CN113887882A - Reservoir optimal scheduling method considering virtual water based on improved whale optimization algorithm - Google Patents

Reservoir optimal scheduling method considering virtual water based on improved whale optimization algorithm Download PDF

Info

Publication number
CN113887882A
CN113887882A CN202111068338.7A CN202111068338A CN113887882A CN 113887882 A CN113887882 A CN 113887882A CN 202111068338 A CN202111068338 A CN 202111068338A CN 113887882 A CN113887882 A CN 113887882A
Authority
CN
China
Prior art keywords
reservoir
water
information
constraint
max
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.)
Withdrawn
Application number
CN202111068338.7A
Other languages
Chinese (zh)
Inventor
程浩淼
王梦磊
汪靓
程吉林
龚懿
王玉琳
朱腾义
刘畅
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.)
Yangzhou University
Original Assignee
Yangzhou University
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 Yangzhou University filed Critical Yangzhou University
Priority to CN202111068338.7A priority Critical patent/CN113887882A/en
Publication of CN113887882A publication Critical patent/CN113887882A/en
Withdrawn legal-status Critical Current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
    • G06Q10/06—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
    • 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
    • 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
    • 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; 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
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
    • Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Health & Medical Sciences (AREA)
  • Quality & Reliability (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Educational Administration (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Computational Linguistics (AREA)
  • Animal Husbandry (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Mining & Mineral Resources (AREA)
  • Water Supply & Treatment (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Agronomy & Crop Science (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a reservoir optimal scheduling method considering virtual water based on an improved whale optimal algorithm in the field of reservoir optimal scheduling, which comprises the following steps of 1) obtaining reservoir-water receiving area information; 2) establishing a reservoir-water receiving area system database; 3) constructing a reservoir optimal scheduling model considering virtual water: reading data information of each field of the reservoir-water supply area system in the step 2) by using a C + + connection database, taking water supply amount of each water area and annual crop circulation amount of each time period as decision variables, and taking economic benefits of reservoir water supply in a scheduling period
Figure DEST_PATH_IMAGE002
The maximum is an objective function, and simultaneously needs to satisfy the product import, the reservoir water balance constraint, the reservoir water supply constraint, the reservoir capacity constraint, the reservoir water discharge constraint and the nonnegative constraint of decision variables, establishes a reservoir optimal scheduling model considering virtual water for database mutual feedback, and 4) obtains the optimal scheduling scheme of the reservoir optimal model through the improved C-A-WWOA algorithm.

Description

Reservoir optimal scheduling method considering virtual water based on improved whale optimization algorithm
Technical Field
The invention belongs to the technical field of reservoir optimal scheduling, and relates to a reservoir optimal scheduling method.
Background
The reservoir optimal scheduling refers to an optimal selection problem that the total benefit of the reservoir optimal scheduling is the maximum in the whole scheduling period of a single department or a plurality of departments such as power generation, flood control, irrigation and water supply and the like under the condition that the initial water level and the final water level are fixed, the water consumption and water abandon conditions of water coming from different time periods are considered, and the water consumption requirements of users in each time period are met. The reservoir is optimally scheduled, the contradiction between various water using departments can be solved, and the water resources and the hydroenergy resources are economically and reasonably utilized, so that the water reservoir is widely concentrated by scholars at home and abroad. However, the optimal scheduling of the reservoir currently has the following two problems: (1) traditional reservoir optimization only considers the scheduling of physical water, but neglects the circulation of virtual water (water condensed in goods or services), and is not favorable for realizing the optimal allocation of water resources. (2) The reservoir optimal scheduling is an optimal scheduling problem of a complex water conservancy system in a multi-constraint and multi-stage decision process, and the difficulty is how to solve. The method for solving reservoir optimal scheduling generally adopts linear programming, nonlinear programming, dynamic programming, group intelligent optimization algorithm and the like, but the methods have some inevitable problems, such as: linear programming and nonlinear programming have the problems of low solving speed, low efficiency and the like; the dynamic planning has large calculation amount and is easy to generate dimension disaster; and problems of low solving precision, local optimization and the like easily occur in the group intelligent optimization algorithm.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a reservoir optimization scheduling method considering virtual water based on an improved whale optimization algorithm, further considers the circulation of the virtual water, constructs a regional virtual water-reservoir optimization model, and provides a whale optimization algorithm (C-A-WWOA) coupling center migration and double weight factors on the basis of the current solving method, thereby realizing the optimal allocation of regional water resources and obtaining the maximum economic benefit in a scheduling period.
The purpose of the invention is realized as follows: a reservoir optimal scheduling method considering virtual water based on an improved whale optimization algorithm comprises the following steps:
step 1) obtaining reservoir-water receiving area information: acquiring scheduling parameter information, reservoir capacity information and crop information of a water receiving area, and completing the conversion of crop yield and virtual water according to a formula to obtain virtual water information;
step 2) establishing a reservoir-water receiving area system database: constructing a reservoir-water receiving area system database, adding information of each field in the step 1) into the database, then importing corresponding reservoir and water receiving area information data in batches, and updating data information in real time;
step 3), constructing a reservoir optimal scheduling model considering virtual water: using a C + + connection database, reading data information of each field of the reservoir-water supply area system in the step 2), taking water supply quantity of each water area and annual crop circulation quantity in each time period as decision variables, taking the maximum economic benefit maxF of reservoir water supply in a scheduling period as an objective function, and simultaneously meeting the requirements of product import, reservoir water balance constraint, reservoir water supply constraint, reservoir capacity constraint, reservoir water abandon constraint and nonnegative constraint of the decision variables, and establishing a reservoir optimization scheduling model considering virtual water for mutual feedback of the database, wherein the concrete optimization model is as follows:
an objective function:
Figure BDA0003259444670000021
constraint 1, product import constraint:
Figure BDA0003259444670000022
(17)
constraint 2, water balance constraint: vti＝Vti-1+LSti-Xti-Cti-EFti
(18)
Constraint 3, reservoir water supply constraint:
Figure BDA0003259444670000031
(19)
constraint 4, reservoir capacity constraint: vmin≤Vti≤Vmax
(20)
Constraint 5, reservoir water disposal constraint:
Figure BDA0003259444670000032
(21)
constraint 6, variable non-negative constraint: xti,ar≥0,IMPty≥0
(22)
In the formula: ti is the current time interval, ti belongs to [1, T ∈](ii) a ar is the current water supply area, and ar belongs to [1, N ]](ii) a Ty is the crop type, and ty belongs to [1, P ]]；INCarIncome for reservoir to supply water to ar-th area, Xti,arFor water supply of reservoir to ar-th region during ti, EXParThe cost of supplying water to the ar-th area from the reservoir; INCtyFor income of imported agricultural products ty, EXPtyCost for imported produce ty; IMP (impact resistance)tyFor importing agricultural productsQuality, PRDtyFor local production of agricultural products ty, TDtyIs the demand of the agricultural product ty; vtiThe water storage capacity V of the reservoir at the beginning of the ti period and the end of the ti-1 periodti-1Is the initial water storage capacity, LS, of reservoir ti-1 time periodtiThe amount of water supplied to the reservoir at time ti, CtiThe water discharge quantity, EF, of the reservoir at the ti periodtiThe evaporation and leakage quantity in the ti period of the reservoir, XtiWater supply for the reservoir ti period; SK is the annual maximum water supply of the reservoir, V0Is the initial water storage volume, VTThe water storage capacity of the reservoir in T period, gamma is the leakage rate of the water supply pipeline, DarMinimum water demand for the ar-th area; vminIs the minimum storage capacity, V, of the reservoirmaxThe maximum storage capacity of the reservoir;
step 4) model solving method: and (3) carrying out automatic iterative optimization solution on the reservoir optimization scheduling model considering the virtual water, which is constructed in the steps 1) to 3), through coupling center migration and a whale algorithm with double weight factors, so as to obtain an optimal reservoir scheduling scheme.
As a further limitation of the present invention, the specific steps for solving in step 4) are as follows:
step1 parameter setting: the number of rows Num and columns Dim of the variable matrix Va are set to ti ar + ty, the current iteration number T, and the maximum iteration number TmaxUpper and lower limits X of water supply amount in each time intervalti,ar,min＝Dti,arAnd Xti,ar,maxAnd crop demand TDtyA result variable Res for obtaining the value of max F, and a result moment RES of 1 XDim for obtaining the value of Va (i) corresponding to max F;
step2 center wander initialization: generating a variable matrix Va of Num multiplied by Dim, and regarding each row of the variable matrix Va as a group of candidate solutions, wherein the candidate solutions are formed by Xti,arAnd IMPtyComposition is carried out; updating random solutions by equation 8
Figure BDA0003259444670000041
Generating the opposites of each random solution of the matrix Va by equation 9
Figure BDA0003259444670000042
At the same time solve the random
Figure BDA0003259444670000043
Adopting a central migration strategy 10 to carry out migration and generating a migration solution
Figure BDA0003259444670000044
And then to
Figure BDA0003259444670000045
And
Figure BDA0003259444670000046
the fitness of the population is compared and selected as an evaluation index, the optimal solution in the population is reserved, and the initialization of the population is completed; the specific formula is as follows:
Figure BDA0003259444670000047
Figure BDA0003259444670000048
Figure BDA0003259444670000049
in the formula: i denotes the number of rows of the matrix Va, i ∈ [1, Num]And up is the upper limit X of the variableti,ar,maxOr 0.5TDtyDown is the lower limit X of the variableti,ar,minOr 0, xi is [0,1 ]]A random number in between;
Figure BDA00032594446700000412
the ith random solution generated for t iterations,
Figure BDA00032594446700000410
generating the ith opponent solution for the t iterations of opponent learning;
Figure BDA00032594446700000411
for t iterationsThe i-th wander solution generated by the center wander, δ is a wander coefficient, whose value is [0,1 ]]A random number in between;
step3 constraint processing: read reservoir data V0、Vmin、Vmax、TDty、Dti,ar、LStiAnd EFtiIn combination with randomly generated Xti,arAnd IMPtyCalculating a constraint value Xti,ar,min、Xti,ar,maxAnd VtiThen, carrying out constraint processing on the optimized variable matrix Va, wherein the constraint processing comprises a formula 2-7, deleting matrix rows Va (i) which do not meet the constraint, regenerating through center migration, repeating the above processes until all candidate solution matrixes Va meet the constraint, and entering the next process;
step4 iterates the update process: in the t iteration, firstly, the fitness value is calculated to obtain the optimal solution
Figure BDA0003259444670000051
And updating the algorithm parameters according to the corresponding formula, including: a. a, C, omega1、ω2And p; on the basis, according to the values of p and | A |, each whale respectively carries out an iterative updating strategy of surrounding predation, random or spiral bubbles; the specific updating mode is as follows: when p is<0.5, | A | is less than or equal to 1, and the formula 12 is adopted to carry out the enclosing predation updating; when p is<0.5，|A|>1, performing random updating according to formula 11; when p is more than or equal to 0.5, adopting a formula 13 to update the spiral bubbles; subsequently, correcting and updating the boundary-crossing whales according to the boundary neighborhood update 14 to obtain a population of the t +1 th generation; the specific update formula is as follows:
random search update mode: va (i)t+1＝Va(rand)t-A·|C·Va(rand)t-Va(i)t| (26)
Enclosing a predation updating mode:
Figure BDA0003259444670000052
spiral bubble updating mode:
Figure BDA0003259444670000053
and (3) a boundary neighborhood updating mode:
Figure BDA0003259444670000054
in the formula: va (i)tIs the ith group of candidate solutions in t iterations;
Figure BDA0003259444670000055
the optimal solution in t iterations; va (rand)tA set of solutions randomly selected in the t iteration matrices Va; a and C are matrix coefficients, respectively expressed as: a ═ 2a · (r)1-1) and C ═ 2a · r2(ii) a a is convergence factor, a ═ α · (T/T)max)3+β·(t/Tmax)2+γ·(t/Tmax)1+ λ, T is the current iteration number, TmaxFor the maximum number of iterations, α ═ 3.6, β ═ 7.8, γ ═ 6.2, and λ ═ 2.0; r is1、r2Is [0,1 ]]A random vector of (a); wherein ω is1＝1-sin(π·t/(2·Tmax))、ω2＝1+sin(π·t/(2·Tmax))、
Figure BDA0003259444670000056
D' represents the distance between the ith candidate solution and the optimal solution in t iterations; b is a spiral shape constant with the value of 1; l is [ -1,1 [ ]]The distance between the candidate solution and the optimal solution is determined by the size of the random number, wherein l is the shortest distance of-1 and l is the farthest distance of 1; u (0,0.1) denotes obedience [0,0.1 ]]Are evenly distributed in between.
Step5 judges the iteration condition: when T is less than or equal to TmaxAnd repeating Step4 until the iteration is exited, and outputting the result as a txt file, wherein the result information comprises:
(1) virtual water information VWty、
(2) Model parameter information, including water supply net gain INCar-EXParCirculation net income INCty-EXPtyAnd crop demand TDty、
(3) Optimum water supply Xti,arAnd the inlet amount of agricultural products IMPty、
(4) Maximum economic benefit max F.
As a further limitation of the invention, Step4 obtains the optimal solution
Figure BDA0003259444670000061
The specific process is as follows: in t iterations, calculating a fitness value max F of Va (i), comparing Res with max F, and keeping Res and RES unchanged if max F is less than or equal to Res; if max F>Res, then max F is assigned to Res, and Va (i) is assigned to RES until the calculation of Va is completed, at which time
Figure BDA0003259444670000062
As a further limitation of the present invention, the specific method of outputting the result as a txt file in Step5, adding a path in the program and creating a txt file, opening the txt file, adding header field information to the file, namely: (1) virtual water information VWtyAnd (2) model parameter information including water supply net income INCar-EXParCirculation net profit (INC)ty-EXPtyAnd crop demand TDty(3) optimum Water supply amount Xti,arAnd the inlet amount of agricultural products IMPtyAnd (4) maximum economic benefit max F; then, the corresponding data are read, virtual water: VW1And VW2(ii) a Water supply net gain: (INC)1-EXP1)、(INC2-EXP2) And (INC)3-EXP3) (ii) a Circulation net gain: (INC)1-EXP1) And (INC)2-EXP2) (ii) a Crop demand: TD1And TD2Writing into the corresponding header position of the file, wherein Xti,arAnd IMPtyThe data information is located in the result matrix RES; and the data information corresponding to the max F is positioned in Res, the data is read in sequence and written into the corresponding position of the file, and the file is closed after the data information is written into the corresponding position of the file.
As a further limitation of the present invention, the scheduling parameter information in step 1) includes a reservoir scheduling cycle, a total number T of scheduling time periods, a total number N of reservoir water supply areas, a type P of agricultural products imported from the water receiving area, a runoff coefficient a, and a pipeline leakage rate γ.
As the inventionFurther limiting, the reservoir capacity information in step 1) includes the current time period ti and the reservoir capacity information V0、Vmax、VminLS water supply condition of reservoir at each time intervaltiAnd the evaporation leakage quantity EF of each period of the reservoirtiAnd rainfall information at each time period.
As a further limitation of the present invention, the crop information of the catchment area in step 1) includes: crop area number ar, and crop water demand D of each area in each time periodti,arAnd crop yield information PRDtyDistribution cost EXPtyAnd revenue information INCty。
As a further limitation of the present invention, the virtual water information, i.e. the virtual water amount in step 1), is obtained by calculating through an agricultural product-virtual water conversion formula, wherein the specific formula is as follows:
Figure BDA0003259444670000071
wherein: ty is crop type, VWtyFor the consumable water footprint of the crop ty, ETtyGreen and blue evapotranspiration of crops in the growing period, YtyIs the yield of the crop ty.
As a further limitation of the invention, in the step 2), constructing a reservoir-water receiving area database according to the information of each field of the reservoir-water receiving area, and adding the field information, wherein the field information comprises a reservoir dispatching cycle, a dispatching time interval total number T, a reservoir water supply area total number N, a water receiving area inlet agricultural product type P, a runoff coefficient a and a pipeline leakage rate gamma; information V of reservoir capacity at current time interval ti0、Vmax、VminLS water supply condition of reservoir at each time intervaltiAnd the evaporation leakage quantity EF of each period of the reservoirtiAnd rainfall information at each time period; crop area number ar, and crop water demand D of each area in each time periodti,arAnd crop yield information PRDtyDistribution cost EXPtyAnd revenue information INCty(ii) a After field addition is completed, is LStiAnd Dti,arAdding the primary key index, and importing the data corresponding to each field in batch,and finishing the database construction.
Compared with the prior art, the invention has the beneficial effects that:
1) the method constructs a reservoir-water receiving area system database, and stores reservoir scheduling parameter data, reservoir capacity data, water receiving area crop data and virtual water amount calculation data into the database in batches, so that data can be conveniently inquired, read and updated in real time, and data sharing among multiple users can be easily realized;
2) the method reads the information of the reservoir-water receiving area system database in real time, automatically generates a reservoir optimal scheduling model which is mutually fed with the database and takes virtual water into consideration, and realizes the optimal allocation of regional water resources; the model supplies water (X) to each water area in each time periodti,ar) And annual crop flux (IMP)ty) Taking the maximum economic benefit (maxF) of reservoir water supply in the dispatching period as an objective function as a decision variable, and simultaneously satisfying the product import, the reservoir water balance constraint, the reservoir water supply constraint, the reservoir capacity constraint, the reservoir water abandon constraint and the decision variable (X)ti,arAnd IMPty) Non-negative constraints of (d);
3) the reservoir optimal scheduling model solution is based on a whale optimization algorithm, and the C-A-WWOA is further improved and provided, so that the convergence precision and speed of solving the optimal scheduling problem of the complex water conservancy system are improved; the method is initialized through center migration, and boundary neighborhood updating is adopted for boundary-crossing whales, so that the population quality is improved, and the convergence speed of the algorithm is improved; secondly, the algorithm parameters are improved, the whale population optimizing process is optimized, and the optimizing precision of the algorithm is improved; the method introduces and improves the weight strategy of the particle swarm algorithm, provides a double-weight disturbance updating strategy, solves the problem that the algorithm is easy to fall into local optimization in the later period, and further improves the precision.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts;
FIG. 1 is a technical scheme of the present invention.
FIG. 2 is a graph of the revenue convergence curve of the C-A-WWOA algorithm of the present invention in an embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the 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 obtained by a person of ordinary skill in the art based on the embodiments of the present invention without any creative effort belong to the protection scope of the present invention;
selecting Nanjing Liuhe district Jinniushan reservoir as an experimental research object, wherein the maximum storage capacity is 9315 km3Xingli library capacity 5165 km3The Xingli water level is 21.5m, and the dead storage capacity is 260 ten thousand m3Dead water level 13.5m, water collection area 124.14km2According to different crop types in the water receiving area, different areas are generalized according to the types of crops; the specific known scheduling parameter information, reservoir capacity information, crop information of the water receiving area and virtual water information are shown in tables 1-4.
As shown in fig. 1, the specific implementation steps are as follows:
(1) obtaining reservoir-water receiving area information: obtaining scheduling parameter information (table 1), reservoir capacity information (table 2) and crop information (tables 3 and 4) of a water receiving area, and completing conversion of crop yield and virtual water according to a formula to obtain virtual water information, wherein the scheduling parameter information comprises a reservoir scheduling period, a scheduling time period total (T), a reservoir water supply area total (N), a water receiving area inlet agricultural product type (P), a runoff coefficient (a) and a pipeline leakage rate (gamma); reservoir capacity information including current time interval (ti), reservoir capacity information (V)0、Vmax、Vmin) And water supply condition of reservoir at each time interval (LS)ti) Evaporation leakage (EF) of reservoir at each periodti) And rainfall information at each time period; crop information in the catchment area, including: crop areaField number (ar), water demand for crops in each area at each time period (D)ti,ar) Crop yield information (PRD)ty) Distribution cost (EXP)ty) And revenue Information (INC)ty) (ii) a The virtual water information is the virtual water quantity, which is calculated by the conversion formula of agricultural products and virtual water. The specific formula is as follows:
Figure BDA0003259444670000091
wherein: ty is crop type, VWtyFor the consumable water footprint of the crop ty, ETtyGreen and blue evapotranspiration of crops in the growing period, YtyIs the yield of the crop ty.
(2) Establishing a reservoir-water receiving area system database: constructing a reservoir-water receiving area system database, adding the field information in the step (1) into the database, then leading in the corresponding reservoir and water receiving area information data in batches, and updating the data information in real time, wherein the method specifically comprises the following steps: constructing a reservoir-water receiving area database, and adding the field information, wherein the field information comprises a reservoir dispatching cycle, a dispatching time interval total number (T), a reservoir water supply area total number (N), a water receiving area inlet agricultural product type (P), a runoff coefficient (a) and a pipeline leakage rate (gamma); current time interval (ti), reservoir capacity information (V)0、Vmax、Vmin) And water supply condition of reservoir at each time interval (LS)ti) Evaporation leakage (EF) of reservoir at each periodti) And rainfall information at each time period; crop area number (ar), and crop water demand (D) for each area at each time periodti,ar) Crop yield information (PRD)ty) Distribution cost (EXP)ty) And revenue Information (INC)ty). After field addition is completed, is LStiAnd Dti,arAnd adding a primary key index, and importing the data corresponding to each field in batch to complete the database construction.
(3) Constructing a reservoir optimal scheduling model considering virtual water: c + + is used for connecting a database, data information of each field of the reservoir-water supply area system in the step (2) is read, and a reservoir optimal scheduling model which is mutually fed with the database and takes virtual water into consideration is automatically generated and used as the modelWater supply (X) to each water area at each time intervalti,ar) And annual crop flux (IMP)ty) Taking the maximum economic benefit (maxF) of reservoir water supply in the dispatching period as an objective function as a decision variable, and simultaneously satisfying the product import, the reservoir water balance constraint, the reservoir water supply constraint, the reservoir capacity constraint, the reservoir water abandon constraint and the decision variable (X)ti,arAnd IMPty) Is not negatively constrained. The specific optimization model is as follows:
an objective function:
Figure BDA0003259444670000101
constraint 1 (product import constraint):
Figure BDA0003259444670000102
(33)
constraint 2 (water balance constraint): vti＝Vti-1+LSti-Xti-Cti-EFti
(34)
Constraint 3 (reservoir water supply constraint):
Figure BDA0003259444670000111
(35)
constraint 4 (reservoir capacity constraint): vmin≤Vti≤Vmax
(36)
Constraint 5 (reservoir water disposal constraint):
Figure BDA0003259444670000112
(37)
constraint 6 (variable non-negative constraint) Xti,ar≥0,IMPty≥0
(38)
In the formula: ti is the current time interval, ti belongs to [1, T ∈]. ar is the current water supply area, and ar belongs to [1, N ]]. . Ty is the crop type, and ty belongs to [1, P ]]。INCarFrom reservoir to ar-th areaIncome of regional water supply (yuan/m)3)，Xti,arWater supply (m) to the ar-th area for the reservoir during the ti period3)，EXParCost (yuan/m) for supplying water from reservoir to ar-th area3)；INCtyFor the income (Yuan/Kg) of imported agricultural products, EXPtyThe cost (yuan/Kg) of the imported produce ty. IMP (impact resistance)tyIs quality (t), PRD of imported agricultural productstyIs the local production (t), TD, of the agricultural product tyty: demand for agricultural product ty. Vti: water storage capacity at the beginning of ti period and at the end of ti-1 period, Vti-1: initial water storage amount LS of reservoir ti-1 time periodti: water volume in reservoir ti period, Cti: water discharge, EF, in the ti period of the reservoirti: evaporation and leakage in reservoir ti period, Xti: water supply amount of the reservoir ti period. SK: annual maximum water supply of reservoir, V0: initial water storage volume of reservoir, VT: water storage capacity of reservoir at time T, γ: leakage rate of water supply pipe, Dar: minimum water requirement of the ar-th area. VminIs the minimum storage capacity, V, of the reservoirmaxThe maximum storage capacity of the reservoir.
(4) The model solving method comprises the following steps: and (4) carrying out automatic iterative optimization solution on the reservoir optimization scheduling model considering the virtual water, which is constructed in the steps (1) to (3), through a whale algorithm (C-A-WWOA) coupling center migration and double weight factors. The specific steps of C-A-WWOA are as follows:
step 1C-A-WWOA algorithm parameter setting: the number of rows (Num) and columns (Dim ═ ti · ar + ty) of the variable matrix Va, the current number of iterations (T), and the maximum number of iterations (T) are setmax) Upper and lower limits of water supply amount in each time period (X)ti,ar,min＝Dti,arAnd Xti,ar,max) And crop demand (TD)ty) Result variable (Res: to obtain the value of max F) and the resulting moment of 1 × Dim (RES: to obtain the va (i) value for max F).
Step2 center wander initialization: generating a variable matrix Va of Num multiplied by Dim, and regarding each row of the variable matrix Va as a group of candidate solutions, wherein the candidate solutions are formed by Xti,arAnd IMPtyAnd (4) forming. Updating the random solution by (equation 8)
Figure BDA0003259444670000121
The opposite points of each random solution of the matrix Va are generated by (equation 9)
Figure BDA0003259444670000122
At the same time solve the random
Figure BDA0003259444670000123
) Adopting a central migration strategy (formula 10) to perform migration and generate a migration solution
Figure BDA0003259444670000124
And then to
Figure BDA0003259444670000125
And
Figure BDA0003259444670000126
the fitness (objective function) of the population is compared and selected as an evaluation index, the optimal solution in the evaluation index is reserved, and the initialization of the population is completed. The specific formula is as follows:
Figure BDA0003259444670000127
Figure BDA0003259444670000128
Figure BDA0003259444670000129
in the formula: i denotes the number of rows of the matrix Va, i ∈ [1, Num]Up is the upper limit of the variable (X)ti,ar,maxOr 0.5TDty) Down is the lower limit of the variable (X)ti,ar,minOr 0), ξ is [0,1 ]]A random number in between;
Figure BDA00032594446700001210
the ith random solution generated for t iterations,
Figure BDA00032594446700001211
generating the ith opponent solution for the t iterations of opponent learning;
Figure BDA00032594446700001212
the ith wander solution generated for t iterations of center wander, δ is the wander coefficient, whose value is [0,1 ]]A random number in between.
Step3 constraint processing: read reservoir data (V)0、Vmin、Vmax、TDty、Dti,ar、LStiAnd EFti) In combination with randomly generated Xti,arAnd IMPtyCalculating a constraint value (X)ti,ar,min、Xti,ar,maxAnd Vti) And then, carrying out constraint processing (including a formula 2-7) on the optimized variable matrix Va, deleting matrix rows Va (i) which do not meet the constraint, regenerating through center migration, repeating the above processes until all candidate solution matrixes Va meet the constraint, and entering the next process.
Step4 iterates the update process: in the t iteration, firstly, the fitness value is calculated to obtain the optimal solution
Figure BDA0003259444670000131
The specific process is as follows: in t iterations, an fitness value (max F) of va (i) is calculated and Res is compared to max F. If max F is less than or equal to Res, keeping Res and RES unchanged; if max F>Res, then max F is assigned to Res, and Va (i) is assigned to RES until the calculation of Va is completed, at which time
Figure BDA0003259444670000132
And updating algorithm parameters (including a, A, C, omega) according to corresponding formulas1、ω2And p). On this basis, each whale is subjected to an iterative update strategy encompassing predation, random or spiral bubbles, respectively, depending on the p and | a | values. The specific updating mode is as follows: when p is<0.5, | A | < 1, adopting (formula 12) to carry out surrounding predation updating; when p is<0.5，|A|>1, performing random update according to (equation 11); when p is more than or equal to 0.5, the spiral bubble is renewed by adopting (formula 13). Then, based on the boundary neighborhoodAnd (4) updating (formula 14) to update the boundary-crossing whales in a correction manner to obtain the population of the t +1 th generation. The specific update formula is as follows:
random search update mode: va (i)t+1＝Va(rand)t-A·|C·Va(rand)t-Va(i)t| (42)
Enclosing a predation updating mode:
Figure BDA0003259444670000133
spiral bubble updating mode:
Figure BDA0003259444670000134
and (3) a boundary neighborhood updating mode:
Figure BDA0003259444670000135
in the formula: va (i)tIs the ith group of candidate solutions in t iterations;
Figure BDA0003259444670000136
the optimal solution in t iterations; va (rand)tA randomly selected set of solutions in the t iteration matrix Va. A and C are matrix coefficients, respectively expressed as: a ═ 2a · (r)1-1) and C ═ 2a · r2(ii) a a is convergence factor, a ═ α · (T/T)max)3+β·(t/Tmax)2+γ·(t/Tmax)1+ λ, T is the current iteration number, TmaxFor the maximum number of iterations, α ═ 3.6, β ═ 7.8, γ ═ 6.2, and λ ═ 2.0; r is1、r2Is [0,1 ]]The random vector of (2). Wherein ω is1＝1-sin(π·t/(2·Tmax))、ω2＝1+sin(π·t/(2·Tmax))、
Figure BDA0003259444670000141
D' represents the distance of the ith candidate solution from the optimal solution in t iterations. b is a helical shape constant with a value of 1. l is [ -1,1 [ ]]The distance between the candidate solution and the optimal solution is determined by the size of the random number in (1), wherein l is the closest distance to-1 and l is the farthest distance to 1.
Step5 judges the iteration condition: when T is less than or equal to TmaxAnd repeating Step4 until exiting the iteration, outputting the result as a txt file, adding a path in the program and creating the txt file, opening the txt file, and adding header field information to the file, namely: (1) virtual water information (VW)ty) And (2) model parameter information including (water supply net profit (INC)ar-EXPar) Circulation net profit (INC)ty-EXPty) And crop demand (TD)ty) (3) optimum water supply amount (X)ti,ar) And the amount of agricultural product Imported (IMP)ty) And (4) maximum economic benefit max F. Then, the corresponding data (virtual water: VW) are read respectively1And VW2(ii) a Water supply net gain: (INC)1-EXP1)、(INC2-EXP2) And (INC)3-EXP3) (ii) a Circulation net gain: (INC)1-EXP1) And (INC)2-EXP2) (ii) a Crop demand: TD1And TD2) And writing the corresponding header position of the file. Wherein, Xti,arAnd IMPtyThe data information is located in the result matrix RES; and the data information corresponding to the max F is positioned in Res, the data is read in sequence and written into the corresponding position of the file, and the file is closed after the data information is written into the corresponding position of the file.
Fig. 2 shows a revenue convergence curve in the embodiment of the present invention.
Table 1 scheduling parameter information
Figure BDA0003259444670000142
TABLE 2 reservoir capacity information
Figure BDA0003259444670000143
Figure BDA0003259444670000151
TABLE 3 crop information in water receiving areas
Figure BDA0003259444670000152
TABLE 4 Water requirement for crops at different periods of time (D)ti,ar)：m3Per mu
Figure BDA0003259444670000153
TABLE 5 output results of virtual water information, model parameter information, optimal water supply and agricultural product import and maximum economic benefit
Figure BDA0003259444670000161
Note: INCar-EXPar: water supply net profit for area ar (ar ∈ [1,3 ]]Respectively, a wheat area, a rice area and a rape area); INCty-EXPty: net yield of Ty circulation of crops (Ty E [1,2 ]]Respectively representing wheat and rice), TDty: represents the demand of the crop ty; xti,ar: representing the amount of water supplied to the region ar during the ti period (ti e [1,12 ]])；IMPty: representing the annual flux of the crop product ty.
Table 5 shows output result information including virtual Water information (VW) according to an embodiment of the present inventionty) Model parameter information (net profit on water supply: (INC)ar-EXPar) And circulation net income: (INC)ty-EXPty) And crop demand (TD)ty) Optimum water supply amount (X) for each region in each periodti,ar) Agricultural product Intake (IMP)ty) And the maximum profit (max F), and the optimal scheduling of the reservoir can be realized according to the information in the table 5.
The above description of the embodiments is only intended to facilitate the understanding of the method of the invention and its core ideas; it should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (9)

1. A reservoir optimal scheduling method considering virtual water based on an improved whale optimization algorithm is characterized by comprising the following steps:
step 1) obtaining reservoir-water receiving area information: acquiring scheduling parameter information, reservoir capacity information and crop information of a water receiving area, and completing the conversion of crop yield and virtual water according to a formula to obtain virtual water information;
step 2) establishing a reservoir-water receiving area system database: constructing a reservoir-water receiving area system database, adding information of each field in the step 1) into the database, then importing corresponding reservoir and water receiving area information data in batches, and updating data information in real time;
step 3), constructing a reservoir optimal scheduling model considering virtual water: using a C + + connection database, reading data information of each field of the reservoir-water supply area system in the step 2), taking water supply quantity of each water area and annual crop circulation quantity in each time period as decision variables, taking the maximum economic benefit maxF of reservoir water supply in a scheduling period as an objective function, and simultaneously meeting the requirements of product import, reservoir water balance constraint, reservoir water supply constraint, reservoir capacity constraint, reservoir water abandon constraint and nonnegative constraint of the decision variables, and establishing a reservoir optimization scheduling model considering virtual water for mutual feedback of the database, wherein the concrete optimization model is as follows:
an objective function:
Figure FDA0003259444660000011
constraint 1, product import constraint:
Figure FDA0003259444660000012
constraint 2, water balance constraint: vti＝Vti-1+LSti-Xti-Cti-EFti (3)
Constraint 3, reservoir water supply constraint:
Figure FDA0003259444660000013
constraint 4, reservoir capacity constraint: vmin≤Vti≤Vmax (5)
Constraint 5, reservoir water disposal constraint:
Figure FDA0003259444660000021
constraint 6, variable non-negative constraint: xti,ar≥0,IMPty≥0 (7)
In the formula: ti is the current time interval, ti belongs to [1, T ∈](ii) a ar is the current water supply area, and ar belongs to [1, N ]](ii) a Ty is the crop type, and ty belongs to [1, P ]]；INCarIncome for reservoir to supply water to ar-th area, Xti,arFor water supply of reservoir to ar-th region during ti, EXParThe cost of supplying water to the ar-th area from the reservoir; INCtyFor income of imported agricultural products ty, EXPtyCost for imported produce ty; IMP (impact resistance)tyFor quality of imported agricultural products, PRDtyFor local production of agricultural products ty, TDtyIs the demand of the agricultural product ty; vtiThe water storage capacity V of the reservoir at the beginning of the ti period and the end of the ti-1 periodti-1Is the initial water storage capacity, LS, of reservoir ti-1 time periodtiThe amount of water supplied to the reservoir at time ti, CtiThe water discharge quantity, EF, of the reservoir at the ti periodtiThe evaporation and leakage quantity in the ti period of the reservoir, XtiWater supply for the reservoir ti period; SK is the annual maximum water supply of the reservoir, V0Is the initial water storage volume, VTThe water storage capacity of the reservoir in T period, gamma is the leakage rate of the water supply pipeline, DarMinimum water demand for the ar-th area; vminIs the minimum storage capacity, V, of the reservoirmaxThe maximum storage capacity of the reservoir;
step 4) model solving method: and (3) carrying out automatic iterative optimization solution on the reservoir optimization scheduling model considering the virtual water, which is constructed in the steps 1) to 3), through coupling center migration and a whale algorithm with double weight factors, so as to obtain an optimal reservoir scheduling scheme.
2. The optimal reservoir dispatching method considering virtual water based on the improved whale optimization algorithm as claimed in claim 1, wherein the specific steps of solving in the step 4) are as follows:
step1 parameter setting: the number of rows Num and columns Dim of the variable matrix Va are set to ti ar + ty, the current iteration number T, and the maximum iteration number TmaxUpper and lower limits X of water supply amount in each time intervalti,ar,min＝Dti,arAnd Xti,ar,maxAnd crop demand TDtyA result variable Res for obtaining the value of max F, and a result moment RES of 1 XDim for obtaining the value of Va (i) corresponding to max F;
step2 center wander initialization: generating a variable matrix Va of Num multiplied by Dim, and regarding each row of the variable matrix Va as a group of candidate solutions, wherein the candidate solutions are formed by Xti,arAnd IMPtyComposition is carried out; updating random solutions by equation 8
Figure FDA0003259444660000031
Generating the opposites of each random solution of the matrix Va by equation 9
Figure FDA0003259444660000032
At the same time solve the random
Figure FDA0003259444660000033
Adopting a central migration strategy 10 to carry out migration and generating a migration solution
Figure FDA0003259444660000034
And then to
Figure FDA0003259444660000035
And
Figure FDA0003259444660000036
the fitness of the population is compared and selected as an evaluation index, the optimal solution in the population is reserved, and the initialization of the population is completed; the specific formula is as follows:
Figure FDA0003259444660000037
Figure FDA0003259444660000038
Figure FDA0003259444660000039
in the formula: i denotes the number of rows of the matrix Va, i ∈ [1, Num]And up is the upper limit X of the variableti,ar,maxOr 0.5TDtyDown is the lower limit X of the variableti,ar,minOr 0, xi is [0,1 ]]A random number in between;
Figure FDA00032594446600000310
the ith random solution generated for t iterations,
Figure FDA00032594446600000311
generating the ith opponent solution for the t iterations of opponent learning;
Figure FDA00032594446600000312
the ith wander solution generated for t iterations of center wander, δ is the wander coefficient, whose value is [0,1 ]]A random number in between;
step3 constraint processing: read reservoir data V0、Vmin、Vmax、TDty、Dti,ar、LStiAnd EFtiIn combination with randomly generated Xti,arAnd IMPtyCalculating a constraint value Xti,ar,min、Xti,ar,maxAnd VtiThen, carrying out constraint processing on the optimized variable matrix Va, wherein the constraint processing comprises a formula 2-7, deleting matrix rows Va (i) which do not meet the constraint, regenerating through center migration, repeating the above processes until all candidate solution matrixes Va meet the constraint, and entering the next process;
step4 iterationThe new process comprises the following steps: in the t iteration, firstly, the fitness value is calculated to obtain the optimal solution
Figure FDA00032594446600000313
And updating the algorithm parameters according to the corresponding formula, including: a. a, C, omega1、ω2And p; on the basis, according to the values of p and | A |, each whale respectively carries out an iterative updating strategy of surrounding predation, random or spiral bubbles; the specific updating mode is as follows: when p is<0.5, | A | is less than or equal to 1, and the formula 12 is adopted to carry out the enclosing predation updating; when p is<0.5，|A|>1, performing random updating according to formula 11; when p is more than or equal to 0.5, adopting a formula 13 to update the spiral bubbles; subsequently, correcting and updating the boundary-crossing whales according to the boundary neighborhood update 14 to obtain a population of the t +1 th generation; the specific update formula is as follows:
random search update mode: va (i)t+1＝Va(rand)t-A·|C·Va(rand)t-Va(i)t| (11)
Enclosing a predation updating mode:
Figure FDA0003259444660000041
spiral bubble updating mode:
Figure FDA0003259444660000042
and (3) a boundary neighborhood updating mode:
Figure FDA0003259444660000043
in the formula: va (i)tIs the ith group of candidate solutions in t iterations;
Figure FDA0003259444660000044
the optimal solution in t iterations; va (rand)tA set of solutions randomly selected in the t iteration matrices Va; a and C are matrix coefficients, respectively expressed as: a ═ 2a · (r)1-1) and C ═ 2a · r2(ii) a a is convergence factor, a ═ α · (T/T)max)3+β·(t/Tmax)2+γ·(t/Tmax)1+ λ, T is the current iteration number, TmaxFor the maximum number of iterations, α ═ 3.6, β ═ 7.8, γ ═ 6.2, and λ ═ 2.0; r is1、r2Is [0,1 ]]A random vector of (a); wherein ω is1＝1-sin(π·t/(2·Tmax))、ω2＝1+sin(π·t/(2·Tmax))、
Figure FDA0003259444660000045
D' represents the distance between the ith candidate solution and the optimal solution in t iterations; b is a spiral shape constant with the value of 1; l is [ -1,1 [ ]]The distance between the candidate solution and the optimal solution is determined by the size of the random number, wherein l is the shortest distance of-1 and l is the farthest distance of 1; u (0,0.1) denotes obedience [0,0.1 ]]Are evenly distributed in between.
Step5 judges the iteration condition: when T is less than or equal to TmaxAnd repeating Step4 until the iteration is exited, and outputting the result as a txt file, wherein the result information comprises:
(1) virtual water information VWty、
(2) Model parameter information, including water supply net gain INCar-EXParCirculation net income INCty-EXPtyAnd crop demand TDty、
(3) Optimum water supply Xti,arAnd the inlet amount of agricultural products IMPty、
(4) Maximum economic benefit max F.
3. The optimal reservoir dispatching method considering virtual water based on improved whale optimization algorithm as claimed in claim 2, wherein the optimal solution is obtained in Step4
Figure FDA0003259444660000051
The specific process is as follows: in t iterations, calculating a fitness value max F of Va (i), comparing Res with max F, and keeping Res and RES unchanged if max F is less than or equal to Res; if max F>Res, then max F is assigned to Res, and Va (i) is assigned to RES until the calculation of Va is completed, at which time
Figure FDA0003259444660000052
4. The optimal reservoir dispatching method considering virtual water based on the improved whale optimization algorithm as claimed in claim 2, wherein the specific method of outputting the result as txt file in Step5, adding path in the program and creating txt file, opening txt file, adding header field information to the file, namely: (1) virtual water information VWtyAnd (2) model parameter information including water supply net income INCar-EXParCirculation net profit (INC)ty-EXPtyAnd crop demand TDty(3) optimum Water supply amount Xti,arAnd the inlet amount of agricultural products IMPtyAnd (4) maximum economic benefit max F; then, the corresponding data are read, virtual water: VW1And VW2(ii) a Water supply net gain: (INC)1-EXP1)、(INC2-EXP2) And (INC)3-EXP3) (ii) a Circulation net gain: (INC)1-EXP1) And (INC)2-EXP2) (ii) a Crop demand: TD1And TD2Writing into the corresponding header position of the file, wherein Xti,arAnd IMPtyThe data information is located in the result matrix RES; and the data information corresponding to the max F is positioned in Res, the data is read in sequence and written into the corresponding position of the file, and the file is closed after the data information is written into the corresponding position of the file.
5. The optimal reservoir scheduling method considering virtual water based on the improved whale optimization algorithm as claimed in any one of claims 1-4, wherein the scheduling parameter information in step 1) comprises a reservoir scheduling cycle, a total number T of scheduling periods, a total number N of reservoir water supply areas, a type P of inlet agricultural products of a water receiving area, a runoff coefficient a and a pipeline leakage rate γ.
6. The optimal scheduling method of reservoir considering virtual water based on improved whale optimization algorithm as claimed in any one of claims 1-4, wherein the reservoir capacity information in step 1),including, the current time interval ti, reservoir storage capacity information V0、Vmax、VminLS water supply condition of reservoir at each time intervaltiAnd the evaporation leakage quantity EF of each period of the reservoirtiAnd rainfall information at each time period.
7. The optimal reservoir scheduling method considering virtual water based on the improved whale optimization algorithm according to any one of claims 1-4, wherein the crop information of the catchment area in the step 1) comprises: crop area number ar, and crop water demand D of each area in each time periodti,arAnd crop yield information PRDtyDistribution cost EXPtyAnd revenue information INCty。
8. The optimal reservoir scheduling method considering virtual water based on the improved whale optimization algorithm as claimed in any one of claims 1-4, wherein the virtual water information, namely the amount of virtual water, in step 1) is calculated by an agricultural product-virtual water conversion formula, and the specific formula is as follows:
Figure FDA0003259444660000061
wherein: ty is crop type, VWtyFor the consumable water footprint of the crop ty, ETtyGreen and blue evapotranspiration of crops in the growing period, YtyIs the yield of the crop ty.
9. The optimal reservoir scheduling method considering virtual water based on the improved whale optimization algorithm according to any one of claims 1-4, wherein the information of each field of the reservoir-water receiving area in the step 2) is used for constructing a reservoir-water receiving area database, and the information of the fields is added, wherein the field information comprises a reservoir scheduling period, a total scheduling time period T, a total number N of reservoir water supply areas, a type P of inlet agricultural products of the water receiving area, a runoff coefficient a and a pipeline leakage rate gamma; information V of reservoir capacity at current time interval ti0、Vmax、VminAnd each of the reservoirsTime interval water supply condition LStiAnd the evaporation leakage quantity EF of each period of the reservoirtiAnd rainfall information at each time period; crop area number ar, and crop water demand D of each area in each time periodti,arAnd crop yield information PRDtyDistribution cost EXPtyAnd revenue information INCty(ii) a After field addition is completed, is LStiAnd Dti,arAnd adding a primary key index, and importing the data corresponding to each field in batch to complete the database construction.
CN202111068338.7A 2021-09-13 2021-09-13 Reservoir optimal scheduling method considering virtual water based on improved whale optimization algorithm Withdrawn CN113887882A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111068338.7A CN113887882A (en) 2021-09-13 2021-09-13 Reservoir optimal scheduling method considering virtual water based on improved whale optimization algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111068338.7A CN113887882A (en) 2021-09-13 2021-09-13 Reservoir optimal scheduling method considering virtual water based on improved whale optimization algorithm

Publications (1)

Publication Number Publication Date
CN113887882A true CN113887882A (en) 2022-01-04

Family

ID=79009225

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111068338.7A Withdrawn CN113887882A (en) 2021-09-13 2021-09-13 Reservoir optimal scheduling method considering virtual water based on improved whale optimization algorithm

Country Status (1)

Country Link
CN (1) CN113887882A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115619189A (en) * 2022-11-09 2023-01-17 中国南方电网有限责任公司 Waste water scheduling method and device considering cascade hydroelectric waste water flow limitation

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115619189A (en) * 2022-11-09 2023-01-17 中国南方电网有限责任公司 Waste water scheduling method and device considering cascade hydroelectric waste water flow limitation
CN115619189B (en) * 2022-11-09 2023-11-14 中国南方电网有限责任公司 Water discarding scheduling method and device considering cascade hydroelectric water discarding flow limit

Similar Documents

Publication Publication Date Title
CN106981888B (en) Wind based on multi-source complementation stores the multiple target dynamic dispatching method of Thermal and Hydroelectric Power Systems
CN110276698B (en) Distributed renewable energy transaction decision method based on multi-agent double-layer collaborative reinforcement learning
CN107527119A (en) Water resources optimal operation method based on improved multi-target quantum genetic algorithm
CN109670650B (en) Multi-objective optimization algorithm-based solving method for cascade reservoir group scheduling model
CN109034563A (en) A kind of increment power distribution network source net lotus collaborative planning method of multi-agent Game
CN107067190A (en) The micro-capacitance sensor power trade method learnt based on deeply
CN109948847B (en) Reservoir group scheduling method based on multi-objective evolutionary algorithm
CN110569958B (en) High-dimensional complex water distribution model solving method based on hybrid artificial bee colony algorithm
CN111310999A (en) Warehouse mobile robot path planning method based on improved ant colony algorithm
CN105023053B (en) Power energy allocation method and system
CN113822496A (en) Multi-unit thermal power plant heat supply mode and parameter online optimization method
CN108710970B (en) Multi-target scheduling parallel dimension reduction method for giant cascade hydroelectric system
CN112001752A (en) Multi-virtual power plant dynamic game transaction behavior analysis method based on limited rationality
CN113887882A (en) Reservoir optimal scheduling method considering virtual water based on improved whale optimization algorithm
CN110889553A (en) Real-time water distribution method based on optimization strategy
CN115526550A (en) Spot market organization method suitable for new energy power and conventional thermal power to compete on same station
CN109299829A (en) Photovoltaic greenhouse is micro- can the polymorphic energy storage configuration method of net system and system
CN116029405A (en) Multi-target dynamic water distribution method based on irrigation area canal system
CN112005679B (en) Water and fertilizer integrated distribution method for precision agriculture
CN112214885B (en) Irrigation area crop irrigation water quantity optimal distribution method under insufficient irrigation condition
CN110598946A (en) Flood prevention material rescue distribution method based on non-dominated artificial bee colony
CN111864811A (en) Photovoltaic capacity configuration method of water-light complementary system considering power grid output demand
CN113887943A (en) Pump station optimal scheduling method based on virtual water
CN113159399A (en) Agricultural machinery scheduling optimization method and system considering field transfer and operation planning
CN116796790A (en) Yield prediction and irrigation system optimization method based on crop water deficiency degree

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
WW01 Invention patent application withdrawn after publication
WW01 Invention patent application withdrawn after publication

Application publication date: 20220104