[go: up one dir, main page]

CN114819659A - Reservoir optimal scheduling method based on dynamic optimization algorithm - Google Patents

Reservoir optimal scheduling method based on dynamic optimization algorithm Download PDF

Info

Publication number
CN114819659A
CN114819659A CN202210470612.1A CN202210470612A CN114819659A CN 114819659 A CN114819659 A CN 114819659A CN 202210470612 A CN202210470612 A CN 202210470612A CN 114819659 A CN114819659 A CN 114819659A
Authority
CN
China
Prior art keywords
reservoir
point
minimum
water
knee
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210470612.1A
Other languages
Chinese (zh)
Inventor
王万良
吴菲
陈忠馗
李国庆
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University of Technology ZJUT
Original Assignee
Zhejiang University of Technology ZJUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang University of Technology ZJUT filed Critical Zhejiang University of Technology ZJUT
Priority to CN202210470612.1A priority Critical patent/CN114819659A/en
Publication of CN114819659A publication Critical patent/CN114819659A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Geometry (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

提出一种基于动态优化算法的水库优化调度方法，包括：采集水库运行参数,设置为初始化种群popt；数学建模；设置水库的约束条件；检测环境变化；将水库种群进行非支配排序、分层，选择第一层作为非支配解集popNon；找到边缘个体及其组成的线或者面，存储到几个B中；计算点到直线或平面的距离；将第m目标值均匀划分为k个区域，在每个分区中选择到线或者平面距离最大的点作为第一目标值的knee点；计算边界参考点；将计算出的边界参考点加入到KN中记为更新knee点NKN，进行拥挤度距离计算、排序，删除拥挤度距离最小的点；预测环境变化后knee点的新位置；计算knee点在决策空间中的位置；预测环境变化后的非支配解集

Figure DDA0003622283550000011
获得预测新种群；利用优化算法优化总体；迭代结束。

Figure 202210470612

A reservoir optimization scheduling method based on dynamic optimization algorithm is proposed, which includes: collecting reservoir operating parameters and setting them as initial population pop t ; mathematical modeling; setting reservoir constraints; detecting environmental changes; layer, select the first layer as the non-dominated solution set pop Non ; find the edge individuals and the lines or surfaces they consist of, and store them in several B; calculate the distance from the point to the line or plane; divide the mth target value into k evenly In each partition, select the point with the largest distance from the line or plane as the knee point of the first target value; calculate the boundary reference point; add the calculated boundary reference point to KN and record it as the updated knee point NKN, and perform Calculate and sort the crowding degree distance, and delete the point with the smallest crowding degree distance; predict the new position of the knee point after the environment changes; calculate the position of the knee point in the decision space; predict the non-dominated solution set after the environment change

Figure DDA0003622283550000011
Obtain the predicted new population; use the optimization algorithm to optimize the population; the iteration ends.

Figure 202210470612

Description

Reservoir optimal scheduling method based on dynamic optimization algorithm
Technical Field
The invention relates to a reservoir optimal scheduling method based on a dynamic optimization algorithm
Background
With economic development, large-scale reservoir group optimization scheduling strategies are gradually improved and updated, reservoir scheduling integrates various target factors such as power generation, reservoir water supply, flood discharge, user water demand and the like, target decision is continuously changed along with the passage of time, and in order to comprehensively plan and coordinate various targets and simultaneously meet multi-party constraints, the reservoir optimization problem needs to be summarized into a dynamic optimization problem. Unlike static scheduling, the static scheduling problem is often the optimal scheme in an ideal state, but the reservoir environment is dynamically changed, so the reservoir optimization scheduling problem belongs to a typical multi-stage decision problem. In a real-world problem, it often happens that a plurality of targets conflict with each other and the targets change with time, and when one of the targets increases, the remaining sub-targets change in a weakening manner. For these problems, a dynamic optimization algorithm is required to track the pareto front or pareto set solutions that change over time.
To solve tracking the pareto front and the pareto solution, one usually tries to find a well-distributed pareto front, but this approach undoubtedly increases the computational burden. The challenge of this problem is therefore to maintain the search power of the algorithm as the environment changes, with appropriate diversity and convergence strategies.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a reservoir optimal scheduling method based on a dynamic optimization algorithm, which comprises the following steps:
suppose that the reservoir scheduling problem needs to meet two objectives: 1. a maximum power generation target; 2. and (5) a water abandoning target of the minimum-year reservoir. In addition to satisfying two goals, set constraint adjustment, the constraint conditions need to be satisfied: 1. balance constraint of reservoir water amount; 2. reservoir capacity constraints and initial condition constraints; 3. a flow limit constraint; 4. and (5) restraining the generated output.
In order to meet the above conditions, the specific implementation steps are as follows:
s1, acquiring time period t: coefficient of generated power k t Average discharge flow of reservoir
Figure BDA0003622283530000011
Average storage capacity of reservoir
Figure BDA0003622283530000012
Average water demand of user
Figure BDA0003622283530000013
Setting the collected data as an initialization population pop t Let N be 100.
S2, carrying out mathematical modeling on each parameter of the reservoir. The reservoir needs to meet two goals, respectively: maximum annual energy production f 1 And minimum annual water reject f 2 The relation formula of the two target values and each parameter of the reservoir is as follows:
Figure BDA0003622283530000021
Figure BDA0003622283530000022
wherein the target value f 1 Represents the maximum annual energy production, f 2 Expressing the total amount of water abandoned by the minimum year reservoir, as shown in formula (1)
Figure BDA0003622283530000023
Respectively representing the mean water level of the reservoir at time T and the mean water level of the tailwater of the hydropower station at time T, T t Is the length of the t period, and n is the number of the period of one year.
S3 setting the constraint condition of the reservoir. While achieving the above objective function, the reservoir still needs to satisfy the following constraints:
(1) reservoir water balance constraint
Figure BDA0003622283530000024
I t The amount of the water put in the warehouse is expressed,
Figure BDA0003622283530000025
indicating water loss of reservoir, V t Indicating the reservoir capacity at the end of time t.
(2) And (4) constraint of storage capacity limitation:
Figure BDA0003622283530000026
the maximum and minimum limits are expressed as the capacity of normal impounded water and the capacity of dead reservoir, respectively.
(3) Reservoir flow restriction:
Figure BDA0003622283530000027
the maximum and minimum limits are expressed as the minimum and maximum let-down flows of the plant during the time period t,
Figure BDA0003622283530000028
indicating the average let down flow.
(4) And (3) power generation force constraint:
Figure BDA0003622283530000029
the limits of the maximum value and the minimum value respectively represent the minimum power generation capacity and the maximum power generation capacity of the plant during the period t,
Figure BDA00036222835300000210
the average power generation force is represented.
S4, detecting the environmental change, and if the environmental change does not exist, turning to the step S16; if the environment has changed, the process goes to S5.
S5, carrying out non-domination sorting on the reservoir populations, layering the reservoir populations subjected to non-domination sorting, and selecting a first layer as a non-domination solution set pop Non Here, a good set of reservoir solutions that meet all reservoir objectives and constraints can be understood.
S6, searching edge individuals in the non-dominant solution set, finding lines or planes formed by the edge individuals, and storing the edge individuals into a plurality of B as shown in figure 1.
And S7, calculating the distance from the point to a straight line (two targets) or a plane (three targets) according to the formula (3).
Figure BDA0003622283530000031
S8, uniformly dividing the mth target value into k areas according to the target number M, selecting a point with the maximum distance to the line or plane in each partition as a knee point of the first target value, and if the area is empty, randomly initializing a value as the knee point selected by the area. As shown in fig. 1, the first target value is divided into 4 regions, the farthest non-dominant solutions (asterisk points) from the edge point connection line are respectively selected, then the second target value is divided into 4 regions, the farthest non-dominant solutions (dots) are also selected, and if a point (dotted line point) overlapping with the first target value appears, the second farthest non-dominant solution in the region is selected. The number of knee points obtained is m × k, and the knee points obtained are stored in several KNs.
S9, calculating a boundary reference point Q * The formula is as follows:
Figure BDA0003622283530000032
wherein Q i Representing the ith individual in the non-dominated solution set.
And S10, adding the calculated boundary reference points into the KN, recording the boundary reference points as updated knee points NKN, calculating the crowding degree distance of all the obtained KN individuals, sequencing, and deleting the point with the minimum crowding degree distance.
S11, predicting a new position of the knee point after the environment changes. The change in knee point is shown in fig. 2. Predicting the knee point evolution direction:
Figure BDA0003622283530000033
s12, obtaining a direction vector according to a formula (6)
Figure BDA0003622283530000034
Calculating the position of the knee point at the t +1 moment in the decision space:
Figure BDA0003622283530000041
wherein up i Denotes the maximum value in the i dimension, low i Denotes the minimum in the i dimension, ε t Is a gaussian perturbation.
S13, predicting the non-dominated solution set after the environmental change
Figure BDA0003622283530000042
Figure BDA0003622283530000043
S14, obtaining a new prediction population at the t +1 moment:
Figure BDA0003622283530000044
wherein pop rand Is a random point when
Figure BDA0003622283530000045
Then it will calculate
Figure BDA0003622283530000046
The distance of the crowdedness degree is sorted again, the point with the minimum distance of the crowdedness degree is deleted, and the random point pop rand For ensuring population pop t+1 The size remains unchanged at N-100.
And S15, optimizing the whole by utilizing an optimization algorithm RM-MEDA.
And S16, finishing iteration, and outputting a final pop to obtain an optimal scheme which meets the target of the maximum power generation amount and the minimum water abandon amount and also meets all reservoir constraints.
The invention provides a reservoir optimal scheduling method based on a dynamic optimization algorithm, which has the advantages that:
the method reduces the huge calculation burden caused by the need of intensively searching a well-distributed pareto frontier in the evolution process, and realizes the good balance between the convergence and the diversity by a method of increasing the population diversity by using a special point and a prediction strategy.
Drawings
FIG. 1 is a knee point selection diagram.
FIG. 2 is a graph of prediction of knee point at time t + 1.
Detailed Description
In order that the process of the invention may be more readily understood, the invention will now be described in detail with reference to the examples.
The invention provides a reservoir optimal scheduling method based on a dynamic optimization algorithm, aiming at overcoming the defects in the prior art, and the method comprises the following steps:
suppose that the reservoir scheduling problem needs to meet two objectives: 1. a maximum power generation target; 2. and (5) a water abandoning target of the minimum-year reservoir. In addition to satisfying two goals, set constraint adjustment, the constraint conditions need to be satisfied: 1. balance constraint of reservoir water amount; 2. reservoir capacity constraints and initial condition constraints; 3. a flow limit constraint; 4. power generation output constraint; 5. and (4) final storage capacity constraint.
In order to meet the above conditions, the specific implementation steps are as follows:
s1, acquiring time period t: coefficient of generated power k t Average discharge flow of reservoir
Figure BDA0003622283530000051
Average storage capacity of reservoir
Figure BDA0003622283530000052
Average water demand of user
Figure BDA0003622283530000053
Setting the collected data as an initialization population pop t Let N be 100.
S2, carrying out mathematical modeling on each parameter of the reservoir. The reservoir needs to meet two goals, respectively: maximum annual energy production f 1 And minimum annual water reject f 2 The relation formula of the two target values and each parameter of the reservoir is as follows:
Figure BDA0003622283530000054
Figure BDA0003622283530000055
wherein the target value f 1 Represents the maximum annual energy production, f 2 Expressing the total amount of water abandoned by the minimum year reservoir, as shown in formula (1)
Figure BDA0003622283530000056
Respectively representing the mean water level of the reservoir at time T and the mean water level of the tailwater of the hydropower station at time T, T t Is the length of the t period, and n is the number of the period of one year.
S3 setting the constraint condition of the reservoir. While achieving the above objective function, the reservoir still needs to satisfy the following constraints:
(1) reservoir water balance constraint
Figure BDA0003622283530000057
I t The amount of the water put in the warehouse is expressed,
Figure BDA0003622283530000058
indicating water loss of reservoir, V t Indicating the reservoir capacity at the end of time t.
(5) And (4) constraint of storage capacity limitation:
Figure BDA0003622283530000059
the maximum and minimum limits are expressed as the capacity of normal impounded water and the capacity of dead reservoir, respectively.
(6) Reservoir flow restriction:
Figure BDA0003622283530000061
the maximum and minimum limits are expressed as the minimum and maximum let-down flows of the plant during the time period t respectively,
Figure BDA0003622283530000062
indicating the average let down flow.
(7) And (3) power generation force constraint:
Figure BDA0003622283530000063
the limits of the maximum and minimum values represent the minimum and maximum power generation forces of the plant during the period t respectively,
Figure BDA0003622283530000064
the average power generation force is represented.
S4, detecting the environmental change, and if the environmental change does not exist, turning to the step S16; if the environment has changed, go to S5.
S5, performing non-dominated sorting on the population, layering the population, and selecting a first layer as a non-dominated solution set pop Non Here, a good set of reservoir solutions that meet all reservoir objectives and constraints can be understood.
S6, searching edge individuals in the non-dominated solution set, finding lines or planes formed by the edge individuals, and storing the edge individuals into a plurality of B as shown in figure 1.
And S7, calculating the distance from the point to a straight line (two targets) or a plane (three targets) according to the formula (3).
Figure BDA0003622283530000065
S8, uniformly dividing the mth target value into k areas according to the target number M, selecting a point with the maximum distance to the line or plane in each partition as a knee point of the first target value, and if the area is empty, randomly initializing a value as the knee point selected by the area. As shown in fig. 1, the first target value is divided into 4 regions, the farthest non-dominant solutions (asterisk points) from the connecting line of the edge points are respectively selected, then the second target value is divided into 4 regions, the farthest non-dominant solution (circular point) is also selected, and if a point (dotted line point) overlapping with the first target value appears, the second farthest non-dominant solution in the region is selected. The number of knee points obtained is m × k, and the knee points obtained are stored in several KNs.
S9, calculating a boundary reference point Q * The formula is as follows:
Figure BDA0003622283530000066
wherein Q i Representing the ith individual in the non-dominated solution set.
And S10, adding the calculated boundary reference points into the KN, recording the boundary reference points as updated knee points NKN, calculating the crowding degree distance of all the obtained KN individuals, sequencing, and deleting the point with the minimum crowding degree distance.
S11, predicting a new position of the knee point after the environment changes. The change in knee point is shown in fig. 2. Predicting the knee point evolution direction:
Figure BDA0003622283530000071
s12, obtaining a direction vector according to a formula (6)
Figure BDA0003622283530000072
Calculating the position of the knee point at the t +1 moment in the decision space:
Figure BDA0003622283530000073
wherein up i Denotes the maximum value in the i dimension, low i Denotes the minimum in the i dimension, ε t Is a gaussian perturbation.
S13, predicting the non-dominated solution set after the environmental change
Figure BDA0003622283530000074
Figure BDA0003622283530000075
S14, obtaining a new prediction population at the t +1 moment:
Figure BDA0003622283530000076
wherein pop rand Is a random point when
Figure BDA0003622283530000077
Then it will calculate
Figure BDA0003622283530000078
The distance of the crowdedness degree is sorted again, the point with the minimum distance of the crowdedness degree is deleted, and the random point pop rand For ensuring population pop t+1 The size remains unchanged at N-100.
And S15, optimizing the whole by utilizing an optimization algorithm RM-MEDA.
And S16, finishing iteration, and outputting a final pop to obtain an optimal scheme which meets the target of the maximum power generation amount and the minimum water abandon amount and also meets various reservoir constraints.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.

Claims (2)

1. A reservoir optimal scheduling method based on a dynamic optimization algorithm comprises the following steps:
suppose that the reservoir scheduling problem needs to meet two objectives: 1. a maximum power generation target; 2. a minimum annual reservoir water abandonment target; in addition to satisfying two goals, set constraint adjustment, the constraint conditions need to be satisfied: 1. balance constraint of reservoir water amount; 2. reservoir capacity constraints and initial condition constraints; 3. a flow limit constraint; 4. power generation output constraint;
in order to meet the above conditions, the specific implementation steps are as follows:
s1, collecting time periods t: coefficient of generated power k t Average discharge flow of reservoir
Figure FDA0003622283520000017
Average storage capacity of reservoir
Figure FDA0003622283520000019
Average water demand of user
Figure FDA0003622283520000018
Setting the collected data as an initialization population pop t Setting the size of the population as N to 100;
s2, carrying out mathematical modeling on each parameter of the reservoir; the reservoir needs to meet two goals, respectively: maximum annual energy production f 1 And minimum annual water reject f 2 The relation formula of the two target values and each parameter of the reservoir is as follows:
Figure FDA0003622283520000011
Figure FDA0003622283520000012
wherein the target value f 1 Represents the maximum annual energy production, f 2 Expressing the total amount of water abandoned by the minimum year reservoir, as shown in formula (1)
Figure FDA0003622283520000013
Respectively representing the mean water level of the reservoir at time T and the mean water level of the tailwater of the hydropower station at time T, T t Is the length of the t time period, and n is the number of time periods of one year;
s3, setting the constraint conditions of the reservoir; while achieving the above objective function, the reservoir still needs to satisfy the following constraints:
(1) reservoir water balance constraint
Figure FDA0003622283520000014
I t The amount of the water put in the reservoir is indicated,
Figure FDA0003622283520000015
indicating water loss of reservoir, V t Indicating the storage capacity of the reservoir at the end of time t;
(2) and (4) constraint of storage capacity limitation:
Figure FDA0003622283520000016
the maximum value limit and the minimum value limit are respectively expressed as the storage capacity of normal water storage and the dead storage capacity;
(3) reservoir flow restriction:
Figure FDA0003622283520000021
the maximum and minimum limits are expressed as the minimum and maximum let-down flows of the plant during the time period t respectively,
Figure FDA0003622283520000022
represents the average let-down flow;
(4) and (3) power generation force constraint:
Figure FDA0003622283520000023
the limits of the maximum value and the minimum value respectively represent the minimum power generation capacity and the maximum power generation capacity of the plant during the period t,
Figure FDA0003622283520000024
represents an average power generation force;
s4, detecting the environmental change, and if the environmental change does not exist, turning to the step S16; go to S5 if the environment has changed;
s5, carrying out non-domination sequencing on the reservoir populations, layering the reservoir populations subjected to non-domination sequencing, and selecting the first layer as a non-domination solution set pop Non Here, a good set of reservoir solutions that meet all reservoir objectives and constraints can be understood;
s6, searching edge individuals in the non-dominated solution set, finding lines or planes formed by the edge individuals, and storing the edge individuals into a plurality of B;
s7, calculating the distance from the point to a straight line (two targets) or a plane (three targets) according to a formula (3);
Figure FDA0003622283520000025
s8, uniformly dividing the mth target value into k areas according to the target number M, selecting a point with the largest distance to a line or plane in each partition as a knee point of the first target value, and if the area is empty, randomly initializing a value as the knee point selected by the area;
s9, calculating a boundary reference point Q * The formula is as follows:
Figure FDA0003622283520000026
wherein Q i Representing the ith individual in the non-dominant solution set;
s10, adding the calculated boundary reference point into the KN, recording the boundary reference point as an updated knee point NKN, calculating the crowding degree distance of all the obtained KN individuals, sequencing, and deleting the point with the minimum crowding degree distance;
s11, predicting a new position of a knee point after the environment changes; the change in knee point is shown in FIG. 2; predicting the knee point evolution direction:
Figure FDA0003622283520000031
s12, obtaining a direction vector according to a formula (6)
Figure FDA0003622283520000032
Calculating the position of the knee point at the t +1 moment in the decision space:
Figure FDA0003622283520000033
wherein up i Denotes the maximum value in the i dimension, low i Denotes the minimum in the i dimension, ε t Is Gaussian disturbance;
s13, predicting the non-dominated solution set after the environmental change
Figure FDA0003622283520000034
Figure FDA0003622283520000035
S14, obtaining a new prediction population at the t +1 moment:
Figure FDA0003622283520000036
wherein pop rand Is a random point when
Figure FDA0003622283520000037
Then it will calculate
Figure FDA0003622283520000038
The distance of the crowdedness degree is sorted again, the point with the minimum distance of the crowdedness degree is deleted, and the random point pop rand For ensuring population pop t+1 The size is kept unchanged when N is 100;
s15, optimizing the whole by utilizing an optimization algorithm RM-MEDA;
and S16, finishing iteration, and outputting a final pop to obtain an optimal scheme which meets the target of the maximum power generation amount and the minimum water abandon amount and also meets all reservoir constraints.
2. The reservoir optimal scheduling method based on the dynamic optimization algorithm as claimed in claim 1, wherein: step S8, dividing the first target value into 4 areas, selecting the farthest non-dominated solution from the edge point connection line, dividing the second target value into 4 areas, selecting the farthest non-dominated solution, and selecting the second farthest non-dominated solution in the area if the point is repeated with the first target value; the number of knee points obtained is m × k, and the knee points obtained are stored in several KNs.
CN202210470612.1A 2022-04-28 2022-04-28 Reservoir optimal scheduling method based on dynamic optimization algorithm Pending CN114819659A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210470612.1A CN114819659A (en) 2022-04-28 2022-04-28 Reservoir optimal scheduling method based on dynamic optimization algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210470612.1A CN114819659A (en) 2022-04-28 2022-04-28 Reservoir optimal scheduling method based on dynamic optimization algorithm

Publications (1)

Publication Number Publication Date
CN114819659A true CN114819659A (en) 2022-07-29

Family

ID=82510034

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210470612.1A Pending CN114819659A (en) 2022-04-28 2022-04-28 Reservoir optimal scheduling method based on dynamic optimization algorithm

Country Status (1)

Country Link
CN (1) CN114819659A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117634698A (en) * 2023-12-08 2024-03-01 华润电力技术研究院有限公司 A short-term wind power power prediction method, device and related components

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106228232A (en) * 2016-07-07 2016-12-14 淮北师范大学 A kind of dynamic multi-objective based on fuzzy reasoning Population forecast strategy teaching optimization method
US9811061B1 (en) * 2001-05-18 2017-11-07 The Energy Authority, Inc. Method for management and optimization of hydropower generation and consumption
CN109583638A (en) * 2018-11-16 2019-04-05 新疆大学 A kind of multistage reservoir optimizing and dispatching method based on mixing cuckoo optimization algorithm
CN109902855A (en) * 2019-01-14 2019-06-18 昆明电力交易中心有限责任公司 A multi-objective optimization and power distribution method for cascade key hydropower stations in a market environment
CN110097088A (en) * 2019-04-08 2019-08-06 燕山大学 A kind of dynamic multi-objective evolvement method based on transfer learning Yu particular point strategy
CN111861137A (en) * 2020-06-28 2020-10-30 中国电建集团昆明勘测设计研究院有限公司 Parallel Multi-objective Scheduling Method for Cascade Reservoir Groups

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9811061B1 (en) * 2001-05-18 2017-11-07 The Energy Authority, Inc. Method for management and optimization of hydropower generation and consumption
CN106228232A (en) * 2016-07-07 2016-12-14 淮北师范大学 A kind of dynamic multi-objective based on fuzzy reasoning Population forecast strategy teaching optimization method
CN109583638A (en) * 2018-11-16 2019-04-05 新疆大学 A kind of multistage reservoir optimizing and dispatching method based on mixing cuckoo optimization algorithm
CN109902855A (en) * 2019-01-14 2019-06-18 昆明电力交易中心有限责任公司 A multi-objective optimization and power distribution method for cascade key hydropower stations in a market environment
CN110097088A (en) * 2019-04-08 2019-08-06 燕山大学 A kind of dynamic multi-objective evolvement method based on transfer learning Yu particular point strategy
CN111861137A (en) * 2020-06-28 2020-10-30 中国电建集团昆明勘测设计研究院有限公司 Parallel Multi-objective Scheduling Method for Cascade Reservoir Groups

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张小潭 等: "基于拥挤距离的多目标粒子群优化算法在漳河水库优化调度中的应用", 水电能源科学, vol. 31, no. 04, 25 April 2013 (2013-04-25), pages 42 - 45 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117634698A (en) * 2023-12-08 2024-03-01 华润电力技术研究院有限公司 A short-term wind power power prediction method, device and related components

Similar Documents

Publication Publication Date Title
CN103164742B (en) A kind of server performance Forecasting Methodology based on particle group optimizing neural network
CN106779153B (en) An optimization method for cargo space allocation in an intelligent three-dimensional warehouse
CN111814251A (en) Multi-objective and multi-modal particle swarm optimization method based on Bayesian adaptive resonance
CN110363343B (en) A hybrid adaptive intelligent optimal scheduling method and system for hydropower station groups
CN113469426A (en) Photovoltaic output power prediction method and system based on improved BP neural network
CN117973600A (en) A reservoir water level prediction method based on multi-source data fusion integration
CN115390565B (en) A dynamic path planning method and system for unmanned boat based on improved D-star algorithm
Chiu et al. Using a hybrid genetic algorithm–simulated annealing algorithm for fuzzy programming of reservoir operation
Azimi et al. A novel clustering algorithm based on data transformation approaches
Niu et al. Artificial intelligence-based response surface progressive optimality algorithm for operation optimization of multiple hydropower reservoirs
CN111353534B (en) A Graph Data Category Prediction Method Based on Adaptive Fractional Gradients
CN116307533B (en) Reservoir group flood control dispatching intelligent method, system and medium
CN116108982A (en) Cooperative search method and system for multi-objective scheduling of reservoir groups
CN116644700A (en) Analog circuit design method and device
CN118333345B (en) An optimization scheduling method for water-photovoltaic complementary system based on multi-objective whale algorithm
CN118114945A (en) A dynamic multi-objective optimization control system and method for giant reservoir water level in flood season
CN118521037A (en) Agile satellite scheduling method and system based on hybrid local search algorithm
CN114819659A (en) Reservoir optimal scheduling method based on dynamic optimization algorithm
CN119417182A (en) A multi-objective optimization method for reservoir group dispatching interval under uncertain conditions
CN119474644A (en) A small sample regression prediction method for tunnel blasting effect
CN112132469B (en) A reservoir group scheduling method and system based on multi-swarm cooperative particle swarm algorithm
CN115409387B (en) Reservoir optimal scheduling method and system based on improved differential evolution
Oyama et al. Real-coded adaptive range genetic algorithm and its application to aerodynamic design
CN120657877B (en) Provincial power grid fire storage day-ahead active power scheduling method coping with extreme scenes
CN117421969A (en) A wind speed prediction method based on BSSA algorithm optimizing LSSVM model

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20220729