CN110428099B - Reservoir multi-target water supply capacity optimization method based on particle swarm algorithm - Google Patents

Reservoir multi-target water supply capacity optimization method based on particle swarm algorithm Download PDF

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CN110428099B
CN110428099B CN201910696110.9A CN201910696110A CN110428099B CN 110428099 B CN110428099 B CN 110428099B CN 201910696110 A CN201910696110 A CN 201910696110A CN 110428099 B CN110428099 B CN 110428099B
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林鹏飞
游进军
贾玲
蒋云钟
方国华
汪林
薛志春
付敏
刘鼎
闫腾
姚懿真
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Abstract

The invention discloses a reservoir multi-target water supply capacity optimization method based on a particle swarm algorithm, which comprises the following steps of: s1, randomly generating an initial agricultural dispatching line; s2, acquiring the water supply amount and guarantee rate of the reservoir to each water user; s3, primarily selecting an agricultural dispatching line; s4, acquiring the water supply capacity range of the selected primary agricultural dispatching line; s5, acquiring the maximum water supply capacity of the selected primary agricultural dispatching line; s6, obtaining an individual optimal value and a global optimal value of the agricultural dispatching line; s7, optimizing the primary agricultural dispatching line by updating the individual optimal value and the global optimal value; and S8, taking the agricultural dispatching line which has the same water supply capacity as the agricultural dispatching line optimized last time and continuously reaches a preset algebra as an output. The invention can obtain the maximum water supply capacity meeting the requirement of double guarantee rates of certain agricultural irrigation water and urban water, and solves the problem that the prior art cannot handle reservoir water supply with multiple targets and multiple guarantee rates.

Description

Reservoir multi-target water supply capacity optimization method based on particle swarm algorithm
Technical Field
The invention relates to the field of reservoir water supply, in particular to a reservoir multi-target water supply capacity optimization method based on a particle swarm algorithm.
Background
With the rapid development of urbanization, the water demand of cities and towns is increased rapidly, and the water supply task of the reservoirs in cities and towns is increased. Meanwhile, the agricultural water demand of reservoir design is reduced under the influence of factors such as reduction of irrigation area and development of water-saving technology. With the increasingly prominent ecological problems of rivers, the health of the rivers and the ecological water requirement of the rivers are widely regarded, and the reservoir built in early stage lacks the consideration of the ecological water supply target during the design. Therefore, the water supply task of the water supply reservoir is converted from a single target to multiple targets, and the multiple water supply targets need to be considered when the water supply capacity of the reservoir is calculated. Particularly, for the reservoir serving as a town water supply source, the maximum water supply capacity can be effectively calculated, so that the method has great practical significance and necessity for reasonably planning the scale of a town water plant and guaranteeing the urban water safety.
Because the importance degrees of the water supply targets of the reservoirs are different, the water supply guarantee rates required by different water supply targets are different, and therefore the guarantee rates of a plurality of water supply targets are required to be used as constraint conditions when the water supply capacity of the reservoirs is calculated. The traditional method for calculating the water supply capacity of the reservoir mainly comprises a typical year method and a long-series regulation calculation method, but the traditional method can obtain a reasonable result only when single target water supply quantity and water supply guarantee rate are processed, and the problem of multiple targets and multiple guarantee rates cannot be effectively processed.
Disclosure of Invention
Aiming at the defects in the prior art, the reservoir multi-target water supply capacity optimization method based on the particle swarm optimization solves the problem that the prior art cannot handle multi-target multi-guarantee rate.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
the provided reservoir multi-target water supply capacity optimization method based on the particle swarm algorithm comprises the following steps:
s1, randomly generating a plurality of primary agricultural dispatching lines between the normal storage capacity and the dead storage capacity of the reservoir;
s2, obtaining the water supply amount and guarantee rate of the reservoir to each water user; wherein each water using party comprises ecological water, town water and agricultural irrigation water, and the priority of the ecological water is higher than that of the town water and is higher than that of the agricultural irrigation water;
s3, selecting a primary agricultural dispatching line which accords with the water supply amount and the guarantee rate of each water user of the reservoir;
s4, acquiring a water supply capacity range corresponding to the selected primary agricultural dispatching line according to the guarantee rate of the town water and the agricultural irrigation water;
s5, subdividing the water supply capacity range corresponding to the selected primary agricultural dispatching line by adopting a midsplit algorithm to obtain the maximum water supply capacity of the selected primary agricultural dispatching line;
s6, comparing the water supply capacities of the same agricultural dispatching line in different iterative algebras in a bisection algorithm, and selecting an agricultural dispatching line segment corresponding to the maximum water supply capacity as an individual optimal value; taking the agricultural dispatching line corresponding to the maximum water supply capacity in all iterative processes as a global optimal value; the initial generation dispatching lines are respectively generated randomly by the initial generation dispatching lines;
s7, updating the individual optimal value and the global optimal value by adopting a particle swarm algorithm to optimize the primary agricultural dispatching line to obtain an optimized agricultural dispatching line;
s8, judging whether the current optimized agricultural dispatching line has the same water supply capacity as the last optimized agricultural dispatching line and continuously reaches a preset algebra, and if so, taking the current optimized agricultural dispatching line as a final agricultural dispatching line and outputting the final agricultural dispatching line; otherwise, the optimized agricultural dispatching line is used as a new initial agricultural dispatching line, and the step S2 is returned.
The invention has the beneficial effects that: aiming at the reservoir with multiple water supply tasks, the invention can simultaneously consider three water supply tasks of ecology, cities and towns and optimize the water supply capacity of an agricultural dispatching line and the cities and towns based on the particle swarm algorithm to obtain the maximum water supply capacity meeting the requirement of double guarantee rates of certain agricultural irrigation water and urban water, thereby solving the problem that the prior art can not process the reservoir water supply with multiple targets and multiple guarantee rates.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram showing the process of the evolution of the town water supply capacity when the guarantee rate of water supply for town water is 95% and the guarantee rate of water supply for agricultural irrigation is 90% in the embodiment;
FIG. 3 is a schematic diagram showing the process of evolution of the urban water supply capacity when the urban water supply guarantee rate is 95% and the agricultural irrigation water supply guarantee rate is 75% in the embodiment;
FIG. 4 is a schematic diagram showing the process of the evolution of the urban water supply capacity when the urban water supply guarantee rate is 95% and the agricultural irrigation water supply guarantee rate is 50% in the embodiment;
FIG. 5 is a schematic diagram of the evolution process of 1-10 generations of the global optimal agricultural dispatching line under the target conditions of the town water supply guarantee rate of 95% and the agricultural water supply guarantee rate of 90%;
FIG. 6 is a schematic diagram of the evolution process of 11-20 generations of the global optimal agricultural dispatching line under the target conditions of the town water supply guarantee rate of 95% and the agricultural water supply guarantee rate of 90%;
FIG. 7 is a schematic diagram of the evolution process of 21-30 generations of the global optimal agricultural dispatching line under the target conditions of the town water supply guarantee rate of 95% and the agricultural water supply guarantee rate of 90%;
FIG. 8 is a diagram of the evolution process of 31-40 generations of the global optimal agricultural dispatching line under the target conditions of the town water supply guarantee rate of 95% and the agricultural water supply guarantee rate of 90%;
FIG. 9 is a schematic diagram of the evolution process of 41-50 generations of the global optimal agricultural dispatching line under the target conditions of the town water supply guarantee rate of 95% and the agricultural water supply guarantee rate of 90%;
FIG. 10 is a schematic diagram of the evolution process of 51-60 generations of the global optimal agricultural dispatching line under the target conditions of the town water supply guarantee rate of 95% and the agricultural water supply guarantee rate of 90%;
FIG. 11 is a schematic diagram of the evolution process of 61-70 generations of the global optimal agricultural dispatching line under the target conditions of the urban water supply guarantee rate of 95% and the agricultural water supply guarantee rate of 90%;
FIG. 12 is a schematic diagram of the evolution process of the 71-80 generations of the global optimal agricultural dispatching line under the target conditions of the town water supply guarantee rate of 95% and the agricultural water supply guarantee rate of 90%;
FIG. 13 is a schematic diagram of the evolution process of 81-90 generations of the global optimal agricultural dispatching line under the target conditions of the town water supply guarantee rate of 95% and the agricultural water supply guarantee rate of 90%;
FIG. 14 is a schematic diagram of the evolution process of 91-144 generations of the global optimal agricultural dispatching line under the target conditions of the town water supply guarantee rate of 95% and the agricultural water supply guarantee rate of 90%.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in figure 1, the reservoir multi-target water supply capacity optimization method based on the particle swarm algorithm comprises the following steps:
s1, randomly generating a plurality of primary agricultural dispatching lines between the normal storage capacity and the dead storage capacity of the reservoir;
s2, acquiring the water supply amount and guarantee rate of the reservoir to each water user; wherein each water using party comprises ecological water, town water and agricultural irrigation water, and the priority of the ecological water is higher than that of the town water and that of the agricultural irrigation water;
s3, selecting a primary agricultural dispatching line which accords with the water supply amount and the guarantee rate of each water user by the reservoir;
s4, acquiring a water supply capacity range corresponding to the selected primary agricultural dispatching line according to the guarantee rate of the town water and the agricultural irrigation water;
s5, subdividing the water supply capacity range corresponding to the selected primary agricultural dispatching line by adopting a midsplit algorithm to obtain the maximum water supply capacity of the selected primary agricultural dispatching line;
s6, comparing the water supply capacity of the same agricultural dispatching line in different iteration generations in a bisection algorithm, and selecting the agricultural dispatching line segment corresponding to the maximum water supply capacity as an individual optimal value; taking the agricultural dispatching line corresponding to the maximum water supply capacity in all iterative processes as a global optimal value; wherein the individual optimal values of the initial generation dispatching lines are the dispatching lines randomly generated by the respective initial generation;
s7, updating the individual optimal value and the global optimal value by adopting a particle swarm algorithm to optimize the primary agricultural dispatching line to obtain an optimized agricultural dispatching line;
s8, judging whether the current optimized agricultural dispatching line has the same water supply capacity as the last optimized agricultural dispatching line and continuously reaches a preset algebra, and if so, taking the current optimized agricultural dispatching line as a final agricultural dispatching line and outputting the final agricultural dispatching line; otherwise, the optimized agricultural dispatching line is used as a new initial agricultural dispatching line, and the step S2 is returned.
The specific method of step S1 is:
according to the formula
Figure BDA0002149407800000051
Generating an nth primary agricultural dispatch line particle in an m period
Figure BDA0002149407800000052
Selecting a particle of the primary agricultural dispatching line in each time period to form a primary agricultural dispatching line, wherein the time length of each primary agricultural dispatching line is a time period; wherein V Death by death Is the dead storage capacity of the reservoir; r is a random number between 0 and 1; v Storage m The normal storage capacity of the reservoir in the mth period.
The specific method for acquiring the water supply demand and the guarantee rate of the reservoir to each water user in the step S2 comprises the following substeps:
s2-1, according to the formula
Figure BDA0002149407800000053
Obtaining the storage capacity V of the initial reservoir at the ith time interval i Further obtaining the initial storage capacity V of the reservoir Beginning of the design ＝V 1 (ii) a Wherein V Powder, i-1 The water storage capacity of the upper reservoir at the end of the ith time interval; v Death by death Is the dead storage capacity of the reservoir; v Storage, i Normal reservoir capacity for the ith time period;
s2-2, according to the formula
V Front, i ＝W In, i +V i
Obtaining the water storage volume V before the water supply of the reservoir in the ith time period Front, i (ii) a Wherein W In, i The water quantity of the warehouse is the i-th time period;
s2-3, according to the formula
Figure BDA0002149407800000061
Obtaining the leakage loss W of the reservoir in the ith period Lossiei i (ii) a Wherein k is a leakage coefficient;
s2-4, according to the formula
Figure BDA0002149407800000062
Obtaining the water supply amount W available for ecological water in the ith period Shengke, i According to the formula
Figure BDA0002149407800000063
Obtaining the water supply demand W of the ecological water in the ith period Raw, i (ii) a Wherein D Raw, i The minimum water demand for ecological water in the ith period;
s2-5, according to the formula
V After birth, i ＝V Front, i -W Decrease, i -W Raw, i
Obtaining the water storage volume V after the reservoir provides ecological water in the ith time period After birth, i ；
S2-6, according to the formula
Figure BDA0002149407800000064
Obtaining water supply amount V available for town water in ith period City of, i According to the formula
Figure BDA0002149407800000071
Obtaining the water supply demand W of urban water in the ith period City, i (ii) a Wherein
Figure BDA0002149407800000072
The maximum water demand of town water;
s2-7, according to the formula
V After city, i ＝V After birth, i -W City, i
Obtaining the water storage volume V after the reservoir provides the urban water in the ith period After city, i ；
S2-8, according to the formula
Figure BDA0002149407800000073
Figure BDA0002149407800000074
Obtaining the water supply amount V available for agricultural irrigation water in the ith period Nongke, i According to the formula
Figure BDA0002149407800000075
Obtaining the water supply demand W of agricultural irrigation water in the ith period Agricultural, i (ii) a Wherein mod (i, M) represents the remainder of i divided by M, M being the total number of time periods;
Figure BDA0002149407800000076
all the primary agriculture dispatching lines in the ith time period; d Agricultural, i The maximum water demand for agricultural irrigation water;
s2-9, for each water user, dividing the time period number of the actual water supply quantity equal to the water supply demand quantity by the total time period number according to historical data to obtain the guarantee rate of each water user; wherein the time length of each primary agricultural dispatching line is a time period.
The specific method of step S4 is:
and for each selected primary agricultural dispatching line, gradually increasing the water supply amount of the reservoir to the urban water by taking T as a step length from 0 until the urban water supply guarantee rate or the agricultural irrigation water supply guarantee rate is lower than a corresponding threshold value, taking the water supply amount at the moment as an upper water supply limit, and taking the water supply amount obtained by subtracting the step length from the water supply amount at the moment as a lower water supply limit to obtain the water supply capacity range corresponding to the selected primary agricultural dispatching line.
The specific method of step S5 includes the following substeps:
s5-1, according to the formula
Figure BDA0002149407800000081
Obtaining the result of adopting a bisection algorithm for the water supply range of the selected primary agricultural dispatching line for the first time
Figure BDA0002149407800000082
Wherein
Figure BDA0002149407800000083
For selected water supply capacity range of primary agricultural dispatching lineA lower limit;
Figure BDA0002149407800000084
the upper limit of the water supply capacity range of the selected primary agricultural dispatching line;
s5-2, adopting the same method as the step S5-1, and according to the value formula
Figure BDA0002149407800000085
Figure BDA0002149407800000086
Figure BDA0002149407800000087
Iterating until satisfying
Figure BDA0002149407800000088
Obtaining the maximum water supply capacity of the selected nth primary agricultural dispatching line
Figure BDA0002149407800000089
Wherein δ is an iteration ending parameter which is a constant; j is more than or equal to 2;
Figure BDA00021494078000000810
a result obtained by adopting a bisection algorithm in the last water supply range during the j-1 th iteration;
Figure BDA00021494078000000811
the lower limit of the water supply capacity obtained in the jth iteration;
Figure BDA00021494078000000812
the water supply capacity upper limit obtained in the jth iteration is obtained;
Figure BDA00021494078000000813
the upper limit of the water supply capacity obtained in the j-1 th iteration;
Figure BDA00021494078000000814
the water supply capacity lower limit obtained in the j-1 th iteration is obtained; p Town counting The guarantee rate of the town water consumption is calculated through the current water supply scheme; p Town restraint The minimum constraint value of the guarantee rate of the town water; p Agricultural computing Calculating the agricultural irrigation water guarantee rate according to the current water supply scheme; p is Agricultural constraints The minimum constraint value is the guarantee rate of agricultural irrigation water.
The specific method of step S7 includes the following substeps:
s7-1, according to the formula respectively
Figure BDA0002149407800000091
Figure BDA0002149407800000092
Updating the evolution speed of a particle swarm
Figure BDA0002149407800000093
And position
Figure BDA0002149407800000094
Wherein
Figure BDA0002149407800000095
The evolution speed of the particles in the mth time period of the nth agricultural dispatching line after the e-time iteration is obtained;
Figure BDA0002149407800000096
the position of the particle of the nth agricultural dispatching line in the mth time period after the e iteration is represented as the e generation agricultural dispatching line; c. C 1 And c 2 Are all constants; r 1 And R 2 Are all [0, 1]A random number in between; w is the momentum term number;
Figure BDA0002149407800000097
the evolution speed of the particles in the mth time period of the nth agricultural dispatching line after the e-1 iteration;
Figure BDA0002149407800000098
the optimal value of the individual after the e-1 iteration is obtained;
Figure BDA0002149407800000099
the global optimum value after the e-1 iteration is obtained;
Figure BDA00021494078000000910
the particle position of the mth time period of the nth agricultural dispatching line after the e-1 iteration; e is more than or equal to 1, when e is equal to 1,
Figure BDA00021494078000000911
in order to be the initial speed of evolution,
Figure BDA00021494078000000912
is an initial individual optimum value for the initial individual,
Figure BDA00021494078000000913
for the initial global optimum value to be the one,
Figure BDA00021494078000000914
the particle position of the nth agricultural dispatching line in the mth time period;
s7-2, according to the formula
Figure BDA00021494078000000915
Correcting the e-th generation agricultural dispatching line to obtain the e-second optimized agricultural dispatching line
Figure BDA00021494078000000916
In the specific implementation process, the value range of the momentum term w in step S7-1 is [0.1, 0.9 ]. Each session is one full month in length and each time period is one full year. And calculating according to the importance degree of the water supply target and the sequence of ecological water supply, urban water supply and agricultural irrigation water supply. Wherein, the ecological target is preferentially satisfied, and water is supplied to the ecological water users as long as the water storage capacity of the reservoir is higher than the dead reservoir capacity. Since the guarantee rate of the ecological water supply is generally equal to or higher than other water supply targets before other water supply targets, ecological guarantee rate constraints are not set separately. And calculating the water demand of the agricultural water supply task by a rating method according to the area of the reservoir irrigation area and the irrigation rating. The water supply of cities and towns and agriculture can be controlled by an agricultural dispatching line, the water storage above the agricultural dispatching line can be used for agriculture, and the water quantity below the dispatching line can only supply water for cities and towns and ecological users.
In one embodiment of the invention, the akatian reservoir of the third city of Hainan province is taken as an example, the akatian reservoir is the largest hydraulic engineering in the east of the third city of Hainan province, is positioned at the downstream of the West river of the Tengqiao, has the dam site distance of 46km from the third city of China, and is a medium-sized reservoir which mainly uses town water supply for irrigation and flood control and comprehensive development and utilization. The rainwater collection area above the red field reservoir dam site is 221km 2 7710 km Total reservoir Capacity 3 Xingli reservoir capacity 4740 km 3 1220 km dead stock 3 . The main water supply targets of the red field reservoir at present are urban water, agricultural irrigation and ecological water, and the urban water supplied by the red field reservoir is treated by a green field water plant to reach the standard and then supplied to urban areas. At present, the water supply of the green field water plant accounts for about six times of the total water supply of the city with three centers, exceeds the design water supply capacity of the water plant and is in an overload operation state. In order to reasonably determine the scale of the green field water plant and ensure the urban water demand of the third-generation city, the maximum urban water supply of the red field reservoir needs to be recalculated by comprehensively considering the competitive relationship between agricultural irrigation water and ecological guarantee water. The reservoir engineering parameters are as follows: the normal reservoir capacity is 5960 km in the non-flood season (10-6 months in the next year) 3 (ii) a In flood season (7-10 months) 5081 km 3 (ii) a The dead storage capacity is 1220 ten thousand meters 3 . The reservoir operation parameters are as follows: the inflow data of the reservoir adopts the monthly-scale warehousing flow of 55 continuous years in 1 month-201O 12 months in 1956; the initial volume of the library calculated was 6000 km 3 (ii) a Leakage coefficient of0.008; the maximum allowable destruction depth for towns is 1% and the maximum allowable destruction depth for agriculture is 30%.
Speed of iteration
Figure BDA0002149407800000101
Has a variation range of [ -100 ten thousand m 3 100 ten thousand meters 3 ]The initial velocity is randomly generated within the interval, c 1 And c 2 Is 2, w is in the interval [0.4, 0.9]]The step length T is 200 ten thousand m 3 . The water supply targets of the water reservoir of the red field are mainly town, agriculture and ecology. The agricultural water demand is calculated according to the irrigation area and the irrigation quota. Under the condition of irrigation area under the current state of the supermarket, the water requirement of irrigation hair of 50 percent, 75 percent and 90 percent of rainfall frequency years is 1249 ten thousand meters respectively 3 1383 km 3 1603 ten thousand meters 3 The distribution process in the year is shown in table 1. Setting the flow of the ecological environment under the red farmland dam to be 0.2m according to the water environment functional area target of the vine bridge west river 3 /s。
Table 1: the agricultural water demand is distributed in the process units in the year under different rainfall frequencies: wanm 3
Figure BDA0002149407800000111
The process of the evolution of the town water supply capacity under different guarantee rate target conditions is shown in the figure 2, the figure 3 and the figure 4. Wherein, when the guarantee rate of urban water supply is 95% and the guarantee rate of agricultural water supply is 90%, the water supply capacity of the primary generation urban water supply is 596.46 ten thousand meters 3 Monthly, the water supply capacity of cities and towns reaches a maximum of 648.13 km when the generation is advanced to 44 generations 3 And a month. When the guarantee rate of urban water supply is 95% and the guarantee rate of agricultural water supply is 75%, the water supply capacity of the first generation of urban water supply is 675.73 ten thousand meters 3 Monthly, when the generation is advanced to 42, the water supply capacity of cities and towns reaches a maximum of 696.68 km 3 And a month. When the guarantee rate of urban water supply is 95% and the guarantee rate of agricultural water supply is 50%, the water supply capacity of the first generation of urban water supply is 704.52 ten thousand meters 3 Monthly, when the generation is advanced to 14, the water supply capacity of cities and towns reaches a maximum of 723.43 km 3 And a month. Therefore, the invention can realize the overlapping of water supply capacityAnd the generation evolution has feasibility for calculating the water supply capacity of the multi-target reservoir.
As shown in fig. 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14, the optimal agricultural dispatching line has a large evolution range from generation 1 to generation 40, and the main changes occur in a dry season when the water supply amount is small and the competition of water supply targets is large. The evolutionary amplitude of the agricultural dispatching lines of 41-80 generations is small, and only small amplitude correction exists in individual months. The global optimal agricultural schedule line remains substantially unchanged after the 81 generations. After iteration to 144 generations according to the evolution process of town water supply capacity, the town water supply capacity reaches the maximum value and does not change any more. However, the agricultural dispatching line still has small-amplitude adjustment after 144 generations, which is mainly because the method takes the guarantee rate as an optimization target, and although the water supply amount of the dispatching line meeting the same guarantee rate requirement has a certain difference, the calculated water supply guarantee rate has no difference when the water supply amount can not meet the water demand in the period, which is consistent with the actual situation. In addition, in order to avoid the situation of local optimum, when the maximum water supply capacity of the current generation is the same as the maximum value of the maximum water supply capacity of the previous generation, the agricultural dispatching line of the current generation is selected as the current global optimum agricultural dispatching line. Therefore, the evolution process of the agricultural dispatching line in the method is reasonable.
In conclusion, the invention can simultaneously consider three water supply tasks of ecology, cities and towns and agriculture aiming at the reservoir with multiple water supply tasks, optimizes the water supply capacity of an agricultural dispatching line and the cities and towns based on the particle swarm optimization, obtains the maximum water supply capacity meeting the requirement of double guarantee rates of certain agricultural irrigation water and urban water, and solves the problem that the prior art cannot handle multiple targets and multiple guarantee rates.

Claims (7)

1. A particle swarm algorithm-based reservoir multi-target water supply capacity optimization method is characterized by comprising the following steps:
s1, randomly generating a plurality of primary agricultural dispatching lines between the normal storage capacity and the dead storage capacity of the reservoir;
s2, obtaining the water supply amount and guarantee rate of the reservoir to each water user; wherein each water using party comprises ecological water, town water and agricultural irrigation water, and the priority of the ecological water is higher than that of the town water and that of the agricultural irrigation water;
s3, selecting a primary agricultural dispatching line which accords with the water supply amount and the guarantee rate of each water user of the reservoir;
s4, acquiring a water supply capacity range corresponding to the selected primary agricultural dispatching line according to the guarantee rate of the town water and the agricultural irrigation water;
s5, subdividing the water supply capacity range corresponding to the selected primary agricultural dispatching line by adopting a midsplit algorithm to obtain the maximum water supply capacity of the selected primary agricultural dispatching line;
s6, comparing the water supply capacity of the same agricultural dispatching line in different iteration generations in a bisection algorithm, and selecting the agricultural dispatching line segment corresponding to the maximum water supply capacity as an individual optimal value; taking the agricultural dispatching line corresponding to the maximum water supply capacity in all iterative processes as a global optimal value; wherein the individual optimal values of the initial generation dispatching lines are the dispatching lines randomly generated by the respective initial generation;
s7, updating the individual optimal value and the global optimal value by adopting a particle swarm algorithm to optimize the primary agricultural dispatching line to obtain an optimized agricultural dispatching line;
s8, judging whether the current optimized agricultural dispatching line has the same water supply capacity as the last optimized agricultural dispatching line and continuously reaches a preset algebra, and if so, taking the current optimized agricultural dispatching line as a final agricultural dispatching line and outputting the final agricultural dispatching line; otherwise, the optimized agricultural dispatching line is used as a new initial agricultural dispatching line, and the step S2 is returned;
the specific method of step S5 includes the following substeps:
s5-1, according to the formula
Figure FDA0003467421780000021
Obtaining the result of adopting a bisection algorithm for the water supply range of the selected primary agricultural dispatching line for the first time
Figure FDA0003467421780000022
Wherein
Figure FDA0003467421780000023
The lower limit of the water supply capacity range of the selected primary agricultural dispatching line;
Figure FDA0003467421780000024
the upper limit of the water supply capacity range of the selected primary agriculture dispatching line;
s5-2, adopting the same method as the step S5-1, and according to the value formula
Figure FDA0003467421780000025
P Town counting ≥P Town restraint And P is Agricultural computing ≥P Agricultural restraint
Figure FDA0003467421780000026
P Town counting ≥P Town restraint And P is Agricultural computing ＜P Agricultural restraint
Figure FDA0003467421780000027
P Town counting ＜P Town restraint
Iterating until satisfying
Figure FDA0003467421780000028
Obtaining the maximum water supply capacity of the selected nth primary agricultural dispatching line
Figure FDA0003467421780000029
Wherein δ is an iteration ending parameter which is a constant; j is more than or equal to 2;
Figure FDA00034674217800000210
a result obtained by adopting a bisection algorithm through the last water supply range during the j-1 iteration;
Figure FDA00034674217800000211
the lower limit of the water supply capacity obtained in the jth iteration;
Figure FDA00034674217800000212
n is the upper limit of the water supply capacity obtained in the jth iteration;
Figure FDA00034674217800000213
the upper limit of the water supply capacity obtained in the j-1 th iteration;
Figure FDA00034674217800000214
the water supply capacity lower limit obtained in the j-1 th iteration is obtained; p is Town counting The guarantee rate of the town water consumption is calculated through the current water supply scheme; p Town restraint The minimum constraint value of the guarantee rate of the town water; p Agricultural computing Calculating the agricultural irrigation water guarantee rate according to the current water supply scheme; p is Agricultural restraint The minimum constraint value is the guarantee rate of agricultural irrigation water.
2. The particle swarm algorithm-based reservoir multi-target water supply capacity optimization method according to claim 1, wherein the specific method of the step S1 is as follows:
according to the formula
Figure FDA00034674217800000215
Generating the nth primary agricultural dispatch line particle in an m period
Figure FDA00034674217800000216
And selecting a particle of the primary agricultural dispatching line in each time period to form a primary agricultural dispatching line,the time length of each primary agricultural dispatching line is a time period; wherein V Death by death Is the dead storage capacity of the reservoir; r is a random number between 0 and 1; v Storage m The normal storage capacity of the reservoir in the mth period.
3. The method for optimizing the multi-target water supply capacity of the reservoir based on the particle swarm optimization algorithm according to claim 1, wherein the specific method for acquiring the water supply demand and the guarantee rate of the reservoir to each water user in the step S2 comprises the following sub-steps:
s2-1, according to the formula
Figure FDA0003467421780000031
Obtaining the storage capacity V of the initial reservoir at the ith time interval i Further obtaining the initial storage capacity V of the reservoir First stage ＝V 1 (ii) a Wherein V Powder, i-1 The water storage capacity of the reservoir at the last time period of the ith time period; v Death by death Is the dead storage capacity of the reservoir; v Storage i Normal reservoir capacity for the ith time period;
s2-2, according to the formula
V Front, i ＝W In, i +V i
Obtaining the water storage V before the reservoir supplies water in the ith time period Front, i (ii) a Wherein W In, i The water quantity of the warehouse is the i-th time period;
s2-3, according to the formula
Figure FDA0003467421780000032
Obtaining the leakage loss W of the reservoir in the ith period Lossiei i (ii) a Wherein k is the leakage coefficient;
s2-4, according to the formula
Figure FDA0003467421780000033
Obtaining the water supply amount W available for ecological water in the ith period Shengke, i According to the formula
Figure FDA0003467421780000041
Obtaining the water supply demand W of the ecological water in the ith period Raw, i (ii) a Wherein D Raw, i The minimum water demand for ecological water in the ith period;
s2-5, according to the formula
V After birth, i ＝V Front, i -W Decrease, i -W Raw, i
Obtaining the water storage volume V after the reservoir provides ecological water in the ith time period After birth, i ；
S2-6, according to the formula
Figure FDA0003467421780000042
Obtaining water supply amount V available for town water in ith period City of, i According to the formula
Figure FDA0003467421780000043
Obtaining the water supply demand W of urban water in the ith period City, i (ii) a Wherein
Figure FDA0003467421780000044
The maximum water demand of town water;
s2-7, according to the formula
V After city, i ＝V After birth, i -W City, i
Obtaining the water storage volume V after the reservoir provides the urban water in the ith period After city, i ；
S2-8, according to the formula
Figure FDA0003467421780000045
Figure FDA0003467421780000046
Obtaining the water supply amount V which can be used for agricultural irrigation water in the ith period Nongke, i And according to the formula
Figure FDA0003467421780000051
Obtaining the water supply demand W of agricultural irrigation water in the ith period Agricultural, i (ii) a Wherein mod (i, M) represents the remainder of i divided by M, M being the total number of time periods;
Figure FDA0003467421780000052
all the primary agricultural dispatching lines in the ith time period; d Agricultural, i The maximum water demand for agricultural irrigation water;
s2-9, for each water user, dividing the time period number of the actual water supply quantity equal to the water supply demand quantity by the total time period number according to the historical data to obtain the guarantee rate of each water user; wherein the time length of each primary agricultural dispatching line is a time period.
4. The particle swarm algorithm-based reservoir multi-target water supply capacity optimization method according to claim 1, wherein the specific method of the step S4 is as follows:
and for each selected primary agricultural dispatching line, gradually increasing the water supply amount of the reservoir to the urban water by taking T as a step length from 0 until the urban water supply guarantee rate or the agricultural irrigation water supply guarantee rate is lower than a corresponding threshold value, taking the water supply amount at the moment as an upper water supply limit, and taking the water supply amount obtained by subtracting the step length from the water supply amount at the moment as a lower water supply limit to obtain the water supply capacity range corresponding to the selected primary agricultural dispatching line.
5. The particle swarm algorithm-based reservoir multi-target water supply capacity optimization method according to claim 2, wherein the specific method of the step S7 comprises the following sub-steps:
s7-1, respectively according to the formula
Figure FDA0003467421780000053
Figure FDA0003467421780000054
Updating the evolution speed of a particle swarm
Figure FDA0003467421780000055
And position
Figure FDA0003467421780000056
Wherein
Figure FDA0003467421780000057
The evolution speed of the particles in the mth time period of the nth agricultural dispatching line after the e-time iteration is obtained;
Figure FDA0003467421780000058
the position of the particles in the mth time period of the nth agricultural dispatching line after the e-time iteration is represented as the e-th generation agricultural dispatching line; c. C 1 And c 2 Are all constants; r is 1 And R 2 Are all [0, 1]A random number in between; w is the momentum term number;
Figure FDA0003467421780000059
the evolution speed of the particles in the mth time period of the nth agricultural dispatching line after the e-1 iteration;
Figure FDA0003467421780000061
the optimal value of the individual after the e-1 iteration is obtained;
Figure FDA0003467421780000062
the global optimum value after the e-1 iteration is obtained;
Figure FDA0003467421780000063
the particle position of the nth time interval of the nth agricultural dispatching line after the e-1 iteration; e is more than or equal to 1, when e is equal to 1,
Figure FDA0003467421780000064
in order to be the initial speed of evolution,
Figure FDA0003467421780000065
is an initial individual optimum value for the initial individual,
Figure FDA0003467421780000066
for the initial global optimum value to be the one,
Figure FDA0003467421780000067
a particle position of the mth time period of the nth agricultural dispatch line;
s7-2, according to the formula
Figure FDA0003467421780000068
Correcting the e-th generation agricultural dispatching line to obtain the e-second optimized agricultural dispatching line
Figure FDA0003467421780000069
6. The method for optimizing the multi-target water supply capacity of the reservoir based on the particle swarm optimization algorithm according to claim 5, wherein the value range of the momentum term w in the step S7-1 is [0.1, 0.9 ].
7. The particle swarm algorithm-based reservoir multi-objective water supply capacity optimization method according to claim 2, wherein each time period is a whole month and each time period is a whole year.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102156914A (en) * 2011-03-30 2011-08-17 东华大学 Method for cooperatively and optimally allocating water volume in non-flood season
CN102708248A (en) * 2012-05-10 2012-10-03 湖北省电力公司 Dispatching function optimization method based on multi-objective genetic algorithm
CN103942612A (en) * 2014-04-08 2014-07-23 河海大学 Cascade reservoir optimal operation method based on adaptive particle swarm optimization algorithm
CN105243458A (en) * 2015-11-10 2016-01-13 河海大学 Reservoir dispatching method based on multi-target shuffled frog leaping and differential algorithms
CN106529166A (en) * 2016-11-04 2017-03-22 河海大学 Method for regional water-source optimized configuration based on MAEPSO algorithm
CN106951985A (en) * 2017-03-06 2017-07-14 河海大学 A kind of step reservoir Multiobjective Optimal Operation method based on improvement artificial bee colony algorithm

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102156914A (en) * 2011-03-30 2011-08-17 东华大学 Method for cooperatively and optimally allocating water volume in non-flood season
CN102708248A (en) * 2012-05-10 2012-10-03 湖北省电力公司 Dispatching function optimization method based on multi-objective genetic algorithm
CN103942612A (en) * 2014-04-08 2014-07-23 河海大学 Cascade reservoir optimal operation method based on adaptive particle swarm optimization algorithm
CN105243458A (en) * 2015-11-10 2016-01-13 河海大学 Reservoir dispatching method based on multi-target shuffled frog leaping and differential algorithms
CN106529166A (en) * 2016-11-04 2017-03-22 河海大学 Method for regional water-source optimized configuration based on MAEPSO algorithm
CN106951985A (en) * 2017-03-06 2017-07-14 河海大学 A kind of step reservoir Multiobjective Optimal Operation method based on improvement artificial bee colony algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
基于引水限制调度线的并联水库***供水优化算法及应用;林鹏飞等;《水利水电技术》;20180228;第49卷(第2期);第8-14页 *
基于调度线的水库多目标可供水量计算方法与应用;季海萍等;《水利水电技术》;20140430;第45卷(第4期);第117-121页 *

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