CN115659641B - Water bloom prevention and control oriented water engineering multi-objective optimization scheduling method - Google Patents
Water bloom prevention and control oriented water engineering multi-objective optimization scheduling method Download PDFInfo
- Publication number
- CN115659641B CN115659641B CN202211316048.4A CN202211316048A CN115659641B CN 115659641 B CN115659641 B CN 115659641B CN 202211316048 A CN202211316048 A CN 202211316048A CN 115659641 B CN115659641 B CN 115659641B
- Authority
- CN
- China
- Prior art keywords
- water
- reservoir
- objective
- ant
- algae bloom
- 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.)
- Active
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 181
- 230000002265 prevention Effects 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000005457 optimization Methods 0.000 title claims abstract description 22
- 241000192710 Microcystis aeruginosa Species 0.000 title description 48
- 241001206881 Myrmeleon inconspicuus Species 0.000 claims abstract description 50
- 238000010248 power generation Methods 0.000 claims abstract description 35
- 238000009792 diffusion process Methods 0.000 claims abstract description 25
- 241000195493 Cryptophyta Species 0.000 claims abstract description 21
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 12
- 230000005012 migration Effects 0.000 claims abstract description 11
- 238000013508 migration Methods 0.000 claims abstract description 11
- 238000012851 eutrophication Methods 0.000 claims abstract description 7
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims abstract 4
- 229910052698 phosphorus Inorganic materials 0.000 claims abstract 4
- 239000011574 phosphorus Substances 0.000 claims abstract 4
- 238000004364 calculation method Methods 0.000 claims description 16
- 238000003860 storage Methods 0.000 claims description 16
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 12
- 241000257303 Hymenoptera Species 0.000 claims description 9
- 239000003344 environmental pollutant Substances 0.000 claims description 9
- 231100000719 pollutant Toxicity 0.000 claims description 9
- 241000258923 Neuroptera Species 0.000 claims description 8
- 238000011144 upstream manufacturing Methods 0.000 claims description 7
- 229910052757 nitrogen Inorganic materials 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 238000000746 purification Methods 0.000 claims description 4
- 230000015556 catabolic process Effects 0.000 claims description 3
- 238000006731 degradation reaction Methods 0.000 claims description 3
- 241000282320 Panthera leo Species 0.000 claims 1
- 238000010276 construction Methods 0.000 claims 1
- 239000006185 dispersion Substances 0.000 claims 1
- 238000005295 random walk Methods 0.000 claims 1
- 238000004088 simulation Methods 0.000 description 6
- 230000008901 benefit Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000004927 fusion Effects 0.000 description 3
- 238000004140 cleaning Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000011664 nicotinic acid Substances 0.000 description 2
- 239000002957 persistent organic pollutant Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 241000894007 species Species 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000013505 freshwater Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明提供一种面向水华防控的水工程多目标优化调度方法，方法包括：步骤一，构建面向生态、水量和发电需求的多目标函数，以各时段水库出库流量为决策变量的面向水华防控的水工程联合调度模型；步骤二，运用一维对流扩散方程模拟水华水质扩散运移，以评估生态、水量和发电调度目标函数；步骤三，采用多目标蚁狮优化算法求解调度模型，获取可协同优化水华防控与水量、发电调度目标的帕累托解集，提供水工程多目标优化调度方案；步骤四，布设水体脱氮除磷装置于下游水体富营养化区域，为水华防控提供工程措施。
The present invention provides a multi-objective optimal dispatching method for water projects oriented to algae bloom prevention and control. The method includes: Step 1: Constructing a multi-objective function oriented to ecology, water quantity and power generation demand, with the reservoir outflow flow in each period as the decision variable. Water engineering joint dispatch model for algae bloom prevention and control; Step 2, use the one-dimensional convection-diffusion equation to simulate the diffusion and migration of algae bloom water quality to evaluate the ecological, water quantity and power generation dispatch objective functions; Step 3, use the multi-objective ant lion optimization algorithm to solve the problem The dispatch model obtains a Pareto solution set that can collaboratively optimize algae bloom prevention and control, water quantity, and power generation dispatch targets, and provides a multi-objective optimal dispatch plan for water projects; step four is to deploy water denitrification and phosphorus removal devices in downstream water eutrophication areas. , providing engineering measures for algae bloom prevention and control.
Description
Technical Field
The invention belongs to the technical field of reservoir dispatching, and particularly relates to a water engineering multi-objective optimization dispatching method for water bloom prevention and control.
Background
Bloom is a natural ecological phenomenon of mass propagation of algae in fresh water, is a characteristic of eutrophication of water, is commonly found in water with low flow rates such as lakes, reservoirs and the like, and has serious adverse effects on ecosystems, social and economic development and human health. In recent years, important water areas such as the middle and downstream of Han river, the Taihu lake, the Yunnan pond and the like in China have multiple times of water bloom, and many scholars develop research work around the aspects of water bloom cause, control and prevention and control and the like. However, water bloom prevention and control researches are mostly carried out in lakes at present, the combination of water bloom prevention and control and water engineering scheduling is lacking, and meanwhile, the water engineering scheduling technology (non-engineering measures) and the denitrification and dephosphorization device (engineering measures) are too little in fusion, so that the water bloom prevention and control effect is difficult to reach the expected target. The technical difficulties and challenges of water engineering scheduling include: (1) the difficulty of realizing the cooperative scheduling of water bloom prevention and control, water quantity allocation and power generation is high, and the comprehensive benefits of the cooperative scheduling of water engineering are difficult to fully develop; (2) the water engineering dispatching technology and the denitrification and dephosphorization device are lack to be fused.
Disclosure of Invention
The invention aims to solve the problems, and aims to provide a water engineering multi-objective optimization scheduling method and a water bloom treatment device for water bloom prevention and control, which are used for providing a water engineering scheduling method capable of cooperatively optimizing water bloom prevention and control, water volume allocation and power generation scheduling targets by deep fusion of water bloom water quality diffusion migration simulation and reservoir scheduling by applying theoretical methods such as hydrology, bionic evolution algorithm and the like; by arranging the denitrification and dephosphorization device, the self-cleaning capability of the water body is improved, and organic pollutants are degraded. The invention can provide theoretical basis and technical support for scientific formulation of reservoir dispatching scheme related to water bloom prevention and control in watershed.
In order to achieve the above object, the present invention adopts the following scheme:
the invention provides a water engineering multi-objective optimization scheduling method for water bloom prevention and control, which comprises the following steps:
step one, constructing a scheduling model: constructing a multi-objective function oriented to ecological, water quantity and power generation requirements, and a water engineering joint scheduling model oriented to water bloom prevention and control by taking reservoir outlet flow of each period as a decision variable;
step two, water bloom water quality diffusion migration simulation: simulating water bloom water quality diffusion migration by using a one-dimensional convection diffusion equation so as to evaluate ecological, water quantity and power generation scheduling objective functions;
step three, generating a scheduling scheme: solving a scheduling model by adopting a multi-objective ant lion optimization algorithm, obtaining a pareto solution set which can cooperatively optimize water bloom prevention and control, water quantity and power generation scheduling objectives, and providing a water engineering multi-objective optimization scheduling scheme;
and step four, arranging a water body denitrification and dephosphorization device in a downstream water body eutrophication area, and providing engineering measures for water bloom prevention and control.
Preferably, in the first step, the multi-objective function refers to a scheduling objective facing ecological, water quantity and power generation requirements; the minimized ecological objective function is as follows:
wherein F is 1 Representing ecological objectives, Y l,m (t) a value representing an mth index of the t period of the first section; y is Y l,m * The threshold value of the water bloom outbreak of the mth index of the first section is represented, L is the total section number, T is the total period number, and M is the total index number;representing a step function.
Preferably, the step function is defined as follows:
(1) for the forward index, namely, the higher the index value is, the lower the water bloom outbreak probability is, and the forward index mainly comprises the ecological section flow index:
wherein the ecological section flow is related to reservoir delivery flow in each period of water engineering, namely the ecological section flow is represented by (3):
in which Q l (t) represents the ecological section flow rate of the first section t period; q (Q) jin (t) represents the discharge flow of the adjacent nearest reservoir upstream of the first section in the period t; q j Representing the interval inflow flow of the jth tributary between the first section and the upstream adjacent nearest reservoir;
(2) for negative indexes, namely, the higher the index value is, the higher the water bloom outbreak probability is, and the indexes mainly comprise total nitrogen TN and total nitrogen TP:
preferably, the brix schedule objective functions of maximizing the water storage amount and maximizing the power generation amount are respectively as follows:
wherein F is 2 And F 3 Respectively storing water and generating power scheduling targets; v (V) i (t+1) is the storage capacity of the ith reservoir at the time t+1; v (V) i max The maximum reservoir capacity allowed in the ith reservoir dispatching period can be generally taken as a reservoir capacity value corresponding to the normal high water level of the reservoir; k (k) i Representing the ith reservoir output coefficient; q (Q) fd,i (t) is the power generation flow of the ith reservoir at the moment t; h i (t) is the power generation water head of the ith reservoir at the moment t; Δt is the calculated period length.
Preferably, in the first step, the reservoir water storage schedule needs to satisfy the following constraint conditions: reservoir water quantity balance constraint, water level amplitude constraint, reservoir water level boundary constraint, delivery flow constraint, output constraint, water taking node water quantity calculation, water using node water quantity calculation, backwater node water quantity calculation and avionics junction constraint.
Preferably, in the second step, a one-dimensional convection diffusion module is used to simulate the water bloom water quality diffusion and migration process so as to evaluate the ecological, water quantity and power generation scheduling objective function, wherein the one-dimensional convection diffusion equation is as follows:
wherein, C is the concentration of pollutants, mg/L; d is the diffusion coefficient of the pollutant; a is the cross-sectional water passing area, m 2 The method comprises the steps of carrying out a first treatment on the surface of the Q is flow, m 3 The method comprises the steps of carrying out a first treatment on the surface of the K is degradation coefficient, s -1 ；C 2 Is the concentration of source and sink, mg/L; q is the point source flow of the pollutant, m 3 S; x is a space coordinate, m; t is the time step, s.
Preferably, in the second step, the following constraint needs to be satisfied:
initial concentration of water quality index
Wherein C is i,n (t) and C s,n (t) represents the nth water quality index concentration of the ith reservoir and the nth water area for water withdrawal respectively;and->The initial concentrations of the nth water quality indexes of the ith reservoir and the nth water area are respectively shown.
Preferably, in the third step, the solving of the scheduling model by using the multi-objective ant lion optimization algorithm is performed as follows:
(1) data initialization: initializing iteration times, population size and reservoir delivery flow in each period, wherein the reservoir delivery flow in each period corresponds to the position of an ant lion in an ant lion optimization algorithm;
(2) determining elite ant lion: randomly initializing the positions of ants and ant lions within the boundary range of a solution space, calculating the fitness value of all the populations, sequencing the ant lions in descending order, and naming the ant lion with the best fitness value in the ant lion population as elite ant lion;
(3) randomly matching each ant with an ant lion by roulette, updating upper and lower boundary values of a walk range according to the matched ant lion positions, enabling the ants to walk randomly near the selected ant lion and near elite ant lion respectively, and taking an average value as the initial position of the next generation of the ants;
(4) in each iteration, the ant and ant lion population is assigned an initial value again, the adaptability values of the ant and ant lion population are recalculated, the position 50% before the adaptability value is taken as the position of the new generation of ant lion, and the ant lion position with the best adaptability value is taken as the position of the new generation of elite ant lion;
(5) judging termination criteria: if the current iteration number is smaller than the maximum iteration number t max Repeating the step (3); otherwise, the calculation is terminated and the final result is output.
Preferably, in the fourth step, the water body denitrification and dephosphorization device is arranged in a downstream water body eutrophication area to provide engineering measures for water bloom prevention and control, and the treatment equipment comprises a mobile water quality purification device and a fixed denitrification and dephosphorization system.
Compared with the prior art, the application has the following beneficial effects:
1. from the perspective of water bloom prevention and control, a water engineering multi-objective optimization scheduling model for water bloom prevention and control is constructed and efficiently solved based on a water bloom water quality diffusion migration simulation method by applying theoretical methods such as hydrology and a bionic evolution algorithm, and the water bloom prevention and control, water yield allocation and power generation scheduling targets can be cooperatively optimized through deep fusion of the water bloom water quality diffusion migration simulation and reservoir scheduling.
2. From the perspective of water bloom treatment, when a water bloom event occurs, the self-cleaning capability of the water body is improved and organic pollutants are degraded by arranging a denitrification and dephosphorization device.
Drawings
Fig. 1 is a flowchart of a water engineering multi-objective optimization scheduling method for water bloom prevention and control according to an embodiment of the invention.
Fig. 2 is a flowchart for solving a water engineering multi-objective optimization scheduling model for water bloom prevention and control according to an embodiment of the present invention.
FIG. 3 is a diagram of a mobile water purification apparatus and a diagram of a stationary denitrification and dephosphorization treatment system according to an embodiment of the present invention.
Fig. 4 is a Pareto solution set distribution diagram of the average concentration of water quality and the lowest flow rate of the water bloom prevention and control section according to the embodiment of the invention.
Detailed Description
The technical scheme of the invention is described in detail below with reference to the attached drawings, examples and comparative examples.
< example >
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be described below in conjunction with the embodiments of the present invention. Taking two sections of a river reservoir and a downstream as an example, a water engineering multi-objective optimization scheduling method facing water bloom prevention and control is adopted to realize multi-objective balanced optimization of water bloom prevention and control, water supply, water supplement and power generation. As shown in fig. 1, the method comprises the following steps:
step one, constructing a scheduling model: and constructing a water engineering joint scheduling model for water bloom prevention and control, which is oriented to multiple objective functions of ecology, water quantity and power generation requirements and takes the delivery flow of each period as a decision variable, as shown in (a) of fig. 1.
Step two, water bloom water quality diffusion migration simulation: and simulating water bloom water quality diffusion migration by using a one-dimensional convection diffusion module so as to evaluate ecological, water quantity and power generation scheduling objective functions.
Step three, generating a scheduling scheme: and solving a scheduling model by adopting a multi-objective ant lion optimization algorithm to obtain a pareto solution set which can cooperatively optimize water bloom prevention and control, water quantity and power generation scheduling objectives, and providing a water engineering multi-objective optimization scheduling scheme, as shown in (b) in fig. 1.
As shown in fig. 2, data initialization is performed according to (1): determining the initial population quantity of ants and ant lions; (2) determining elite ant lion; (3) updating the ant position; (4) updating the ant lion position and the elite ant lion position; (5) judging the basic flow of the termination criterion, solving a water bloom prevention and control oriented water engineering joint scheduling model, and acquiring a pareto solution set to generate a scheduling scheme to guide reservoir water storage scheduling and risk decision.
And step four, when the water bloom event occurs, arranging a water body denitrification and dephosphorization device in the water area, wherein the device can be withdrawn from operation after the water quality is improved as shown in figure 3, and gradually forming an aquatic ecological balance system.
In this embodiment, in the first step, the multiple objective function refers to a scheduling objective for ecology, water quantity, and power generation requirements; the minimized ecological objective function is as follows:
wherein F is 1 Representing ecological objectives, Y l,m (t) a value representing an mth index of the t period of the first section; y is Y l,m * The threshold value of the water bloom outbreak of the mth index of the first section is represented, L is the total section number, T is the total period number, and M is the total index number;representing a step function.
Further, the step function is defined as follows:
(1) for the forward index, namely, the higher the index value is, the lower the water bloom outbreak probability is, and the forward index mainly comprises the ecological section flow index:
wherein the ecological section flow is related to reservoir delivery flow in each period of water engineering, namely the ecological section flow is represented by (3):
in which Q l (t) represents the ecological section flow rate of the first section t period; q (Q) jin (t) represents the discharge flow of the adjacent nearest reservoir upstream of the first section in the period t; q j Representing the interval inflow flow of the jth tributary between the first section and the upstream adjacent nearest reservoir;
(2) for negative indexes, namely, the higher the index value is, the higher the water bloom outbreak probability is, and the indexes mainly comprise total nitrogen TN and total nitrogen TP:
preferably, the brix schedule objective functions of maximizing the water storage amount and maximizing the power generation amount are respectively as follows:
wherein F is 2 And F 3 Respectively storing water and generating power scheduling targets; v (V) i (t+1) is the storage capacity of the ith reservoir at the time t+1; v (V) i max The maximum reservoir capacity allowed in the ith reservoir dispatching period can be generally taken as a reservoir capacity value corresponding to the normal high water level of the reservoir; k (k) i Representing the ith reservoir output coefficient; q (Q) fd,i (t) is the power generation flow of the ith reservoir at the moment t; h i (t) is the power generation water head of the ith reservoir at the moment t; Δt is the calculated period length.
In the first step, reservoir water storage scheduling needs to meet the following constraint conditions: reservoir water quantity balance constraint, water level amplitude constraint, reservoir water level boundary constraint, delivery flow constraint, output constraint, water taking node water quantity calculation, water using node water quantity calculation, backwater node water quantity calculation and avionics junction constraint.
Balance constraint of reservoir water quantity
V i (t+1)＝V i (t)+[I i (t)-O i (t)]Δt (7)
Wherein V is i (t+1) is the storage capacity of the ith reservoir at the time t+1; v (V) i (t) is the storage capacity of the ith reservoir at the time t; i i (t) represents the flow rate of the ith reservoir in the warehouse at the t-th period; o (O) i (t) represents the discharge flow rate of the ith reservoir in the t period.
Water level constraint
Wherein Z is i (t) represents the reservoir level at time t of the ith reservoir;and->The lower limit and the upper limit of the output of the ith reservoir are respectively taken as the original design water storage scheduling line and the stage flood limit water level.
Water level amplitude constraint
|Z i (t+1)-Z i (t)|≤δ i (9)
In delta i Is the water level amplitude threshold value of the ith reservoir at the adjacent moment.
Reservoir level boundary constraint
In the method, in the process of the invention,water for indicating the beginning of the reservoir schedule periodBit value.
Delivery flow constraints
In the method, in the process of the invention,and->The lower limit and the upper limit of the output of the ith reservoir are respectively determined by ecological requirements and navigation requirements, and the upper limit is determined by the drainage capacity.
Force constraint
P i min (t)≤P i (t)≤P i max (t) (12)
P i (t)＝k i Q fd,i (t)·H i (t) (13)
Wherein P is i (t) represents the output value of the ith reservoir hydropower station in the ith period, P i (t)＝k i Q fd,i (t)·H i (t)；P i min (t) represents a lower limit of the output of the ith reservoir hydropower station at the ith period; p (P) i max And (t) represents the upper limit of the output of the ith reservoir hydropower station in the ith period, and is determined by comprehensively considering the rated output, the blocked capacity, the peak regulation requirement and the like of the unit.
Water intake node water quantity calculation
R s (t)＝Q sy,s (t)+Q qj,s (t)-W qu,s (t) (14)
W qu,s (t)≤W xu,s (t) (15)
Wherein R is j (t) represents the flow in the river after taking water in the t period of the s water area; q (Q) sy,s (t) and Q qj,s (t) represents the inflow of water and interval upstream of the t-th period of the s-th water-using zone, respectively; w (W) qu,s (t) and W xu,s (t) represents the water intake and water demand, respectively, of the s-th water zone in the t-th period.
Water consumption calculation at water node
W tui,s (t)＝(1-α)W qu,s (t) (16)
In which W is tui,s (t) represents the water withdrawal amount of the water zone for the s-th time period; alpha represents the average water consumption rate of the calculated basin.
Water quantity calculation for backwater node
Q h,s (t)＝R s (t)+W tui,s (t) (17)
In which Q h,s And (t) represents the catchment node flow of the ith period of the ith water area.
Air armature new york bundle
Generally, a navigation electricity hub is built at the downstream of the reservoir, the functions of shipping and electricity generation are mainly exerted, the regulating capacity is small, and for such engineering constraint, simulation is carried out according to the conventional scheduling regulation, as follows:
O hd,k (t)＝ψ hd,k [I hd,k (t),Z hd,k (t)] (18)
wherein O is hd,k (t)，I hd,k (t) and Z hd,k (t) represents outflow, inflow and water level, respectively, of the kth aeronautical armature button during the t-th period; psi phi type hd,k The normal scheduling procedure for the kth avionics hub is indicated.
In the second step, a one-dimensional convection diffusion module is used for simulating the water bloom water quality diffusion migration process so as to evaluate the ecological, water quantity and power generation scheduling objective function, wherein the one-dimensional convection diffusion equation is as follows:
wherein, C is the concentration of pollutants, mg/L; d is the diffusion coefficient of the pollutant; a is the cross-sectional water passing area, m 2 The method comprises the steps of carrying out a first treatment on the surface of the Q is flow, m 3 The method comprises the steps of carrying out a first treatment on the surface of the K is degradation coefficient, s -1 ；C 2 Is the concentration of source and sink, mg/L; q is the point source flow of the pollutant, m 3 S; x is a space coordinate, m; t is the time step, s.
Preferably, in the second step, the following constraint needs to be satisfied:
initial concentration of water quality index
Wherein C is i,n (t) and C s,n (t) represents the nth water quality index concentration of the ith reservoir and the nth water area for water withdrawal respectively;and->The initial concentrations of the nth water quality indexes of the ith reservoir and the nth water area are respectively shown.
In the third step, the multi-objective ant lion optimization algorithm is used for solving the scheduling model, and the method comprises the following steps:
(1) data initialization: initializing iteration times, population size and reservoir delivery flow in each period, wherein the reservoir delivery flow in each period corresponds to the position of an ant lion in an ant lion optimization algorithm;
(2) determining elite ant lion: randomly initializing the positions of ants and ant lions within the boundary ranges of solution spaces (7) - (18), (20) and (21), calculating the fitness values of all the species groups according to formulas (1) - (6) and (19), sequencing the species groups according to descending order, and naming the ant lion with the best fitness value in the ant lion species group as elite ant lion;
(3) randomly matching each ant with an ant lion by roulette, updating upper and lower boundary values of a walk range according to the matched ant lion positions, enabling the ants to walk randomly near the selected ant lion and near elite ant lion respectively, and taking an average value as the initial position of the next generation of the ants;
(4) in each iteration, the ant and ant lion population is assigned an initial value again, the adaptability values of the ant and ant lion population are recalculated, the position 50% before the adaptability value is taken as the position of the new generation of ant lion, and the ant lion position with the best adaptability value is taken as the position of the new generation of elite ant lion;
(5) judging termination criteria: if the current iteration number is smaller than the maximum iteration number t max Repeating the step (3); otherwise, the calculation is terminated and the final result is output.
In the fourth step, a water body denitrification and dephosphorization device is arranged in a downstream water body eutrophication area to provide engineering measures for water bloom prevention and control, and the treatment equipment comprises a mobile water quality purification device and a fixed denitrification and dephosphorization system.
As shown in fig. 4, as the average concentration of the sections TP and TP is reduced, the power generation amount of the hydropower station is increased, and the residual water amount of the reservoir volume is reduced; the minimum flow of the section and the generated energy show a cooperative increasing relation, and the increase of the minimum flow and the generated energy can lead to the decrease of the residual water quantity of the reservoir capacity. In addition, with the conventional scheduling result as a dividing line, the Pareto solution set may be divided into A, B two areas. Obviously, the generating capacity of the Pareto solution set in the area A, the average concentration and the minimum flow of the sections TP and TP are inferior to those of the conventional dispatching solution, but the benefit of the residual reservoir capacity of the reservoir is better; and the section TP, TP average concentration, minimum flow, hydropower station power generation amount and reservoir capacity remaining benefits of the Pareto solution set of the zone B are all superior to those of the conventional scheduling solution. The multi-target Pareto solution set obtained by the embodiment can aim at ecological, water quantity and power generation targets, and the comprehensive benefits of water engineering joint scheduling are improved.
The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions in a similar manner without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (5)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211316048.4A CN115659641B (en) | 2022-10-26 | 2022-10-26 | Water bloom prevention and control oriented water engineering multi-objective optimization scheduling method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211316048.4A CN115659641B (en) | 2022-10-26 | 2022-10-26 | Water bloom prevention and control oriented water engineering multi-objective optimization scheduling method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115659641A CN115659641A (en) | 2023-01-31 |
| CN115659641B true CN115659641B (en) | 2024-03-08 |
Family
ID=84991135
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211316048.4A Active CN115659641B (en) | 2022-10-26 | 2022-10-26 | Water bloom prevention and control oriented water engineering multi-objective optimization scheduling method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115659641B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119204359B (en) * | 2024-11-28 | 2025-03-14 | 水利部交通运输部国家能源局南京水利科学研究院 | Water resources coordinated allocation method and system for drought risk response |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010264384A (en) * | 2009-05-14 | 2010-11-25 | Matsue Doken Kk | Method for removing water bloom |
| CN107798199A (en) * | 2017-11-09 | 2018-03-13 | 华中科技大学 | A kind of Hydropower Unit parameter closed-loop identification method |
| CN112700080A (en) * | 2020-08-20 | 2021-04-23 | 国家电网公司西北分部 | Multistage optimal scheduling method for cascade hydropower |
| CN113516373A (en) * | 2021-06-18 | 2021-10-19 | 中国科学院南京地理与湖泊研究所 | Blue algae bloom prevention and control integrated technology system |
| CN113642268A (en) * | 2021-07-15 | 2021-11-12 | 中国环境科学研究院 | River water bloom prediction method and system |
| CN114358379A (en) * | 2021-12-02 | 2022-04-15 | 武汉大学 | Optimization method, device and system for dispatching line of cascade reservoirs with dry and limited water level |
| CN114492233A (en) * | 2022-01-11 | 2022-05-13 | 中国长江三峡集团有限公司 | A Basin Water Simulation Method Based on WebGIS Platform and Considering Comprehensive Utilization Requirements |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112159062B (en) * | 2020-09-07 | 2022-06-24 | 中国环境科学研究院 | A method for multi-interface treatment and restoration of river and lake water environment |
-
2022
- 2022-10-26 CN CN202211316048.4A patent/CN115659641B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010264384A (en) * | 2009-05-14 | 2010-11-25 | Matsue Doken Kk | Method for removing water bloom |
| CN107798199A (en) * | 2017-11-09 | 2018-03-13 | 华中科技大学 | A kind of Hydropower Unit parameter closed-loop identification method |
| CN112700080A (en) * | 2020-08-20 | 2021-04-23 | 国家电网公司西北分部 | Multistage optimal scheduling method for cascade hydropower |
| CN113516373A (en) * | 2021-06-18 | 2021-10-19 | 中国科学院南京地理与湖泊研究所 | Blue algae bloom prevention and control integrated technology system |
| CN113642268A (en) * | 2021-07-15 | 2021-11-12 | 中国环境科学研究院 | River water bloom prediction method and system |
| CN114358379A (en) * | 2021-12-02 | 2022-04-15 | 武汉大学 | Optimization method, device and system for dispatching line of cascade reservoirs with dry and limited water level |
| CN114492233A (en) * | 2022-01-11 | 2022-05-13 | 中国长江三峡集团有限公司 | A Basin Water Simulation Method Based on WebGIS Platform and Considering Comprehensive Utilization Requirements |
Non-Patent Citations (1)
| Title |
|---|
| Exploring a multi-objective optimization operation model of water projects for boosting synergies and water quality improvement in big river systems;Di Zhu 等;Journal of Environmental Management;第345卷;第1-13页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115659641A (en) | 2023-01-31 |
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 | |
| CN101833609B (en) | River ecological flow maintenance-orientated reservoir optimizing and dispatching method | |
| CN105956714B (en) | A new group search method for optimal scheduling of cascade reservoir groups | |
| CN103679285A (en) | Reservoir group combined operation scheduling system and method for improving river and lake relationship | |
| CN102629106B (en) | Water supply control method and water supply control system | |
| CN109523059B (en) | An intelligent optimization method and system for ecological dispatching of cascade hydropower station reservoirs | |
| CN102004835B (en) | A Risk-Oriented Method for Compiling Optimal Scheduling Diagrams for Hydropower Stations | |
| CN105676890B (en) | Three-dimensional and Yi Shang step reservoir operating level during flood season dynamic control method | |
| CN114331033B (en) | Collaborative scheduling method and device for running water level of cascade reservoir in hydro-fluctuation period | |
| CN114663254B (en) | Water resource-grain-energy-ecological cooperative regulation and control method | |
| CN109214110B (en) | Long-distance water lifting project optimized scheduling method | |
| CN115659641B (en) | Water bloom prevention and control oriented water engineering multi-objective optimization scheduling method | |
| CN104036334A (en) | Cascade hydropower station multi-object optimal scheduling mixed searching method coupling peak shaving and navigation demands | |
| CN103088784A (en) | Cascade reservoir flood control water level real-time dynamic control method | |
| CN109598408B (en) | Annual water quantity scheduling plan compilation method considering water use fairness and importance | |
| CN104268653A (en) | Cascade reservoir optimal scheduling method based on ESP | |
| CN107609679A (en) | The preferred method for drafting of multi-parameter and system of a kind of annual-storage reservoir power generation dispatching figure | |
| CN114400698B (en) | Optimal carbon reduction operation method for high-proportion clean energy power grid power supply | |
| CN107704965A (en) | A Reservoir Prosperity Scheduling Diagram Optimization Method Considering the Demands of Ecological Protection | |
| CN111080157A (en) | Method and system for scheduling phosphorus discharge amount of cascade hydropower station | |
| CN105825437A (en) | Complicated reservoir group common water supply task distribution method | |
| CN106845712A (en) | The extracting method of scheduling rule is decomposed in polymerization towards big system multi-reservoir | |
| CN117575289B (en) | Reservoir optimal scheduling method with water quantity-water quality multi-objective cooperation | |
| CN113190963A (en) | Clearing method for virtual power plant participating in electric power peak regulation auxiliary service market | |
| CN114169679A (en) | A day-ahead risk scheduling method for wind-solar-water multi-energy complementarity considering output stability |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |