CN112686432A - Multi-objective hydropower-wind power optimal scheduling model method - Google Patents

Multi-objective hydropower-wind power optimal scheduling model method Download PDF

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CN112686432A
CN112686432A CN202011494328.5A CN202011494328A CN112686432A CN 112686432 A CN112686432 A CN 112686432A CN 202011494328 A CN202011494328 A CN 202011494328A CN 112686432 A CN112686432 A CN 112686432A
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reservoir
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顾巍
叶志伟
徐志刚
徐慧
董新华
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Hubei University of Technology
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Abstract

The invention relates to the field of wind power and hydropower dispatching, in particular to a multi-objective hydropower-wind power optimal dispatching model method, which comprises the steps of constructing an initial scene of wind power uncertainty and runoff uncertainty, and acquiring wind speed of a wind power plant and upstream inflow of a reservoir in different probability scenes in a dispatching cycle; establishing a multi-target hydropower-wind power optimal scheduling model considering uncertainty scenes of wind power output and upstream water flow with different probabilities by taking system node power shortage and reservoir lower discharge as targets and reservoir capacity and power grid load as constraints; reducing the initialized scene by adopting a scene reduction criterion; and solving the model by adopting a multi-objective algorithm based on uncertainty Pareto sorting to obtain an uncertainty Pareto solution set and a scheduling scheme for reservoir operation power generation and discharge. The method effectively improves the comprehensive utilization in the complex power comprehensive system and provides a new method for solving the hydropower-wind power multi-standard optimization scheduling problem.

Description

Multi-objective hydropower-wind power optimal scheduling model method
Technical Field
The invention belongs to the field of wind power and hydropower dispatching, and particularly relates to a multi-objective hydropower-wind power optimal dispatching model method.
Background
Reservoir scheduling is to adjust natural runoff, makes it satisfy needs such as electricity generation, flood control, water supply. Wind power is used as clean energy, the production process of the wind power has small influence on the environment, but the power generation of the wind power is unstable due to the randomness and the intermittence of the wind power. A combined operation system consisting of water, electricity and wind power can fully utilize the electric energy of the wind power, but other tasks of the reservoir except for power generation also need to be considered. When the reservoir is scheduled, the power generation requirement is usually met, and meanwhile, the downstream flood control requirement is considered, the basic river discharge flow is met, and meanwhile, the discharge flow is reduced as much as possible. The amount of generated power is directly related to the magnitude of the downward discharge flow and the magnitude of the water head, which is in contrast to the requirement of flood control that the downward discharge flow is as small as possible. Therefore, the requirements of power generation and flood control need to be considered when a water reservoir dispatching plan is made, and meanwhile, under the requirement of reservoir fine management, the influence and challenge on system dispatching modeling and implementation are brought by wind power uncertainty and reservoir upstream water inflow uncertainty in a wind power-hydropower system, so that the method becomes one of the difficulties of relevant research.
In the research of related problems at present, Monte Carlo simulation or fuzzy membership modeling is usually adopted for uncertain processing of wind power, and a hidden stochastic model such as a hidden Markov model and a display stochastic model is usually adopted for uncertainty of water inflow of the water power to directly consider uncertain information into a solving algorithm such as stochastic dynamic programming. In an actual system including hydroelectric power-wind power, influences of various different characteristic factors such as uncertainty of wind power output and uncertainty of upstream water volume of a reservoir need to be considered at the same time, so that a unified model is urgently needed to be researched to scientifically and accurately model the uncertain wind power-hydroelectric power scheduling problem.
The method is characterized in that the uncertainty of the upstream incoming water and the wind power uncertainty are considered, multi-objective wind power-hydropower dispatching modeling is carried out, and under the constraint condition of considering reservoir capacity, power grid load and the like, the mode of adjusting the generating flow and the discharging flow of the reservoir can be adopted, so that the output of a hydropower-wind power integrated system and the flood control safety of the downstream of the reservoir can be effectively adjusted and controlled, and the multi-objective optimized operation of the aspects of the economy, the safety and the like of the reservoir can be realized. The problems are characterized by complex constraint conditions, multiple targets, nonlinearity and influence of uncertain factors. The method is characterized in that a Pareto optimal solution set of reservoir operation scheduling is obtained by using scene planning and a Pareto optimal multi-objective optimization algorithm, utilizing the capability of the scene planning to process complex uncertainty factors and combining the good nonlinear problem solving and robustness of the Pareto optimal multi-objective optimization algorithm, and a feasible and effective method is provided for decision making.
Disclosure of Invention
Aiming at the problems in the background art, the invention provides a hydropower-wind power multi-objective optimization scheduling problem solved by a multi-objective algorithm based on uncertainty Pareto sequencing.
In order to solve the technical problems, the invention adopts the following technical scheme: a multi-objective hydropower-wind power optimal scheduling model method comprises the following steps:
step 1, constructing an initial scene of wind power uncertainty and runoff uncertainty, and acquiring wind speed of a wind power plant and upstream water inflow of a reservoir in different probability scenes in a scheduling period;
step 2, establishing a multi-target hydropower-wind power optimization scheduling model considering uncertainty scenes of different probability wind power output and upstream water inflow by taking the system node power shortage and the reservoir discharge capacity as targets and the reservoir capacity and the power grid load as constraints;
step 3, reducing the wind speed of the wind power plant with different probabilities generated initially and the upstream water scene of the reservoir by adopting a scene reduction criterion;
and 4, solving the model in the step 3 by adopting a multi-objective algorithm based on uncertainty Pareto sequencing to obtain an uncertainty Pareto solution set and a scheduling scheme for reservoir operation power generation and discharge.
In the multi-target hydropower-wind power optimized dispatching model method, the construction of the initial scene of wind uncertainty and runoff uncertainty in the step 1 comprises sampling the uncertain wind speed obeying the Weber distribution and the uncertain water coming from the upstream of the reservoir obeying the normal distribution by adopting a Monte Carlo simulation method, and generating sequences of t time periods, namely the upstream water { Q } respectively1，Q2，…，QtAnd wind speed { W }1，W2，…，WtT is day; generating M initial scenes, wherein M takes the value of [1000, 10000%]。
In the multi-objective hydropower-wind power optimization scheduling model method, the step 2 is realized by establishing a multi-objective optimization model by taking minimum power shortage and minimum flood control scheduling risk as targets, and the specific steps are as follows:
step 2.1, establishing a minimum objective function of the power shortage, wherein the hydropower and wind power combined power generation meets the requirements of a power generation plan, and the objective function is as follows:
Figure BDA0002841656470000021
wherein L isiElectric load, PH, for period iiFor hydroelectric power of period i, PWiThe output of the wind power is the output of the wind power in the period i;
step 2.1.1, pHiThe calculation method of (2) is as follows:
PHi＝K·Qi·Hi
wherein K is the output coefficient of the hydropower station; qiThe average generated flow at the moment i; hiAverage head for period i;
step 2.1.2, PWiThe calculation method of (2) is as follows:
Figure BDA0002841656470000031
wherein,
Figure BDA0002841656470000032
rated output power of the fan; v. ofCITo cut into the wind speed; v. ofCOCutting out the wind speed; v. ofRRated wind speed, viAverage wind speed for period i;
step 2.2, establishing a flood control dispatching risk minimum objective function, wherein the minimum drainage flow meets the flood control dispatching requirement, and the objective function is as follows:
Figure BDA0002841656470000033
establishing a multi-objective optimization model:
Figure BDA0002841656470000034
in the above multi-objective hydropower-wind power optimization scheduling model method, the implementation of step 3 includes: establishing a scene reduction criterion based on a multi-target association rule, wherein the association dissimilarity measure between scenes is CR(Si,Sj) The distance measure between the scenes is the following formula, and a k-means algorithm is adopted to perform scene clustering so as to reduce the scenes:
Figure BDA0002841656470000035
the scene reduction method based on the multi-target association rule is represented as follows:
Figure BDA0002841656470000036
the method comprises the following specific steps:
step 3.1, calculating Euclidean distances among scene vectors;
3.2, adopting a k-means algorithm to perform scene clustering so as to reduce scenes;
step 3.2.1, randomly selecting K scenes, calculating Euclidean distances and distance measures between the selected scenes and other scenes, and classifying the other scenes into K classifications according to the distance;
step 3.2.2, the center of each cluster is divided again, the mean value of all vectors in the cluster is used as the center, and then all individuals in the group are divided again;
and 3.2.3, calculating a new clustering center, judging whether the clustering center changes or not, skipping to the step 3.2.2 if the clustering center changes, stopping clustering if the clustering center does not change, outputting a scene vector nearest to each clustering center to form a reduced scene set, and calculating the proportion of each cluster in all scenes.
In the above multi-objective hydropower-wind power optimization scheduling model method, the implementation of step 4 includes: comparing the dominance relation between the targets under random conditions by adopting a random dominance method; according to different scenes, combining constraint conditions of reservoir scheduling and the multi-target optimization scheduling model in the step 2, and solving by adopting a distribution estimation algorithm to obtain a multi-target Pareto optimal solution set considering scene probability under a prediction scene set; sequentially solving multi-target solution sets of all probability scenes, analyzing the dominance relation among different solutions in the solution sets under different probabilities, and establishing a Pareto optimal solution set under the uncertain condition; the method comprises the following specific steps:
step 4.1, initializing a reservoir flow parameter X ═ X by using a random scene j in the reduced scene set1,i,x2,i,x3,i,x4,i..,xn,i]，xn,iInitializing the flow variable for the flow of the ith individual in the nth time period in a randomly generated mode in a defined domain range, wherein the value of i is [100,500 ]]The definition domain is the maximum discharge flow of the reservoir, the initial evolution algebra counter Flag is 0, the maximum evolution algebra Tmax is set, and the range is [200,1000]Initialization of NsetjCset is empty set;
4.2, calculating the function value of each individual on the f1 target and the f2 target;
4.3, according to the Pareto optimal method, if the current evolution is the first generation evolution, analyzing the current population, and finding out all Pareto optimal solutions(ii) a If not, analyzing all individuals in the current population and the previous generation Pareto optimal solution set, and finding out the Pareto optimal solution set Nset of the combined setj(M), M takes the value of 200;
4.4, evolving the current population by adopting a multi-target distribution estimation algorithm, randomly selecting i/4 individuals in Nset (M), selecting i/4 individuals with dominance from the current evolved population to form a distribution parameter calculation population Cset (i/2), and calculating the mean value Qmean of the individuals in the population in each time periodiAnd variance Qsdti；
Step 4.5, using the mean and variance, and adopting a normal distribution sampling method N (Qmean)i，Qsdti) Generating i new individuals, wherein Flag is Flag +1, judging whether Flag is greater than Tmax, if Flag is greater than Tmax, finishing calculation, and outputting a Pareto optimal solution set NsetjIf the value is less than Tmax, skipping to step 4.2;
step 4.6, judging whether all the reduced scenes are completely calculated; if yes, outputting all Pareto optimal solution sets NsetjIf not, skipping to step 4.1.
Compared with the prior art, the invention has the beneficial effects that: the wind power uncertainty and the reservoir water inflow uncertainty are processed by a scene planning method, a multi-objective optimization scheduling model of the hydroelectric and wind power output and the downstream flood control water amount based on the scene planning is established, a multi-objective algorithm based on uncertainty Pareto sequencing is provided for solving a hydroelectric-wind power multi-objective optimization scheduling problem, the comprehensive utilization efficiency in a complex power comprehensive system is effectively improved, and a new method is provided for solving the hydroelectric-wind power multi-objective optimization scheduling problem.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the following embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention is further illustrated by the following examples, which are not to be construed as limiting the invention.
The embodiment provides a multi-target hydropower-wind power optimal scheduling method, which aims at system node electricity shortage and reservoir discharge capacity, and aims at modeling and solving a multi-target hydropower-wind power optimal scheduling problem in consideration of different probability wind power output and upstream water inflow, and has positive significance in improving economic benefits and social benefits of a reservoir and improving the reservoir operation management level.
The embodiment is realized by the following technical scheme: a multi-objective hydropower-wind power optimal scheduling model method comprises the following steps:
s1: constructing an initial scene of wind power uncertainty and runoff uncertainty, and acquiring wind speed of a wind power plant and upstream inflow of a reservoir in different probability scenes in a scheduling period;
s2: establishing a multi-objective hydroelectric-wind power optimal scheduling model considering wind power output of different probabilities and a multi-uncertainty scene of upstream water inflow by taking the system node power shortage and the reservoir lower discharge as targets and considering reservoir capacity, power grid load and the like as constraints;
s3: reducing an initialization scene, namely the wind speed of a wind power plant with different initially generated probabilities and a water scene at the upstream of a reservoir by adopting a scene reduction criterion;
s4: and solving a Pareto solution set with uncertainty and a scheduling scheme of reservoir operation power generation and discharge by using a multi-objective algorithm based on Pareto sorting with uncertainty for the model in the S3.
S1, constructing an initial scene of wind power uncertainty and runoff uncertainty, and sampling the uncertain wind speed and the uncertain water in the upstream of the reservoir, which are subjected to Weber distribution and normal distribution, by adopting a Monte Carlo simulation method to generate sequences of t time periods, wherein the sequences are respectively upstream water { Q }1，Q2，…，QtAnd wind speed { W }1，W2，…，WtAnd the time scale is days. M initial scenes are generated by adopting the method, and the value of M can be [1000,10000 ]]。
S2, establishing a target of minimum power shortage and minimum flood control scheduling risk, and establishing a multi-objective optimization model:
s2.1: establishing a minimum objective function of the power shortage, wherein the hydropower and wind power combined power generation needs to meet the requirements of a power generation plan, and the objective function is as follows:
Figure BDA0002841656470000061
wherein L isiElectric load, PH, for period iiFor hydroelectric power of period i, PWiThe output of the wind power is the output of the wind power in the period i.
S2.1.1，PHiThe calculation method of (2) is as follows:
PHi＝K·Qi·Hi
wherein K is the output coefficient of the hydropower station; qiThe average generated flow at the moment i; hiIs the average head over period i.
S2.1.2，PWiThe calculation method of (2) is as follows:
Figure BDA0002841656470000062
wherein,
Figure BDA0002841656470000063
rated output power of the fan; v. ofCITo cut into the wind speed; v. ofCOCutting out the wind speed; v. ofRRated wind speed, viIs the average wind speed over period i.
S2.2: establishing a flood control scheduling risk minimum objective function, wherein the flood control scheduling requires the minimum discharge flow, and the objective function is as follows:
Figure BDA0002841656470000071
establishing a multi-objective optimization model:
Figure BDA0002841656470000072
s3, different prediction scenes have different Pareto optimal solution sets, a scene reduction criterion based on a multi-target association rule is established, and the association dissimilarity measure among the scenes is CR(Si,Sj) The distance measure between the scenes is that a k-means algorithm is adopted to perform scene clustering so as to reduce the scenes:
Figure BDA0002841656470000073
the scene reduction method based on the multi-target association rule is represented as follows:
Figure BDA0002841656470000074
s3.1, calculating Euclidean distances among scene vectors;
and S3.2, carrying out scene clustering by adopting a k-means algorithm so as to reduce the scene.
The hierarchical clustering algorithm is specifically realized as follows:
s3.2.1, randomly selecting K scenes, calculating Euclidean distances between the selected scenes and other scenes, and classifying the other scenes into K classifications according to the distance;
s3.2.2, repartitioning the center of each cluster, taking the mean value of all vectors in the cluster as the center, and then repartitioning all individuals in the population;
s3.2.3, calculating new cluster centers, judging whether the cluster centers change or not, if so, jumping to S3.2.2, if not, stopping clustering, outputting a scene vector nearest to each cluster center to form a reduced scene set, and calculating the proportion of each cluster in all scenes.
S4, adopting an uncertain Pareto distribution estimation algorithm, and adopting a random dominance method to compare the dominance relation between targets under random conditions; according to different scenes, combining constraint conditions of reservoir scheduling and the multi-target optimized scheduling model in S2, and solving by adopting a distribution estimation algorithm to obtain a multi-target Pareto optimal solution set considering scene probability under a prediction scene set; and sequentially solving the multi-target solution sets of all probability scenes, analyzing the dominance relation among different solutions in the solution sets under different probabilities, and establishing a Pareto optimal solution set under the uncertain condition.
S4.1: initializing reservoir flow parameter X ═ X with a random scene j in the reduced scene set1,i,x2,i,x3,i,x4,i..,xn,i]，xn,iThe flow for the ith individual during the nth time period. The initialization of the flow variable is carried out in a randomly generated mode within a defined domain, i is generally a value of 100,500]The definition domain is the maximum discharge flow of the reservoir, the initial evolution algebra counter Flag is 0, the maximum evolution algebra Tmax is set, and the general range is 200,1000]Initialization of NsetjCset is empty.
S4.2: calculating the function value of each individual on the f1 target and the f2 target;
s4.3: according to a Pareto optimal method, if the current evolution is the first generation, analyzing the current population, and finding out all Pareto optimal solutions; if not, analyzing all individuals in the current population and the previous generation Pareto optimal solution set by the first generation evolution, and finding out the Pareto optimal solution set Nset of the combined setj(M), which may generally take the value of 200;
s4.4: randomly selecting i/4 individuals in the evolution Nset (M) of the current population by adopting a multi-target distribution estimation algorithm, selecting i/4 individuals with dominance from the current evolutionary population to form a distribution parameter calculation population Cset (i/2), and calculating the mean value Qmean of the individuals in the population in each time periodiAnd variance Qsdti；
S4.5: using mean and variance, a normal distribution sampling method N (Qmean) is usedi，Qsdti) Generating i new individuals, wherein Flag is Flag +1, judging whether Flag is greater than Tmax, if Flag is greater than Tmax, finishing calculation, and outputting a Pareto optimal solution set NsetjAnd if the value is less than Tmax, jumping to S4.2.
S4.6: if all the reduced scenes are calculated, outputting all Pareto optimal solution sets NsetjAnd jumps to S4.1 otherwise.
During specific implementation, a certain reservoir is responsible for irrigation and water supply of a downstream irrigation area, the initial water level is 69m, the scheduling end-of-term water level is 69.02m, the downstream water level is 37m, the maximum power generation output is 2.4 ten thousand kw, and the installed capacity of wind power is 1.05 ten thousand kw
The grid demand output is shown in table 1 below.
TABLE 1
Figure BDA0002841656470000081
Forecasting the water coming from the upstream of each day to be normally distributed with the parameters of N (15,3.1) in m3The wind speed follows the Weber distribution, the shape parameter is k is 2, the scale parameter c is 11.4, vCIIs 3 m/s; v. ofcoIs 20 m/s; v. ofRIs 14 m/s.
Examples include a hydroelectric power station and a wind power station, taking into account wind speed uncertainty of wind power and uncertainty of water coming upstream of the reservoir. Monte Carlo simulation is adopted, ten-day water inflow forecast of the reservoir is generated by normal distribution sampling, and 10-day wind speed forecast is generated by Weber distribution sampling. The set of scene vectors generated is:
C1＝{Q1{16.6,16.0,26.1,17.3,20.7,10.9,23.6,7.9,10.8,14.4},
W1{12.9,6.8,10.3,14.7,19.9,12.2,11.2,9.6,17.4,7.5}}
randomly generating 1000 scenes, taking Euclidean distance between the scenes as a method for measuring the distance between the scenes, selecting a k value of 5 by adopting a k-means algorithm to perform clustering calculation, and counting the proportion P of individuals in each cluster to the total sceneskAnd obtains the central scene of each cluster.
According to a scheduling model S2 model, solving is carried out by adopting an uncertainty distribution estimation algorithm, the parameters are set to be that the individual number i is 200, the maximum evolution algebra is MaxT, 500 is taken, and a pareto optimal solution set is obtained by calculation (the solution set comprises 1000 pareto optimal solutions, and 20 of the solutions are selected for display):
Figure BDA0002841656470000091
Figure BDA0002841656470000101
when the result is used, after the actual water inflow and rainfall on the first day are monitored, the classification to which the actual scene belongs is judged according to the scene distance, if the actual scene belongs to the classification, the pareto optimal solution set of the scheduling result of the scene is adopted, and the closest scheme is selected from the solution set according to the actual target requirement to serve as the scheduling guidance scheme.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.

Claims (5)

1. A multi-objective hydropower-wind power optimal scheduling model method is characterized by comprising the following steps:
step 1, constructing an initial scene of wind power uncertainty and runoff uncertainty, and acquiring wind speed of a wind power plant and upstream water inflow of a reservoir in different probability scenes in a scheduling period;
step 2, establishing a multi-target hydropower-wind power optimization scheduling model considering uncertainty scenes of different probability wind power output and upstream water inflow by taking the system node power shortage and the reservoir discharge capacity as targets and the reservoir capacity and the power grid load as constraints;
step 3, reducing the wind speed of the wind power plant with different probabilities generated initially and the upstream water scene of the reservoir by adopting a scene reduction criterion;
and 4, solving the model in the step 3 by adopting a multi-objective algorithm based on uncertainty Pareto sequencing to obtain an uncertainty Pareto solution set and a scheduling scheme for reservoir operation power generation and discharge.
2. The method for the multi-objective hydropower-wind power optimized dispatching model of claim 1, wherein the step 1 of constructing the initial scene of uncertainty of wind power and uncertainty of runoff comprises the steps of sampling uncertainty wind speed which follows Weber distribution and reservoir upstream uncertainty incoming water which follows normal distribution by adopting a Monte Carlo simulation method to generate sequences of t time periods, wherein the sequences are respectively upstream incoming water { Q1，Q2，...，QtAnd wind speed { W }1，W2，...，WtT is day; generating M initial scenes, wherein M takes the value of [1000, 10000%]。
3. The multi-objective hydropower-wind power optimization scheduling model method of claim 1, wherein the implementation of the step 2 comprises establishing a multi-objective optimization model with the objective of minimum power shortage and minimum flood control scheduling risk, and the specific steps are as follows:
step 2.1, establishing a minimum objective function of the power shortage, wherein the hydropower and wind power combined power generation meets the requirements of a power generation plan, and the objective function is as follows:
Figure FDA0002841656460000011
wherein L isiElectric load, PH, for period iiFor hydroelectric power of period i, PWiThe output of the wind power is the output of the wind power in the period i;
step 2.1.1, pHiThe calculation method of (2) is as follows:
PHi＝K·Qi·Hi
wherein K is the output coefficient of the hydropower station; qiThe average generated flow at the moment i; hiAverage head of i period；
Step 2.1.2, PWiThe calculation method of (2) is as follows:
Figure FDA0002841656460000021
wherein,
Figure FDA0002841656460000022
rated output power of the fan; v. ofCITo cut into the wind speed; v. ofCOCutting out the wind speed; v. ofRRated wind speed, viAverage wind speed for period i;
step 2.2, establishing a flood control dispatching risk minimum objective function, wherein the minimum drainage flow meets the flood control dispatching requirement, and the objective function is as follows:
Figure FDA0002841656460000023
establishing a multi-objective optimization model:
Figure FDA0002841656460000024
4. the multi-objective hydropower-wind power optimization scheduling model method of claim 1, wherein the step 3 is realized by: establishing a scene reduction criterion based on a multi-target association rule, wherein the association dissimilarity measure between scenes is CR(Si，Sj) The distance measure between the scenes is the following formula, and a k-means algorithm is adopted to perform scene clustering so as to reduce the scenes:
Figure FDA0002841656460000025
the scene reduction method based on the multi-target association rule is represented as follows:
Figure FDA0002841656460000026
the method comprises the following specific steps:
step 3.1, calculating Euclidean distances among scene vectors;
3.2, adopting a k-means algorithm to perform scene clustering so as to reduce scenes;
step 3.2.1, randomly selecting K scenes, calculating Euclidean distances and distance measures between the selected scenes and other scenes, and classifying the other scenes into K classifications according to the distance;
step 3.2.2, the center of each cluster is divided again, the mean value of all vectors in the cluster is used as the center, and then all individuals in the group are divided again;
and 3.2.3, calculating a new clustering center, judging whether the clustering center changes or not, skipping to the step 3.2.2 if the clustering center changes, stopping clustering if the clustering center does not change, outputting a scene vector nearest to each clustering center to form a reduced scene set, and calculating the proportion of each cluster in all scenes.
5. The multi-objective hydropower-wind power optimization scheduling model method of claim 1, wherein the step 4 is realized by: comparing the dominance relation between the targets under random conditions by adopting a random dominance method; according to different scenes, combining constraint conditions of reservoir scheduling and the multi-target optimization scheduling model in the step 2, and solving by adopting a distribution estimation algorithm to obtain a multi-target Pareto optimal solution set considering scene probability under a prediction scene set; sequentially solving multi-target solution sets of all probability scenes, analyzing the dominance relation among different solutions in the solution sets under different probabilities, and establishing a Pareto optimal solution set under the uncertain condition; the method comprises the following specific steps:
step 4.1, initializing a reservoir flow parameter X ═ X by using a random scene j in the reduced scene set1，i，x2，i，x3，i，x4，i..，xn，i]，xn，iInitializing the flow variable for the flow of the ith individual in the nth time period in a randomly generated mode in a defined domain range, wherein the value of i is [100,500 ]]The definition domain is the maximum discharge flow of the reservoir, the initial evolution algebra counter Flag is 0, the maximum evolution algebra Tmax is set, and the range is [200,1000]Initialization of NsetjCset is empty set;
4.2, calculating the function value of each individual on the f1 target and the f2 target;
4.3, analyzing the current population to find out all Pareto optimal solutions if the current evolution is the first generation evolution according to a Pareto optimal method; if not, analyzing all individuals in the current population and the previous generation Pareto optimal solution set, and finding out the Pareto optimal solution set Nset of the combined setj(M), M takes the value of 200;
4.4, evolving the current population by adopting a multi-target distribution estimation algorithm, randomly selecting i/4 individuals in Nset (M), selecting i/4 individuals with dominance from the current evolved population to form a distribution parameter calculation population Cset (i/2), and calculating the mean value Qmean of the individuals in the population in each time periodiAnd variance Qsdti；
Step 4.5, using the mean and variance, and adopting a normal distribution sampling method N (Qmean)i，Qsdti) Generating i new individuals, wherein Flag is Flag +1, judging whether Flag is greater than Tmax, if Flag is greater than Tmax, finishing calculation, and outputting a Pareto optimal solution set NsetjIf the value is less than Tmax, skipping to step 4.2;
step 4.6, judging whether all the reduced scenes are completely calculated; if yes, outputting all Pareto optimal solution sets NsetjIf not, skipping to step 4.1.
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