CN110188912B - Improved pollen algorithm-based surface water and underground water combined scheduling optimization method - Google Patents
Improved pollen algorithm-based surface water and underground water combined scheduling optimization method Download PDFInfo
- Publication number
- CN110188912B CN110188912B CN201910151472.XA CN201910151472A CN110188912B CN 110188912 B CN110188912 B CN 110188912B CN 201910151472 A CN201910151472 A CN 201910151472A CN 110188912 B CN110188912 B CN 110188912B
- Authority
- CN
- China
- Prior art keywords
- water
- reservoir
- formula
- constraint
- pollen
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention discloses a surface water and underground water combined dispatching optimization method based on an improved pollen algorithm, which comprises the following steps of firstly, establishing an objective function of a reservoir optimization target by taking the maximum comprehensive target of power generation, irrigation and ecology of a reservoir as the optimization target, wherein the constraint conditions comprise: water balance constraint, node water quantity constraint, underground water time interval water intake constraint, reservoir water level constraint, reservoir discharge constraint and power station output constraint are constraint conditions; and secondly, introducing a computer parallel technology to improve a pollen algorithm, and carrying out optimization calculation on the objective function to obtain the generated energy, the runoff in and out of the reservoir, the groundwater intake rate and the optimal reservoir water level. The surface water and underground water combined dispatching optimization method based on the improved pollen algorithm has important practical significance and application value for enriching the reservoir group optimized dispatching method, improving the surface water and underground water combined dispatching benefit of the northwest arid region and improving the comprehensive utilization benefit of water resources and the regional ecological benefit.
Description
Technical Field
The invention belongs to the technical field of surface water and underground water combined dispatching optimization, and particularly relates to a surface water and underground water combined dispatching optimization method based on an improved pollen algorithm.
Background
At present, water resources in arid regions in China are relatively in short supply, and sustainable development of the arid regions is seriously influenced. At present, a surface water-underground water combined scheduling mode becomes an important measure for solving the water resource shortage in arid areas. The reservoir is used as an important part in the underground water and surface water combined dispatching, and the operation mode of the reservoir is paid special attention; however, the optimal scheduling of reservoirs is non-linear, high dimensional, complex. At present, the solution method for reservoir optimal scheduling is mainly divided into a traditional method and an optimization method. (1) The traditional methods, such as linear programming, dynamic programming, gradual optimization algorithm and the like, have the defects of long calculation time, dimension disaster, low convergence speed and the like; (2) Optimization methods such as a particle swarm algorithm, a genetic algorithm, a cuckoo algorithm and the like have the defects of easy falling into local optimization, low search efficiency and the like.
The pollen Algorithm (FPA) is an efficient bionic optimization Algorithm proposed by Yang 2010. The FPA principle is simple and easy to understand, the number of built-in parameters is small, the calculation is simple, and the method has the advantages of strong robustness and global search capability. The method is widely applied to the field of system optimization, such as text clustering analysis, wireless sensor network clustering, power system economic load distribution and the like. However, when solving the high-dimensional and nonlinear problems, the pollen algorithm has the defects of an intelligent algorithm, such as slow convergence speed in the later period and easy convergence to a local optimal solution.
Disclosure of Invention
The invention aims to provide a surface water and underground water combined dispatching optimization method based on an improved pollen algorithm, which has the characteristics of improving the combined operation capacity of a reservoir group and improving the utilization efficiency of water resources in arid regions.
The technical scheme adopted by the invention is that the surface water and underground water combined dispatching optimization method based on the improved pollen algorithm is implemented according to the following steps:
MinF＝c 1 F 1 +c 2 F 2 +c 3 F 3 (1)，
in the formula (1), F is a comprehensive target; f 1 The unit of the generated energy of the power station of the reservoir is hundred million kW.h.c 1 A weight coefficient of power station generation capacity of the reservoir; f 2 For irrigation water shortage, unit is hundred million m 3 ，c 2 The weight coefficient is the irrigation water shortage; f 3 In unit of hundred million m for ecological water shortage 3 ，c 3 In ecological water deficitA weight coefficient; c. C 1 、c 2 And c 3 All provided by basin management decision maker, respectively 0.5,0.4 and 0.1;
in the formula (2), m represents a hydropower station number of the reservoir; t represents the number of time segments; Δ t represents the period of time; k is a radical of m Representing the output coefficient of the mth power station; q (m, t) represents the generated flow of the mth power station in m 3 S; h (m, t) represents the power generation head of the mth power station in m;
expresses the irrigation water shortage of the t-th time period and has unit of hundred million m 3 ；
Expresses the ecological water shortage in the t-th time period and has unit of hundred million m 3 ；
And 2, improving a pollen algorithm by adopting a computer parallel technology, and performing optimization calculation on the objective function to obtain the underground water exploitation rate and the optimal reservoir water level.
The invention is also characterized in that:
in step 1, the constraint conditions of the objective function include:
and (3) water balance constraint: v (m, T + 1) = V (m, T) + (QI (m, T) -QO (m, T)) T + Δ W (3),
and (3) node water quantity constraint: QI (m, t + 1) = QI (m, t) + QR (m, t) -QS (m, t) (4),
restriction of water intake in underground water period: RG (route group) t min ≤RG t ≤RG t max (5)，
Reservoir water level constraint: z min (m,t)≤Z(m,t)≤Z max (m,t) (6)，
Reservoir discharge restraint: QO min (m,t)≤QO(m,t)≤QO max (m,t) (7)，
Power station output constraint: n is a radical of min (m,t)≤N(m,t)≤N max (m,t) (8)，
In the formula (3), V (m, t + 1) respectively represent the initial and final storage capacities of the mth reservoir in the tth period, and the units are all hundred million m 3 (ii) a QO (m, t) represents the tth time of the mth reservoirRun-off of the section in m 3 S; Δ W represents the amount of water lost in the evaporation and leakage process, and the unit is hundred million m 3 It can be ignored;
in the formula (4), QI (m, t + 1) respectively represent the runoff of the beginning and end warehousing of the t-th time period of the mth reservoir, and the unit is m 3 S; QR (m, t) represents the interval warehousing of the t-th time period of the mth reservoir, and the unit is m 3 S; QS (m, t) represents the exchange flow rate of the mth reservoir in the tth period of time, and is expressed in m 3 /s；
In formula (5), RG t,min Indicating the lower limit of groundwater intake rate, RG, in the t-th period t,max Representing the groundwater intake rate upper limit in the t-th period;
in the formula (6), Z min (m, t) represents a lower water level limit of the ith period of the mth power plant, Z max (m, t) represents the upper water level limit of the ith time period of the mth power station, and the unit is m;
QO in the formula (7) min (m, t) represents the lower limit of ex-warehouse flow, QO, of the mth power station during the tth period max (m, t) represents the upper limit of the ex-warehouse flow of the mth power station in the tth time period, and the unit is m 3 /s；
In the formula (8), N min (m, t) represents the minimum contribution of the mth power station during the tth period, N max (m, t) represents the maximum contribution of the mth power station during the tth period in MW.
The step 2 is implemented according to the following steps:
step 2.1, initializing a pollen algorithm, wherein initial parameters comprise an initial population N pop Maximum number of iterations T max The conversion probability p of the pollination mode;
step 2.2, according to the constraint conditions and the initial population N pop Calculating to obtain a decision variable, taking the decision variable as an initial pollen gamete, and adopting a calculation formula as follows:
X i,j ＝H min (j)+rand(H max (j)-H min (j))i∈[1,N pop ],j∈[1,D] (9)，
in formula (9), X i,j Representing the position of the ith pollen gamete in the jth dimension space; h max (j) Upper bound constraint for j-dimensional spatial position of pollen gameteThe water level upper limit constraint of reservoir moon end or the exploitation rate upper limit of underground water; h min (j) Representing the lower limit constraint of the j-th dimensional space position of the pollen gamete, namely the lower limit constraint of the water level at the lunar end of the reservoir or the lower limit of the exploitation rate of underground water; rand denotes [0,1 ]]A random number of intervals; d represents a spatial dimension;
2.3, performing parallel task decomposition and integration on the initial pollen gametes through a computer parallel technology, and outputting an optimal population;
step 2.4, performing iterative processing on the optimal population by adopting a pollen algorithm, and if the current iterative times T is less than or equal to T max If yes, step 2.5 is carried out, otherwise, step 2.6 is skipped;
step 2.5, continuously generating a uniform distribution function Rand, if Rand is larger than p, carrying out global pollination on pollen gametes, otherwise, carrying out local pollination, and continuously iterating, wherein the number of iterations is increased by 1;
the global pollination formula is:
wherein the content of the first and second substances,
the formula of local pollination is:
in the formula (10), the reaction mixture is,
respectively representing solutions of t and t +1 generation; l represents a step value; g represents a global optimum; Γ (λ) is the standard gamma function; λ represents a model parameter; s, s 0 Are all step size parameters;
in formula (11), ε is a random number between 0 and 1 that follows a uniform distribution;
are respectively the same plant but differentPollen gamete of plant, rand stands for [0,1 ]]A random number of intervals;
step 2.6 until T > T max And then, obtaining the optimal pollen gamete, namely the optimal reservoir water level or groundwater intake rate, by stopping judgment.
In step 2.3, the specific process of parallel task decomposition and integration is as follows:
task distribution and transmission are carried out on m Worker terminals through a Client terminal of a computer, a fitness function of each Worker terminal is calculated, the fitness function is calculated by adopting a target function, namely a formula (1), and meanwhile, the dominant population is updated; after circulating for n times, the Worker end feeds back the calculation result to the Client end and outputs the optimal population;
the task allocation formula is as follows: n = N pop /m (12)，
In the formula (12), m represents the number of Worker terminals, and n represents the number of cycles.
The invention has the beneficial effects that:
(1) The invention discloses a surface water and underground water combined dispatching optimization method based on an improved pollen Algorithm, which introduces a computer Parallel technology and provides a method for solving different combined dispatching models by the improved pollen Algorithm (P-FPA), wherein the advantages of the P-FPA Algorithm are mainly represented as follows: the global optimization capability is improved, the later population diversity is enlarged, and the local optimal solution is avoided;
(2) According to the method, the optimal scheduling model of the maximum comprehensive benefit of the hydropower station is established and solved, and the optimal reservoir water level and the optimal groundwater intake rate are output, namely the optimal reservoir water level and the optimal groundwater mining process; and the high efficiency and stability of the algorithm (P-FPA) are verified through an algorithm test.
Drawings
FIG. 1 is a flow chart of a surface water and underground water combined dispatching optimization method based on an improved pollen algorithm;
FIG. 2 is a comparison graph of evolution process of the selection particle swarm algorithm (PSO), the cuckoo search algorithm (CS) and the improved pollen algorithm (P-FPA) of the present invention;
FIG. 3 is a black river basin node diagram;
FIG. 4 is a diagram of a "97" water diversion protocol;
FIG. 5 is a water map of the end of the yellow temple in the black river valley;
FIG. 6 is a flow chart of the diameter of the entrance and exit of the yellow temple in the black river basin;
FIG. 7 is a schematic diagram of the black river step reservoir output process;
FIG. 8 is a graph of the warranty years for upstream irrigation and downstream ecological water in the black river basin;
FIG. 9 is a schematic view of the amount of water drained under the sense gorges in the black river basin;
fig. 10 is a schematic diagram of the amount of discharged water in a downstream key period of the black river basin.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
As shown in FIG. 1, the surface water and underground water combined dispatching optimization method based on the improved pollen algorithm is implemented according to the following steps:
MinF＝c 1 F 1 +c 2 F 2 +c 3 F 3 (1)，
in the formula (1), F is a comprehensive target; f 1 The unit of the power station generating capacity of the reservoir is hundred million kW.h.c 1 The weight coefficient of the power station generated energy of the reservoir; f 2 For irrigation water shortage, unit is hundred million m 3 ，c 2 The weight coefficient is the irrigation water shortage; f 3 Is ecological water shortage with unit of hundred million m 3 ，c 3 Is a weight coefficient of the ecological water shortage; c. C 1 、c 2 And c 3 All provided by basin management decision maker, respectively 0.5,0.4 and 0.1;
in the formula (2), m represents a hydropower station number of the reservoir; t represents the number of time segments; Δ t represents the duration; k m representing the output coefficient of the mth power station; q (m, t) represents the generated flow of the mth power station in m 3 S; h (m, t) represents the power generation head of the mth power station in m;
expresses the irrigation water shortage of the t-th time period and has unit of hundred million m 3 ；
Expresses the ecological water shortage in the t-th time period and has unit of hundred million m 3 ；
Wherein, the constraint condition specifically includes:
and (3) water balance constraint: v (m, T + 1) = V (m, T) + (QI (m, T) -QO (m, T)) T + Δ W (3),
node water quantity constraint: QI (m, t + 1) = QI (m, t) + QR (m, t) -QS (m, t) (4),
restriction of water intake in underground water period: RG (route group) t min ≤RG t ≤RG t max (5)，
Reservoir water level constraint: z min (m,t)≤Z(m,t)≤Z max (m,t) (6)，
Reservoir discharge restraint: QO min (m,t)≤QO(m,t)≤QO max (m,t) (7)，
Power station output constraint: n is a radical of hydrogen min (m,t)≤N(m,t)≤N max (m,t) (8)，
In the formula (3), V (m, t + 1) respectively represent the initial and final storage capacities of the mth reservoir in the tth period, and the units are all hundred million m 3 (ii) a QO (m, t) represents the delivery runoff of the mth reservoir in the tth time period and has the unit of m 3 S; Δ W represents the amount of water lost in the evaporation and leakage process, and the unit is hundred million 3 It can be ignored;
in the formula (4), QI (m, t + 1) respectively represent the runoff of the beginning and end warehousing of the t-th time period of the mth reservoir, and the unit is m 3 S; QR (m, t) represents the interval warehousing of the t-th time period of the mth reservoir, and the unit is m 3 S; QS (m, t) represents the exchange flow rate of the mth reservoir in the tth period of time, and is expressed in m 3 /s；
In the formula (5)，RG t,min Indicates the lower limit of groundwater intake rate, RG, in the t-th period t,max Representing the groundwater intake rate upper limit in the t-th period;
in formula (6), Z min (m, t) represents a lower water level limit of the ith period of the mth power plant, Z max (m, t) represents the upper water level limit of the ith time period of the mth power station, and the unit is m;
QO in formula (7) min (m, t) represents the lower limit of the outbound traffic, QO, of the mth power station in the tth time period max (m, t) represents the upper limit of the ex-warehouse flow of the mth power station in the tth time period, and the unit is m 3 /s；
In formula (8), N min (m, t) represents the minimum contribution of the mth power station during the tth period, N max (m, t) represents the maximum contribution of the mth power station in the tth time period, and the unit is MW;
step 2.1, initializing a pollen algorithm, wherein initial parameters comprise an initial population N pop Maximum number of iterations T max The conversion probability p of the pollination mode;
step 2.2, according to the constraint conditions and the initial population N pop Calculating to obtain a decision variable, taking the decision variable as an initial pollen gamete, and calculating according to the following formula:
X i,j ＝H min (j)+rand(H max (j)-H min (j))i∈[1,N pop ],j∈[1,D] (9)，
in the formula (9), X i,j Representing the position of the ith pollen gamete in the jth dimension space; h max (j) The upper limit of the j-dimensional space position of the pollen gamete is the upper limit of the water level at the end of the reservoir month or the upper limit of the exploitation rate of underground water; h min (j) Representing the lower limit constraint of the j-th dimensional space position of the pollen gamete, namely the lower limit constraint of the water level at the lunar end of the reservoir or the lower limit of the exploitation rate of underground water; rand denotes [0,1 ]]A random number of intervals; d represents a spatial dimension;
2.3, performing parallel task decomposition and integration on the initial pollen gametes through a computer parallel technology, and outputting an optimal population;
the specific process of parallel task decomposition and integration is as follows:
task distribution and transmission are carried out on m Worker terminals through a Client terminal of a computer, a fitness function of each Worker terminal is calculated, the fitness function is calculated by adopting a target function, namely a formula (1), and meanwhile, the dominant population is updated; after circulating for n times, the Worker end feeds back the calculation result to the Client end and outputs the optimal population;
the task allocation formula is as follows: n = N pop /m (12)，
In the formula (12), m represents the number of Worker ends, and n represents the cycle number;
step 2.4, iteration processing is carried out on the optimal population by adopting a pollen algorithm, and if the current iteration time T is less than or equal to T max If yes, performing the step 2.5, otherwise, jumping to the step 2.6;
step 2.5, continuously generating a uniform distribution function Rand, if Rand is larger than p, carrying out global pollination on pollen gametes, otherwise, carrying out local pollination, and continuously iterating, wherein the number of iterations is increased by 1;
the global pollination formula is:
wherein, the first and the second end of the pipe are connected with each other,
the formula of local pollination is as follows:
in the formula (10), the reaction mixture is,
respectively representing the solutions of t generation and t +1 generation; l represents a step value; g represents a global optimum; Γ (λ) is the standard gamma function; λ represents a model parameter; s, s 0 Are all step length parameters;
in formula (11), ε is a random number between 0 and 1 that obeys uniform distribution;
pollen gamete of the same plant and different plants respectively, and rand represents [0, 1')]A random number of intervals;
step 2.6 until T > T max And (4) obtaining the optimal pollen gamete, namely the optimal reservoir water level, the optimal underground water mining rate, the optimal generating capacity and the runoff from and to the reservoir, by stopping judgment.
In order to verify the optimal scheduling performance of the improved pollen algorithm (P-FPA) on the reservoir based on the computer parallel technology, the method takes the maximum power generation amount of the black river step reservoir as a target, selects open water years (1976.5-1977.6) data to calculate, and simultaneously selects a Particle Swarm Optimization (PSO) and a Cuckoo Search algorithm (CS) as reference algorithms. The initialization parameters of the above 3 algorithms are as follows: population size n =60; number of iterations T max =400; the test of this embodiment adopts MATLAB (2016) language programming environment, and runs on a 4-core PC with a memory of 4GB and a CPU speed of 2.7 GHz. In view of the instability of the algorithm, the present embodiment runs each of the 3 algorithms 10 times independently, and the evolutionary process of the 3 algorithms is shown in fig. 2, and the comprehensive analysis can obtain:
(1) The PSO algorithm, the CS algorithm and the P-FPA algorithm are converged about 100, 200 and 300 evolutionary algebra respectively, so that the P-FPA algorithm has stronger global search capability and is not easy to fall into precocity compared with the PSO algorithm and the CS algorithm.
(2) According to the later crowding degree of the evolutionary curve cluster of the 3 algorithms, the curve cluster of the P-FPA algorithm is most gathered, the curve cluster of the CS algorithm is next to the curve cluster of the PSO algorithm is most dispersed, and therefore the stability of the calculation result of the P-FPA algorithm is better. The evaluation indexes of the calculation results of the 3 algorithms are counted, and are shown in table 1.
TABLE 1 result statistics of calculation of step power generation by PSO algorithm, CS algorithm and P-FPA algorithm
From the analysis of table 1, it can be seen that:
(1) The average power generation amount calculated by the P-FPA algorithm is the largest, the average power generation amount calculated by the CS algorithm is the next to the average power generation amount calculated by the PSO algorithm is the smallest; the power generation variance calculated by the P-FPA algorithm is the minimum, the power generation variance calculated by the CS algorithm is the second order, and the power generation variance calculated by the PSO algorithm is the maximum; therefore, the result stability of the P-FPA algorithm for calculating the cascade power generation amount is good.
(2) The average calculation time of the P-FPA algorithm is the minimum, the average calculation time of the PSO algorithm is the second order, and the average calculation time of the CS algorithm is the maximum; therefore, the efficiency of calculating the cascade power generation amount by the P-FPA algorithm is high.
In conclusion, the optimization capability, stability and calculation efficiency of the improved pollen algorithm (P-FPA) based on the computer parallel technology all show excellent performance, so that the improved pollen algorithm can be used as a preferred scheme for reservoir optimization scheduling.
Examples
The embodiment takes the black river basin as an application object. The black river basin is sequentially divided into three sections from top to bottom according to water demand and an economic structure: the place above the Yingjiangxia is the upstream, and is used as a main source area of runoff and a main hydropower development base, and comprises an upward Longshou primary stage, a Longshou secondary stage, a small lonshan, a big lonshan, a second dragon mountain, a third gulf, a Baozi river and a Huangzang temple; a midstream is arranged between the ingales and the sense gorges, and a plurality of irrigation areas are distributed along the shore and are main water areas of the watershed; the lower reaches below the sense gorge are the main water consumption areas with rare precipitation and fragile ecology. The black river basin node map is shown in fig. 3.
The water demand of the black river basin mainly comprises midstream irrigation and underwater ecological water demand. According to achievements of 'recent treatment plan of the drainage basin of the black river' and 'regulations on development and utilization of water resources of the black river': the water required for swimming in the black river basin comprises: the water requirement of the three generations (industry, production and life) and the agricultural water requirement are respectively from underground waterThe supply is combined with a ground water-surface water supply. The downstream of the black river basin mainly refers to a river section below the cross section of the sense gorge, the yellow committee is referred to for stipulation, and the water demand of the downstream river channel is based on a '97' water diversion scheme, which is specifically shown in fig. 4: when the bird falls into the gorges, the water is 12.9-17.1 hundred million m 3 When the water quantity distributed by the sense isthmus is determined by interpolation of 2 adjacent representative points, the water quantity distributed by the sense isthmus is less than 12.9 hundred million m when the water of the oriole falls into the isthmus 3 Or more than 17.1 hundred million m 3 And then the upward or downward extending curve obtains the amount of drainage under the sense isthmus.
In this embodiment, 1956.5-2012.6 are selected as the scheduling period, where 7-11 months in each year are the flood season and ten days are the calculation period. The basic calculation parameters are set as follows: annual allowable underground water production of 4.8 hundred million m 3 (ii) a The maximum production rate in time interval is 0.15; normal water storage level 2628m, dead water level 2580m and ecological base flow restriction 9m of Huangzang temple reservoir 3 S; the parameters of the black river step reservoir are shown in table 2; the settings of the scheduling scheme are shown in table 3.
TABLE 2 Black river step reservoir parameters
Table 3 setup of scheduling schemes
| Scheme(s) | Scheduling | Decision variables | |
| 1 | Surface water dominated + groundwater | Reservoir level | |
| 2 | Groundwater domination + surface water compensation | Rate of underground water production |
According to the parameters, an optimized dispatching model of the black river step reservoir is established, and an objective function of the optimized dispatching model is calculated by adopting a pollen algorithm (P-FPA) improved based on a computer parallel technology, so that optimized data of the temple reservoir and the step power station in the black river basin are obtained, and are respectively shown in figures 5-7.
The maximum water levels of the Huangzang temple reservoirs in the scheme 1 and the scheme 2 are 2622.63m and 2628m respectively, the minimum water levels are dead water levels 2580m, and the water level constraints of the maximum water level 2628m and the minimum water level 2580m are met; minimum warehouse-out flow of yellow temple reservoir in scheme 1 and scheme 2 is 9m 3 S, satisfy minimum ecological base flow 9m 3 The requirements are as follows. The similar processes of entering and leaving the reservoir in the scheme 1 and the scheme 2 are obtained by the figure 6, and the similar processes are shown in that the discharge flow rate is small in the non-flood season, the discharge flow rate is large in the flood season, the water storage and discharge rules of the reservoir are met, and the discharge flow rate constraint is met. It is shown in fig. 7 that the maximum output of the step power stations of the scheme 1 and the scheme 2 is 221.96MW and 257.80MW, respectively, and the minimum output is 109.87MW and 97.80MW, respectively, and the output of each time period satisfies the output constraint.
In conclusion, the optimization of the water level, the warehousing-in and warehousing-out and the output process of the black river step reservoir by the improved pollen algorithm (P-FPA) based on the computer parallel technology meets the reasonableness and the reliability, so the P-FPA algorithm is suitable for the long-term optimal scheduling of the black river step reservoir.
Because the water resource problem of the black river basin is contradictory and particularly the competition between the water consumption of the midstream and the downstream is intense, different targets are respectively selected from the upstream, the midstream and the downstream for comprehensive analysis.
(1) Analyzing the power generation and output of the power station at the upstream of the black river basin:
the output process of the scheme 2 is obviously larger than that of the scheme 1 obtained from the graph 7, and the annual average power generation amounts of the scheme 1 and the scheme 2 are respectively 23.56 hundred million kW.h and 26.61 hundred million kW.h obtained through calculation of the target function, so that the scheme 2 is more favorable for improving the power generation target.
(2) The downstream ecological water is analyzed by the irrigation of the midstream of the black river basin:
as can be seen from fig. 8, the midstream irrigation and the downstream ecological water of scheme 1 and the midstream irrigation and the downstream ecological water of scheme 2 have a certain competitive relationship, which is specifically represented as follows: in the full water year, the irrigation target is easy to meet, and the ecological target is easy to destroy; in the dry water year, the ecological target is easy to meet, and the irrigation target is easy to destroy. The main reason is that the water requirement and the process of midstream irrigation are fixed, so that the requirement is easily met in the year of rich water; and the curve of the '97 water diversion scheme' of the downstream ecological water demand shows unreasonable extension, so that the ecology in dry water is easy to meet, and the ecology in rich water is difficult to meet.
(3) And (3) analyzing the amount of the drainage under the sense gorges and the critical periods (4 and 8 months) in the black river basin:
the method comprises a sense gorge diarrhea index scheme 1 and a simulated diarrhea scheme 2, wherein the diarrhea index of the sense gorge in the dry water year can be improved by the scheme 1 and the scheme 2 obtained from the graph 9; however, the effect of the full-water years is not ideal, and mainly caused by unreasonable extension of the 97 water-splitting scheme curve.
A case 1 of a water demand value for justice gorges to let down and a case 2 of simulating the water demand value for letting down are provided, and it is shown in fig. 10 that the amount of let-down water in case 2 is significantly larger than that in case 1, so that the water demand in the critical period at the downstream of the black river basin is basically met, and the improvement of the downstream ecology is facilitated; the annual average guarantee rate of the water discharge amount under the key period of the scheme 1 is only 67 percent and is far lower than that of the scheme 2.
In summary, the statistical values of the long-series optimization results of the above schemes are shown in table 4.
TABLE 4 Long series optimization statistics
| Scheme(s) | |
|
| Irrigation assurance Rate/%) | 70 | 56 |
| Ecological assurance rate/%) | 51 | 68 |
| Underground water exploitation amount for many years/ten thousand meters 3 | 35963 | 39021 |
| Irrigation for years of water shortage per ten thousand meters 3 | 1471 | 1562 |
| Annual average power generation/hundred million kW.h | 23.56 | 23.61 |
From table 4 it follows: the irrigation guarantee rates of the two schemes are close to each other, and the requirements of irrigation design guarantee rate (50%) can be met; the average generating capacity of the scheme 2 for many years is superior to that of the scheme 1, so that the regulation and control mode of the scheme 2 is more favorable for the development of hydroelectric energy sources at the upstream of the black river basin; the perennial water shortage for irrigation in the scheme 2 is obviously greater than that in the scheme 1, the critical-period water demand of the downstream of the black river basin is basically met, and the method has extremely important significance for improving the fragile downstream ecological environment; the annual underground water exploitation amount of scheme 2 is 4.8 hundred million m in the annual underground water exploitation 3 Within the constraint range of (2), the underground water can be fully and separately mined, and meanwhile, the harm of underground water recharge is reduced.
In conclusion, the midstream irrigation and the downstream ecological water of the black river basin are in a mutual competitive relationship, and the actual characteristics of the black river basin are met. The scheme comparison and analysis shows that the scheduling mode of the scheme 2 is slightly inferior to that of the scheme 1 in the aspect of irrigation, but on the basis of meeting the irrigation guarantee rate (50%), the combined scheduling effect of surface water and underground water can be exerted to the greatest extent, the power generation amount of a cascade power station is increased, the downstream ecological water consumption is increased, particularly the downstream critical-period ecological water demand is met, and the method has a special significance for the development of seriously damaged plants in the downstream of the black river. Therefore, the invention adopts the scheme 2, namely, the black river basin is scheduled by using the underground water as the main scheduling mode and the surface water compensation mode.
The invention relates to a surface water and underground water combined dispatching optimization method based on an improved pollen algorithm, which has the advantages that: (1) The reliability and the rationality of the scheduling optimization method are verified by verifying the comprehensive benefits corresponding to the optimal reservoir water level, the reservoir entrance and exit and the output process; (2) The invention enriches the method for the combined scheduling optimization of the underground water and the surface water in the arid region, improves the combined operation level of the reservoir group and the comprehensive utilization rate of water resources, improves the ecological benefit of the arid region, and has important practical significance and application value.
Claims (3)
1. The surface water and underground water combined dispatching optimization method based on the improved pollen algorithm is characterized by comprising the following steps:
step 1, taking the maximum comprehensive objective of power generation, irrigation and ecology of a reservoir as an optimization objective, wherein the objective function of the optimization objective is as follows:
Min F＝c 1 F 1 +c 2 F 2 +c 3 F 3 (1)，
in the formula (1), F is a comprehensive target; f 1 The unit of the generated energy of the power station of the reservoir is hundred million kW.h.c 1 The weight coefficient of the power station generated energy of the reservoir; f 2 For irrigation water shortage, unit is hundred million m 3 ，c 2 The weight coefficient is the irrigation water shortage; f 3 In unit of hundred million m for ecological water shortage 3 ，c 3 Is a weight coefficient of the ecological water shortage; c. C 1 、c 2 And c 3 All provided by basin management decision maker, respectively 0.5,0.4 and 0.1;
in the formula (2), m represents a hydropower station number of the reservoir; t represents the number of time segments; Δ t represents the period of time; k is a radical of formula m Representing the output coefficient of the mth power station; q (m, t) represents the generated flow rate of the mth power station in m 3 S; h (m, t) represents the power generation head of the mth power station in m;
expresses the irrigation water shortage of the t-th time period and has unit of hundred million m 3 ；
The ecological water shortage in the t-th time period is expressed in hundred million m 3 ；
Step 2, improving a pollen algorithm by adopting a computer parallel technology, and performing optimization calculation on the objective function to obtain the groundwater intake rate and the optimal reservoir water level;
the method is implemented according to the following steps:
step 2.1, initializing a pollen algorithm, wherein initial parameters comprise an initial population N pop Maximum number of iterations T max The conversion probability p of the pollination mode;
step 2.2, according to the constraint conditions and the initial population N pop Calculating to obtain a decision variable, taking the decision variable as an initial pollen gamete, and calculating according to the following formula:
X i，j ＝H min (j)+rand(H max (j)-H min (j))i∈[1，N pop ]，j∈[1，D] (9)，
in the formula (9), X i , j Representing the position of the ith pollen gamete in the jth dimension space; h max (j) The upper limit of the j-dimensional space position of the pollen gamete is the upper limit of the water level at the end of the reservoir month or the upper limit of the exploitation rate of underground water; h min (j) Representing the lower limit constraint of the j-dimensional space position of the pollen gamete, namely the lower limit constraint of the water level at the reservoir month end or the lower limit of the underground water exploitation rate; rand denotes [0,1 ]]A random number of intervals; d represents a spatial dimension;
step 2.3, performing parallel task decomposition and integration on the initial pollen gamete through a computer parallel technology, and outputting an optimal population;
step 2.4, the pollen algorithm is adopted to carry out iterative processing on the optimal population, and if the current iteration time T is less than or equal to T max If yes, step 2.5 is carried out, otherwise, step 2.6 is skipped;
step 2.5, continuously generating an even distribution function Rand, if Rand is larger than p, carrying out global pollination on pollen gametes, otherwise, carrying out local pollination and continuously iterating, and adding 1 to the iteration times;
the global pollination formula is:
wherein the content of the first and second substances,
the formula of local pollination is as follows:
in the formula (10), the compound represented by the formula (10),
respectively representing solutions of t and t +1 generation; l represents a step value; g represents a global optimum; Γ (λ) is the standard gamma function; λ represents a model parameter; s, s 0 Are all step length parameters;
in formula (11), ε is a number between 0 and 1 that follows a uniform distributionThe number of machines;
pollen gamete of the same plant and different plants respectively, and rand represents [0, 1')]A random number of intervals;
step 2.6, until T is more than T max And then, obtaining the optimal pollen gamete, namely the optimal reservoir water level or groundwater intake rate, by stopping judgment.
2. The improved pollen algorithm-based surface water and underground water combined dispatching optimization method as claimed in claim 1, wherein in step 1, the constraint conditions of the objective function comprise:
and (3) water balance constraint: v (m, T + 1) = V (m, T) + (QI (m, T) -QO (m, T)) T + Δ W (3),
and (3) node water quantity constraint: QI (m, t + 1) = QI (m, t) + QR (m, t) -QS (m, t) (4),
restriction of water intake in underground water period:
reservoir water level constraint: z min (m,t)≤Z(m,t)≤Z max (m,t) (6)，
Reservoir discharge restraint: QO min (m,t)≤QO(m,t)≤QO max (m,t) (7)，
Power station output restraint: n is a radical of min (m,t)≤N(m,t)≤N max (m,t) (8)，
In the formula (3), V (m, t + 1) respectively represent the initial and final storage capacities of the mth reservoir in the tth period, and the units are all hundred million m 3 (ii) a QO (m, t) represents the delivery runoff of the mth reservoir in the tth time period and has the unit of m 3 S; Δ W represents the amount of water lost in the evaporation and leakage process, and the unit is hundred million m 3 Negligible;
in the formula (4), QI (m, t + 1) respectively represent the runoff of the beginning and end warehousing of the t-th time period of the mth reservoir, and the unit is m 3 S; QR (m, t) represents the interval warehousing of the mth reservoir in the tth time period, and the unit is m 3 S; QS (m, t) represents the exchange flow rate of the mth reservoir in the tth period of time m 3 /s；
In formula (5), RG t,min Indicates the lower limit of groundwater intake rate, RG, in the t-th period t,max Representing the upper limit of groundwater intake rate in the t-th time period;
in formula (6), Z min (m, t) represents a lower water level limit of the ith period of the mth power station, Z max (m, t) represents the upper limit of the water level of the ith period of the mth power station, and the unit is m;
QO in the formula (7) min (m, t) represents the lower limit of the outbound traffic, QO, of the mth power station in the tth time period max (m, t) represents the upper limit of the ex-warehouse flow of the mth power station in the tth time period, and the unit is m 3 /s；
In formula (8), N min (m, t) represents the minimum contribution of the mth power station during the tth period, N max (m, t) represents the maximum contribution of the mth power station during the tth period in MW.
3. The improved pollen algorithm-based surface water and underground water combined dispatching optimization method as claimed in claim 1, wherein in step 2.3, the parallel task decomposition and integration comprises the following specific processes:
task distribution and transmission are carried out on m Worker terminals through a Client terminal of a computer, a fitness function of each Worker terminal is calculated, the fitness function adopts a dispatching calculation mode of the target function, namely a formula (1), and meanwhile, the dominant population is updated; after circulating for n times, the Worker end feeds back the calculation result to the Client end, and outputs the optimal population;
the task allocation formula is as follows: n = N pop /m (12)，
In the formula (12), m represents the number of Worker terminals, and n represents the number of cycles.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910151472.XA CN110188912B (en) | 2019-02-28 | 2019-02-28 | Improved pollen algorithm-based surface water and underground water combined scheduling optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910151472.XA CN110188912B (en) | 2019-02-28 | 2019-02-28 | Improved pollen algorithm-based surface water and underground water combined scheduling optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110188912A CN110188912A (en) | 2019-08-30 |
| CN110188912B true CN110188912B (en) | 2023-01-24 |
Family
ID=67713720
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910151472.XA Active CN110188912B (en) | 2019-02-28 | 2019-02-28 | Improved pollen algorithm-based surface water and underground water combined scheduling optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110188912B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112215715A (en) * | 2020-09-30 | 2021-01-12 | 中国水利水电科学研究院 | Irrigation area water and soil resource balance optimization configuration method based on underground water level regulation |
| CN113326632B (en) * | 2021-06-19 | 2022-09-23 | 南昌工程学院 | Optimization correction method for backward-thrust reservoir warehousing flow process |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017071230A1 (en) * | 2015-10-30 | 2017-05-04 | 南京南瑞集团公司 | Method for short-term optimal scheduling of multi-agent hydropower station group |
| 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 |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102156914B (en) * | 2011-03-30 | 2014-06-18 | 东华大学 | Method for cooperatively and optimally allocating water volume in non-flood season |
| US20170270624A1 (en) * | 2016-03-15 | 2017-09-21 | Waterfind USA, Inc. | Systems and Methods for Imputing Groundwater Usage in a Groundwater Basin |
| WO2017158438A2 (en) * | 2016-03-15 | 2017-09-21 | Ipq Pty Ltd | Systems and methods for imputing groundwater usage in a groundwater basin |
| CN107609683B (en) * | 2017-08-24 | 2021-02-12 | 西安理工大学 | Firefly algorithm-based cascade reservoir group scheduling optimization method |
| CN108869156A (en) * | 2018-08-06 | 2018-11-23 | 丁伟 | A kind of water storage, irrigation（Adjust flood）, power generation Mountainous areas water resources development and utilization scheme |
-
2019
- 2019-02-28 CN CN201910151472.XA patent/CN110188912B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017071230A1 (en) * | 2015-10-30 | 2017-05-04 | 南京南瑞集团公司 | Method for short-term optimal scheduling of multi-agent hydropower station group |
| 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 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110188912A (en) | 2019-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gao et al. | Optimal dispatching of wind-PV-mine pumped storage power station: A case study in Lingxin Coal Mine in Ningxia Province, China | |
| Ming et al. | Hydropower reservoir reoperation to adapt to large-scale photovoltaic power generation | |
| CN106682810B (en) | Long-term operation method of cross-basin cascade hydropower station group under dynamic production of giant hydropower station | |
| CN112184070B (en) | Multi-objective optimization scheduling method and system for cascade hydropower station with cooperative ecological flow demand | |
| CN109858173B (en) | Multi-target tangent method for joint scheduling of wind, light and water complementary power generation system | |
| Qi et al. | Optimal configuration of concentrating solar power in multienergy power systems with an improved variational autoencoder | |
| CN109861302B (en) | Master-slave game-based energy internet day-ahead optimization control method | |
| CN111080157B (en) | Method and system for scheduling phosphorus discharge amount of cascade hydropower station | |
| Jiang et al. | Comprehensive assessment of wind resources and the low-carbon economy: An empirical study in the Alxa and Xilin Gol Leagues of inner Mongolia, China | |
| CN110188912B (en) | Improved pollen algorithm-based surface water and underground water combined scheduling optimization method | |
| CN108711878B (en) | Photovoltaic power station multi-type component capacity configuration method considering output characteristics | |
| CN115271304A (en) | Cross-basin water transfer engineering optimal scheduling method based on climate change | |
| CN114154744A (en) | Capacity expansion planning method and device of comprehensive energy system and electronic equipment | |
| CN111144655A (en) | Combined optimization method for site selection, volume fixing and power distribution network frame of distributed power supply | |
| CN111428970B (en) | Large-scale hydropower station group cross-province capacity analysis model and solving method | |
| CN115293442A (en) | Water and wind energy source system balanced scheduling model based on distribution robust optimization | |
| CN111079086B (en) | Multi-element joint distribution-based multiple risk assessment method for water resource system | |
| CN113363976B (en) | Scene graph-based wind-solar-water complementary power generation system medium-term optimization scheduling method | |
| CN110163420B (en) | Multi-target ecological scheduling method and system based on decomposition culture evolution algorithm | |
| CN114123313B (en) | Method for simulating new energy power system digestion by time sequence production | |
| Song et al. | Optimal water allocation scheme in integrated water-ecosystem-economy system | |
| Jia et al. | Optimal operation of cascade hydro-wind-photovoltaic complementary generation system with vibration avoidance strategy | |
| Li et al. | Research on short-term joint optimization scheduling strategy for hydro-wind-solar hybrid systems considering uncertainty in renewable energy generation | |
| CN109038655A (en) | It rations the power supply and requires the mating stored energy capacitance calculation method in lower large-sized photovoltaic power station | |
| Zhang et al. | Self-optimization simulation model of short-term cascaded hydroelectric system dispatching based on the daily load curve |
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 |