the active power generation of the photovoltaic power generation in a future period after the current moment. The input of the first-day-within prediction model is the active power generation of the photovoltaic power generation in a period before the current moment;

[0032] Based on the minute-level electrical power of the load in history, a second-day-within prediction model of the load electrical power is established by using an autoregressive prediction method. The output of the second-day-within prediction model is the electrical power consumption of the load in a future period after the current moment. The input of the second-day-within prediction model is the electrical power consumption of the load in a period before the current moment.

[0033] Step S103, construct a day-ahead prediction and scheduling optimization model for the basic group of hydropower stations clusters according to the first-day-ahead prediction model and the second-day-ahead prediction model, and adopt a two-stage conditional optimization method to form a first scheduling plan for the basic group of hydropower stations clusters.

[0034] In this step, define the predicted value of the minimum power generation of the photovoltaic power generation system on the second day as and the predicted value of the minimum electrical power consumption of the load on the second day as , is the termination time on the second day, is the predicted value of the power generation of the photovoltaic power generation system at the Tth moment, is the predicted value of the electrical power consumption of the load at the Tth moment;

[0035] Assume that the number of hydropower stations used for the second-day scheduling in the regulating group of hydropower stations clusters is , the th hydropower station in the regulating group of hydropower stations clusters has an installed capacity of , the maximum regulation capacity of the regulated group of hydropower stations is , and according to the predicted minimum power generation value of the photovoltaic power generation system on the next day and the predicted minimum power consumption value of the load on the next day, calculate the output power range of the basic group of hydropower stations at each moment on the next day as ;

[0036] Taking the minimum water consumption of each hydropower station in the basic group of hydropower stations as the objective function, establish the first-stage optimal dispatching model of each hydropower station in the basic group of hydropower stations, and use the classical optimization method to solve the minimum water consumption of the basic group of hydropower stations;

[0037] Taking the minimum water consumption of the basic group of hydropower stations as the condition and the minimum switching of each hydropower station in the basic group of hydropower stations as the objective function, establish the second-stage optimal dispatching model of each hydropower station in the basic group of hydropower stations, and use the classical optimization method to solve the first dispatching plan of the basic group of hydropower stations.

[0038] It should be noted that the optimization variables in the first-stage optimal dispatching model of each hydropower station in the basic group of hydropower stations are determined. Among them, the optimization variables are the operating powers of each hydropower station in the basic group of hydropower stations at each moment on the next day. Define the th hydropower station in the basic group of hydropower stations at the output power at the moment is ;

[0039] Determine the optimization objective function representing the overall water consumption of the hydropower stations in the basic group of hydropower stations , and the expression is:

[0040] ,

[0041] In the formula, is the number of hydropower stations in the basic group of hydropower stations, is the head of the kth hydropower station in the basic group of hydropower stations, is the termination time on the next day, is the functional relationship between the water consumption of each hydropower station and the head and operating power;

[0042] Determine the operating constraints of each hydropower station in the basic group of hydropower stations, and the expression is:

[0043] ,

[0044] ,

[0045] ,

[0046] In the formula, is the maximum ramp rate of the kth hydropower station in the basic group hydropower station cluster, is the installed capacity of the kth hydropower station in the basic group of hydropower station clusters, For load The predicted power consumption value at the time, Photovoltaic power generation system The predicted power generation value at the time, The first hydropower station in the basic group Hydropower stations in Output power at the moment;

[0047] The optimization objective function is solved by using the classical optimization method to obtain the minimum water consumption of the basic group of hydropower station clusters. .

[0048] Specifically, the optimization variables in the second-stage optimization scheduling model of each hydropower station in the basic group hydropower station cluster are determined, wherein the optimization variables include the operating power and operating status of each hydropower station in the basic group hydropower station cluster at each moment on the second day, the first Hydropower stations in The output power and operating status at the moment are and , is a binary optimization variable of 0-1. =1, indicating the first Hydropower stations in Always in the start-up state, =0, indicating the first Hydropower stations in Always in shutdown state;

[0049] Determine the conditional objective function that represents the minimum number of state switching times of the hydropower stations in the basic group hydropower station cluster under the condition of minimum overall water consumption , the expression is:

[0050] ,

[0051] Where, The first hydropower station in the basic group Hydropower stations in The operating status at all times, is the minimum water consumption of the basic group hydropower station cluster, The optimization objective function for the overall water consumption of hydropower stations in the basic group hydropower station cluster;

[0052] Determine the operating constraints of each hydropower station in the basic group hydropower station cluster, expressed as:

[0053] ,

[0054] ,

[0055] ,

[0056] Where, is the maximum ramp rate of the kth hydropower station in the basic group hydropower station cluster, is the installed capacity of the kth hydropower station in the basic group of hydropower station clusters, For load The predicted power consumption value at the time, Photovoltaic power generation system The predicted power generation value at the time, The first hydropower station in the basic group Hydropower stations in Output power at the moment;

[0057] The conditional objective function is solved using a classical optimization method, and the operating power and operating status of each hydropower station in the basic group hydropower station cluster at each moment of the next day are obtained to form a first scheduling plan for the basic group hydropower station cluster.

[0058] Step S104: construct an intraday forecasting scheduling optimization model for the hydropower station cluster of the regulation group based on the first intraday forecasting model and the second intraday forecasting model, and adopt a two-stage conditional optimization method to form a second scheduling plan for the hydropower station cluster of the regulation group.

[0059] In this step, define The predicted power generation value of the photovoltaic power generation system at this moment is and in The power consumption forecast value of the load at the moment is , is the intraday forecast length, For photovoltaic power generation systems The predicted power generation value at the time, For load The predicted value of power consumption at the moment;

[0060] Assume that During the period, the hydropower station cluster of the regulation group is at any time The number of hydropower stations is , the first in the hydropower station cluster of the regulation group At any given moment, a hydropower station The power generation is , the basic group hydropower station cluster as a whole at any time The power generation is , is the number of hydropower stations in the basic group hydropower station cluster, is the th hydropower station in the basic group hydropower station cluster at any moment output power, then the expression of the constraint condition for the power generation of the hydropower stations in the regulating group hydropower station cluster is:

[0061] ,

[0062] In the formula, is the predicted value of the power consumption of the load at any moment , is the predicted value of the power generation of the photovoltaic power generation system at any moment , is the allowable threshold for the imbalance between load power consumption and power generation of the power source;

[0063] Taking the minimum water consumption of each hydropower station in the regulating group hydropower station cluster as the objective function, establish the third-stage optimal scheduling model of each hydropower station in the regulating group hydropower station cluster, and use the classical optimization method to solve the minimum water consumption of the regulating group hydropower station cluster;

[0064] Taking the minimum water consumption of the regulating group hydropower station cluster as the condition and the minimum switching of each hydropower station in the regulating group hydropower station cluster as the objective function, establish the fourth-stage optimal scheduling model of each hydropower station in the regulating group hydropower station cluster, and use the classical optimization method to solve the optimal scheduling sequence of each hydropower station in the regulating group hydropower station cluster for a period of time in the future at the current moment. Among them, the scheduling plan of each hydropower station in the regulating group hydropower station cluster at the first predicted moment in the optimal scheduling sequence is the second scheduling plan of the regulating group hydropower station cluster.

[0065] It should be noted that the optimization variables in the third-stage optimal scheduling model of each hydropower station in the regulating group hydropower station cluster are determined. The optimization variables are the operating powers of each hydropower station in the regulating group hydropower station cluster for a period of time in the future at the current moment. Define the output power of the th hydropower station in the regulating group hydropower station cluster at any moment in the period as ;

[0066] Determine the optimization objective function representing the overall water consumption of the hydropower stations in the regulating group hydropower station cluster , and the expression is:

[0067] ,

[0068] In the formula, is the head of the kth hydropower station in the regulating group hydropower station cluster, It is the functional relationship between the water consumption, head, and operating power of each hydropower station;

[0069] Determine the operating constraints of each hydropower station in the regulated hydropower station cluster. The expression is:

[0070] ,

[0071] ,

[0072] In the formula, is the maximum ramp rate of the th hydropower station in the regulated hydropower station cluster, is the installed capacity of the th hydropower station in the regulated hydropower station cluster, is the power generation of the th hydropower station in the regulated hydropower station cluster at time;

[0073] Use the classical optimization method to solve the optimization objective function , and obtain the minimum water consumption of the regulated hydropower station cluster.

[0074] Specifically, determine the optimization variables in the fourth-stage optimal scheduling model of each hydropower station in the regulated hydropower station cluster. Among them, the optimization variables include the operating power and operating status of each hydropower station in the regulated hydropower station cluster in a future period of time. Assume that at any time in the period, the output power of the th hydropower station in the regulated hydropower station cluster is and the operating status is , is a binary optimization variable of 0-1. When = 1, it means that the th hydropower station in the regulated hydropower station cluster is in the operating state at any time . When = 0, it means that the th hydropower station in the regulated hydropower station cluster is in the shutdown state at any time ;

[0075] Determine the conditional objective function that represents the minimum number of state switches of the hydropower stations in the regulated hydropower station cluster under the condition of minimum overall water consumption, and the expression is:

[0076] ,

[0077] In the formula, To optimize the objective function of the overall water consumption of the hydropower stations in the hydropower station cluster, To regulate the minimum water consumption of the hydropower station cluster, The jth hydropower station in the basic group hydropower station cluster is The operating status at the moment;

[0078] Determine the operating constraints of each hydropower station in the hydropower station cluster of the regulation group, and the expression is:

[0079] ,

[0080] ,

[0081] Where, It is the first hydropower station in the regulation group The maximum ramp rate of a hydropower station, It is the first hydropower station in the regulation group The installed capacity of each hydropower station;

[0082] Solve the conditional objective function using classical optimization methods , and obtain the operating power and operating status of each hydropower station in the regulation group hydropower station cluster in the future period, forming an optimized scheduling sequence for each hydropower station in the regulation group hydropower station cluster in the future period.

[0083] In a specific embodiment, it is determined whether to perform the day-ahead forecast scheduling optimization of the small hydropower cluster. If the day-ahead forecast scheduling optimization of the small hydropower cluster is performed, the first scheduling scheme is performed; if not, it is determined whether to continue to perform the intraday forecast scheduling optimization of the small hydropower cluster;

[0084] If the intraday forecast scheduling optimization of the small hydropower cluster is executed, the second scheduling plan is carried out; if not, the scheduling of the small hydropower cluster is completed and the restart instruction is waited for.

[0085] In summary, in the actual application of current distributed hydropower and photovoltaic power generation systems, most distributed hydropower and photovoltaic clusters operate independently, lacking unified optimal scheduling management. Existing research considers unified control of distributed resources. Some research considers transforming small hydropower stations into micro-pumped storage systems and establishing an optimization model for improving photovoltaic power consumption and an intraday rolling optimization strategy. This method endows the transformed pumped storage power station with dual characteristics of power source and load, but increases the transformation cost and ignores the complementarity between hydropower and photovoltaic power generation and the ability to cooperate with photovoltaic power generation. Some research is based on the complementary characteristics of small hydropower stations and photovoltaic power generation, calculates the output power of the hydropower station cluster according to the power system sending end face, or realizes the coordinated control of the small hydropower cluster by optimizing the complementary power consumption model of the distributed water / light power generation cluster. However, on the one hand, the relevant research ignores the differences between hydropower stations in the small hydropower cluster, especially the performance in terms of response speed, regulation energy, and energy loss, which will result in less than satisfactory performance in the coordinated control process. On the other hand, the relevant research ignores the differences shown by hydropower stations with different performances in the small hydropower cluster in terms of the prediction scheduling strategy at different time scales. Therefore, the present invention proposes a conditional objective optimization scheduling method for time-sharing and grouping of a distributed light-water cluster. Compared with the prior art, the technical solution of the present application considers the global and low time resolution characteristics of day-ahead prediction and the accuracy and high time resolution characteristics of intraday rolling prediction, effectively groups the small hydropower clusters in the distribution network, and establishes a scheduling strategy for the small hydropower station cluster with different time scales. In addition, factors such as water consumption and switching of small hydropower are considered in the scheduling strategy, and a two-stage conditional objective optimization method is established to improve the operating performance of the small hydropower cluster while cooperating with photovoltaic power generation to supply load power consumption. The present application will be conducive to promoting the matching degree of the coordinated operation of the distributed small hydropower cluster and the distributed photovoltaic power generation system, and improving the overall energy utilization rate of the power generation cluster and the stability of power supply to the load.

[0086] Please refer to Figure 2 , which shows a structural block diagram of a conditional objective optimization scheduling system for time-sharing and grouping of a light-water cluster according to the present application.

[0087] As Figure 2 shown, the conditional objective optimization scheduling system 200 includes a division module 210, a first construction module 220, a second construction module 230, and a third construction module 240.

[0088] Among them, the partitioning module 210 is configured to construct a feature vector representing the operation ability of a hydropower station by using the technical parameters of each hydropower station in the hydropower station cluster in the distribution network, and use a machine learning method to divide each hydropower station into a basic group hydropower station cluster and a regulating group hydropower station cluster; the first construction module 220 is configured to construct a first day-ahead prediction model and a first intraday prediction model of the photovoltaic power generation based on the historical data of the photovoltaic power generation system and the historical data of the load, as well as a second day-ahead prediction model and a second intraday prediction model of the load power consumption; the second construction module 230 is configured to construct a day-ahead prediction scheduling optimization model of the basic group hydropower station cluster according to the first day-ahead prediction model and the second day-ahead prediction model, and adopt a two-stage conditional optimization method to form a first scheduling plan for the basic group hydropower station cluster; the third construction module 240 is configured to construct an intraday prediction scheduling optimization model of the regulating group hydropower station cluster according to the first intraday prediction model and the second intraday prediction model, and adopt a two-stage conditional optimization method to form a second scheduling plan for the regulating group hydropower station cluster.

[0089] It should be understood that Figure 2 the modules described in Figure 1 correspond to the respective steps in the method described in the reference Figure 2 Therefore, the operations, features, and corresponding technical effects described above for the method also apply to

[0090] In some other embodiments, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the program instructions are executed by a processor, the processor is caused to execute the light-water cluster time-sharing-group conditional target optimization scheduling method in any of the above method embodiments;

[0091] As an implementation manner, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are set as:

[0092] Construct a feature vector representing the operation ability of a hydropower station by using the technical parameters of each hydropower station in the hydropower station cluster in the distribution network, and use a machine learning method to divide each hydropower station into a basic group hydropower station cluster and a regulating group hydropower station cluster;

[0093] Construct a first day-ahead prediction model and a first intraday prediction model of the photovoltaic power generation based on the historical data of the photovoltaic power generation system and the historical data of the load, as well as a second day-ahead prediction model and a second intraday prediction model of the load power consumption;

[0094] Construct a day-ahead prediction scheduling optimization model of the basic group hydropower station cluster according to the first day-ahead prediction model and the second day-ahead prediction model, and adopt a two-stage conditional optimization method to form a first scheduling plan for the basic group hydropower station cluster;

[0095] Construct an intraday prediction and scheduling optimization model for the regulating group hydropower station cluster based on the first-day prediction model and the second-day prediction model, and adopt a two-stage conditional optimization method to form the second scheduling plan for the regulating group hydropower station cluster.

[0096] A computer-readable storage medium may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the time-sharing-grouping conditional objective optimization scheduling system of the optical and hydropower cluster, etc. In addition, the computer-readable storage medium may include high-speed random access memory, and may also include memories, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the computer-readable storage medium may optionally include a memory remotely provided with respect to the processor, and these remote memories can be connected to the time-sharing-grouping conditional objective optimization scheduling system of the optical and hydropower cluster through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0097] Figure 3 is a schematic structural diagram of the electronic device provided by an embodiment of the present invention, as Figure 3 shown, the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected through a bus or other means, Figure 3 taking the connection through the bus as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running non-volatile software programs, instructions, and modules stored in the memory 320, that is, implements the time-sharing-grouping conditional objective optimization scheduling method of the optical and hydropower cluster in the above method embodiment. The input device 330 can receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the time-sharing-grouping conditional objective optimization scheduling system of the optical and hydropower cluster. The output device 340 may include display devices such as a display screen.

[0098] The above electronic device can execute the method provided by the embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in this embodiment, reference can be made to the method provided by the embodiment of the present invention.

[0099] As an implementation manner, the above electronic device is applied to a conditional target optimization scheduling system for optical water cluster time-sharing and grouping, and is used for a client, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0100] Adopt the technical parameters of each hydropower station in the hydropower station cluster in the distribution network to construct a feature vector representing the operation ability of the hydropower station, and use machine learning methods to divide each hydropower station into a basic group hydropower station cluster and a regulating group hydropower station cluster;

[0101] Based on the historical data of the photovoltaic power generation system and the historical data of the load, construct a first day-ahead prediction model and a first intra-day prediction model of the photovoltaic power generation power, and a second day-ahead prediction model and a second intra-day prediction model of the load power consumption;

[0102] According to the first day-ahead prediction model and the second day-ahead prediction model, construct a day-ahead prediction scheduling optimization model for the basic group hydropower station cluster, and adopt a two-stage conditional optimization method to form a first scheduling plan for the basic group hydropower station cluster;

[0103] According to the first intra-day prediction model and the second intra-day prediction model, construct an intra-day prediction scheduling optimization model for the regulating group hydropower station cluster, and adopt a two-stage conditional optimization method to form a second scheduling plan for the regulating group hydropower station cluster.

[0104] Through the description of the above implementation manners, those skilled in the art can clearly understand that each implementation manner can be realized by means of software plus a necessary general hardware platform, and of course, it can also be realized by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0105] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A time-sharing and group-based conditional target optimization scheduling method for optical and water clusters, characterized in that: include: The technical parameters of each hydropower station in the distribution network cluster are used to construct a feature vector representing the operating capacity of the hydropower station. The hydropower stations are then divided into a basic group hydropower station cluster and a regulation group hydropower station cluster using machine learning methods. Based on the historical data of the photovoltaic power generation system and the historical load data, a one-day-ahead forecast model and a one-day-intraday forecast model for photovoltaic power generation, as well as a two-day-ahead forecast model and a two-day-intraday forecast model for load power consumption are constructed; A day-ahead forecasting and scheduling optimization model for the basic group of hydropower station clusters is constructed based on the first day-ahead forecasting model and the second day-ahead forecasting model, and a two-stage conditional optimization method is used to form a first scheduling plan for the basic group of hydropower station clusters. The day-ahead forecasting and scheduling optimization model for the basic group of hydropower station clusters is constructed based on the first day-ahead forecasting model and the second day-ahead forecasting model, and a two-stage conditional optimization method is used to form the first scheduling plan for the basic group of hydropower station clusters. The minimum power generation forecast value of the photovoltaic power generation system on the second day is defined as And the minimum power consumption forecast value of the load on the next day is , The end of the second day, is the predicted power generation value of the photovoltaic power generation system at the Tth moment, is the predicted value of load power consumption at time T; Assume that the number of hydropower stations in the regulation group hydropower station cluster for the next day's dispatch is , the first in the hydropower station cluster of the regulation group The installed capacity of the hydropower station is , then the maximum regulation capacity of the hydropower station cluster of the regulation group is According to the predicted value of the minimum power generation of the photovoltaic power generation system on the second day and the predicted value of the minimum power consumption of the load on the second day, the output power range of the hydropower station cluster of the regulation group at each moment on the second day is calculated as follows: ; Taking the minimum water consumption of the entire hydropower station cluster as the objective function, a first-stage optimization scheduling model for each hydropower station in the basic group hydropower station cluster is established, and the minimum water consumption of the basic group hydropower station cluster is solved using classical optimization methods. Taking the minimum water consumption of the basic hydropower station cluster as the condition and the minimum switching of each hydropower station in the basic hydropower station cluster as the objective function, a second-stage optimization scheduling model for each hydropower station in the basic hydropower station cluster is established, and the first scheduling plan of the basic hydropower station cluster is solved using classical optimization methods. The objective function is to establish a first-stage optimization scheduling model for each hydropower station in the basic group hydropower station cluster, taking the minimum water consumption of the entire hydropower station cluster as the objective function, and using the classical optimization method to solve the minimum water consumption of the basic group hydropower station cluster. The results include: Determine the optimization variables in the first phase optimization scheduling model of each hydropower station in the basic group hydropower station cluster, where the optimization variables are the output power of each hydropower station in the basic group hydropower station cluster at each time on the second day, and define the first phase optimization scheduling model of each hydropower station in the basic group hydropower station cluster. Hydropower stations in The output power at the moment is ; Determine the optimization objective function that represents the overall water consumption of hydropower stations in the basic group of hydropower stations , the expression is: ， Where, is the number of hydropower stations in the basic group hydropower station cluster, is the head of the kth hydropower station in the basic group of hydropower station clusters, The end of the second day, The functional relationship between water consumption, hydraulic head and output power of each hydropower station; Determine the operating constraints of each hydropower station in the basic group hydropower station cluster, expressed as: ， ， ， Where, is the maximum ramp rate of the kth hydropower station in the basic group hydropower station cluster, is the installed capacity of the kth hydropower station in the basic group of hydropower station clusters, For load The predicted power consumption value at the time, Photovoltaic power generation system The predicted power generation value at the time, The first hydropower station in the basic group Hydropower stations in Output power at the moment; The optimization objective function is solved by using the classical optimization method to obtain the minimum water consumption of the basic group of hydropower station clusters. ; The second-stage optimization scheduling model for each hydropower station in the basic group hydropower station cluster is established based on the minimum water consumption of the basic group hydropower station cluster and the minimum state switching number of each hydropower station in the basic group hydropower station cluster as the objective function, and the first scheduling plan for the basic group hydropower station cluster is solved using the classical optimization method. Determine the optimization variables in the second-stage optimization scheduling model of each hydropower station in the basic group hydropower station cluster, where the optimization variables include the output power and operating status of each hydropower station in the basic group hydropower station cluster at each time on the second day, the first Hydropower stations in The output power and operating status at the moment are and , is a binary optimization variable of 0-1. =1, indicating the first Hydropower stations in Always in the start-up state, =0, indicating the first Hydropower stations in Always in shutdown state; Determine the conditional objective function that represents the minimum number of state switching times of the hydropower stations in the basic group hydropower station cluster under the condition of minimum overall water consumption , the expression is: ， Where, The first hydropower station in the basic group Hydropower stations in The operating status at all times, is the minimum water consumption of the basic group hydropower station cluster, The optimization objective function for the overall water consumption of hydropower stations in the basic group hydropower station cluster; Determine the operating constraints of each hydropower station in the basic group hydropower station cluster, expressed as: ， ， ， Where, is the maximum ramp rate of the kth hydropower station in the basic group hydropower station cluster, is the installed capacity of the kth hydropower station in the basic group of hydropower station clusters, For load The predicted power consumption value at the time, Photovoltaic power generation system The predicted power generation value at the time, The first hydropower station in the basic group Hydropower stations in Output power at the moment; Solving the conditional objective function using a classical optimization method, and obtaining the output power and operating status of each hydropower station in the basic group hydropower station cluster at each time on the second day, thereby forming a first scheduling plan for the basic group hydropower station cluster; Constructing an intraday forecasting scheduling optimization model for the hydropower station cluster of the regulation group based on the first intraday forecasting model and the second intraday forecasting model, and adopting a two-stage conditional optimization method to form a second scheduling plan for the hydropower station cluster of the regulation group, wherein constructing an intraday forecasting scheduling optimization model for the hydropower station cluster of the regulation group based on the first intraday forecasting model and the second intraday forecasting model, and adopting a two-stage conditional optimization method to form the second scheduling plan for the hydropower station cluster of the regulation group includes: Defined in The predicted power generation value of the photovoltaic power generation system at this moment is and in The power consumption forecast value of the load at the moment is , is the intraday forecast length, For photovoltaic power generation systems The predicted power generation value at the time, For load The predicted value of power consumption at the moment; Assume that During the period, the hydropower station cluster of the regulation group is at any time The number of hydropower stations is , the first in the hydropower station cluster of the regulation group At any given moment, a hydropower station The power generation is , the basic group hydropower station cluster as a whole at any time The power generation is , is the number of hydropower stations in the basic group hydropower station cluster, The first hydropower station in the basic group At any given moment, a hydropower station The output power of the hydropower station cluster is regulated by the constraint condition of the power generation power of the hydropower station cluster: , Where, For any moment The load power consumption forecast value, For any moment The predicted value of the generated power of the photovoltaic power generation system, It is the permissible threshold for the imbalance between load power consumption and power generation; Taking the minimum water consumption of the entire hydropower station cluster as the objective function, a third-stage optimization scheduling model for each hydropower station in the regulation group is established, and the minimum water consumption of the regulation group hydropower station cluster is solved using classical optimization methods. Taking the minimum water consumption of the regulating group hydropower station cluster as a condition and the minimum number of state switching of each hydropower station in the regulating group hydropower station cluster as the objective function, a fourth-stage optimal scheduling model for each hydropower station in the regulating group hydropower station cluster is established, and the classical optimization method is used to solve the optimal scheduling sequence of each hydropower station in the regulating group hydropower station cluster for a period of time in the future at the current moment, wherein the scheduling scheme of each hydropower station in the regulating group hydropower station cluster at the first predicted moment in the optimal scheduling sequence is the second scheduling scheme of the regulating group hydropower station cluster; The objective function is to establish a third-stage optimization scheduling model for each hydropower station in the regulating group hydropower station cluster, taking the minimum water consumption of the entire hydropower station in the regulating group hydropower station cluster as the objective function, and using the classical optimization method to solve the minimum water consumption of the regulating group hydropower station cluster. Determine the optimization variables in the third stage optimization scheduling model of each hydropower station in the regulation group hydropower station cluster. The optimization variables are the output power of each hydropower station in the regulation group hydropower station cluster in the future at the current moment, which are defined in Any time during the period The regulation group under the hydropower station cluster The output power of a hydropower station is ; Determine the optimization objective function that represents the overall water consumption of the hydropower plants in the regulation group hydropower plant cluster , the expression is: ， Where, is the head of the kth hydropower station in the hydropower station cluster of the regulation group, The functional relationship between water consumption, hydraulic head and output power of each hydropower station; Determine the operating constraints of each hydropower station in the hydropower station cluster of the regulation group, and the expression is: ， ， Where, It is the first hydropower station in the regulation group The maximum ramp rate of a hydropower station, It is the first hydropower station in the regulation group The installed capacity of the hydropower station, It is the first hydropower station in the regulation group Hydropower stations in Power generation at the moment; Solve the optimization objective function using classical optimization methods , get the minimum water consumption of the hydropower station cluster in the regulation group ; The fourth-stage optimization scheduling model for each hydropower station in the regulating group hydropower station cluster is established based on the minimum water consumption of the regulating group hydropower station cluster and the minimum state switching number of each hydropower station in the regulating group hydropower station cluster as the objective function, and the optimal scheduling sequence for each hydropower station in the regulating group hydropower station cluster in the future at the current moment is solved using the classical optimization method. The following includes: Determine the optimization variables in the fourth stage optimization scheduling model of each hydropower station in the regulation group hydropower station cluster, where the optimization variables include the output power and operating status of each hydropower station in the regulation group hydropower station cluster in the future period. Any time during the period The regulation group under the hydropower station cluster The output power of a hydropower station is and the operating status is , is a binary optimization variable of 0-1. =1, indicating the first At any given moment, a hydropower station In operation, when =0, indicating the first At any given moment, a hydropower station In shutdown state; Determine the conditional objective function that represents the minimum number of state switching times of the hydropower stations in the regulation group under the condition of minimum overall water consumption , the expression is: ， Where, To optimize the objective function of the overall water consumption of the hydropower stations in the hydropower station cluster, To regulate the minimum water consumption of the hydropower station cluster, The jth hydropower station in the basic group hydropower station cluster is The operating status at the moment; Determine the operating constraints of each hydropower station in the hydropower station cluster of the regulation group, and the expression is: ， ， Where, It is the first hydropower station in the regulation group The maximum ramp rate of a hydropower station, It is the first hydropower station in the regulation group The installed capacity of each hydropower station; Solve the conditional objective function using classical optimization methods , and obtain the output power and operating status of each hydropower station in the regulation group hydropower station cluster in the future period, forming the optimized scheduling sequence of each hydropower station in the regulation group hydropower station cluster in the future period.

2. The method for optimizing the time-sharing and grouping conditional target scheduling of optical and water clusters according to claim 1, characterized in that: The method uses the technical parameters of each hydropower station in the hydropower station cluster in the distribution network to construct a feature vector representing the operating capacity of the hydropower station, and uses a machine learning method to divide each hydropower station into a basic group hydropower station cluster and a regulation group hydropower station cluster, including: Define indicators that characterize the regulation speed and regulation range of hydropower stations as characteristic variables for hydropower station grouping, and use expert experience to define the importance of different characteristic variables; Based on the feature vectors and feature importance of each hydropower station in the hydropower station cluster, the clustering method in machine learning is used to divide each hydropower station into a basic group hydropower station cluster and a regulation group hydropower station cluster.

3. The method for optimizing the time-sharing and grouping conditional target scheduling of optical and water clusters according to claim 1, characterized in that: The method of constructing a one-day-ahead prediction model and a one-day-intraday prediction model for photovoltaic power generation based on photovoltaic power generation system historical data and load historical data, as well as a two-day-ahead prediction model and a two-day-intraday prediction model for load power consumption, includes: Based on the historical hourly power generation and meteorological data of the photovoltaic power generation system, a machine learning method is used to establish a one-day-ahead prediction model for photovoltaic power generation. The output of the one-day-ahead prediction model is the power generation of the photovoltaic power generation system on the next day. The input of the one-day-ahead prediction model is the photovoltaic power generation and meteorological data of the same date on the next day. The prediction error of the photovoltaic power generation system on each day is statistically analyzed to form a predicted value for the minimum power generation of the photovoltaic power generation system on the next day. Based on the hourly power consumption history of the load, a neural network is used to establish a two-day-ahead prediction model for the load power consumption. The output of the two-day-ahead prediction model is the load power consumption on the next day. The input of the two-day-ahead prediction model is the load power consumption on the same date in history. The prediction error of the load power consumption on each day is statistically analyzed to form a predicted value for the minimum load power consumption. Based on the minute-level power generation data and meteorological data of photovoltaic power generation history, an autoregressive prediction method is used to establish a first-day prediction model for photovoltaic power generation. The output of the first-day prediction model is the photovoltaic active power generation in the future period after the current moment, and the input of the first-day prediction model is the photovoltaic active power generation in the period before the current moment. Based on the historical minute-level electric power consumption of the load, the autoregressive prediction method is used to establish a second-day prediction model for the load electric power consumption. The output of the second-day prediction model is the load electric power consumption in the future period after the current moment, and the input of the second-day prediction model is the load electric power consumption in the period before the current moment.

4. A time-sharing and group-based conditional target optimization scheduling system for light and water clusters, characterized by: include: a partitioning module configured to use technical parameters of each hydropower station in the hydropower station cluster within the distribution network to construct a feature vector representing the operating capacity of the hydropower station, and to use a machine learning method to divide each hydropower station into a basic group hydropower station cluster and a regulation group hydropower station cluster; A first building module is configured to build a first-day-ahead forecast model and a first-day-intraday forecast model for photovoltaic power generation, and a second-day-ahead forecast model and a second-day-intraday forecast model for load power based on photovoltaic power generation system historical data and load historical data; The second construction module is configured to construct a day-ahead forecasting and scheduling optimization model for the basic group of hydropower station clusters based on the first day-ahead forecasting model and the second day-ahead forecasting model, and adopt a two-stage conditional optimization method to form a first scheduling plan for the basic group of hydropower station clusters. The construction of the day-ahead forecasting and scheduling optimization model for the basic group of hydropower station clusters based on the first day-ahead forecasting model and the second day-ahead forecasting model, and adopting a two-stage conditional optimization method to form the first scheduling plan for the basic group of hydropower station clusters includes: The minimum power generation forecast value of the photovoltaic power generation system on the second day is defined as And the minimum power consumption forecast value of the load on the next day is , The end of the second day, is the predicted power generation value of the photovoltaic power generation system at the Tth moment, is the predicted value of load power consumption at time T; Assume that the number of hydropower stations in the regulation group hydropower station cluster for the next day's dispatch is , the first in the hydropower station cluster of the regulation group The installed capacity of the hydropower station is , then the maximum regulation capacity of the hydropower station cluster of the regulation group is According to the predicted value of the minimum power generation of the photovoltaic power generation system on the second day and the predicted value of the minimum power consumption of the load on the second day, the output power range of the hydropower station cluster of the regulation group at each moment on the second day is calculated as follows: ; Taking the minimum water consumption of the entire hydropower station cluster as the objective function, a first-stage optimization scheduling model for each hydropower station in the basic group hydropower station cluster is established, and the minimum water consumption of the basic group hydropower station cluster is solved using classical optimization methods. Taking the minimum water consumption of the basic hydropower station cluster as the condition and the minimum number of state switching of each hydropower station in the basic hydropower station cluster as the objective function, a second-stage optimization scheduling model for each hydropower station in the basic hydropower station cluster is established, and the first scheduling plan of the basic hydropower station cluster is solved using classical optimization methods. The objective function is to establish a first-stage optimization scheduling model for each hydropower station in the basic group hydropower station cluster, taking the minimum water consumption of the entire hydropower station cluster as the objective function, and using the classical optimization method to solve the minimum water consumption of the basic group hydropower station cluster. The results include: Determine the optimization variables in the first phase optimization scheduling model of each hydropower station in the basic group hydropower station cluster, where the optimization variables are the output power of each hydropower station in the basic group hydropower station cluster at each time on the second day, and define the first phase optimization scheduling model of each hydropower station in the basic group hydropower station cluster. Hydropower stations in The output power at the moment is ; Determine the optimization objective function that represents the overall water consumption of hydropower stations in the basic group of hydropower stations , the expression is: ， Where, is the number of hydropower stations in the basic group hydropower station cluster, is the head of the kth hydropower station in the basic group of hydropower station clusters, The end of the second day, The functional relationship between water consumption, hydraulic head and output power of each hydropower station; Determine the operating constraints of each hydropower station in the basic group hydropower station cluster, expressed as: ， ， ， Where, is the maximum ramp rate of the kth hydropower station in the basic group hydropower station cluster, is the installed capacity of the kth hydropower station in the basic group of hydropower station clusters, For load The predicted power consumption value at the time, Photovoltaic power generation system The predicted power generation value at the time, The first hydropower station in the basic group Hydropower stations in Output power at the moment; The optimization objective function is solved by using the classical optimization method to obtain the minimum water consumption of the basic group of hydropower station clusters. ; The second-stage optimization scheduling model for each hydropower station in the basic group hydropower station cluster is established based on the minimum water consumption of the basic group hydropower station cluster and the minimum state switching number of each hydropower station in the basic group hydropower station cluster as the objective function, and the first scheduling plan for the basic group hydropower station cluster is solved using the classical optimization method. Determine the optimization variables in the second-stage optimization scheduling model of each hydropower station in the basic group hydropower station cluster, where the optimization variables include the output power and operating status of each hydropower station in the basic group hydropower station cluster at each time on the second day, the first Hydropower stations in The output power and operating status at the moment are and , is a binary optimization variable of 0-1. =1, indicating the first Hydropower stations in Always in the start-up state, =0, indicating the first Hydropower stations in Always in shutdown state; Determine the conditional objective function that represents the minimum number of state switching times of the hydropower stations in the basic group hydropower station cluster under the condition of minimum overall water consumption , the expression is: ， Where, The first hydropower station in the basic group Hydropower stations in The operating status at all times, is the minimum water consumption of the basic group hydropower station cluster, The optimization objective function for the overall water consumption of hydropower stations in the basic group hydropower station cluster; Determine the operating constraints of each hydropower station in the basic group hydropower station cluster, expressed as: ， ， ， Where, is the maximum ramp rate of the kth hydropower station in the basic group hydropower station cluster, is the installed capacity of the kth hydropower station in the basic group of hydropower station clusters, For load The predicted power consumption value at the time, Photovoltaic power generation system The predicted power generation value at the time, The first hydropower station in the basic group Hydropower stations in Output power at the moment; Solving the conditional objective function using a classical optimization method, and obtaining the output power and operating status of each hydropower station in the basic group hydropower station cluster at each time on the second day, thereby forming a first scheduling plan for the basic group hydropower station cluster; The third construction module is configured to construct an intraday forecasting scheduling optimization model for the hydropower station cluster of the regulation group based on the first intraday forecasting model and the second intraday forecasting model, and adopt a two-stage conditional optimization method to form a second scheduling plan for the hydropower station cluster of the regulation group. The construction of the intraday forecasting scheduling optimization model for the hydropower station cluster of the regulation group based on the first intraday forecasting model and the second intraday forecasting model, and adopting a two-stage conditional optimization method to form the second scheduling plan for the hydropower station cluster of the regulation group includes: Defined in The predicted power generation value of the photovoltaic power generation system at this moment is and in The power consumption forecast value of the load at the moment is , is the intraday forecast length, For photovoltaic power generation systems The predicted power generation value at the time, For load The predicted value of power consumption at the moment; Assume that During the period, the hydropower station cluster of the regulation group is at any time The number of hydropower stations is , the first in the hydropower station cluster of the regulation group At any given moment, a hydropower station The power generation is , the basic group hydropower station cluster as a whole at any time The power generation is , is the number of hydropower stations in the basic group hydropower station cluster, The first hydropower station in the basic group At any given moment, a hydropower station The output power of the hydropower station cluster is regulated by the constraint condition of the power generation power of the hydropower station cluster: , Where, For any moment The load power consumption forecast value, For any moment The predicted value of the generated power of the photovoltaic power generation system, It is the permissible threshold for the imbalance between load power consumption and power generation; Taking the minimum water consumption of the entire hydropower station cluster as the objective function, a third-stage optimization scheduling model for each hydropower station in the regulation group is established, and the minimum water consumption of the regulation group hydropower station cluster is solved using classical optimization methods. Taking the minimum water consumption of the regulating group hydropower station cluster as a condition and the minimum number of state switching of each hydropower station in the regulating group hydropower station cluster as the objective function, a fourth-stage optimal scheduling model for each hydropower station in the regulating group hydropower station cluster is established, and the classical optimization method is used to solve the optimal scheduling sequence of each hydropower station in the regulating group hydropower station cluster for a period of time in the future at the current moment, wherein the scheduling scheme of each hydropower station in the regulating group hydropower station cluster at the first predicted moment in the optimal scheduling sequence is the second scheduling scheme of the regulating group hydropower station cluster; The objective function is to establish a third-stage optimization scheduling model for each hydropower station in the regulating group hydropower station cluster, taking the minimum water consumption of the entire hydropower station in the regulating group hydropower station cluster as the objective function, and using the classical optimization method to solve the minimum water consumption of the regulating group hydropower station cluster. Determine the optimization variables in the third stage optimization scheduling model of each hydropower station in the regulation group hydropower station cluster. The optimization variables are the output power of each hydropower station in the regulation group hydropower station cluster in the future at the current moment, which are defined in Any time during the period The regulation group under the hydropower station cluster The output power of a hydropower station is ; Determine the optimization objective function that represents the overall water consumption of the hydropower plants in the regulation group hydropower plant cluster , the expression is: ， Where, is the head of the kth hydropower station in the hydropower station cluster of the regulation group, The functional relationship between water consumption, hydraulic head and output power of each hydropower station; Determine the operating constraints of each hydropower station in the hydropower station cluster of the regulation group, and the expression is: ， ， Where, It is the first hydropower station in the regulation group The maximum ramp rate of a hydropower station, It is the first hydropower station in the regulation group The installed capacity of the hydropower station, It is the first hydropower station in the regulation group Hydropower stations in Power generation at the moment; Solve the optimization objective function using classical optimization methods , get the minimum water consumption of the hydropower station cluster in the regulation group ; The fourth-stage optimization scheduling model for each hydropower station in the regulating group hydropower station cluster is established based on the minimum water consumption of the regulating group hydropower station cluster and the minimum state switching number of each hydropower station in the regulating group hydropower station cluster as the objective function, and the optimal scheduling sequence for each hydropower station in the regulating group hydropower station cluster in the future at the current moment is solved using the classical optimization method. The following includes: Determine the optimization variables in the fourth stage optimization scheduling model of each hydropower station in the regulation group hydropower station cluster, where the optimization variables include the output power and operating status of each hydropower station in the regulation group hydropower station cluster in the future period. Any time during the period The regulation group under the hydropower station cluster The output power of a hydropower station is and the operating status is , is a binary optimization variable of 0-1. =1, indicating the first At any given moment, a hydropower station In operation, when =0, indicating the first At any given moment, a hydropower station In shutdown state; Determine the conditional objective function that represents the minimum number of state switching times of the hydropower stations in the regulation group under the condition of minimum overall water consumption , the expression is: ， Where, To optimize the objective function of the overall water consumption of the hydropower stations in the hydropower station cluster, To regulate the minimum water consumption of the hydropower station cluster, The jth hydropower station in the basic group hydropower station cluster is The operating status at the moment; Determine the operating constraints of each hydropower station in the hydropower station cluster of the regulation group, and the expression is: ， ， Where, It is the first hydropower station in the regulation group The maximum ramp rate of a hydropower station, It is the first hydropower station in the regulation group The installed capacity of each hydropower station; Solve the conditional objective function using classical optimization methods , and obtain the output power and operating status of each hydropower station in the regulation group hydropower station cluster in the future period, forming the optimized scheduling sequence of each hydropower station in the regulation group hydropower station cluster in the future period.

Images