CN110188912A - Based on the surface water and groundwater combined dispatching optimization method for improving pollen algorithm - Google Patents
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Abstract
The invention discloses based on the surface water and groundwater combined dispatching optimization method for improving pollen algorithm, first, using the maximum integration objective of the power generation of reservoir, irrigation and ecology as optimization aim, the objective function of reservoir optimizing target is established, constraint condition includes: that water balance constraint, node water quantity restraint, the water intaking constraint of underground water period, reservoir level constraint, the constraint of reservoir aerial drainage and output of power station are constrained to constraint condition；Secondly, Import computer concurrent technique improves pollen algorithm, and calculating is optimized to above-mentioned objective function, obtains generated energy, goes out two Phase flow, underground water water intaking rate and optimal reservoir level.The present invention is based on the surface water and groundwater combined dispatching optimization methods for improving pollen algorithm, for abundant Optimal Scheduling of Multi-reservoir System method, arid region of Northwest China surface water and groundwater combined dispatching benefit, raising water resources comprehensive utilization benefit and regional ecological benefits are improved, there is important practical significance and application value.
Description
Technical field
The invention belongs to surface water and groundwater combined dispatching optimisation technique fields, and in particular to based on improvement pollen algorithm
Surface water and groundwater combined dispatching optimization method.
Background technique
Currently, the water resource of China arid area is relatively short, the sustainable development of arid area has been seriously affected.
At this stage, surface water-underground water combined dispatching mode has become the important measure for solving water resources in arid area shortage.And reservoir is made
For the pith in underground water and surface water combined dispatching, the method for operation is especially paid close attention to；However the optimization tune of reservoir
Degree is non-linear, higher-dimension, complexity.Currently, the method for solving of the Optimized Operation of reservoir is broadly divided into conventional method and optimization side
Method.(1) conventional method, such as linear programming, Dynamic Programming, progressive optimal algorithm, but above method exist calculate the time it is long,
Dimension calamity, the defects of convergence rate is slow；(2) optimization method, such as particle swarm algorithm, genetic algorithm, cuckoo algorithm, but more than
There is the defects of easily falling into local optimum, search efficiency is slow in method.
Pollen algorithm (Flower Pollination Algorithm, FPA) is that one kind that Yang is proposed in 2010 is efficient
Bionic optimization algorithm.FPA principle is easily understood, parameter-embedded few, calculates simply, has stronger robustness and global search energy
The advantages of power.It has been widely used in system optimization field, such as Clustering Analysis of Text, wireless sensor network cluster, electric power
Systematic economy sharing of load etc..But pollen algorithm haves the defects that intelligent algorithm when solving higher-dimension, nonlinear problem, as after
Phase convergence rate is slow, is easy to converge on locally optimal solution.
Summary of the invention
The object of the present invention is to provide based on the surface water and groundwater combined dispatching optimization method for improving pollen algorithm, tool
It is improved the combined operating ability of multi-reservoir, improves the characteristics of water resources in arid area utilization efficiency.
The technical scheme adopted by the invention is that based on the surface water and groundwater combined dispatching optimization for improving pollen algorithm
Method is specifically implemented according to the following steps:
Step 1, using the maximum integration objective of the power generation of reservoir, irrigation and ecology as optimization aim, the function of optimization aim
Are as follows:
MinF=c1F1+c2F2+c3F3(1),
In formula (1), F is integration objective；F1For the power station generated energy of reservoir, unit is hundred million kWh, c1For the power station of reservoir
The weight coefficient of generated energy；F2For irrigation water shortage amount, unit is hundred million m3, c2For the weight coefficient of irrigation water shortage amount；F3It is lacked for ecology
Water, unit are hundred million m3, c3For the weight coefficient of ecological water deficit；c1、c2And c3It is provided by watershed management policymaker, respectively
0.5,0.4 and 0.1；
In formula (2), m indicates the power station number of reservoir；Number of segment when t is indicated；Δ t indicates period length； kmIndicate m
The power factor in power station；Q (m, t) indicates the generating flow in the power station m, unit m3/s；The hair in h (m, t) the expression power station m
Electric head, unit m；Indicate that the irrigation water shortage amount of t period, unit are hundred million m3；Indicate the ecological holographic of t period
Amount, unit are hundred million m3；
Step 2 improves pollen algorithm using computer parallel technology, and optimizes calculating to objective function, obtains ground
It is lauched coefficient of mining and optimal reservoir level.
The features of the present invention also characterized in that:
In step 1, bound for objective function includes:
Water balance constraint: V (m, t+1)=V (m, t)+(QI (m, t)-QO (m, t)) T+ Δ W (3),
Node water quantity restraint: QI (m, t+1)=QI (m, t)+QR (m, t)-QS (m, t) (4),
Underground water period water intaking constraint: RGt min≤RGt≤RGt max(5),
Reservoir level constraint: Zmin(m,t)≤Z(m,t)≤Zmax(m, t) (6),
Reservoir aerial drainage constraint: QOmin(m,t)≤QO(m,t)≤QOmax(m, t) (7),
Output of power station constraint: Nmin(m,t)≤N(m,t)≤Nmax(m, t) (8),
In formula (3), V (m, t), V (m, t+1) respectively indicate the whole story storage capacity of the t period of m reservoir, and unit is hundred million
m3；QO (m, t) indicates the outbound runoff of the t period of m reservoir, unit m3/s；Δ W indicates the loss of evaporation, leakage process
Water, unit are hundred million m3, can be neglected；
In formula (4), QI (m, t), QI (m, t+1) respectively indicate the whole story two Phase flow of the t period of m reservoir, unit
It is m3/s；QR (m, t) indicates the section storage of the t period of m reservoir, unit m3/s；QS (m, t) indicates m reservoir
The t period switching traffic, unit m3/s；
In formula (5), RGt,minIndicate the underground water water intaking rate lower limit of t period, RGt,maxIndicate the underground water of t period
The water intaking rate upper limit；
In formula (6), Zmin(m, t) indicates the water level lower limit of i-th period in the power station m, Zmax(m, t) indicates the power station m
The water level upper limit of i-th period, unit are m；
In formula (7), QOmin(m, t) indicates the storage outflow lower limit of the t period in the power station m, QOmax(m, t) indicates m
The storage outflow upper limit of the t period in power station, unit are m3/s；
In formula (8), Nmin(m, t) indicates the minimum load of the t period in the power station m, Nmax(m, t) indicates the power station m
The maximum output of t period, unit MW.
Step 2 is specifically implemented according to the following steps:
Step 2.1, initialization pollen algorithm, initial parameter includes initial population Npop, maximum number of iterations Tmax, pollination side
The transition probability p of formula；
Step 2.2, foundation constraint condition and initial population NpopDecision variable is calculated, using decision variable as initial
Pollen gamete, calculation formula are as follows:
Xi,j=Hmin(j)+rand(Hmax(j)-Hmin(j))i∈[1,Npop], j ∈ [1, D] (9),
In formula (9), Xi,jIndicate i-th of pollen gamete in the position of jth dimension space；Hmax(j) empty for pollen gamete jth dimension
Between position the upper limit constraint, i.e., the reservoir the end of month the water level upper limit constraint or underground water the coefficient of mining upper limit；Hmin(j) flower is indicated
The lower limit of powder gamete jth dimension space position constrains, i.e. the water level lower limit constraint at the reservoir the end of month or the coefficient of mining lower limit of underground water；
The random number in rand expression [0,1] section；D representation space dimension；
Step 2.3 carries out parallel task decomposition to initial pollen gamete by computer parallel technology and integrates, and exports
Optimal population；
Step 2.4 is iterated processing to optimal population using pollen algorithm, if current iteration number t≤Tmax, then into
Row step 2.5, otherwise gos to step 2.6；
Step 2.5 continues to generate uniformly distributed function Rand, if Rand > p, carries out at global pollination to pollen gamete
Otherwise reason carries out local pollination processing, and continue iteration, and the number of iterations adds 1；
Overall situation pollination formula are as follows:
Wherein,
Part pollination formula are as follows:
In formula (10),Respectively indicate t, the solution in t+1 generation；L indicates step value；G indicates global optimum；Γ
(λ) is the gamma function of standard；λ indicates model parameter；S, s0It is step parameter；
In formula (11), ε is the equally distributed random number of obedience between 0-1；Respectively same plant, it is different
The pollen gamete of plant, rand indicate the random number in [0,1] section；
Step 2.6, until t ﹥ TmaxWhen, optimal pollen gamete, i.e., optimal reservoir level or ground are obtained by terminating judgement
It is lauched water intaking rate.
In step 2.3, parallel task is decomposed and integrated detailed process are as follows:
Task distribution and transmission are carried out to the m end Worker by the end Client of computer, for each end Worker
Its fitness function is calculated, fitness function is calculated using objective function, i.e. formula (1), while carrying out the update of dominant population；
After recycling n times, calculated result is fed back to the end Client by the end Worker, and exports optimal population；
Task distributes formula are as follows: n=Npop/ m (12),
In formula (12), m indicates the number at the end Worker, and n indicates cycle-index.
The beneficial effects of the present invention are:
(1) the present invention is based on the surface water and groundwater combined dispatching optimization method for improving pollen algorithm, calculating is introduced
Machine concurrent technique, and propose and improve pollen algorithm (Parallel Flower Pollination Algorithm, P-FPA)
The advantage of the method for solving different integrated distribution models, P-FPA algorithm is mainly shown as: global optimizing ability is improved, after expansion
Phase population diversity, avoids locally optimal solution；
(2) present invention is by establishing and solving power station maximum comprehensive benefit Optimal Operation Model, output optimization reservoir water
Position and underground water water intaking rate, i.e., optimal reservoir level and best underground water recovery process；And algorithm is demonstrated by test of heuristics
(P-FPA) high efficiency and stability.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts for the surface water and groundwater combined dispatching optimization method for improving pollen algorithm；
Fig. 2 is that selection particle swarm algorithm (PSO), cuckoo searching algorithm (CS) and the present invention improve pollen algorithm (P-
FPA evolutionary process comparison chart)；
Fig. 3 is Heihe River basin node diagram；
Fig. 4 is that " 97 " divide water conceptual scheme；
Fig. 5 is the temple Huang Zang ten days end hydrograph in Heihe River basin；
Fig. 6 is that the temple Huang Zang goes out two Phase flow figure in Heihe River basin；
Fig. 7 is Heihe step reservoir power output process schematic；
Fig. 8 is the guarantee time figure of the irrigation of the Middle Reaches of Heihe River and the ecological water in downstream；
Fig. 9 is just gorge amount of water to be discharged schematic diagram in Heihe River basin；
Figure 10 is Heihe River basin downstream critical period amount of water to be discharged schematic diagram.
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
As shown in Figure 1, the present invention is based on the surface water and groundwater combined dispatching optimization methods for improving pollen algorithm, specifically
It follows the steps below to implement:
Step 1, using the maximum integration objective of the power generation of reservoir, irrigation and ecology as optimization aim, the objective function of optimization
Are as follows:
MinF=c1F1+c2F2+c3F3(1),
In formula (1), F is integration objective；F1For the power station generated energy of reservoir, unit is hundred million kWh, c1For the power station of reservoir
The weight coefficient of generated energy；F2For irrigation water shortage amount, unit is hundred million m3, c2For the weight coefficient of irrigation water shortage amount；F3It is lacked for ecology
Water, unit are hundred million m3, c3For the weight coefficient of ecological water deficit；c1、c2And c3It is provided by watershed management policymaker, respectively
0.5,0.4 and 0.1；
In formula (2), m indicates the power station number of reservoir；Number of segment when t is indicated；Δ t indicates period length； kmIndicate m
The power factor in power station；Q (m, t) indicates the generating flow in the power station m, unit m3/s；The hair in h (m, t) the expression power station m
Electric head, unit m；Indicate that the irrigation water shortage amount of t period, unit are hundred million m3；Indicate the ecological holographic of t period
Amount, unit are hundred million m3；
Wherein, constraint condition specifically includes:
Water balance constraint: V (m, t+1)=V (m, t)+(QI (m, t)-QO (m, t)) T+ Δ W (3),
Node water quantity restraint: QI (m, t+1)=QI (m, t)+QR (m, t)-QS (m, t) (4),
Underground water period water intaking constraint: RGt min≤RGt≤RGt max(5),
Reservoir level constraint: Zmin(m,t)≤Z(m,t)≤Zmax(m, t) (6),
Reservoir aerial drainage constraint: QOmin(m,t)≤QO(m,t)≤QOmax(m, t) (7),
Output of power station constraint: Nmin(m,t)≤N(m,t)≤Nmax(m, t) (8),
In formula (3), V (m, t), V (m, t+1) respectively indicate the whole story storage capacity of the t period of m reservoir, and unit is hundred million
m3；QO (m, t) indicates the outbound runoff of the t period of m reservoir, unit m3/s；Δ W indicates the loss of evaporation, leakage process
Water, unit are hundred million m3, can be neglected；
In formula (4), QI (m, t), QI (m, t+1) respectively indicate the whole story two Phase flow of the t period of m reservoir, unit
It is m3/s；QR (m, t) indicates the section storage of the t period of m reservoir, unit m3/s；QS (m, t) indicates m reservoir
The t period switching traffic, unit m3/s；
In formula (5), RGt,minIndicate the underground water water intaking rate lower limit of t period, RGt,maxIndicate the underground water of t period
The water intaking rate upper limit；
In formula (6), Zmin(m, t) indicates the water level lower limit of i-th period in the power station m, Zmax(m, t) indicates the power station m
The water level upper limit of i-th period, unit are m；
In formula (7), QOmin(m, t) indicates the storage outflow lower limit of the t period in the power station m, QOmax(m, t) indicates m
The storage outflow upper limit of the t period in power station, unit are m3/s；
In formula (8), Nmin(m, t) indicates the minimum load of the t period in the power station m, Nmax(m, t) indicates the power station m
The maximum output of t period, unit MW；
Step 2 improves pollen algorithm using computer parallel technology, and optimizes calculating to objective function, is sent out
Electricity goes out two Phase flow, mining of groundwater rate and optimal reservoir level；It is specifically implemented according to the following steps:
Step 2.1, initialization pollen algorithm, initial parameter includes initial population Npop, maximum number of iterations Tmax, pollination side
The transition probability p of formula；
Step 2.2, foundation constraint condition and initial population NpopDecision variable is calculated, using the decision variable as
Initial pollen gamete, calculation formula are as follows:
Xi,j=Hmin(j)+rand(Hmax(j)-Hmin(j))i∈[1,Npop], j ∈ [1, D] (9),
In formula (9), Xi,jIndicate i-th of pollen gamete in the position of jth dimension space；Hmax(j) empty for pollen gamete jth dimension
Between position the upper limit constraint, i.e., the reservoir the end of month the water level upper limit constraint or underground water the coefficient of mining upper limit；Hmin(j) flower is indicated
The lower limit of powder gamete jth dimension space position constrains, i.e. the water level lower limit constraint at the reservoir the end of month or the coefficient of mining lower limit of underground water；
The random number in rand expression [0,1] section；D representation space dimension；
Step 2.3 carries out parallel task decomposition to initial pollen gamete by computer parallel technology and integrates, and exports
Optimal population；
Wherein, parallel task decomposes and integrated detailed process are as follows:
Task distribution and transmission are carried out to the m end Worker by the end Client of computer, for each end Worker
Its fitness function is calculated, fitness function is calculated using objective function, i.e. formula (1), while carrying out the update of dominant population；
After recycling n times, calculated result is fed back to the end Client by the end Worker, and exports optimal population；
Task distributes formula are as follows: n=Npop/ m (12),
In formula (12), m indicates the number at the end Worker, and n indicates cycle-index；
Step 2.4 is iterated processing to optimal population using pollen algorithm, if current iteration number t≤Tmax, then into
Row step 2.5, otherwise gos to step 2.6；
Step 2.5 continues to generate uniformly distributed function Rand, if Rand > p, carries out at global pollination to pollen gamete
Otherwise reason carries out local pollination processing, and continue iteration, and the number of iterations adds 1；
Overall situation pollination formula are as follows:
Wherein,
Part pollination formula are as follows:
In formula (10),Respectively indicate t, the solution in t+1 generation；L indicates step value；G indicates global optimum；Γ
(λ) is the gamma function of standard；λ indicates model parameter；S, s0It is step parameter；
In formula (11), ε is the equally distributed random number of obedience between 0-1；Respectively same plant, it is different
The pollen gamete of plant, rand indicate the random number in [0,1] section；
Step 2.6, until t ﹥ TmaxWhen, optimal pollen gamete, i.e., optimal reservoir level, underground are obtained by terminating judgement
Water coefficient of mining, generated energy and two Phase flow out.
In order to verify, the present invention is based on computer parallel technologies to improve pollen algorithm (P-FPA) to the Optimized Operation of reservoir
Can, the present invention chooses normal flow year (1976.5-1977.6) data and carries out using the maximum generating watt of Heihe step reservoir as target
It calculates, simultaneous selection particle swarm algorithm (Particle Swarm Optimization, PSO) and cuckoo searching algorithm
(Cuckoo Search, CS) is used as comparator algorithm.Above-mentioned 3 kinds of algorithm initialization parameters are as follows: population scale n=60；Iteration time
Number Tmax=400；The test of the present embodiment uses MATLAB (2016) Language environment, saves as 4GB, CPU speed inside
It is run in the 4 core PC machine that degree is 2.7GHz.In view of the unstability of algorithm, the present embodiment is by each isolated operation of above-mentioned 3 kinds of algorithms
10 times, the evolutionary process of 3 kinds of algorithms is obtained as shown in Fig. 2, comprehensive analysis can obtain:
(1) PSO algorithm, CS algorithm and P-FPA algorithm are respectively 100,200 and 300 or so convergences in evolutionary generation, therefore
P-FPA algorithm of the invention is stronger compared to the ability of searching optimum of PSO algorithm and CS algorithm, is not easy to fall into " precocity ".
(2) by the later period crowding of the evolution set of curves of 3 kinds of algorithms it is found that the set of curves of P-FPA algorithm of the invention most
For aggregation, the set of curves of CS algorithm takes second place, and the set of curves of PSO algorithm dissipates the most, therefore the calculating knot of P-FPA algorithm of the invention
The stability of fruit is preferable.The calculated result evaluation index of above-mentioned 3 kinds of algorithms is counted, as shown in table 1.
Table 1PSO algorithm, CS algorithm and P-FPA algorithm calculate the result statistics of step generated energy
It is analyzed from table 1:
(1) P-FPA algorithm of the invention calculates resulting average generated energy maximum, and CS algorithm calculates resulting average power generation
Amount is taken second place, and it is minimum that PSO algorithm calculates resulting average generated energy；P-FPA algorithm of the invention calculates resulting generated energy variance
Minimum, CS algorithm calculate resulting generated energy variance and take second place, and it is maximum that PSO algorithm calculates resulting generated energy variance；Therefore it is of the invention
P-FPA algorithm calculate step generated energy result stability it is preferable.
(2) average calculation times of P-FPA algorithm of the invention are minimum, and the average calculation times of PSO algorithm take second place, and CS is calculated
The average calculation times of method are maximum；Therefore the efficiency that P-FPA algorithm of the invention calculates step generated energy is higher.
To sum up, the present invention is based on optimizing ability, stability and meters that computer parallel technology improves pollen algorithm (P-FPA)
Efficiency is calculated, outstanding performance is shown, therefore can be used as the preferred embodiment of optimizing scheduling of reservoir.
Embodiment
The present embodiment is using Heihe River basin as application.According to water demand and economic structure by Heihe River basin from top to bottom
Be divided into three sections: warbler falls gorge, and the above are upstreams, as the main source area and main hydroelectric development base of runoff, including according to
Secondary upward imperial monic grade, dragon first second level, The Solitary Hill, The Great Hermit Hill, two Longshan, San Daowan, Aguarius river, Huang Zangsi；Warbler fall gorge with
It is middle reaches between just gorge, it is the main Water District in basin that bank, which is distributed multiple irrigated areas,；The following are downstream, precipitation in just gorge
Rareness, ecology fragility are main water consumption areas.Heihe River basin node diagram is as shown in Figure 3.
It includes that water demand for natural service is irrigated and need to be swum under water in middle reaches that Heihe River basin, which needs water mainly,.According to " Heihe River basin is administered in the recent period
Planning " and " Heihe water resources development and utilization conservation regulation " achievement: it includes: the three lives (industry, life that the Middle Reaches of Heihe River, which needs water,
Produce, life) need water, agricultural to need water, respectively by underground water supply and underground water-surface water joint supply.Heihe River basin downstream master
Criticize adopted gorge section or less section, with reference to Huang, committee can be provided, downstream river course need water be subject to " 97 " divide water scheme, it is specific as schemed
Shown in 4: being hundred million m of 12.9-17.1 when warbler falls gorge water3When, it is true that the water of just gorge distribution mainly represents point interpolation by adjacent 2
It is fixed, when warbler falls gorge water less than 12.9 hundred million m3Or it is greater than 17.1 hundred million m3, then upwardly or downwardly extend under curve acquisition justice gorge
Water discharged amount.
It is schedule periods that the present embodiment, which chooses 1956.5-2012.6, wherein the annual 7-11 month is flood season, it is when calculating with ten days
Section.Basic calculating parameter setting is as follows: the average annual permitted pumping discharge of underground water is 4.8 hundred million m3；Period maximum coefficient of mining 0.15；Huang hiding
Temple reservoir operation scheme 2628m, level of dead water 2580m, ecological basic flow constrain 9m3/s；The parameter of Heihe step reservoir such as 2 institute of table
Show；The setting of scheduling scheme is as shown in table 3.
2 Heihe step reservoir parameter of table
The setting of 3 scheduling scheme of table
| Scheme | Scheduling mode | Decision variable |
| 1 | Based on surface water+underground water compensation | Reservoir level |
| 2 | Based on underground water+surface water compensation | Mining of groundwater rate |
According to above-mentioned parameter, the Optimal Operation Model of Heihe step reservoir is established, is changed using based on computer parallel technology
The objective function of above-mentioned Optimal Operation Model is calculated into pollen algorithm (P-FPA), obtains Huang Zang Si Shui in Heihe River basin
The optimization data in library and step hydropower station, it is as shown in Figure 5-Figure 7 respectively.
The peak level that the temple Huang Zang reservoir in scheme 1 and scheme 2 is obtained by Fig. 5 is respectively 2622.63m and 2628m, most
Low water level is level of dead water 2580m, meets peak level 2628m, the restriction of water level of lowest water level 2580m；Scheme 1 and scheme 2
The minimum storage outflow of the middle temple Huang Zang reservoir is 9m3/ s meets minimum ecological base flow 9m3/ s requirement.1 He of scheme is obtained by Fig. 6
Scheme 2 to go out storage process similar, show as that non-flood period letdown flow is small, and flood season letdown flow is big, meet reservoir and store, discharge water
Rule, while meeting letdown flow constraint.The maximum output for obtaining 2 step hydropower station of scheme 1 and scheme by Fig. 7 is respectively
221.96MW and 257.80MW, minimum load is respectively 109.87MW and 97.80MW, and the power output of day part is all satisfied power output about
Beam.
To sum up, the present invention is based on computer parallel technology improve pollen algorithm (P-FPA) to the water level of Heihe step reservoir,
It is put in storage out and the optimization of power output process is all satisfied reasonability and reliability, therefore P-FPA algorithm of the invention is suitable for Heihe step
The Long-term Optimal Dispatch of reservoir.
Since the water resources problems of Heihe River basin are particularly thorny, especially dog-eat-dog between midstream and downstream water, therefore
Selection different target, which is swum, from upper, middle and lower respectively carries out comprehensive analysis.
(1) the power station power generation of Heihe River basin upstream, power output are analyzed:
It show that the power output process of scheme 2 is significantly greater than the power output process of scheme 1 by Fig. 7, passes through the calculating to objective function
The annual average power generation for obtaining scheme 1 and scheme 2 is respectively 23.56 hundred million kWh and 26.61 hundred million kWh, therefore scheme 2 is more advantageous to
Improve power generation target.
(2) to the irrigation of the Middle Reaches of Heihe River, the ecological water in downstream is analyzed:
It is obtained by Fig. 8, the middle reaches of scheme 1 are irrigated and the middle reaches irrigation and downstream ecology use of downstream ecology water and scheme 2
Certain competitive relation, specific manifestation is presented in water are as follows: in the abundance of water time, irrigates target and readily satisfies, Ecological Target is easily destroyed；
In dry years, Ecological Target is readily satisfied, and is irrigated target and is easily destroyed.Main cause is middle reaches irrigation requirement and process is
Certain, therefore satisfaction is easy to get in the high flow year；And downstream ecology needs water unreasonable because " 97 points of water schemes " curve shows
Extension, lead to that low flow year ecology readily satisfies and high flow year ecology is difficult to meet.
(3) gorge amount of water to be discharged just in Heihe River basin and downstream critical period (4, August) amount of water to be discharged are analyzed:
It is let out under just gorge and lets out scheme 2 under index scheme 1 and simulation, show that scheme 1 and scheme 2 can improve low water by Fig. 9
Year justice lets out index under gorge；But it lets out that effect is undesirable under the high flow year, essentially consists in " 97 points of water schemes " curve and do not conform to
Caused by reason extends.
It lets out to need to let out under water number scheme 1 and simulation under just gorge and needs water number scheme 2, the amount of water to be discharged of scheme 2 is obtained by Figure 10
The significantly greater than amount of water to be discharged of scheme 1 substantially meets the critical period water requirement in Heihe River basin downstream, and being more advantageous to improves downstream life
State；And averagely fraction only has 67% to 1 critical period of scheme amount of water to be discharged for many years, is far below scheme 2.
To sum up, the long serial optimum results statistical value for counting above-mentioned each side's case is as shown in table 4.
The long serial optimum results statistical value of table 4
| Scheme | Scheme 1 | Scheme 2 |
| Ensurance probability of irrigation water/% | 70 | 56 |
| Ecological fraction/% | 51 | 68 |
| Underground water many years yield/ten thousand m3 | 35963 | 39021 |
| Irrigate many years water deficit/ten thousand m3 | 1471 | 1562 |
| Mean annual energy production/hundred million kWh | 23.56 | 23.61 |
Obtained by table 4: the ensurance probability of irrigation water of two schemes is close, is able to satisfy wanting for irrigation system design (50%)
It asks；The Mean annual energy production of scheme 2 is better than scheme 1, therefore the control methods of scheme 2 are more advantageous to the water power of Heihe River basin upstream
Energy development；The irrigation many years water deficit of scheme 2 is significantly greater than the irrigation many years water deficit of scheme 1, substantially meets Heihe River basin
The critical period water requirement in downstream has extremely important meaning for the downstream ecology environment for improving fragile；The underground water of scheme 2
Many years yield allows to exploit 4.8 hundred million m in underground water every year3Restriction range in, therefore can abundant exploiting groundwater, reduce simultaneously
The harm of groundwater recharge.
To sum up, the middle reaches of Heihe River basin are irrigated and relationship of vying each other is presented in downstream ecology water, meet Heihe River basin
Actual features.It is obtained by scheme comparison's analysis, although the scheduling mode of scheme 2 is slightly inferior to scheme 1 in terms of irrigation, full
On the basis of sufficient ensurance probability of irrigation water (50%), surface water-underground water combined dispatching effect can be utmostly played, step is increased
Power station generated energy increases downstream ecology water, especially meets downstream critical period water demand for natural service, this is for seriously being broken
Bad downstream of Heihe river development of plants has special significance.Therefore, the present invention uses scheme 2, i.e., based on underground water, surface water
The scheduling mode of compensation is scheduled Heihe River basin.
The present invention is based on the surface water and groundwater combined dispatching optimization methods for improving pollen algorithm, the advantage is that: (1)
The present invention demonstrates scheduling of the present invention by verifying optimal reservoir level, going out the corresponding comprehensive benefit of process that is put in storage and contributes
The reliability and reasonability of optimization method；(2) present invention enriches underground water and surface water the combined dispatching optimization to drought-hit area
Method, improves the combined operating level and water resources comprehensive utilization rate of multi-reservoir, while also improving the ecological benefits of drought-hit area, tool
There are important practical significance and application value.
Claims (4)
1. based on improve pollen algorithm surface water and groundwater combined dispatching optimization method, which is characterized in that specifically according to
Lower step is implemented:
Step 1, using the maximum integration objective of the power generation of reservoir, irrigation and ecology as optimization aim, the target of the optimization aim
Function are as follows:
Min F=c1F1+c2F2+c3F3(1),
In formula (1), F is integration objective；F1For the power station generated energy of reservoir, unit is hundred million kWh, c1It generates electricity for the power station of reservoir
The weight coefficient of amount；F2For irrigation water shortage amount, unit is hundred million m3, c2For the weight coefficient of irrigation water shortage amount；F3For ecological holographic
Amount, unit are hundred million m3, c3For the weight coefficient of ecological water deficit；c1、c2And c3It is provided by watershed management policymaker, respectively
0.5,0.4 and 0.1；
In formula (2), m indicates the power station number of reservoir；Number of segment when t is indicated；Δ t indicates period length；kmIndicate the power station m
Power factor；Q (m, t) indicates the generating flow in the power station m, unit m3/s；H (m, t) indicates the productive head in the power station m,
Unit is m；Indicate that the irrigation water shortage amount of t period, unit are hundred million m3；Indicate the ecological holographic amount of t period, unit
For hundred million m3；
Step 2 improves pollen algorithm using computer parallel technology, and optimizes calculating to the objective function, obtains ground
It is lauched water intaking rate and optimal reservoir level.
2. the surface water and groundwater combined dispatching optimization method according to claim 1 based on improvement pollen algorithm,
It is characterized in that, in step 1, the bound for objective function includes:
Water balance constraint: V (m, t+1)=V (m, t)+(QI (m, t)-QO (m, t)) T+ Δ W (3),
Node water quantity restraint: QI (m, t+1)=QI (m, t)+QR (m, t)-QS (m, t) (4),
Underground water period water intaking constraint: RGt min≤RGt≤RGt max(5),
Reservoir level constraint: Zmin(m,t)≤Z(m,t)≤Zmax(m, t) (6),
Reservoir aerial drainage constraint: QOmin(m,t)≤QO(m,t)≤QOmax(m, t) (7),
Output of power station constraint: Nmin(m,t)≤N(m,t)≤Nmax(m, t) (8),
In formula (3), V (m, t), V (m, t+1) respectively indicate the whole story storage capacity of the t period of m reservoir, and unit is hundred million m3；QO
(m, t) indicates the outbound runoff of the t period of m reservoir, unit m3/s；Δ W indicates the loss water of evaporation, leakage process
Amount, unit are hundred million m3, can be neglected；
In formula (4), QI (m, t), QI (m, t+1) respectively indicate the whole story two Phase flow of the t period of m reservoir, and unit is
m3/s；QR (m, t) indicates the section storage of the t period of m reservoir, unit m3/s；The t of QS (m, t) expression m reservoir
The switching traffic of period, unit m3/s；
In formula (5), RGt,minIndicate the underground water water intaking rate lower limit of t period, RGt,maxIndicate the underground water water intaking rate of t period
The upper limit；
In formula (6), Zmin(m, t) indicates the water level lower limit of i-th period in the power station m, ZmaxWhen (m, t) indicates the i-th of the power station m
The water level upper limit of section, unit is m；
In formula (7), QOmin(m, t) indicates the storage outflow lower limit of the t period in the power station m, QOmax(m, t) indicates the power station m
The t period the storage outflow upper limit, unit is m3/s；
In formula (8), Nmin(m, t) indicates the minimum load of the t period in the power station m, NmaxWhen (m, t) indicates the t in the power station m
The maximum output of section, unit MW.
3. the surface water and groundwater combined dispatching optimization method according to claim 1 or 2 based on improvement pollen algorithm,
It is characterized in that, the step 2 is specifically implemented according to the following steps:
Step 2.1, initialization pollen algorithm, initial parameter includes initial population Npop, maximum number of iterations Tmax, Pollination Modes
Transition probability p；
Step 2.2, according to the constraint condition and initial population NpopDecision variable is calculated, using the decision variable as
Initial pollen gamete, calculation formula are as follows:
Xi,j=Hmin(j)+rand(Hmax(j)-Hmin(j))i∈[1,Npop], j ∈ [1, D] (9),
In formula (9), Xi,jIndicate i-th of pollen gamete in the position of jth dimension space；HmaxIt (j) is pollen gamete jth dimension space position
The upper limit constraint set, i.e. the water level upper limit constraint at the reservoir the end of month or the coefficient of mining upper limit of underground water；Hmin(j) indicate that pollen is matched
The lower limit of sub- jth dimension space position constrains, i.e. the water level lower limit constraint at the reservoir the end of month or the coefficient of mining lower limit of underground water；rand
Indicate the random number in [0,1] section；D representation space dimension；
Step 2.3 carries out parallel task decomposition to the initial pollen gamete by computer parallel technology and integrates, and exports
Optimal population；
Step 2.4 is iterated processing to optimal population using the pollen algorithm, if current iteration number t≤Tmax, then into
Row step 2.5, otherwise gos to step 2.6；
Step 2.5 continues to generate uniformly distributed function Rand, if Rand > p, carries out global pollination processing to pollen gamete, no
Then, local pollination processing is carried out, and continues iteration, and the number of iterations adds 1；
Overall situation pollination formula are as follows:
Wherein,
Part pollination formula are as follows:
In formula (10),Respectively indicate t, the solution in t+1 generation；L indicates step value；G indicates global optimum；Γ (λ) is
The gamma function of standard；λ indicates model parameter；S, s0It is step parameter；
In formula (11), ε is the equally distributed random number of obedience between 0-1；Respectively same plant, different plants
Pollen gamete, rand indicate [0,1] section random number；
Step 2.6, until t ﹥ TmaxWhen, optimal pollen gamete, i.e., optimal reservoir level or underground water are obtained by terminating judgement
Water intaking rate.
4. the surface water and groundwater combined dispatching optimization method according to claim 3 based on improvement pollen algorithm,
It is characterized in that, in step 2.3, the parallel task is decomposed and integrated detailed process are as follows:
Task distribution and transmission are carried out to the m end Worker by the end Client of computer, for each end Worker
Its fitness function is calculated, the fitness function uses the scheduling calculation of the objective function, i.e. formula (1), simultaneously
Carry out the update of dominant population；After recycling n times, calculated result is fed back to the end Client by the end Worker, and exports optimal population；
Task distributes formula are as follows: n=Npop/ m (12),
In formula (12), m indicates the number at the end Worker, and n indicates cycle-index.
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