CN103500369A - Water resource optimum dispatching method based on mixed shaping plan and decomposition - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000007493 shaping process Methods 0.000 title claims abstract description 19
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 16
- 238000004422 calculation algorithm Methods 0.000 claims description 20
- 238000005457 optimization Methods 0.000 claims description 14
- 238000010206 sensitivity analysis Methods 0.000 claims description 7
- 238000004904 shortening Methods 0.000 claims description 4
- 230000008901 benefit Effects 0.000 abstract description 14
- 238000013178 mathematical model Methods 0.000 abstract 1
- 238000010276 construction Methods 0.000 description 20
- 239000000203 mixture Substances 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 3
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- 230000008878 coupling Effects 0.000 description 2
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Abstract
The invention discloses a water resource optimum dispatching method based on mixed shaping plan and decomposition. The problem is decomposed through analyzing the special structure of the problem, a local solution after the decomposition is obtained, and then, the special structure of the problem is utilized to obtain the optimum scheme of the special problem. The water resource optimum dispatching method has the advantages that a geographical module is divided through clustering, a mixed shaping plan mathematical model is built, the special structure of the problem is developed, and in addition, the problem is decomposed into small independent problems, next, the decomposed local solution is obtained, then, the local solution is recombined by using the special structure of the problem, the optimum scheme of the specific problem is obtained, and in addition, the optimality of the scheme is proved.
Description
Technical field
The present invention relates to geography information water resource water supply dispatching engineering application, relate in particular to a kind of water resource optimal scheduling method based on mixing shaping planning and decomposing.
Background technology
The China's Arid area is wide, locality causes crop yield low and unstable because of shortage of water resources, be subject to numerous constraints such as geographic element, natural resources due to water resource, zones of different Water Resources Allocation situation significant difference, but, in the large system of whole geographical space, the basic principle that water resource is distributed rationally is identical, realize that water resource utilizes maximizing the benefits, comprise economic benefit, social benefit and ecological benefits, wherein economic benefit is to determine the important factor in order of hydraulic engineering construction.Under the considering of economic benefit target, should improve as far as possible the economic output of crop and reduce the construction cost of water supply project.When under investment constraint condition, affect the factor of irrigation water resource by adjusting, maximize the water resource economic benefit, rationally and economically increase irrigation efficiency, and analyze the investment marginal value; And, when not considering the investment constraint, how maximizing the water resource economic benefit, the investment of simultaneous minimization water supply project, find its maximum marginal value point.
And water resource scheduling is a multiple goal, multiple constraint, multi-level large scale optimization problem, data scale is large, and data dimension is high.Existing extensive water resource dispatching method mainly divides three major types both at home and abroad at present: heuristic, suboptimum (being similar to) method and composition decomposition method.
Heuristic mainly is based on the general name of a class global search method of experience, comprises heredity, population, simulated annealing, neural network etc.When running into the NP-Hard problem or can't realizing, or can't in finite time, travel through a kind of feasible program that can't guarantee the quality of solution of solution space under the condition of existing computing technique.Water resource assignment Design of Problems variable is many, and scale is large, generally can't directly be configured, and the heuristic that relies on experience has obtained application comparatively widely.Find fast solution although heuritic approach has advantages of, also exist and can not guarantee that solution is the most excellent remarkable shortcoming.At first, although heuristic can provide a kind of preferably Water Resources Allocation scheme, can't estimate the quality of this scheme, and can only provide a kind of scheme relatively preferably.Although there are now a lot of technology for jumping out local solution, still can not guarantee the quality of separating for numerous realistic problems.Secondly, these algorithms all depend on initial solution, and the design of some operator quality, and these technology are not mature enough, do not have theory that enough supports are provided.Finally, because these algorithms are only searched for according to experience, could not utilize well the characteristics of particular problem, go to obtain the quality that speed is conciliate faster.
Suboptimum (being similar to) method is compared heuristic, can guarantee theoretically the quality of separating, and its result is not necessarily optimum, but also in the scope that can bear, and can be than Exact Solution consumption resource still less.Solving on resource allocation problem, obtained numerous researchers' attention in recent years.The application of suboptimum (being similar to) method on engineering also seldom, because suboptimum (being similar to) method is to separate close all the time, itself is asking for suboptimum exactly, and for a particular problem, designing a feasible approximate data, has been difficult, designs a good approximate schemes, extremely difficult especially, therefore can say that the quality of these schemes more depends on deviser's inspiration.
The composition decomposition method is a kind of decomposition method in the face of extensive problem, and this large and change littlely, the method broken the whole up into parts is a kind of important means that solves extensive problem.The key of decomposing is how to guarantee global optimum, also just says how to go to combine local optimum under the prerequisite that obtains local problem's optimum solution, obtains global optimum.But combination local in reality is not often global optimum.The composition decomposition method is a kind of means preferably really, but how to go to combine local optimum and obtain the primitive solution optimum, need problem to possess some special construction, not all problem can both be decomposed, only decompose and can bring the scheme of a bad luck by force, even just a scheme without foundation.
In sum, heuristic and composition decomposition method can't guarantee the quality (quality of separating) of resulting dispatching method, and suboptimum (being similar to) method is to have sacrificed under the prerequisite of quality of partial solution, exchange a kind of compromise of time for, although can prove to a certain extent the quality of solution, but it is a kind of approximate all the time, can't obtain best practice.
Summary of the invention
The present invention seeks to: a kind of water resource optimal scheduling method based on mixing shaping planning and decomposing is provided, special construction by problem analysis, decompose this problem, obtain the local solution after decomposing, the special construction that recycles this problem obtains the optimal case of this particular problem.
Technical scheme of the present invention is: a kind of water resource optimal scheduling method based on mixing shaping planning and decomposing, it comprises: input data module, threshold module, large system coordination decomposition algorithm, mixing shaping planning algorithm, subproblem Combinatorial Optimization algorithm, sensitivity analysis algorithm and output module, and comprise the steps:
S1, according to the input data module information definite threshold module in threshold value;
S2, the large system coordination decomposition algorithm of combination are divided into several irrigated areas by whole water resource geographical space, provide natural cause, geographic factor and the artificial engineering factor of water resource to be set to variable simultaneously, and set the geographical attribute of these factors as irrigated area;
S3, irrigated area is divided into etc. to the plot (grid) of size, the attribute key element in unified each plot;
S4, cluster is carried out to according to attribute in these plot, and obtains the irrigated area type:
S5, determine crop species and shortening that in the shaping planning algorithm, different grids may be planted, and to carrying out its combination constraint;
S6, determine the water resources optimal operation target, in conjunction with optimization aim, set optimized algorithm;
S7, by objective function by each decomposing land, optimization subproblem of each self-forming;
S8, sensitivity analysis is carried out in each plot, obtain marginal value.
On the basis of technique scheme, further comprise following attached technical scheme:
Described step S4 further comprises:
S4a, remember that the factor of each grid is mistake! Do not find Reference source., comprising natural cause, geographic factor and artificial engineering factor;
S4b, calculate the similarity between any two adjacent grids, adopt the cosine similarity here,
coｓ（Ｘ
１，Ｘ
２)＝＜Ｘ
１，Ｘ
２＞/｜｜Ｘ
１||||X
2||；
S4c, a given similar threshold value mistake! Do not find Reference source.If, mistake! Do not find Reference source., aggregate into same class, otherwise regard as inhomogeneity.
Advantage of the present invention is:
Provided a kind of framework of finding optimum water resource engineering construction and scheduling scheme thereof.By the geographical module of clustering, set up and mix shaping mathematics for programming model, develop the special construction of this problem, and this PROBLEM DECOMPOSITION is become to independent minor issue, the local solution of reentrying after decomposing, then utilize the special construction of this problem to reconfigure local solution, obtain the optimal case of this particular problem, and prove its optimality.
The accompanying drawing explanation
Below in conjunction with drawings and Examples, the invention will be further described:
The schematic diagram that Fig. 1 is factor in each link of the present invention;
Fig. 2 is schematic flow sheet of the present invention;
Embodiment
Embodiment: shown in Fig. 1-2, the invention provides a kind of water resource optimal scheduling method based on mixing shaping planning and decomposing, it comprises:
1. according to the information definite threshold of raw data, in conjunction with large system coordination decomposition algorithm, whole water resource geographical space is divided into to several irrigated areas, provide natural cause, geographic factor and the artificial engineering factor of water resource to be set to variable simultaneously, set these variablees as regional geographical attribute.The big or small plot such as divide the area into, Parcel division is become to the grid of 500m*500m, the attribute key element of unified each grid.Finally these grids are carried out to cluster according to attribute, cluster guarantees the neighborhood connective (being neighborhood similarity) of geographic area, so just can obtain the irrigated area type:
A, remember that the factor of each grid is mistake! Do not find Reference source., comprising natural cause, geographic factor and artificial engineering factor.
B, calculate the similarity between any two adjacent grids, adopt the cosine similarity here,
coｓ（Ｘ
１，Ｘ
２)＝＜Ｘ
１，Ｘ
２＞/||X
1||||X
2||
C, a given similar threshold value mistake! Do not find Reference source.If, mistake! Do not find Reference source., aggregate into same class, otherwise regard as inhomogeneity.
2. determine crop species and the shortening that in the shaping planning algorithm, different grids may be planted, and to carrying out its combination constraint; But determine different engineering construction type irrigation quantities and the expense of pouring water, to each plot engineering kind constraint, the maximum water lift constraint of engineering, engineering construction constraint, the constraint of engineering water lift construction cost.
Above two point selection factors constraints can mean with the 0-1 variable, as:
ν
tirefer to whether select i kind crop or crop combination; β
tirefer to and whether select i kind crop or crop combination
3. determine the water resources optimal operation target, in conjunction with optimization aim, set optimized algorithm.
4. the decomposition of problem and solving with optimality reconfigures.For any one target, require except there is coupling in objective function, other constraint is all separately independently, and objective function is the linear combination of the target in each plot, and objective function is pressed to each decomposing land, optimization subproblem of each self-forming.
5. the optimization problem due to plot is to mix the shaping linear programming, can carry out sensitivity analysis to each plot so, obtains marginal value, thus known prime investment direction.Can, to engineering construction planning, provide direction sign reference and effect and guarantee like this.
For understanding in more detail the present invention, specifically be described below:
Step1. water resource is built the structure of scheduling model.
A) by water condition, whole water resource geographical space is divided into to several areas.Because each regional geographical environment differs, can not unify to treat.And block out according to some fixed sizes, can cause the sharp increase (exponential increase) of scale, therefore need to first reasonably divide.First find out natural cause (as rainfall, underground water etc.), geographic factor (as planted crop species, river etc.) and artificial engineering factor (as water lift, collection rain etc.) that water resource can be provided, the attribute using these factors as geographical piece.Then, Parcel division is become to the grid of 500m*500m, the kind of the attribute key element of unified each grid.Finally these grids are carried out to cluster according to attribute, cluster guarantees the field connectedness of geographic area, so just can obtain a zone and divide (division hereinafter all refers to the division of the area type that this newly obtains).As shown in Figure 1, concrete clustering algorithm step is as follows:
1. the factor of remembering each grid is mistake! Do not find Reference source., comprising natural cause, geographic factor and artificial
Engineering factor.
2. calculate the similarity between any two adjacent grids, adopt the cosine similarity here,
cos(X
1，X
2)=＜X
1，X
2＞/||X
1||||X
2||
3. a given similar threshold value mistake! Do not find Reference source.If, mistake! Do not find Reference source., aggregate into same class, otherwise regard as inhomogeneity.
4. repeating step 2 and 3.
B) utilize the 0-1 variable to set up the selection factor model for each division.
The crop species difference that each little plot may be planted, and be not can plant all crops in each plot, therefore each plot is existed to the constraint of plantation crop species and combination thereof.Each regional geographic factor differs, the engineering construction cost of certain specific engineering construction and the water yield that can provide are also inconsistent, and the water demand of crop is also inconsistent, not all area can provide the construction condition of all engineering kinds simultaneously, in summary, there are the constraint of engineering kind, the maximum water lift constraint of engineering, engineering construction constraint, the constraint of engineering water lift construction cost.
Above 2 points, be all to carry out crop-planting and the engineering construction factor is selected in certain particular locality, meet separately institute's Constrained in zone separately.Select certain factor, can mean with the 0-1 variable, as
ν
tirefer to whether select i kind crop or crop combination; β
tirefer to and whether select i kind crop or crop combination
In conjunction with optimization aim, the economic benefit of crop, be easy to that issue table is shown as to one and mix the shaping planning problem.
Problem one, under given engineering construction investment, maximizes the model of water resource economic benefit:
Problem two: maximum economic benefit, the model of simultaneous minimization engineering investment cost is as follows:
A
tit is the product of t area crops price and shortening; γ
ti=1 represents the crop combination of t ecological region planting i kind, otherwise means not plant i kind crop; Q
tit is the water resources quantity in t zone; y
i(Q
t) function of moisture and output while being t ecological region planting i kind crop combination; C
tit is the investment quota; λ
tjthat regional t selects j kind water-saving irrigation project type; β
ti=1 means to select i kind irrigated area; γ
ti, η
ti, λ
tjby 0 and 1 combination formed, mean whether to select corresponding type.
Step2. the decomposition of problem and solving with optimality reconfigures.
For any one top problem, except there is coupling in objective function, other constraint is all separately independently, and objective function is the linear combination of the target in each plot, objective function is pressed to each decomposing land, optimization subproblem of each self-forming.A mistake of problem one! Do not find Reference source.Individual subproblem is as follows:
Can prove the locally optimal solution in each plot so, namely the optimum solution of primal problem.Also just say, we only need to solve each intramassif optimization problem, and this small-scale mixing shaping planning, we both can try to achieve optimum solution at short notice, their combination is again global optimum, thereby we can obtain optimum water resource scheduling scheme.The optimality proof is as follows:
A theorem: if mistake! Do not find Reference source.For the objective function of problem one, wherein mistake! Do not find Reference source.The objective function of i subproblem, a mistake! Do not find Reference source., mistake! Do not find Reference source.For the subproblem mistake! Do not find Reference source.Field of definition, mistake! Do not find Reference source.For the optimum solution of problem one, mistake! Do not find Reference source.For the subproblem mistake! Do not find Reference source.Optimum solution, mistake so! Do not find Reference source.。
A proof: establish mistake! Do not find Reference source.For arbitrary feasible solution of problem one, mistake so! Do not find Reference source.For the subproblem mistake! Do not find Reference source.Feasible solution, thereby wrong! Do not find Reference source., i.e. mistake! Do not find Reference source., make mistake! Do not find Reference source.Problem must be demonstrate,proved.
Use the commercial Optimization Software can be in the hope of the optimum solution of all subproblems, use LINGO to solve the specific code of this problem as follows:
Step3. the sensitivity analysis of solution.
Because the optimization problem of little division is to mix the shaping linear programming, can carry out sensitivity analysis to each division so, obtain marginal value, thus known prime investment direction.Can, to engineering construction planning, provide direction sign reference and effect and guarantee like this.
Certainly above-described embodiment is only explanation technical conceive of the present invention and characteristics, and its purpose is to allow the person skilled in the art can understand content of the present invention and implement according to this, can not limit the scope of the invention with this.All equivalent transformation or modification that according to the present invention, the Spirit Essence of main technical schemes is done, within all should being encompassed in protection scope of the present invention.
Claims (2)
1. the water resource optimal scheduling method based on mixing shaping planning and decomposing, it is characterized in that it comprises: input data module, threshold module, large system coordination decomposition algorithm, mixing shaping planning algorithm, subproblem Combinatorial Optimization algorithm, sensitivity analysis algorithm and output module, and comprise the steps:
S1, according to the input data module information definite threshold module in threshold value;
S2, the large system coordination decomposition algorithm of combination are divided into several irrigated areas by whole water resource geographical space, provide natural cause, geographic factor and the artificial engineering factor of water resource to be set to variable simultaneously, and set the geographical attribute of these variablees as irrigated area;
S3, irrigated area is divided into etc. to the plot of size, the attribute key element in unified each plot;
S4, cluster is carried out to according to attribute in these plot, and obtains the irrigated area type:
S5, determine crop species and shortening that in the shaping planning algorithm, different plot may be planted, and to carrying out its combination constraint;
S6, determine the water resources optimal operation target, in conjunction with optimization aim, set optimized algorithm;
S7, by objective function by each decomposing land, optimization subproblem of each self-forming;
S8, sensitivity analysis is carried out in each plot, obtain marginal value.
2. water resource optimal scheduling method according to claim 1, it is characterized in that: described step S4 further comprises:
S4a, remember that the factor of each grid is mistake! Do not find Reference source., comprising natural cause, geographic factor and artificial engineering factor;
S4b, calculate the similarity between any two adjacent grids, adopt the cosine similarity here,
cos(X
1,X
2)=＜X
1，X
2＞/||X
1||||X
2||；
S4c, a given similar threshold value mistake! Do not find Reference source.If, mistake! Do not find Reference source., aggregate into same class, otherwise regard as inhomogeneity.
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