Large reservoir group multi-target water storage scheduling method and system and storage medium

Abstract

The invention relates to a multi-target water storage scheduling method and system for a large reservoir group, and a storage medium. The method comprises the following steps: step 1, collecting watershed hydrological data; 2, determining an optimization target of combined water storage scheduling of the cascade reservoir group, and setting reservoir scheduling constraint conditions; further determining the priority of each scheduling target, and constructing a two-stage segmented optimization model framework; 3, introducing a partition-based large system aggregation-decomposition method, and optimizing by adopting a parameter-simulation-optimization method to obtain an initial Pareto frontier; 4, based on the initial Pareto frontier in the step 3, a parallel successive optimization algorithm is adopted to further improve a water storage strategy obtained through aggregation decomposition, and efficient utilization of water resources of all subareas is achieved. Optimized water storage management of the large reservoir group can be achieved, and the water storage efficiency of the large reservoir group is improved on the premise that the flood control standard of the original design is not lowered. And further utilization of limited water resources in each subarea in the drainage basin is promoted.

Description

Technical Field

[0001] The present application relates to the field of reservoir optimization scheduling methods, and in particular to a multi-objective water storage scheduling method, system and storage medium for a large reservoir group. Background Art

[0002] At present, many research works have discussed in detail the multi-objective optimization scheduling of reservoirs. Yun Ruan et al. used the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) to solve the two-objective optimization problem of a single reservoir, and obtained a sufficient and evenly distributed Pareto front. The results show that the NSGA-II algorithm is robust and stable, and performs well in the multi-objective optimization performance of reservoirs. Through direct strategy search by the NSGA-Ⅱ algorithm, the coordinated optimization solution set of power generation, ecology and water storage is obtained based on historical runoff data, in order to obtain a robust solution that adapts to future climate change conditions. The above studies have well demonstrated that the NSGA-Ⅱ intelligent algorithm can optimize small and medium-sized scheduling systems. However, intelligent algorithms represented by NSGA-Ⅱ still fall into the problem of dimensionality disaster in large multi-objective systems.

[0003] In response to the multi-objective "curse of dimensionality", the basic concept of "surrogate model" (SM) is proposed. Its core is to retain the main characteristic parameters of the initial reservoir system, ignore the connection between reservoirs (hydraulic and electric power), and greatly reduce the running time by reducing the number of decision variables and the internal complex model structure. For water storage scheduling, its primary goal is to fill the reservoir as much as possible at the end of the water storage period without reducing the existing flood control standards, so as to deal with the problems of compensation scheduling in the dry season in a timely manner. This requires that the water storage model should retain the state variables of the reservoir capacity at each moment; secondly, in order to overcome the curse of dimensionality, the SM model should also minimize the number of variables in the reservoir optimization system. On the other hand, cascade reservoirs are often divided into different partitions according to different reservoir characteristic parameters (such as reservoir capacity, installed capacity), their spatial geographical location information, flood control task planning, etc. In theory, reservoirs in the same partition with similar reservoir characteristic parameters can be aggregated into a "virtual reservoir" for decision scheduling. In summary, the joint multi-objective water storage of cascade reservoirs can overcome the curse of dimensionality through the "partition-aggregation-decomposition" method theory, in which the alternative model obtained by aggregation can track the storage capacity status in the form of "water aggregation", and then make corresponding scheduling decisions for the alternative model, and finally distribute the aggregation decision to each independent reservoir according to the decomposition strategy. This method can transform the complex water storage scheduling system into the scheduling problem of small and medium-sized reservoirs in the traditional sense, and achieve the final solution through the "parameter-simulation-optimization" framework structure and mature multi-objective intelligent algorithm. However, the decomposition strategy in the aggregation and decomposition theory sometimes cannot balance the relationship between the target effect and the decomposition efficiency well, thereby limiting its promotion and application in engineering practice. To this end, the present invention further improves the decomposition strategy by using a parallel successive optimization algorithm based on the optimized Pareto frontier obtained by aggregation and decomposition, and ensures the optimization operation efficiency of the algorithm, and finally solves the water resource allocation problem between different reservoirs in the same partition. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a multi-objective water storage scheduling method, system and storage medium for a large-scale reservoir group. On the basis of the optimized Pareto front obtained by aggregate decomposition, a parallel successive optimization algorithm is further used to improve the decomposition strategy, and the algorithm optimization operation efficiency is ensured, so as to finally solve the water resource allocation problem between different reservoirs in the same partition.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a multi-objective water storage scheduling method for a large reservoir group, comprising the following steps:

[0007] Step 1, collecting basin hydrological data;

[0008] Step 2: Determine the optimization target of the joint water storage operation of the cascade reservoir group and set the reservoir operation constraints; further clarify the priority of each operation target and construct a two-stage segmented optimization model framework;

[0009] Step 3: introduce the large system aggregation-decomposition method based on partitioning, and use the parameter-simulation-optimization method to optimize the initial Pareto frontier;

[0010] Step 4: Based on the initial Pareto frontier of step 3, a parallel successive optimization algorithm is used to further improve the water storage strategy obtained by aggregation decomposition to achieve efficient utilization of water resources in each partition.

[0011] The optimization objective of the joint water storage scheduling of the cascade reservoir group in step 2 is:

[0012] Under the condition of not exceeding the existing flood control standards, the reservoir is filled as much as possible at the end of the water storage period, and as much power generation benefits as possible are generated during the period.

[0013] and N k,i (t) = A k,i Q k,i (t)H k,i (t) means,

[0014] Among them, FCR and IE are the multi-year flood control risk and storage rate of the cascade reservoirs, respectively; E k is the power generation of the kth partition;

[0015] VE k,i (y) is the storage capacity of the i-th reservoir in the k-th zone at the end of the impoundment period;

[0016] SU k,i and SD k,i They represent the normal beneficial storage capacity and dead storage capacity of the i-th reservoir in the k-th zone respectively;

[0017] V k,i (t) and SS k,i (t) are the reservoir capacity of the i-th reservoir in the k-th zone at time period t and the reservoir capacity corresponding to the highest safe water level in front of the dam;

[0018] N k,i (t) is the average output of the i-th reservoir in the k-th sub-area during the t-th period; △t is the step interval during the water storage period; A k,i is the average power generation efficiency coefficient of the i-th reservoir in the k-th zone; Q k,i (t) and H k,i (t) are the power generation flow and power generation head of the i-th reservoir in the k-th zone during period t, respectively;

[0019] Y is the number of years in the scheduling period;

[0020] T is the total number of water storage periods each year;

[0021] K is the total number of partitions;

[0022] M k is the number of reservoirs in the kth partition.

[0023] The reservoir dispatching constraint conditions in step 2 are:

[0024] (a) Water balance constraints:

[0025]

[0026] In formula (4), I k,i (t) and O k,i (t) is the inflow and outflow flow of the i-th reservoir in the k-th zone during period t; Φ j,k,i represents the set of reservoirs that are hydraulically connected to the upstream reservoir of the i-th reservoir in the k-th zone; O j (t) is Φ j,k,i The outflow from reservoir j in the set;

[0027] (b) Reservoir capacity constraints:

[0028] SL k,i (t)≤V k,i (t)≤SS k,i (t) (5)

[0029] In formula (5), SL k,i (t) and SS k,i (t) are the minimum and maximum permissible water storage capacity of the i-th reservoir in the k-th zone during period t, respectively;

[0030] (c) Reservoir discharge flow constraints:

[0031] OL k,i (t)≤O k,i (t)≤OU k,i (t), O k,i (t) = Q k,i (t)+QS k,i (t) (6)

[0032] In formula (6), QL k,i (t) and QU k,i (t) are the minimum and maximum allowable discharge flow of the i-th reservoir in the k-th zone during period t, QS k,i (t) is the discharge flow of the reservoir;

[0033] (d) Power station output constraints:

[0034] PL k,i (t)≤N k,t (t)≤PU k,i (t) (7)

[0035] In formula (7), PL k,i and PU k,i They are the minimum and maximum output limits of the i-th reservoir in the k-th zone during period t, respectively;

[0036] (e) Boundary conditions:

[0037]

[0038] In formula (8), Z k,i,begin and Z k,i,end are the initial and final water levels of the ith reservoir in the kth subarea, respectively.

[0039] The large-scale system aggregation-decomposition method based on partition in step 3 adopts the aggregation-decomposition method of the same partition reservoir with water volume as the unit; the optimization method in the parameter-simulation-optimization framework adopts the second-generation non-dominated sorting genetic algorithm NSGA-II. In the aggregation stage, the storage capacity of the aggregated reservoir is given by the formula

[0040] (9) is fitted, and the specific aggregation formula is as follows:

[0041]

[0042]

[0043] In formulas (9)-(10), and are the aggregate storage capacity and runoff of the kth virtual reservoir at time t; I k,i (t) is the runoff from the outside into the i-th reservoir in the k-th sub-area at time t; Eva k (t) is the water loss of the kth partition at time t.

[0044] In the decomposition stage, the output results are decomposed according to the storage capacity ratio of each reservoir, so as to determine the storage capacity status of a single reservoir, as shown in formula (11):

[0045]

[0046] The water storage strategy obtained by further improving the aggregation decomposition by using the parallel successive optimization algorithm is to ensure that the storage rate and flood control risk results of the same partition are not distorted, the storage rate value is not lower than the original aggregation decomposition scheme, and the flood control risk value is not higher than the original aggregation decomposition scheme, so as to maximize the power generation benefit of the partition as much as possible, and should satisfy equations (12) and (13).

[0047]

[0048]

[0049]

[0050] In formulas (12)-(13), and They are the storage capacity and flood control risk of the kth partition obtained by the initial optimization of the NSGA-Ⅱ algorithm.

[0051] In the second aspect, an embodiment of the present application provides a multi-objective water storage and scheduling system for a large-scale reservoir group, including a memory and a processor, wherein the memory includes a program for a multi-objective water storage and scheduling method for a large-scale reservoir group, and the program for the multi-objective water storage and scheduling method for a large-scale reservoir group implements the following steps when executed by the processor: collecting basin hydrological data; determining the optimization target for the joint water storage and scheduling of the cascade reservoir group, and setting reservoir scheduling constraints; further clarifying the priority of each scheduling target, and constructing a two-stage segmented optimization model framework; introducing a partition-based large system aggregation-decomposition method, and using a parameter-simulation-optimization method to optimize the initial Pareto frontier; based on the initial Pareto frontier of the above steps, a parallel successive optimization algorithm is used to further improve the water storage strategy obtained by aggregation and decomposition, so as to achieve efficient utilization of water resources in each partition.

[0052] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the multi-objective water storage scheduling method for a large reservoir group are implemented as described above.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. Scientific and reasonable, close to engineering practice:

[0055] The "parameter-simulation-optimization" method based on partition aggregation decomposition of the present invention can effectively solve the "dimensionality curse" problem of multi-objective water storage scheduling of cascade reservoirs; thus providing a certain possibility for serving the multi-objective water storage optimization scheduling of cascade reservoirs;

[0056] 2. It can provide important and highly operable reference for reservoir dispatching:

[0057] Clarifying the priority of scheduling targets during the water storage period of cascade reservoirs, and adopting a "parallel successive optimization algorithm" can achieve further efficient utilization of limited water resources in the same region. Without increasing the flood control risk of cascade reservoirs and reducing the fullness performance of cascade reservoirs, the overall power generation efficiency of the region reservoir group can be maximized, which is suitable for further promotion and application in flood resource scheduling of giant reservoir groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 It is a specific flow chart of the method of the present invention;

[0060] Figure 2 The following are generalized diagrams of three topological structures of cascade reservoirs;

[0061] Figure 3 Schematic diagram of the aggregation and decomposition principle of cascade reservoirs. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0063] The terms "comprises," "comprising," or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0064] The terms "first", "second", etc. are only used to distinguish one entity or operation from another entity or operation, and should not be understood as indicating or implying relative importance, nor should they be understood as requiring or implying any such actual relationship or order between these entities or operations.

[0065] The present invention aims at the "dimensionality explosion" problem in the multi-objective water storage optimization scheduling of a large-scale reservoir group in the same river basin, comprehensively considers the multi-objective demands of flood control, water storage, power generation, ecology, etc. during the water storage period, adopts a control strategy based on partition aggregation decomposition to reduce the optimization state variables, and constructs a two-stage segmented optimization model framework according to the scheduling target priority, and finally realizes a variety of optimization scheduling schemes by using methods such as "parameter-simulation-optimization" and "parallel successive optimization algorithm" to provide different decision-making preferences for river basin managers.

[0066] The specific implementation process of the present invention is shown in Figure 1, the steps are as follows:

[0067] Step 1: Collect basin hydrological data.

[0068] Collect reservoir hydrological data, including inflow data observed at upstream stations, characteristic curves such as reservoir water level-storage capacity, reservoir water level-maximum discharge capacity, and discharge-reservoir tailwater level; and determine the water topology of the cascade reservoir group in the basin;

[0069] Step 2: Determine the optimization target of the joint water storage operation of the cascade reservoir group and set the reservoir operation constraints; further clarify the priority of each operation target and construct a two-stage segmented optimization model framework;

[0070] Under the condition of not exceeding the existing flood control standards, the reservoir is filled as much as possible at the end of the water storage period, and as much power generation benefits as possible are generated during the period. Its mathematical expression is:

[0071]

[0072]

[0073]

[0074] Among them, FCR and IE are the multi-year flood control risk and storage rate of the cascade reservoirs, respectively, and are the first priority in the two-stage optimization; E k is the power generation of the kth partition, which is the second priority in the two-stage optimization. That is, in water storage scheduling, the primary goal is to consider the fullness rate and flood control risk performance of the cascade reservoir group, followed by the power generation performance of the cascade reservoir.

[0075] VE k,i (y) is the storage capacity of the i-th reservoir in the k-th zone at the end of the impoundment period;

[0076] SU k,i and SD k,i They represent the normal beneficial storage capacity and dead storage capacity of the i-th reservoir in the k-th zone respectively;

[0077] V k,i (t) and SS k,i (t) are the reservoir capacity of the i-th reservoir in the k-th zone at time period t and the reservoir capacity corresponding to the highest safe water level in front of the dam;

[0078] N k,i (t) is the average output of the i-th reservoir in the k-th sub-area during the t-th period; △t is the step interval during the water storage period; A k,i is the average power generation efficiency coefficient of the i-th reservoir in the k-th zone; Q k,i (t) and H k,i (t) are the power generation flow and power generation head of the i-th reservoir in the k-th zone during period t, respectively;

[0079] Y is the number of years in the scheduling period;

[0080] T is the total number of water storage periods each year;

[0081] K is the total number of partitions;

[0082] M k is the number of reservoirs in the kth partition.

[0083] The following constraints are considered in this specific implementation:

[0084] (a) Water balance constraints:

[0085]

[0086] In formula (4), I k,i (t) and O k,i (t) is the inflow and outflow flow of the i-th reservoir in the k-th zone during period t; Φ j,k,i represents the set of reservoirs that are hydraulically connected to the upstream reservoir of the i-th reservoir in the k-th zone; O j (t) is Φ j,k,i The outflow from reservoir j in the set;

[0087] (b) Reservoir capacity constraints:

[0088] SL k,i (t)≤V k,i (t)≤SS k,i (t) (5)

[0089] In formula (5), SL k,i (t) and SS k,i (t) are the minimum and maximum permissible water storage capacity of the i-th reservoir in the k-th zone during period t, respectively;

[0090] (c) Reservoir discharge flow constraints:

[0091] OL k,i (t)≤O k,i (t)≤OU k,i (t), O k,i (t) = Q k,i (t)+QS k,i (t) (6)

[0092] In formula (6), QL k,i (t) and QU k,i (t) are the minimum and maximum allowable discharge flow of the i-th reservoir in the k-th zone during period t, QS k,i (t) is the discharge flow of the reservoir;

[0093] (d) Power station output constraints:

[0094] PL k,i (t)≤N k,t (t)≤PU k,i (t) (7)

[0095] In formula (7), PL k,i and PU k,i They are the minimum and maximum output limits of the i-th reservoir in the k-th zone during period t, respectively;

[0096] (e) Boundary conditions:

[0097]

[0098] In formula (8), Z k,i,begin and Z k,i,end are the initial and final water levels of the ith reservoir in the kth subarea, respectively.

[0099] Step 3: introduce the large system aggregation-decomposition method based on partitioning, and use the "parameter-simulation-optimization" method to optimize the initial Pareto frontier.

[0100] In this specific implementation, when the same sub-district reservoirs aggregate virtual reservoirs based on water volume, it is not necessary to consider the characteristic differences between reservoirs on different tributaries. Figure 2 As shown in Figure 1, regardless of whether the reservoir topology is in series, parallel or hybrid mode, the aggregate reservoir capacity can be fitted by formula (9). The specific aggregate formula is as follows:

[0101]

[0102]

[0103] In formulas (9)-(10), and are the aggregate storage capacity and runoff of the kth virtual reservoir at time t; I k,i (t) is the runoff from the outside into the i-th reservoir in the k-th sub-area at time t; Eva k (t) is the water loss of the kth partition at time t.

[0104] The present invention selects the storage capacity state of the aggregate reservoir (i.e., ) is the optimization variable of the “parameter-simulation-optimization” framework. After obtaining the total output of the virtual aggregate reservoir, the decision needs to be further decomposed into each single reservoir, such as Figure 3 In the decomposition stage, the output results can be decomposed according to the proportion of the available storage capacity of each reservoir, so as to determine the storage capacity status of a single reservoir, as shown in formula (11).

[0105]

[0106] The symbols of the parameters in the formula have been introduced above.

[0107] In this embodiment, the optimization method in the "parameter-simulation-optimization" framework adopts the second generation non-dominated sorting genetic algorithm (NSGA-II).

[0108] Step 4: Based on the initial Pareto frontier of step 3, a parallel successive optimization algorithm is used to further improve the water storage strategy obtained by aggregation decomposition to achieve efficient utilization of water resources in each partition.

[0109] In this specific implementation, the power generation benefit of the partition is maximized under the premise of ensuring that the storage rate and flood risk results of the same partition are not distorted (the storage rate value is not lower than the original aggregate decomposition scheme, and the flood risk value is not higher than the original aggregate decomposition scheme). The mathematical model satisfies the maximum target of formula (3). As for the scheduling constraints, in addition to satisfying the constraints such as water balance, reservoir output, and water level limit, it should also satisfy formulas (12) and (13).

[0110]

[0111]

[0112]

[0113] In formulas (12)-(13), and They are the storage capacity and flood control risk of the kth partition obtained by the initial optimization of the NSGA-Ⅱ algorithm.

[0114] In summary, the present invention aims at the "dimensionality explosion" problem in the multi-objective water storage optimization scheduling of large-scale reservoir groups in the same basin, comprehensively considers the multi-objective needs of flood control, water storage, power generation, ecology, etc. during the water storage period, adopts a control strategy based on partition aggregation decomposition to reduce the optimization state variables, and constructs a two-stage segmented optimization model framework according to the scheduling target priority, and finally achieves a variety of optimization scheduling schemes by using methods such as "parameter-simulation-optimization" and "parallel successive optimization algorithm" to provide different decision preferences for basin managers. The method of the present invention can realize the optimized water storage management of large-scale reservoir groups, and promote the further utilization of limited water resources in each partition in the basin without reducing the original design flood control standards, which has important engineering application value and practical significance.

[0115] The embodiment of the present application provides a large-scale reservoir group multi-objective water storage scheduling system, including a memory and a processor, wherein the memory includes a program of a large-scale reservoir group multi-objective water storage scheduling method, and the program of the large-scale reservoir group multi-objective water storage scheduling method implements the following steps when executed by the processor: collecting basin hydrological data; determining the optimization target of the joint water storage scheduling of the cascade reservoir group, and setting reservoir scheduling constraints; further clarifying the priority of each scheduling target, and constructing a two-stage segmented optimization model framework; introducing a large system aggregation-decomposition method based on partitions, and using a parameter-simulation-optimization method to optimize the initial Pareto frontier; based on the initial Pareto frontier of the above steps, using a parallel successive optimization algorithm to further improve the water storage strategy obtained by aggregation and decomposition, so as to achieve efficient utilization of water resources in each partition.

[0116] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the multi-objective water storage scheduling method for a large reservoir group described above are implemented.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0122] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0123] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0124] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-objective water storage scheduling method for a large reservoir group, characterized in that: The following steps are involved: Step 1, collecting basin hydrological data; Step 2: Determine the optimization target of the joint water storage operation of the cascade reservoir group and set the reservoir operation constraints; further clarify the priority of each operation target and construct a two-stage segmented optimization model framework; Step 3: introduce the large system aggregation-decomposition method based on partitioning, and use the parameter-simulation-optimization method to optimize the initial Pareto frontier; Step 4: Based on the initial Pareto frontier of step 3, a parallel successive optimization algorithm is used to further improve the water storage strategy obtained by aggregation decomposition to achieve efficient utilization of water resources in each partition.

2. A multi-objective water storage scheduling method for a large reservoir group according to claim 1, characterized in that: The optimization objective of the joint water storage scheduling of the cascade reservoir group in step 2 is: Under the condition of not exceeding the existing flood control standards, the reservoir is filled as much as possible at the end of the water storage period, and as much power generation benefits as possible are generated during the period. and express, Among them, FCR and IE are the multi-year flood control risk and storage rate of the cascade reservoirs, respectively; E k is the power generation of the kth partition; VE k,i (y) is the storage capacity of the i-th reservoir in the k-th zone at the end of the impoundment period; SU k,i and SD k,i They represent the normal beneficial storage capacity and dead storage capacity of the i-th reservoir in the k-th zone respectively; V k,i (t) and SS k,i (t) are the reservoir capacity of the i-th reservoir in the k-th zone at time period t and the reservoir capacity corresponding to the highest safe water level in front of the dam; N k,i (t) is the average output of the i-th reservoir in the k-th sub-area during the t-th period; △t is the step interval during the water storage period; A k,i is the average power generation efficiency coefficient of the i-th reservoir in the k-th zone; Q k,i (t) and H k,i (t) are the power generation flow and power generation head of the i-th reservoir in the k-th zone during period t, respectively; Y is the number of years in the scheduling period; T is the total number of water storage periods each year; K is the total number of partitions; M k is the number of reservoirs in the kth partition.

3. A multi-objective water storage scheduling method for a large reservoir group according to claim 1, characterized in that: The reservoir dispatching constraint conditions in step 2 are: (a) Water balance constraints: In formula (4), I k,i (t) and O k,i (t) is the inflow and outflow flow of the i-th reservoir in the k-th zone during period t; Φ j,k,i represents the set of reservoirs that are hydraulically connected to the upstream of the i-th reservoir in the k-th sub-area; O j (t) is Φ j,k,i The outflow from reservoir j in the set; (b) Reservoir capacity constraints: SL k,i (t)≤V k,i (t)≤SS k,i (t) (5) In formula (5), SL k,i (t) and SS k,i (t) are the minimum and maximum permissible water storage capacity of the i-th reservoir in the k-th zone during period t, respectively; (c) Reservoir discharge flow constraints: OL k,i (t)≤O k,i (t)≤OU k,i (t), O k,i (t)=Q k,i (t)+QS k,i (t) (6) In formula (6), QL k,i (t) and QU k,i (t) are the minimum and maximum allowable discharge flow of the i-th reservoir in the k-th zone during period t, QS k,i (t) is the discharge flow of the reservoir; (d) Power station output constraints: PL k,i (t)≤N k,t (t)≤PU k,i (t) (7) In formula (7), PL k,i and PU k,i They are the minimum and maximum output limits of the i-th reservoir in the k-th zone during period t, respectively; (e) Boundary conditions: In formula (8), Z k,i,begin and Z k,i,end are the initial and final water levels of the ith reservoir in the kth subarea, respectively.

4. A multi-objective water storage scheduling method for a large reservoir group according to claim 1, characterized in that: The large-scale system aggregation-decomposition method based on partition in step 3 adopts the aggregation-decomposition method of the same partition reservoir with water volume as the unit; the optimization method in the parameter-simulation-optimization framework adopts the second-generation non-dominated sorting genetic algorithm NSGA-II. In the aggregation stage, the aggregated reservoir capacity is obtained by fitting formula (9). The specific aggregation formula is as follows: In formulas (9)-(10), and are the aggregate storage capacity and runoff of the kth virtual reservoir at time t; I k,i (t) is the runoff from the outside into the i-th reservoir in the k-th sub-area at time t; Eva k (t) is the water loss of the kth partition at time t, and a partition acts as a virtual reservoir.

5. A multi-objective water storage scheduling method for a large reservoir group according to claim 4, characterized in that: In the decomposition stage, the output results are decomposed according to the storage capacity ratio of each reservoir, so as to determine the storage capacity status of a single reservoir, as shown in formula (11):

6. A multi-objective water storage scheduling method for a large reservoir group according to claim 4, characterized in that: The water storage strategy obtained by further improving the aggregation decomposition by using the parallel successive optimization algorithm is to ensure that the storage rate and flood control risk results of the same partition are not distorted, the storage rate value is not lower than the original aggregation decomposition scheme, and the flood control risk value is not higher than the original aggregation decomposition scheme, so as to maximize the power generation benefit of the partition as much as possible, and should satisfy equations (12) and (13). In formulas (12)-(13), and They are the storage capacity and flood control risk of the kth partition obtained by the initial optimization of the NSGA-Ⅱ algorithm.

7. A multi-objective water storage scheduling system for a large reservoir group, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a program of a multi-objective water storage scheduling method for a large-scale reservoir group, and the program of the multi-objective water storage scheduling method for a large-scale reservoir group implements the following steps when executed by the processor: collecting basin hydrological data; determining the optimization target of the joint water storage scheduling of the cascade reservoir group, and setting reservoir scheduling constraints; further clarifying the priority of each scheduling target, and constructing a two-stage segmented optimization model framework; introducing a large system aggregation-decomposition method based on partitioning, and using a parameter-simulation-optimization method to optimize and obtain an initial Pareto frontier; Based on the initial Pareto frontier of the above steps, a parallel successive optimization algorithm is used to further improve the water storage strategy obtained by aggregation and decomposition to achieve efficient utilization of water resources in each zone.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the multi-objective water storage scheduling method for a large reservoir group as described in any one of claims 1-6.

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