Short-term dispatching method and system for generating units under advance water storage at the end of the flood season

Abstract

The present application provides a method and system for short-term dispatch of units under the advance water storage at the end of the flood season in a reservoir. The method includes the following steps: establishing a scheduling plan for advance water storage in a reservoir; based on the set scheduling plan for advance water storage in a reservoir, constructing a short-term dispatch model for the unit with the objective function of maximizing the total power generation of the reservoir during the planning period and satisfying the model operation constraints; using the reservoir water storage at the end of the period as the decision variable of the short-term dispatch model for the unit, and using a genetic algorithm to seek a short-term dispatch strategy for the unit under the optimal power generation criterion. The method for short-term dispatch of units under the advance water storage at the end of the flood season provided in the present application is based on the framework of "advance water storage-unit dispatching-bionic optimization", constructs and efficiently solves a general model for short-term dispatch of units under the water-based electricity mode of a reservoir, and proposes a method for short-term dispatch of units under advance water storage at the end of the flood season in a reservoir, providing a theoretical basis and technical support for the scientific formulation of short-term dispatch plans for units under advance water storage at the end of the flood season in a river basin.

Description

Technical Field

[0001] The present application relates to the technical field of reservoir dispatching, and in particular to a method and system for short-term dispatching of units during early water storage at the end of a flood season in a reservoir. Background Art

[0002] Advance water storage scheduling at the end of the flood season in a reservoir refers to a method of determining the start time of reservoir storage at the end of the flood season within the planned period and the specific value of the water storage level control line, based on the designed phased safe water storage level of the reservoir dam, the meteorological and hydrological forecast information, the water situation information at the moment, the engineering situation and the disaster information of the reservoir basin, and on the premise of meeting the requirements of reservoir water storage, reservoir water discharge capacity and flood control and benefit development. It is one of the effective methods to improve the level of water resource utilization and water supply guarantee capacity in the basin.

[0003] The theories and methods of reservoir water storage scheduling at the end of the flood season are mainly divided into the design flood process line method, fuzzy statistics method, storage capacity compensation method, multi-objective optimization method, etc. According to their mathematical principles, the existing technology mainly focuses on reservoirs or hydropower stations for the scheduling of water storage at the end of the flood season, and develops the technology of water storage scheduling at the end of the flood season, but has not yet carried out the short-term scheduling of units under the early water storage scheduling of reservoirs at the end of the flood season.

[0004] Compared with the hydropower station dispatching under the advance water storage at the end of the flood season, the technical difficulties and challenges of short-term dispatching of units under the advance water storage at the end of the flood season are as follows:

[0005] (1) Finer calculation granularity: not only the optimal output of the hydropower station should be determined, but also the number and combination of operating units in the plant and the output of each operating unit should be determined;

[0006] (2) The model is more complex: It is necessary not only to develop risk-controlled advance water storage scheduling technology for reservoirs at the end of the flood season, but also to develop short-term optimal scheduling technology for units under total water consumption control. Summary of the invention

[0007] The present application provides a method for short-term dispatching of units under early water storage at the end of a flood season in a reservoir, which can solve the technical problem that the objects of early water storage at the end of a flood season in the prior art are mainly reservoirs or hydropower stations, and research on short-term dispatching of units under early water storage at the end of a flood season in a reservoir has not yet been carried out.

[0008] In a first aspect, the embodiment of the present application provides a method for short-term dispatching of a unit during advance water storage at the end of a flood season in a reservoir, comprising the following steps:

[0009] Establish a plan for advance storage and dispatching of reservoirs;

[0010] Based on the set reservoir advance storage scheduling plan, a short-term scheduling model for the unit is constructed with the objective function of maximizing the total power generation of the reservoir during the planning period and satisfying the model operation constraints;

[0011] The reservoir water storage at the end of the period is used as the decision variable of the unit short-term scheduling model, and a genetic algorithm is used to find the unit short-term scheduling strategy under the optimal power generation criterion.

[0012] In conjunction with the first aspect, in one implementation, establishing a reservoir advance water storage scheduling plan specifically includes the following steps:

[0013] Determine the safe water storage levels in stages in front of the dam for the reservoir advance water storage plan;

[0014] The flood control algorithm is used to deduce the flood control high water level and maximum discharge flow of the reservoir advance water storage scheduling plan;

[0015] The flood control algorithm is used to analyze the flood control risks of the reservoir advance water storage scheduling plan.

[0016] In combination with the first aspect, in one embodiment, in the step of using a flood control algorithm to deduce the flood control high water level and maximum downstream flow of the reservoir advance water storage scheduling plan, the flood control process of the water storage scheduling plan satisfies the following reservoir water balance equation, water storage equation, and reservoir advance water storage scheduling line feasible domain constraints.

[0017] In combination with the first aspect, in one implementation, based on the set advance reservoir water storage scheduling plan, a unit short-term scheduling model is constructed with maximizing the total power generation of the reservoir during the planning period as the objective function and satisfying the model operation constraints. The objective function is shown in the following formula:

[0018]

[0019] Where: T is the planning period, U(t) is the number of units that can be put into operation at the tth time, which is less than or equal to the total number of units; w is the number of the put-in-operation unit, which is less than or equal to U(t); g = L(t,w) is the plant number corresponding to the wth put-in-operation unit at the tth time; N g (t) is the output of unit g in the plant.

[0020] In combination with the first aspect, in one embodiment, the advance water storage scheduling plan of the reservoir is set to construct a short-term scheduling model for the unit with the objective function of maximizing the total power generation of the reservoir during the planning period and satisfying the model operation constraints, wherein the constraints include the physical constraints of the reservoir-hydropower station-unit and the constraints of its own characteristic curve.

[0021] In combination with the first aspect, in one embodiment, the reservoir-hydropower station-unit physical constraints include total water volume constraints, reservoir hydropower station water balance constraints, reservoir water level constraints, unit head constraints, unit output limitations, unit water consumption characteristics, hydropower station total output limitations, comprehensive utilization constraints and initial reservoir water level constraints.

[0022] In combination with the first aspect, in one implementation, the reservoir water storage at the end of the time period is used as the decision variable of the unit short-term scheduling model, and a genetic algorithm is used to seek a unit short-term scheduling strategy under the optimal power generation criterion, which specifically includes the following steps:

[0023] Set genetic algorithm parameters and initialize the population;

[0024] Based on the set genetic algorithm parameters, the initialization population and the unit short-term scheduling model, the genetic algorithm is used for iterative optimization scheduling to obtain the unit short-term scheduling strategy under the optimal power generation criterion.

[0025] In combination with the first aspect, in one implementation, the iterative optimization scheduling using a genetic algorithm based on the set genetic algorithm parameters, the initialization population, and the unit short-term scheduling model to obtain the unit short-term scheduling strategy under the optimal power generation criterion specifically includes the following steps:

[0026] Based on the set genetic algorithm parameters, the initialization population and the unit short-term scheduling model, the genetic algorithm is used to perform iterative optimization scheduling to obtain the current iteration number;

[0027] If the current number of iterations is less than the maximum number of iterations, the genetic algorithm is repeatedly used to perform iterative optimization scheduling based on the set genetic algorithm parameters, the initialization population, and the unit short-term scheduling model until the current number of iterations is not less than the maximum number of iterations;

[0028] If the current number of iterations is not less than the maximum number of iterations, the iterative optimization scheduling is terminated. By querying the optimal characteristic curve, the short-term scheduling strategy of the unit under the optimal power generation criterion is obtained, including the optimal number and combination of working units in each period and the distribution of active load among the working units.

[0029] In a second aspect, the embodiment of the present application provides a short-term dispatching system for a unit in advance of water storage at the end of a flood season in a reservoir, comprising:

[0030] The advance water storage module is used to establish the advance water storage scheduling plan for the reservoir;

[0031] A unit dispatching module, which is in communication connection with the advance water storage module, is used to construct a unit short-term dispatching model with maximizing the total power generation of the reservoir within the planning period as the objective function and satisfying the model operation constraints based on the set reservoir advance water storage dispatching plan;

[0032] The bionic optimization module is connected to the unit dispatching module for using the reservoir water storage at the end of the time period as the decision variable of the unit short-term dispatching model and using a genetic algorithm to seek a unit short-term dispatching strategy under the optimal power generation criterion.

[0033] In conjunction with the second aspect, in one implementation, the advance water storage module includes:

[0034] A safe water storage level determination unit is used to determine the safe water storage level in stages in front of the dam for the reservoir advance water storage plan;

[0035] A water level and flow acquisition unit is used to deduce the flood control high water level and maximum discharge flow of the reservoir advance water storage scheduling plan using a flood control algorithm;

[0036] The risk acquisition unit is used to analyze the flood control risks of the reservoir advance water storage scheduling plan using the flood control algorithm.

[0037] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0038] The method for short-term dispatching of units under the condition of advance water storage at the end of the flood season provided in this application is based on the framework of "advance water storage-unit dispatching-bionic optimization", constructs and efficiently solves a general model for short-term dispatching of units under the water-based electricity mode of reservoirs, and proposes a method for short-term dispatching of units under the condition of advance water storage at the end of the flood season. This provides a theoretical basis and technical support for the scientific formulation of short-term dispatching plans for units under advance water storage at the end of the flood season in river basin reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a method for short-term dispatching of a unit during early water storage at the end of a flood season provided in an embodiment of the present application;

[0040] Figure 2 An illustration of the constraints on the phased safe water storage level in front of the dam at the end of the flood season of the Three Gorges Reservoir provided in the embodiment of the present application;

[0041] Figure 3 The Three Gorges Reservoir water storage scheduling diagram at the end of the flood season provided in the embodiment of the present application;

[0042] Figure 4 An illustration of the iterative process of the particle swarm algorithm for short-term dispatch of the Three Gorges Hydropower Station units provided in the embodiment of the present application;

[0043] Figure 5 A schematic diagram of information interaction between the functional modules of a short-term dispatching system for a unit during advance water storage at the end of a flood season provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0046] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0047] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0048] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0049] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0050] First, please refer to Figure 1 The embodiment of the present application provides a method for short-term dispatching of units for early water storage at the end of the flood season in a reservoir, comprising the following steps:

[0051] Step S1, establishing a reservoir advance water storage scheduling plan;

[0052] Step S2: Based on the set advance reservoir water storage scheduling plan, a short-term scheduling model for the unit is constructed with the objective function of maximizing the total power generation of the reservoir during the planning period and satisfying the model operation constraints;

[0053] Step S3: Taking the reservoir water storage at the end of the time period as the decision variable of the unit short-term scheduling model, a genetic algorithm is used to seek the unit short-term scheduling strategy under the optimal power generation criterion.

[0054] The method for short-term dispatching of units under the condition of advance water storage at the end of the flood season provided in this application is based on the framework of "advance water storage-unit dispatching-bionic optimization", constructs and efficiently solves a general model for short-term dispatching of units under the water-based electricity mode of reservoirs, and proposes a method for short-term dispatching of units under the condition of advance water storage at the end of the flood season. This provides a theoretical basis and technical support for the scientific formulation of short-term dispatching plans for units under advance water storage at the end of the flood season in river basin reservoirs.

[0055] In one embodiment, step S1, establishing a reservoir advance water storage scheduling plan, specifically includes the following steps:

[0056] Step S11, determining the safe water storage level in stages in front of the dam of the reservoir advance water storage plan;

[0057] Step S12, using a flood control algorithm to deduce the flood control high water level and the maximum downstream discharge of the advance water storage scheduling plan of the reservoir. Specifically, the advance water storage scheduling plan of the reservoir is input, and the water balance equation and the storage and discharge equation are combined to calculate the reservoir water level and downstream discharge by time period using a flood control algorithm, wherein the maximum reservoir water level is the flood control high water level, and the maximum downstream discharge flow is the maximum downstream discharge flow;

[0058] Step S13: Analyze the flood control risk of the reservoir advance water storage scheduling plan using a flood control algorithm.

[0059] Based on steps S11, S12 and S13, the safe water storage level in stages before the dam is considered. (i is the number of stages at the end of the flood season, e.g. Figure 2 Based on the premise of the above, an advance water storage scheduling scheme Z with different storage start times and staged water storage lines is proposed. l (t)(l is the number of options, such as Figure 3 The flood control risk of the reservoir advance water storage scheduling scheme is analyzed using a flood control algorithm.

[0060] In one embodiment, the step S11, determining the phased safe water storage level in front of the dam of the reservoir advance water storage plan, is specifically implemented as follows:

[0061] Step S111: Draw the maximum water level Z M (P k ) flood risk rate ~Stage-by-stage safe water storage level ~Design flood frequency P j Relationship curve and maximum allowable discharge flow q M (P k ) flood risk rate ~Stage-by-stage safe water storage level ~Design flood frequency P j Relationship curve, where P k is the kth design flood frequency corresponding to the reservoir flood control standard;

[0062] Step S112: Determine the safe water storage level in front of the dam in stages according to the principle of not lowering the flood control standard Specifically, if the downstream flood control standard, reservoir design standard and reservoir verification standard of the reservoir are 1%, 0.1% and 0.01% respectively, then by querying the relationship curve, the phased safe water storage levels corresponding to the design flood frequencies of 1%, 0.1% and 0.01% are found, and the inner envelope is taken to determine the phased safe water storage levels in front of the dam.

[0063] In one embodiment, in the step S12, in which the flood control algorithm is used to deduce the flood control high water level and the maximum downstream flow of the reservoir advance water storage scheduling plan, the flood control process of the water storage scheduling plan satisfies the following constraints:

[0064] A. The reservoir water balance equation is as follows:

[0065]

[0066] Where: V(t) and V(t+1) are the water storage of the reservoir at time t and time t+1 respectively; I(t) and I(t+1) are the inflow of the reservoir at time t and time t+1 respectively; q(t) and q(t+1) are the outflow of the reservoir at time t and time t+1 respectively; Δt is the length of the calculation period.

[0067] B. The water storage equation is as follows:

[0068] q(t)≤f M (Z(t))

[0069] Where: q(t) is the outflow of the reservoir at time t; Z(t) is the water level of the reservoir at time t; f M (·) is the maximum discharge capacity corresponding to a certain water level of the reservoir.

[0070] C. The feasible domain of the reservoir advance water storage scheduling line is as follows:

[0071]

[0072] Where: Z d It is the water storage level of the reservoir at the end of the flood season; Z is the safe water storage level in stages in front of the dam; l (t) is the water storage level at time t of the lth scheme.

[0073] In one embodiment, the step S13, using a flood control algorithm to analyze the flood control risk of the reservoir advance water storage scheduling plan, specifically includes the following steps:

[0074] The maximum water level Z M (P k ) and the maximum allowable discharge flow q M (P k ) is the risk control indicator. Based on the principle of controllable risk, the reservoir advance water storage scheduling plan that does not reduce the flood control standard is selected. The reservoir advance water storage scheduling plan must meet the following controllable risk constraints:

[0075] Z M (I(t),Q qj (t),Z l (t))≤Z M (P k )

[0076] q M (I(t),Q qj (t),Z l (t))≤q M (P k )

[0077] In the formula, Z M (I(t),Q qj (t),Z l (t)) is the high water level of flood control in the water storage scheduling scheme, q M (I(t),Q qj (t),Z l (t)) is the maximum discharge flow of the water storage scheduling scheme, Z M (P k ) is the maximum allowable water storage level, q M (P k ) is the maximum allowable downstream flow rate.

[0078] In one embodiment, step S2, based on the set advance reservoir water storage scheduling plan, construct a unit short-term scheduling model with maximizing the total power generation of the reservoir during the planning period as the objective function and satisfying the model operation constraints, and the objective function is shown in the following formula:

[0079]

[0080] Where: U(t) is the number of units that can be put into operation at the tth time, which is less than or equal to the total number of units; w is the number of the units that can be put into operation, which is less than or equal to U(t); g = L(t,w) is the factory number corresponding to the wth unit that can be put into operation at the tth time; N g (t) is the output of unit g in the plant.

[0081] In one embodiment, the reservoir advance water storage scheduling scheme is based on the setting, and a short-term scheduling model of the unit is constructed with the objective function of maximizing the total power generation of the reservoir during the planning period and satisfying the model operation constraints. The constraints include the physical constraints of the reservoir-hydropower station-unit and the constraints of the own characteristic curve. Among them, the physical constraints of the reservoir-hydropower station-unit include the total water volume constraint, the water balance constraint of the reservoir hydropower station, the reservoir water level constraint, the unit head constraint, the unit output limit, the unit water consumption characteristics, the total output limit of the hydropower station, the comprehensive utilization constraint and the initial reservoir water level constraint. The own characteristic curve constraint specifically includes the reservoir water level-storage capacity relationship curve, the reservoir water level-discharge capacity curve, the downstream water level-flow relationship curve, the unit expected output limit line, and the optimal power characteristic curve of the whole plant. In addition, various variables must be non-negative variables.

[0082] In one embodiment, within the planning period T (1≤t≤T), the physical constraints of the reservoir-hydropower station-unit that the model operation needs to meet are:

[0083] (1) The total water volume constraint is as follows:

[0084]

[0085] Where: Q fd (t) is the power generation flow of the hydropower station at the tth moment; Q qs (t) is the amount of water discharged from the reservoir at the tth moment; is the power generation flow of unit g at time t; W ck (T) is the total water outflow from the reservoir during the planning period T, which is calculated based on the water release process of the reservoir advance water storage scheduling in step 1.

[0086] (2) The water balance constraint of a reservoir and hydropower station is as follows:

[0087]

[0088] Where: V(t) and V(t+1) are the water storage of the reservoir at time t and time t+1 respectively; I(t) and I(t+1) are the inflow of the reservoir at time t and time t+1 respectively; q(t) and q(t+1) are the outflow of the reservoir at time t and time t+1 respectively; Δt is the length of the calculation period.

[0089] (3) The reservoir water level constraint is as follows:

[0090] Z min (t)≤Z(t)≤Z max (t)

[0091] Where: Z(t) is the reservoir water level at time t; Z min (t) and Z max (t) are the minimum and maximum water levels allowed in the reservoir at moment t, respectively. The minimum allowable water level is usually the dead water level, and the maximum allowable water level is the staged safe water level in front of the dam.

[0092] (4) The unit head constraint is as follows:

[0093]

[0094] Where: H g (t) is the net water head of unit g at time t; and are the minimum and maximum water heads of unit g respectively; Z xy (t) is the water level downstream of the unit at time t; ΔH g (t) is the head loss of unit g at the tth moment; φ g (·) is the head loss function of unit g.

[0095] (5) The unit output limit is as follows:

[0096]

[0097] Where: and They are the minimum and maximum output of unit g at the tth moment respectively.

[0098] (6) The water consumption characteristics of the unit are shown as follows:

[0099]

[0100] Where: g (·,·) is the water consumption characteristic curve of unit g, which reflects the relationship between the unit power generation flow and the unit output and water head.

[0101] (7) The total output limit of a hydropower station is as follows:

[0102]

[0103] Where: N(t) is the output of the hydropower station at the tth moment; N min (t) and N max (t) are the minimum and maximum outputs of the hydropower station at moment t respectively.

[0104] (8) Comprehensive Utilization Constraints

[0105]

[0106] Where: The minimum downstream flow rate is determined by comprehensively considering the needs of ecology, water supply, shipping, etc. for the minimum downstream flow rate required for downstream comprehensive utilization.

[0107] (9) The initial reservoir water level constraint is as follows:

[0108] Z cs = Z(1)

[0109] Where: Z cs is the initial reservoir water level during the planning period; Z(1) is the reservoir water level at the start of storage, which is the starting water level in the reservoir advance water storage scheduling plan in step S1.

[0110] In one embodiment, the short-term dispatch of the unit aims to seek the optimal number and combination of the working units in each time period and the distribution of active load among the working units under the optimal power generation criterion. According to the unique conversion relationship characteristics between the key variables of the reservoir type (the water storage of the reservoir at the end of the time period) and the key variables of the hydropower station type (the power generation output and power generation flow of the unit during the time period), considering the water-based power model, the water storage of the reservoir at the end of the time period is selected as the decision variable of the short-term dispatch model of the unit. The step S3, using the water storage of the reservoir at the end of the time period as the decision variable of the short-term dispatch model of the unit, adopting the genetic algorithm to seek the short-term dispatch strategy of the unit under the optimal power generation criterion, specifically includes the following steps:

[0111] Step S31, setting genetic algorithm parameters and initializing the population;

[0112] Step S32: Based on the set genetic algorithm parameters, the initialized population and the unit short-term scheduling model, the genetic algorithm is used to perform iterative optimization scheduling to obtain the unit short-term scheduling strategy under the optimal power generation criterion.

[0113] In one embodiment, the step S31, setting genetic algorithm parameters and initializing the population, is specifically implemented as follows:

[0114] Step S311, genetic algorithm parameters are set as follows:

[0115] Assign crossover probability P c =0.05, mutation probability P m =0.85, population size N pop = 1000 and the maximum number of iterations G max =500;

[0116] Step S312: Population initialization operation is as follows:

[0117] Use real number encoding to encode and randomly generate N pop The decision variable of the scale is the reservoir water level V(t+1) at the end of the period in the water balance equation in the short-term dispatch model of the unit.

[0118] In one embodiment, the step S32 uses a genetic algorithm to perform iterative optimization scheduling, which specifically includes the following steps:

[0119] Step S321: Generate a new offspring population R 0 , the specific implementation is:

[0120] A. Perform a crowded bidding competition selection operation to select individuals with greater fitness to generate the offspring population;

[0121] B. Execution probability P c The crossover operation recombines the chromosomes of the two parents to generate a population of offspring;

[0122] C. Execution probability P m mutation operations to maintain the genetic diversity of the population.

[0123] Generate N based on the three operators in steps A, B, and C pop The size of the new offspring population R 0 .

[0124] Step S322: obtaining the intermediate population, evaluating the fitness of the intermediate population and calculating the crowding distance of the intermediate population, which is specifically implemented as follows:

[0125] For the jth iteration, the evaluation population R m The fitness function used in fitness evaluation is the objective function in the unit short-term scheduling model. The larger the value of the objective function, the better the fitness.

[0126] The population R of the j-1th iteration m-1 and the population R of the jth iteration m Merge to a size of 2·N pop The intermediate population A m ;

[0127] Step S323: Generate a new offspring population based on the intermediate population and evaluate the fitness of the new offspring population, which is specifically implemented as follows:

[0128] Execute the crowded bidding competition to select the operator from the middle population A m Select the size N pop The new parent population A m+1 ;

[0129] For the new parent population A m+1 Perform crossover and mutation operations to generate scale N pop The new offspring group Rm+1 ;

[0130] Evaluate the new offspring population R m+1 , evaluates whether the fitness of the population has improved after selection, crossover and mutation operations, and obtains the current number of iterations.

[0131] In one embodiment, the step S32 of obtaining the short-term dispatch strategy of the unit under the optimal power generation criterion specifically includes the following steps:

[0132] Step S33A: If the current number of iterations is less than the maximum number of iterations G max ,like Figure 4 As shown, based on the set genetic algorithm parameters and the initialization population and the unit short-term scheduling model, steps S321 to S323 are repeatedly executed to use the genetic algorithm for iterative optimization scheduling until the current number of iterations is not less than the maximum number of iterations;

[0133] Step S33B, if the current number of iterations is not less than the maximum number of iterations, terminate the iterative optimization scheduling, and obtain the unit short-term scheduling strategy under the optimal power generation criterion by querying the optimal characteristic curve, including the optimal number and combination of working units in each time period and the distribution results of active load among the working units. The unit short-term scheduling strategy must meet the reservoir-hydropower station-unit physical constraints mentioned in the unit short-term scheduling model and its own characteristic curve constraints.

[0134] Second, as Figure 5 As shown, the embodiment of the present application provides a short-term dispatching system for units in advance of water storage at the end of the flood season in a reservoir, including:

[0135] The advance water storage module is used to establish the advance water storage scheduling plan for the reservoir;

[0136] A unit dispatching module, which is in communication connection with the advance water storage module, is used to construct a unit short-term dispatching model with maximizing the total power generation of the reservoir within the planning period as the objective function and satisfying the model operation constraints based on the set reservoir advance water storage dispatching plan;

[0137] The bionic optimization module is connected to the unit dispatching module for using the reservoir water storage at the end of the time period as the decision variable of the unit short-term dispatching model and using a genetic algorithm to seek a unit short-term dispatching strategy under the optimal power generation criterion.

[0138] In one embodiment, the advance water storage module includes:

[0139] A safe water storage level determination unit is used to determine the safe water storage level in stages in front of the dam for the reservoir advance water storage plan;

[0140] A water level and flow acquisition unit is used to deduce the flood control high water level and maximum discharge flow of the reservoir advance water storage scheduling plan using a flood control algorithm;

[0141] The risk acquisition unit is used to analyze the flood control risks of the reservoir advance water storage scheduling plan using the flood control algorithm.

[0142] Among them, the functional implementation of each module in the short-term scheduling system for units under the above-mentioned reservoir water storage in advance at the end of the flood season corresponds to the steps in the above-mentioned short-term scheduling method for units under the reservoir water storage in advance at the end of the flood season, and their functions and implementation processes will not be repeated here one by one.

[0143] On the third aspect, an embodiment of the present application provides a short-term scheduling device for generating units during early water storage at the end of a flood season in a reservoir. The short-term scheduling device for generating units during early water storage at the end of a flood season in a reservoir can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0144] The communication interface includes input / output (I / O) interface, physical interface and logical interface, etc., which are used to realize the interconnection of devices inside the short-term dispatching equipment of the unit under the advance water storage at the end of the flood season, and the interface used to realize the interconnection between the short-term dispatching equipment of the unit under the advance water storage at the end of the flood season and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display (Display), a keyboard (Keyboard), etc.

[0145] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0146] The processor may be a general-purpose processor, which may call the short-term scheduling program for the unit under the reservoir water storage at the end of the flood season stored in the memory, and execute the short-term scheduling method for the unit under the reservoir water storage at the end of the flood season provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the short-term scheduling program for the unit under the reservoir water storage at the end of the flood season is called may refer to the various embodiments of the short-term scheduling method for the unit under the reservoir water storage at the end of the flood season in the present application, which will not be repeated here.

[0147] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.

[0148] The readable storage medium of the present application stores a short-term scheduling program for units under the condition of early water storage at the end of a flood season in a reservoir. When the short-term scheduling program for units under the condition of early water storage at the end of a flood season in a reservoir is executed by a processor, the steps of the short-term scheduling method for units under the condition of early water storage at the end of a flood season in a reservoir as described above are implemented.

[0149] Among them, the method implemented when the short-term scheduling program of the unit is executed when the reservoir is stored in advance at the end of the flood season can refer to the various embodiments of the short-term scheduling method of the unit when the reservoir is stored in advance at the end of the flood season in this application, and will not be repeated here.

[0150] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0151] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0152] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for short-term dispatching of units during early water storage at the end of a flood season in a reservoir, characterized in that: The following steps are involved: Establish a plan for advance storage and dispatching of reservoirs; The establishment of the reservoir advance water storage scheduling plan specifically includes the following steps: Determine the safe water storage levels in stages in front of the dam for the reservoir advance water storage plan; The flood control algorithm is used to deduce the flood control high water level and maximum discharge flow of the reservoir advance water storage scheduling plan; Use flood control algorithms to analyze flood control risks of reservoir advance water storage scheduling plans; Based on the set reservoir advance storage scheduling plan, a short-term scheduling model for the unit is constructed with the objective function of maximizing the total power generation of the reservoir during the planning period and satisfying the model operation constraints; Based on the set reservoir advance water storage scheduling plan, a unit short-term scheduling model is constructed with the objective function of maximizing the total power generation of the reservoir during the planning period and satisfying the model operation constraints. The objective function E(T) is shown in the following formula: Where: T is the planning period, U(t) is the number of units that can be put into operation at the tth time, which is less than or equal to the total number of units; w is the number of the put-in-operation unit, which is less than or equal to U(t); g = L(t,w) is the plant number corresponding to the wth put-in-operation unit at the tth time; N g (t) is the output of unit g in the plant; The reservoir-hydropower station-unit physical constraints include total water volume constraints, reservoir hydropower station water volume balance constraints, reservoir water level constraints, unit head constraints, unit output limits, unit water consumption characteristics, hydropower station total output limits, comprehensive utilization constraints and initial reservoir water level constraints; The total water volume constraint is as follows: Where: Q fd (t) is the power generation flow of the hydropower station at the tth moment; Q qs (t) is the amount of water discharged from the reservoir at the tth moment; is the power generation flow of unit g at time t; W ck (T) is the total water outflow from the reservoir during the planning period T; The water balance constraint of a reservoir and hydropower station is as follows: Where: V(t) and V(t+1) are the water storage of the reservoir at time t and time t+1 respectively; I(t) and I(t+1) are the inflow of the reservoir at time t and time t+1 respectively; q(t) and q(t+1) are the outflow of the reservoir at time t and time t+1 respectively; Δt is the length of the calculation period; The reservoir water level constraint is as follows: Z min (t)≤Z(t)≤Z max (t) Where: Z(t) is the reservoir water level at time t; Z min (t) and Z max (t) are the minimum and maximum water levels allowed by the reservoir at time t respectively; The unit head constraint is as follows: Where: H g (t) is the net water head of unit g at time t; and are the minimum and maximum water heads of unit g respectively; Z xy (t) is the water level downstream of the unit at time t; ΔH g (t) is the head loss of unit g at the tth moment; φ g (·) is the head loss function of unit g; The unit output limit is as follows: Where: and are the minimum and maximum outputs of unit g at time t respectively; The water consumption characteristics of the unit are shown as follows: Where: g (·,·) is the water consumption characteristic curve of unit g, reflecting the correlation between the unit power generation flow and the unit output and water head; The total output limit of the hydropower station is as follows: Where: N(t) is the output of the hydropower station at the tth moment; N min (t) and N max (t) are the minimum and maximum output of the hydropower station at moment t, respectively; The comprehensive utilization constraint is as follows: Where: The minimum downstream flow required for downstream comprehensive utilization; The initial reservoir water level constraint is as follows: WITH cs =From(1) Where: Z cs is the initial reservoir water level during the planning period; Z(1) is the water level at the reservoir start time; The constraints include reservoir-hydropower station-unit physical constraints and self-characteristic curve constraints; The reservoir water storage at the end of the period is used as the decision variable of the unit short-term scheduling model, and a genetic algorithm is used to find the unit short-term scheduling strategy under the optimal power generation criterion.

2. The method for short-term dispatching of units during advance water storage at the end of a flood season in a reservoir as claimed in claim 1, characterized in that: In the step of using the flood control algorithm to deduce the flood control high water level and maximum discharge flow of the reservoir advance water storage scheduling plan, the flood control process of the water storage scheduling plan satisfies the feasible domain constraints of the reservoir water balance equation, the water storage equation and the reservoir advance water storage scheduling line.

3. The method for short-term dispatching of units during advance water storage at the end of a flood season in a reservoir as claimed in claim 1, characterized in that: The reservoir water storage at the end of the time period is used as the decision variable of the unit short-term scheduling model, and a genetic algorithm is used to seek a unit short-term scheduling strategy under the optimal power generation criterion, which specifically includes the following steps: Set genetic algorithm parameters and initialize the population; Based on the set genetic algorithm parameters, the initialization population and the unit short-term scheduling model, the genetic algorithm is used for iterative optimization scheduling to obtain the unit short-term scheduling strategy under the optimal power generation criterion.

4. The method for short-term dispatching of units during advance water storage at the end of a flood season according to claim 1, characterized in that: The reservoir water storage at the end of the time period is used as the decision variable of the unit short-term scheduling model, and a genetic algorithm is used to seek a unit short-term scheduling strategy under the optimal power generation criterion, which specifically includes the following steps: Based on the set genetic algorithm parameters, the initialization population and the unit short-term scheduling model, the genetic algorithm is used to perform iterative optimization scheduling to obtain the current iteration number; If the current number of iterations is less than the maximum number of iterations, the genetic algorithm is repeatedly used to perform iterative optimization scheduling based on the set genetic algorithm parameters, the initialization population, and the unit short-term scheduling model until the current number of iterations is not less than the maximum number of iterations; If the current number of iterations is not less than the maximum number of iterations, the iterative optimization scheduling is terminated, and the short-term scheduling strategy of the unit under the optimal power generation criterion is obtained by querying the optimal characteristic curve.

5. A system using the method for short-term dispatching of units during advance water storage at the end of a flood season in a reservoir as described in any one of claims 1 to 4, characterized in that: include: The advance water storage module is used to establish the advance water storage scheduling plan for the reservoir; A unit dispatching module, which is in communication connection with the advance water storage module, is used to construct a unit short-term dispatching model with maximizing the total power generation of the reservoir within the planning period as the objective function and satisfying the model operation constraints based on the set reservoir advance water storage dispatching plan; The bionic optimization module is connected to the unit dispatching module for using the reservoir water storage at the end of the time period as the decision variable of the unit short-term dispatching model and using a genetic algorithm to seek a unit short-term dispatching strategy under the optimal power generation criterion.

6. The system according to claim 5, characterized in that The advance water storage module comprises: A safe water storage level determination unit is used to determine the safe water storage level in stages in front of the dam for the reservoir advance water storage plan; A water level and flow acquisition unit is used to deduce the flood control high water level and maximum discharge flow of the reservoir advance water storage scheduling plan using a flood control algorithm; The risk acquisition unit is used to analyze the flood control risks of the reservoir advance water storage scheduling plan using the flood control algorithm.

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