CN111008743A - High-discretization reservoir power generation optimal scheduling method - Google Patents

High-discretization reservoir power generation optimal scheduling method Download PDF

Info

Publication number
CN111008743A
CN111008743A CN201911255453.8A CN201911255453A CN111008743A CN 111008743 A CN111008743 A CN 111008743A CN 201911255453 A CN201911255453 A CN 201911255453A CN 111008743 A CN111008743 A CN 111008743A
Authority
CN
China
Prior art keywords
reservoir
knowledge
power generation
optimal
individuals
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
CN201911255453.8A
Other languages
Chinese (zh)
Inventor
顾巍
叶志伟
徐志刚
徐慧
董新华
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.)
Hubei University of Technology
Original Assignee
Hubei University of Technology
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 Hubei University of Technology filed Critical Hubei University of Technology
Priority to CN201911255453.8A priority Critical patent/CN111008743A/en
Publication of CN111008743A publication Critical patent/CN111008743A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Computation (AREA)
  • Development Economics (AREA)
  • Data Mining & Analysis (AREA)
  • Game Theory and Decision Science (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • General Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Primary Health Care (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention discloses a high-discretization reservoir power generation optimal scheduling method, which comprises the steps of firstly establishing a maximum model of high-discretization space reservoir power generation optimal scheduling; and then, solving a maximum model for optimizing and dispatching the power generation of the reservoir in the high discrete space by adopting a cultural whale optimization algorithm and combining a discrete space mapping continuous strategy. The invention adopts culture algorithm, whale optimization algorithm and discrete space mapping continuous strategy to process the power generation optimization scheduling problem of the reservoir with high discrete space, effectively improves the reservoir scheduling level and provides a new method for solving the reservoir scheduling problem with high discrete constraint.

Description

High-discretization reservoir power generation optimal scheduling method
Technical Field
The invention belongs to the technical field of reservoir optimization scheduling in the field of water resources, and particularly relates to a high-discretization reservoir power generation optimization scheduling method.
Background
Reservoir optimal scheduling is an important means for water resource optimal utilization, and the reservoir generally has the functions of power generation, flood control, irrigation, water supply and the like. In the power generation dispatching of the reservoir, the reservoir optimization dispatching problem is a high-dimensionality, multi-constraint and nonlinear complex optimization problem, and the research on the problem is always a hotspot researched by scholars. When the vibration problem of each unit in the reservoir is considered, the combined feasible region is a highly discrete feasible region, and an infeasible solution is easily trapped during problem solving, so that more challenges are brought.
Therefore, a proper mechanism is needed to be adopted to process the high discretization so as to improve the quality of the solution of the algorithm for solving the reservoir optimal scheduling problem.
Disclosure of Invention
In order to solve the technical problems, the invention provides a high-discretization reservoir power generation optimal scheduling method, which adopts a cultural algorithm, a whale optimal algorithm and a discrete space mapping continuous strategy to process the high-discretization space reservoir power generation optimal scheduling problem, effectively improves the reservoir scheduling level, and provides a new method for the high-discretization constraint reservoir scheduling problem.
The technical scheme adopted by the invention is as follows: the optimal scheduling method for the power generation of the high-discretization reservoir is characterized by comprising the following steps of:
step 1: establishing a maximum mathematical model of reservoir dispatching generating capacity;
Figure BDA0002310123810000011
s.t.
Vt＝Vt-1+It-Qt-St
Vmin≤Vt≤Vmax
Qmin≤Qt≤Qmax
Pmin≤Pt≤Pmax
V0＝VB,VT＝VE
Figure BDA0002310123810000012
whereinP is the generated energy; k is a hydropower station output coefficient; qiThe average generated flow at the moment i; hiAverage head for period i; diHours of the i period; t is the total number of periods in the scheduling period, where VtIs the storage capacity of the reservoir at time t, ItIs the warehousing flow rate, Q, of the reservoir at the time ttGenerating flow of reservoir at time t, StThe water discharge of the reservoir at the time t; vmin、VmaxThe lower limit and the upper limit of the reservoir capacity of the reservoir; qmin、QmaxThe lower limit and the upper limit of reservoir discharge; pmin、PmaxFor reservoir to produce PtLower and upper limits of, V0、VTRegulating the starting water level and the final water level for hydropower dispatching;
Figure BDA0002310123810000021
is the feasible region of the reservoir output;
step 2: and solving a maximum model of the high-dispersion-space reservoir power generation optimal scheduling by adopting a cultural whale optimization algorithm and combining a discrete space mapping continuous strategy to realize the high-dispersion-space reservoir power generation optimal scheduling.
Compared with the existing algorithm, the reservoir power generation optimization scheduling considering the high dispersion condition is provided by the invention, the existing algorithm processes the discrete solution space in a penalty function mode, the penalty function mode easily causes the solution to fall into an infeasible area when the dispersion is high, and in the actual reservoir operation, due to the fact that different generator set vibration infeasible intervals are different, the scheduling problem in the power generation operation of a distribution power station is the high dispersion optimization problem. The invention provides a culture whale optimization algorithm under the high-dispersion condition, integrates the treatment of a high-dispersion space in the algorithm design process, improves the problem of algorithm treatment on high-dispersion reservoir dispatching, integrates the culture algorithm and the whale optimization algorithm, provides the culture whale optimization algorithm, and further improves the capability of the algorithm jumping out of local optimal solution, thereby improving the reservoir power generation optimization dispatching performance of the algorithm, and providing more practical operation management for the reservoir.
The invention relates to a high-discretization reservoir power generation optimal scheduling method, which adopts a cultural algorithm, a whale optimal algorithm and a discrete space mapping continuous strategy to process the high-discretization reservoir power generation optimal scheduling problem, effectively improves the reservoir scheduling level, and provides a new method for the high-discretization-constraint reservoir scheduling problem
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail with reference to the accompanying drawings and examples, it is to be understood that the embodiments described herein are merely illustrative and explanatory of the present invention and are not restrictive thereof.
Referring to fig. 1, the optimal scheduling method for power generation of a high-discretization reservoir provided by the invention comprises the following steps:
step 1: establishing a maximum mathematical model of reservoir dispatching generating capacity;
Figure BDA0002310123810000031
s.t.
Vt＝Vt-1+It-Qt-St
Vmin≤Vt≤Vmax
Qmin≤Qt≤Qmax
Pmin≤Pt≤Pmax
V0＝VB,VT＝VE
Figure BDA0002310123810000032
wherein, P is the generated energy; k is a hydropower station output coefficient; qiThe average generated flow at the moment i; hiAverage head for period i; diHours of the i period; t is the total number of periods in the scheduling period, where VtFor reservoir at time tCarved storage capacity, ItIs the warehousing flow rate, Q, of the reservoir at the time ttGenerating flow of reservoir at time t, StThe water discharge of the reservoir at the time t; vmin、VmaxThe lower limit and the upper limit of the reservoir capacity of the reservoir; qmin、QmaxThe lower limit and the upper limit of reservoir discharge; pmin、PmaxFor reservoir to produce PtLower and upper limits of, V0、VTRegulating the starting water level and the final water level for hydropower dispatching;
Figure BDA0002310123810000033
is the feasible region of the reservoir output;
step 2: and solving a maximum model of the high-dispersion-space reservoir power generation optimal scheduling by adopting a cultural whale optimization algorithm and combining a discrete space mapping continuous strategy to realize the high-dispersion-space reservoir power generation optimal scheduling.
In this embodiment, the specific implementation of step 2 includes the following substeps:
step 2.1: initializing a population, and randomly generating N initial solutions in a solution space, namely randomly generating the positions of N whales;
wherein, the position of whale is marked as x (i,0) ═ Q1,Q2,..QT}，QTThe generating flow of the reservoir at the T-th time period; in [ Q ]min,c，Qmax,c]Random generation of Q over a rangeTA value of (d); qmin,c，Qmax,cAnd the upper and lower limits of the reservoir generating flow in the T-th time period are restricted.
Step 2.2: calculating the fitness value of the whale, and comparing to obtain a solution with the maximum fitness value and a current optimal solution;
by the formula
Figure BDA0002310123810000034
Calculating the power generation capacity as the fitness value of the individual;
and carrying out penalty function processing on the violation of the constraint condition of the equation, namely P + PF (y), wherein PF (y) is a penalty function, and y is a constraint violation quantity.
Step 2.3: analyzing the position of whales in the colony, and updating situation knowledge, standard knowledge, historical knowledge and field knowledge in a cultural algorithm;
in this embodiment, situational knowledge is used to record the preferred individuals during evolution, and the structural description is as follows:
＜E1,E2,....,Es＞
wherein S is knowledge volume, ESThe individuals in the situation knowledge are arranged in a descending order according to individual fitness values for the elite individuals in the evolution process; after the evolution of the individuals in the population space Npop is completed, selecting a better part of the individuals through an acceptance function and submitting the better part of the individuals to a belief space Nbel ef; and if the number of the individuals in the knowledge space exceeds the knowledge capacity S, replacing the poor individuals when a new individual is accepted, and selecting the better individuals to enter the belief space.
In the embodiment, the standard knowledge gives the range of each variable, and is used for providing a standard for an individual and guiding the individual to adjust; the standard knowledge structure is:
＜l1,...lj,...,ln；u1,...uj,...,un＞
wherein ljAnd ujJ is the upper and lower limits of the variable, and n is the dimension of the variable.
In the embodiment, the historical knowledge is used for monitoring the evolutionary process of the population space and guiding the search direction of the population space; the historical knowledge records the optimal solution of the population after each generation of evolution in the population space, and the structure of the historical knowledge is as follows:
＜f*(Mt-h+1),...,f*(Mt)；ξt,h＞
wherein f is*(Mt-h+1)～f*(Mt) Fitness value for successive h generations of optimal individuals, ξt,hDescribing the relative change of the algorithm in the near h generation if ξt,hLess than a convergence threshold ξ0It indicates that the optimal individual of the latest h generation has no significant change, and the algorithm may be trapped in the local optimal solution.
In the embodiment, the domain knowledge is used for predicting the evolution direction, recording a better evolution trend, and jumping out the local optimum and improving the convergence precision by using a local search method; the structure of domain knowledge is:
＜X*；LocalSearch()＞
wherein, X*For the optimal solution obtained in the evolution process, LocalSearch () is a local search operator, and hill climbing search, namely X, is adopted*Whether + -random is more preferable, random is a random value.
Step 2.4: adopting situation knowledge and standard knowledge to spirally update the positions of whales, adopting a food search operator to search in combination with historical knowledge and domain knowledge, recalculating fitness values and updating the positions, sequencing the fitness values of all whales and finding out an optimal solution;
the specific implementation comprises the following substeps:
step 2.4.1: updating the position of whale by adopting situation knowledge and standard knowledge; the specific mathematical model is as follows:
Xi(t+1)＝Xp(t)-A·cos(2πl)·ebl·|C·Xp(t)-Xi(t)|ifli＜Xi(t+1)＜ui
Xi(t+1)＝liifli＞Xi(t+1)
Xi(t+1)＝uiifui＜Xi(t+1)
wherein t is the current iteration number, Xp(t) is the position of the prey at time t, i.e. the position of the optimal solution in the population of the t generation, Xp(t) is the optimal solution of the t-th generation population, Xi(t +1) is the position of the individual i at the t +1 th generation; li,uiParameters that are standard knowledge; b is a constant coefficient, l is [0,1]]Random number in between, A · | C · Xp(t)-Xi(t) | is the step length of hunting enclosure, and a and C are coefficients for controlling the enclosure step length, respectively, and the coefficients are defined as follows:
A＝a·(2·r1-1)
C＝2·r2
wherein r1 and r2 are random numbers between [0,1], a is a control parameter set by adopting situation knowledge and standard knowledge:
Figure BDA0002310123810000051
in the formula tmaxIs the maximum iteration number;
step 2.4.2: searching by adopting a food search operator in combination with historical knowledge and domain knowledge; the specific mathematical model is as follows:
Xi(t+1)＝Xp(t)-A·|C·Xrandom-Xi(t)|+Localsearch()
wherein t is the current iteration number, XrandomFor random historical knowledge location, A. C. Xp(t)-Xi(t) | is the step length surrounded by the hunting, A and C are coefficients for controlling the surrounding step length respectively, and Localsearch () is a neighborhood knowledge parameter;
step 2.5: adopting a discrete space mapping continuous strategy to map the individuals in the infeasible domain, namely mapping and correcting variables violating the constraint through a formula, wherein X is X +/-Xm, and Xm is a violation value;
step 2.6: judging whether the maximum iteration times is reached;
if yes, ending the process;
if not, the rotation is executed to repeat the step 2.2.
The invention adopts culture algorithm, whale optimization algorithm and discrete space mapping continuous strategy to process the power generation optimization scheduling problem of the reservoir with high discrete space, effectively improves the reservoir scheduling level and provides a new method for solving the reservoir scheduling problem with high discrete constraint.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (8)

1. The optimal scheduling method for the power generation of the high-discretization reservoir is characterized by comprising the following steps of:
step 1: establishing a maximum mathematical model of reservoir dispatching generating capacity;
Figure FDA0002310123800000011
s.t.
Vt＝Vt-1+It-Qt-St
Vmin≤Vt≤Vmax
Qmin≤Qt≤Qmax
Pmin≤Pt≤Pmax
V0＝VB,VT＝VE
Figure FDA0002310123800000013
wherein, P is the generated energy; k is a hydropower station output coefficient; qiThe average generated flow at the moment i; hiAverage head for period i; diHours of the i period; t is the total number of periods in the scheduling period, where VtIs the storage capacity of the reservoir at time t, ItIs the warehousing flow rate, Q, of the reservoir at the time ttGenerating flow of reservoir at time t, StThe water discharge of the reservoir at the time t; vmin、VmaxThe lower limit and the upper limit of the reservoir capacity of the reservoir; qmin、QmaxThe lower limit and the upper limit of reservoir discharge; pmin、PmaxFor reservoir to produce PtLower and upper limits of, V0、VTRegulating the starting water level and the final water level for hydropower dispatching;
Figure FDA0002310123800000012
is the feasible region of the reservoir output;
step 2: and solving a maximum model of the high-dispersion-space reservoir power generation optimal scheduling by adopting a cultural whale optimization algorithm and combining a discrete space mapping continuous strategy to realize the high-dispersion-space reservoir power generation optimal scheduling.
2. The optimal scheduling method for power generation of high-discretization reservoir according to claim 1, wherein the step 2 is realized by the following substeps:
step 2.1: initializing a population, and randomly generating N initial solutions in a solution space, namely randomly generating the positions of N whales;
wherein, the position of whale is marked as x (i,0) ═ Q1,Q2,..QT}，QTThe generating flow of the reservoir at the T-th time period; in [ Q ]min,T，Qmax,T]Random generation of Q over a rangeTA value of (d); qmin,c，Qmax,cThe upper limit and the lower limit of the reservoir generating flow in the T-th time period respectively;
step 2.2: calculating the fitness value of the whale, and comparing to obtain a solution with the maximum fitness value and a current optimal solution;
by the formula
Figure FDA0002310123800000021
Calculating the power generation capacity as the fitness value of the individual;
step 2.3: analyzing the position of whales in the colony, and updating situation knowledge, standard knowledge, historical knowledge and field knowledge in a cultural algorithm;
step 2.4: adopting situation knowledge and standard knowledge to spirally update the positions of whales, adopting a food search operator to search in combination with historical knowledge and domain knowledge, recalculating fitness values and updating the positions, sequencing the fitness values of all whales and finding out an optimal solution;
step 2.5: adopting a discrete space mapping continuous strategy to map the individuals in the infeasible domain, namely mapping and correcting variables violating the constraint through a formula, wherein X is X +/-Xm, and Xm is a violation value;
step 2.6: judging whether the maximum iteration times is reached;
if yes, ending the process;
if not, the rotation is executed to repeat the step 2.2.
3. The high-discretization optimal dispatching method for reservoir power generation according to claim 2, wherein the dispatching method comprises the following steps: in step 2.2, a penalty function is performed on the violation of the constraint condition of the equation, that is, P + pf (y), where pf (y) is the penalty function and y is the constraint violation quantity.
4. The high-discretization optimal dispatching method for reservoir power generation according to claim 2, wherein the dispatching method comprises the following steps: in step 2.3, the situational knowledge is used to record the superior individuals in the evolution process, and the structural description is as follows:
＜E1,E2,....,Es＞
wherein S is knowledge volume, ESThe individuals in the situation knowledge are arranged in a descending order according to individual fitness values for the elite individuals in the evolution process; after the evolution of the individuals in the population space Npop is completed, selecting a better part of the individuals through an acceptance function and submitting the better part of the individuals to a belief space Nbel ef; and if the number of the individuals in the knowledge space exceeds the knowledge capacity S, replacing the poor individuals when a new individual is accepted, and selecting the better individuals to enter the belief space.
5. The high-discretization optimal dispatching method for reservoir power generation according to claim 2, wherein the dispatching method comprises the following steps: in step 2.3, the standard knowledge gives the range of each variable, and is used for providing standards for individuals and guiding the individuals to adjust; the standard knowledge structure is:
＜l1,...lj,...,ln；u1,...uj,...,un＞
wherein ljAnd ujJ is the upper and lower limits of the variable, and n is the dimension of the variable.
6. The high-discretization optimal dispatching method for reservoir power generation according to claim 2, wherein the dispatching method comprises the following steps: in step 2.3, historical knowledge is used for monitoring the evolutionary process of the population space and guiding the search direction of the population space; the historical knowledge records the optimal solution of the population after each generation of evolution in the population space, and the structure of the historical knowledge is as follows:
<f*(Mt-h+1)，...，f*(Mt)；ξt，h>
wherein f is*(Mt-h+1)～f*(Mt) Fitness value for successive h generations of optimal individuals, ξt，hDescribing the relative change of the algorithm in the near h generation if ξt，hLess than a convergence threshold ξ0It indicates that the optimal individual of the latest h generation has no significant change, and the algorithm may be trapped in the local optimal solution.
7. The high-discretization optimal dispatching method for reservoir power generation according to claim 2, wherein the dispatching method comprises the following steps: in step 2.3, the domain knowledge is used for predicting the evolution direction, recording a better evolution trend, and jumping out the local optimum and improving the convergence precision by using a local search method; the structure of domain knowledge is:
<X*；LocalSearch()>
wherein, X*For the optimal solution obtained in the evolution process, LocalSearch () is a local search operator, and hill climbing search, namely X, is adopted*Whether + -random is more preferable, random is a random value.
8. The high-discretization optimal dispatching method for power generation of the reservoir according to claim 2, wherein the dispatching method is characterized in that. The specific implementation of step 4.2 comprises the following substeps:
step 2.4.1: updating the position of whale by adopting situation knowledge and standard knowledge; the specific mathematical model is as follows:
Xi(t+1)＝Xi(t)-A·cos(2πl)·ebl·|C·Xp(t)-Xi(t)|if li＜Xi(t+1)＜ui
Xi(t+1)＝liif li＞Xi(t+1)
Xi(t+1)＝uiif ui＜Xi(t+1)
wherein t is the current iteration number, Xp(t) is the position of the prey at time t, i.e. the position of the optimal solution in the population of the t generation, Xp(t) is the optimal solution of the t-th generation population, Xi(t +1) is the position of the individual i at the t +1 th generation; li，uiParameters that are standard knowledge; b is a constant coefficient, l is [0,1]]Random number in between, A · | C · Xp(t)-Xi(t) | is the step length of hunting enclosure, and a and C are coefficients for controlling the enclosure step length, respectively, and the coefficients are defined as follows:
A＝a·(2·r1-1)
C＝2·r2
wherein r1 and r2 are random numbers between [0,1], a is a control parameter set by adopting situation knowledge and standard knowledge:
Figure FDA0002310123800000041
in the formula tmaxIs the maximum iteration number;
step 2.4.2: searching by adopting a food search operator in combination with historical knowledge and domain knowledge; the specific mathematical model is as follows:
Xi(t+1)＝Xi(t)-A·|C·Xrandom-Xi(t)|+Localsearch()
wherein t is the current iteration number, XrandomFor random historical knowledge location, A. C. Xp(t)-Xi(t) | is the step length surrounded by hunting, A and C are the coefficients for controlling the surrounding step length respectively, and Localsearch () is the neighborhood knowledge parameter.
CN201911255453.8A 2019-12-10 2019-12-10 High-discretization reservoir power generation optimal scheduling method Pending CN111008743A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911255453.8A CN111008743A (en) 2019-12-10 2019-12-10 High-discretization reservoir power generation optimal scheduling method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911255453.8A CN111008743A (en) 2019-12-10 2019-12-10 High-discretization reservoir power generation optimal scheduling method

Publications (1)

Publication Number Publication Date
CN111008743A true CN111008743A (en) 2020-04-14

Family

ID=70114227

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911255453.8A Pending CN111008743A (en) 2019-12-10 2019-12-10 High-discretization reservoir power generation optimal scheduling method

Country Status (1)

Country Link
CN (1) CN111008743A (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109345005A (en) * 2018-09-12 2019-02-15 中国电力科学研究院有限公司 A kind of integrated energy system multidimensional optimization method based on improvement whale algorithm
CN109409569A (en) * 2018-09-20 2019-03-01 中国南方电网有限责任公司 The discrete gradient progressive optimal algorithm dispatched for a long time in a kind of multi-reservoir considering direct current transportation constraint
CN109523059A (en) * 2018-10-19 2019-03-26 华中科技大学 A kind of step hydroelectric station reservoir ecological dispatching intelligent optimization method and system
CN109583644A (en) * 2018-11-27 2019-04-05 湖北工业大学 One kind being based on spiral local search reservoir operation method
CN109886589A (en) * 2019-02-28 2019-06-14 长安大学 A method of low-carbon Job-Shop is solved based on whale optimization algorithm is improved
CN110070212A (en) * 2019-03-26 2019-07-30 河南科技学院 A kind of polymorphic type harvester cooperative scheduling optimization method based on modified whale algorithm

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109345005A (en) * 2018-09-12 2019-02-15 中国电力科学研究院有限公司 A kind of integrated energy system multidimensional optimization method based on improvement whale algorithm
CN109409569A (en) * 2018-09-20 2019-03-01 中国南方电网有限责任公司 The discrete gradient progressive optimal algorithm dispatched for a long time in a kind of multi-reservoir considering direct current transportation constraint
CN109523059A (en) * 2018-10-19 2019-03-26 华中科技大学 A kind of step hydroelectric station reservoir ecological dispatching intelligent optimization method and system
CN109583644A (en) * 2018-11-27 2019-04-05 湖北工业大学 One kind being based on spiral local search reservoir operation method
CN109886589A (en) * 2019-02-28 2019-06-14 长安大学 A method of low-carbon Job-Shop is solved based on whale optimization algorithm is improved
CN110070212A (en) * 2019-03-26 2019-07-30 河南科技学院 A kind of polymorphic type harvester cooperative scheduling optimization method based on modified whale algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
吴泽忠、宋菲: "基于改进螺旋更新位置模型的鲸鱼优化算法", 《系统工程理论与实践》 *

Similar Documents

Publication Publication Date Title
CN111555355B (en) Scheduling strategy and optimization method for water-light-storage combined power generation
CN110289631B (en) Method and system for calculating capacity of energy storage device of wind power plant
CN113241803B (en) Energy storage scheduling method based on new energy consumption and computer medium
CN116760047A (en) Power distribution network voltage reactive power control method and system based on safety reinforcement learning algorithm
CN117879118B (en) Operation strategy method of self-adaptive optical storage and filling system
CN112966445B (en) Reservoir flood control optimal scheduling method based on reinforcement learning model FQI
CN109784497B (en) AI model automatic generation method based on computational graph evolution
CN113486593B (en) Wind-storage-hydrogen hybrid energy storage control method based on wind power climbing prediction
CN111325306A (en) ABC-PSO hybrid algorithm-based water supply pump set scheduling method
CN110739726A (en) multi-type power supply capacity long-term planning method considering offshore wind power access
CN117175595B (en) Power grid regulation and control method and system based on multi-level data
CN111008743A (en) High-discretization reservoir power generation optimal scheduling method
CN107958306A (en) A kind of power station random optimization dispatching method based on reference line
CN115459320B (en) Intelligent decision-making method and device for aggregation control of multipoint distributed energy storage system
CN114493090A (en) Intelligent control method and device for comprehensive energy of industrial park
CN114971207A (en) Load optimization distribution method based on improved artificial bee colony algorithm
CN105758028B (en) A kind of hot water reserves control method applied to solar energy central hot-water heating system
CN107069832A (en) A kind of virtual plant power regulating method based on tide energy and wind energy
CN112613654A (en) Comprehensive energy system flexibility evaluation method based on multi-type energy storage
CN118013253B (en) Intelligent load management method and system for hybrid virtual power plant
CN109842119A (en) The method that maintenance data drives robust Model to analyze uncertain wind-powered electricity generation AC-OPF problem
CN113507134B (en) Optimal planning method for planning target year new energy power supply installed capacity
CN115528712B (en) Method and system for balancing energy storage capacities of different areas of source network charge storage
CN115864464B (en) Discharge optimization method suitable for energy storage system
CN113034310A (en) Photovoltaic power generation output power prediction method based on optimized BP neural network

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
RJ01 Rejection of invention patent application after publication

Application publication date: 20200414