CN106951985B - Multi-objective optimal scheduling method for cascade reservoir based on improved artificial bee colony algorithm - Google Patents

Multi-objective optimal scheduling method for cascade reservoir based on improved artificial bee colony algorithm Download PDF

Info

Publication number
CN106951985B
CN106951985B CN201710126746.0A CN201710126746A CN106951985B CN 106951985 B CN106951985 B CN 106951985B CN 201710126746 A CN201710126746 A CN 201710126746A CN 106951985 B CN106951985 B CN 106951985B
Authority
CN
China
Prior art keywords
reservoir
water
flow
period
ith
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
Application number
CN201710126746.0A
Other languages
Chinese (zh)
Other versions
CN106951985A (en
Inventor
张昕怡
方国华
成鹏飞
边广琦
李庆
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hohai University HHU
Original Assignee
Hohai University HHU
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Hohai University HHU filed Critical Hohai University HHU
Priority to CN201710126746.0A priority Critical patent/CN106951985B/en
Publication of CN106951985A publication Critical patent/CN106951985A/en
Application granted granted Critical
Publication of CN106951985B publication Critical patent/CN106951985B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • 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 cascade reservoir multi-target optimization scheduling method based on an improved artificial bee colony algorithm, which comprises the following steps of: s11, acquiring basic information data of the step reservoir system; s12, establishing a multi-target scheduling model including power generation, estuary ecology and water supply according to the reservoir system information; and s13, executing an improved artificial bee colony algorithm to solve the optimal scheduling scheme of the cascade reservoir system. The invention realizes the global optimization of the reservoir scheduling problem, improves the calculation efficiency and precision and provides a new way for solving the multi-target optimization scheduling problem of the cascade reservoir system.

Description

Multi-objective optimal scheduling method for cascade reservoir based on improved artificial bee colony algorithm
Technical Field
The invention belongs to the field of reservoir scheduling in the field of water conservancy and hydropower, and relates to a cascade reservoir multi-objective optimization scheduling method based on an improved artificial bee colony algorithm.
Background
The reservoir optimal scheduling is the optimal scheduling problem of a multi-objective, multi-constraint and complex water conservancy system, and particularly has certain difficulty in solving the optimal scheduling problem of the reservoir system which undertakes multiple tasks of power generation, water supply, ecology and the like.
China has numerous rivers entering the sea, the interaction between the rivers and the ocean at the river-sea junction is obvious, a special and complex natural complex-estuary is formed, the area is often dense in population and developed economically, and the stability of an estuary ecosystem is one of important foundations and guarantees of social sustainable development. With the continuous development and utilization of water resources, a plurality of hydropower stations and reservoirs are often built in a downstream watershed with a estuary to form a step reservoir system, and various hydraulic connections exist in the system, so that a plurality of experts propose to adjust the river flow in the estuary region by means of the regulation and storage capacity of the upstream reservoir so as to maintain the ecological balance of the estuary region. The cascade reservoir combined dispatching takes the comprehensive benefit maximization of the whole system into consideration, namely, the cascade reservoir combined dispatching plays a remarkable role in improving the overall power generation benefit and the like on the basis of guaranteeing the multi-aspect benefits of estuary ecology, water supply and the like, and can obtain more economic benefits than single-reservoir dispatching. Therefore, the development of the watershed cascade reservoir combined optimization scheduling considering the estuary ecological balance becomes a reservoir system scheduling method which is researched more and developed faster at present.
In recent years, intelligent algorithms have been widely applied to reservoir optimal scheduling model solution, such as genetic algorithms, artificial neural networks, chaotic algorithms, and the like, and are gradually and widely applied to reservoir optimal scheduling, wherein artificial bee colony algorithms are less researched in the field of reservoir scheduling. The artificial bee colony algorithm has strong global search capability and is applied to a plurality of industrial fields, but has the defects of weak local search capability, low later convergence speed and the like, so the defects of the artificial bee colony algorithm need to be improved, and the improved artificial bee colony algorithm is applied to the field of multi-target optimized scheduling of the cascade reservoir by combining the problem characteristics of optimized scheduling of the cascade reservoir system.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of weak local searching capability, low later convergence speed and the like of a basic artificial bee colony algorithm, an improved artificial bee colony algorithm is provided, an optimal scheduling scheme of the cascade reservoir system is solved based on the algorithm, the overall optimization of the reservoir scheduling problem is realized, the calculation efficiency and the calculation precision are improved, and a new way is provided for solving the multi-objective optimal scheduling problem of the cascade reservoir system.
The technical scheme is as follows: in order to achieve the above purpose, the invention provides a cascade reservoir multi-objective optimization scheduling method based on an improved artificial bee colony algorithm, which comprises the following steps, as shown in fig. 1-2:
step 1: acquiring basic information data of a cascade reservoir system, establishing a multi-target scheduling model comprising power generation, estuary ecology and water supply, and setting basic parameters of the model, including scheduling cycle time interval, reservoir number, bee colony scale, bee collection and bee colony following scale, individual population vector, exploitation limit of the same honey source and maximum cycle number;
the basic information data required for establishing the multi-objective scheduling model comprises the following steps: in the system, the overflow capacity values Q of a reservoir, a pump, a gate and the like, the initial and final reservoir capacity limit V of the reservoir, the normal water storage level Z of the reservoir, the flood control limit water level Z, the dead water level Z, the relationship curve S-Z of the volume and the water level of the lake and the reservoir, the relationship curve Z-Q of the downstream water level and the downward discharge flow of the reservoir, the output constraint value N of a reservoir generator set and the inflow W are calculated.
The optimal scheduling of the cascade reservoir must meet the target and the requirement of comprehensive utilization, aiming at the problem of medium-long term optimal scheduling of the cascade reservoir of a watershed with a river mouth at the downstream, because the scheduling time interval scale is longer, the flood prevention target is not considered for a moment, the cascade reservoir is divided into a plurality of target functions such as power generation, river mouth ecology, water supply and the like in a constant and quantitative mode, the target functions comprise a power generation target function with the largest generated energy in the scheduling period, an ecology target function with the smallest water demand of the ecological environment at the river mouth, and a reservoir group water supply target function with the smallest reservoir water supply amount relative to the minimum water shortage degree in the scheduling:
(1) maximum generated energy in scheduling period
One of the main purposes of building a hydropower station is power generation, so the maximum generated energy, the maximum total output or the maximum power generation benefit is often taken as an objective function:
Figure GDA0003021399920000021
in the formula: m is the number of the cascade reservoirs, T is the number of time periods divided in a dispatching cycle, delta T is the duration of each time period in the dispatching cycle, and N (i, T) is the output of the ith reservoir in the T-th time period;
(2) minimum water shortage in ecological environment in dispatching period
In order to protect the stability of the ecosystem of the basin and the demand of industrial and agricultural water on both sides, the paper selects the minimum water demand of the ecological environment as an ecological objective function:
Figure GDA0003021399920000022
in the formula: qEcological environment(i,t)The water flow required by the ecological environment of the downstream riverway of the ith reservoir in the tth time period, and Q (i, t) is the discharge flow of the ith reservoir in the tth time period;
the estuary ecological water demand is determined according to a functional demand principle, a time-sharing consideration principle, a multifunctional coordination principle and a river-sharing consideration principle, and comprises the ecological base flow f of an estuary areaBasic flowSand transportation water demand fSand conveying deviceAnd maintaining the water-salt balance water demand f of the estuary systemAnti-saltBecause the ecological water demands can be mutually compatible and overlapped, the ecological environment water demand f at the river mouth is obtained according to the additivity and the maximum value principleEcological environmentThe specific calculation formula is as follows:
fecological environment＝max(fBasic flow,fSand conveying device,fAnti-salt) (3)
(3) Minimum relative water shortage of reservoir water supply in dispatching period
Because estuary areas are generally developed areas and densely populated areas, the water consumption is very large, and estuary water entering sea mouths are generally influenced by salt tides to cause water shortage in the areas, the cascade reservoirs in the watershed with estuaries at the downstream are required to bear the task of supplying water to the estuary areas through various water diversion facilities, and the water supply objective function of the reservoir group can be represented by the relative water shortage degree of the water supply:
Figure GDA0003021399920000031
in the formula: wk(i, t) is the amount of water supplied by the ith reservoir to the kth water supply section during the t-th period, Wk(t) local water supply amount of kth water supply section in the t-th period, Xk(T) is the water demand of the kth water supply zone in the tth period, i is 1,2, …, M, T is 1,2, …, T, K is 1,2, …, K is the number of water supply zones.
And taking the maximum target of the generated energy in the target function as a main target function of the researched scheduling problem, converting the minimum target of the water shortage of the estuary ecological environment into a constraint condition for processing, and preferentially deducting the diversion flow from the reservoir warehousing runoff in the solving process of the water supply target so as to naturally meet the target. The cascade reservoirs at the upstream and downstream of the same river are composed of reservoir groups with different warehousing runoff characteristics and different adjusting performances, and the constraint conditions are generally represented by an equation or an inequality group composed of indexes such as water consumption balance conditions, generating heads, lower discharge flow, hydropower station output, upstream and downstream water levels, rated water and the like:
(1) water balance constraint
The water balance condition of a certain hydropower station of the cascade is linked with the upstream and downstream hydropower stations in time and space, the water balance in time means that the water quantity of the reservoir at different moments of the reservoir must meet a continuity equation, and the water balance in space means that the warehousing flow of the downstream reservoir is the sum of the ex-warehouse flow of the upstream reservoir and the interval flow between the ex-warehouse flow and the upstream reservoir, specifically:
in time:
Figure GDA0003021399920000041
spatially:
I(i+1,t)＝Qdrain device(i,t)+qi-1,i(t) (6)
In the formula: v (I, t) and V (I, t +1) are respectively the reservoir capacity of the ith reservoir at the t th and t +1 th moments, I (I, t) and QDrain device(i, t) is the inlet runoff and the discharge flow of the ith reservoir in the t time period, Wk(I, t) is the water supply amount of the ith reservoir to the kth water supply area in the t period, I (I +1, t) is the warehousing runoff of the (I +1) th reservoir in the t period, and q is the warehousing runoff of the ith reservoir in the t periodi-1,i(t) is the interval confluence of the i-1 th reservoir and the i-th reservoir in the t-th time period;
(2) reservoir discharge restriction
The minimum flow that the reservoir must let down is used for satisfying many-sided requirements such as the shipping base current of reservoir low reaches river course, irrigation water, and during flood season, the flow that lets down of reservoir must control the safety discharge within range of low reaches river course to guarantee the flood control safety of low reaches cities and towns, dykes and the like, and is specific:
Qmin(i,t)<Qdrain device(i,t)<Qmax(i,t) (7)
In the formula: qmin(i,t)、Qmax(i, t) are respectively the minimum flow and the maximum flow allowed to be discharged by the ith reservoir in the tth time period, and the two values are generally comprehensively determined by the minimum flow and the flow fluctuation range of shipping, the limit of the overflow capacity of the hydraulic turbine set and the maximum discharge flow under the limit of flood control;
(3) power station output constraints
The power station output constraint comprises the guaranteed output of the power station, the maximum installed capacity and the output requirement of a power system to the power station, and specifically comprises the following steps:
Nmin(i,t)<N(i,t)<Nmax(i,t) (8)
in the formula: n (i, t) is the power station output of the ith reservoir in the t period, Nmin(i,t)、Nmax(i, t) are respectively the minimum and maximum output allowed by the power station in the t time period of the ith reservoir;
(4) water level or reservoir capacity constraints
The water level constraint or the reservoir capacity constraint is actually a constraint condition, the two can be mutually converted according to a 'reservoir capacity-water level relation curve', the specific application can be freely selected according to the actual situation, the constraint comprises the dead water level of the reservoir, the normal water storage level or the flood control limit water level, the special limitation of the reservoir capacity in the dispatching period and the like, and specifically:
Zmin(i,t)<Z(i,t)<Zmax(i, t) or Vmin(i,t)<V(i,t)<Vmax(i,t) (9)
In the formula: z (i, t) and V (i, t) are respectively the water level and the storage capacity of the ith reservoir in the tth period, and Zmin(i,t)、Zmax(i,t)，Vmin(i,t)、Vmax(i, t) are respectively the lowest highest water level and the minimum maximum storage capacity allowed by the ith reservoir at the tth moment;
(5) non-negative constraint
All decision physical parameters in the model are not negative:
X≥0 (10)
in the formula: and X is a vector formed by decision variables.
Step 2: the method comprises the following steps that the detection bee generates an initial solution of an optimal honey source by adopting chaotic search, namely, an optimal power generation flow sequence is initialized, and the specific method comprises the following steps:
2.1) generating a generating flow sequence (X) of honey sources with individual number SN at the initial moment randomly in a solution space1,X2,…,XSN) As a decision variable requiring chaotic mapping, where XiIs a D-dimensional vector;
2.2) by
Figure GDA0003021399920000051
Represents XiA component in the D-dimensional solution space is to be
Figure GDA0003021399920000052
It is mapped to [0,1 ] according to equation (11)]Chaotic variable in between
Figure GDA0003021399920000053
Figure GDA0003021399920000054
2.3) updating the chaos variable according to equation (12)
Figure GDA0003021399920000055
Figure GDA0003021399920000056
2.4) obtaining decision variables according to the formula (13)
Figure GDA0003021399920000057
Figure GDA0003021399920000058
2.5) New Current Generation sequence (X) thus obtained after chaotic Tent mapping1,X2,…,XSN) I.e. the initial solution.
And step 3: the method comprises the following steps that the leading bees search near the currently reserved optimal honey source by adopting a search strategy of leading index distribution scale factors, and the specific method comprises the following steps:
3.1) each honey source corresponds to a leading bee, and the fitness value W of each honey source at present is calculatediI.e. the comprehensive benefit value corresponding to the power generation flow sequence, the bee colony evolved to the kth step is searched near the currently reserved optimal honey source, and the exponentially distributed scale factors are introduced
Figure GDA0003021399920000061
Improving the search strategy, wherein the search formula is as follows:
Figure GDA0003021399920000062
in the formula (I), the compound is shown in the specification,
Figure GDA0003021399920000063
that is, a new feasible solution is obtained for the neighborhood search if
Figure GDA0003021399920000064
If the value exceeds a certain boundary value of the solution space, the value is taken as the boundary value,
Figure GDA0003021399920000065
is [ -1,1 [ ]]The random number of (a) is set,
Figure GDA0003021399920000066
the method is generated from exponentially distributed random numbers, and specifically comprises the following steps:
Figure GDA0003021399920000067
in the formula, beta is [0,1 ]]The value of m is 1 or 2, rm]Is two random numbers, r 1]∈[α1,0),r[2]∈(0,α2]，α1<0,α2＞0，
Figure GDA0003021399920000068
Is taken as
Figure GDA0003021399920000069
The corresponding fitness value is more optimal
Figure GDA00030213999200000610
Or
Figure GDA00030213999200000611
3.2) if
Figure GDA00030213999200000612
Corresponding fitness value
Figure GDA00030213999200000613
Is superior to
Figure GDA00030213999200000614
Is a fitness value WiThen use
Figure GDA00030213999200000615
Instead of the former
Figure GDA00030213999200000616
Otherwise, the original state is maintained
Figure GDA00030213999200000617
And is not changed.
And 4, step 4: the follower bees adopt a self-adaptive proportion selection strategy to select from the searched honey sources and become leading bees, and the specific method comprises the following steps:
4.1) leading the bee with following the bee sharing honey source information in waving dancing honeycomb, following the bee and according to the fitness of each honey source, adopting the probability that self-adaptation proportion selection mechanism calculated honey source and selecting, specifically do:
Figure GDA00030213999200000618
in the formula, WiThe fitness value (comprehensive effect corresponding to the power generation flow sequence) of each current honey sourceBenefit value), SN is the number of honey source individuals, and the power exponent λ is obtained by applying equation (17):
Figure GDA00030213999200000619
wherein q is a coefficient when
Figure GDA00030213999200000620
When λ is 1; when in use
Figure GDA00030213999200000621
When is lambda<1, where λ → 0, P i1/SN; when in use
Figure GDA00030213999200000622
When is lambda>1, wherein λ → ∞ and Wi<Wmax，P i0; if W isi＝Wmax，P i1/M, wherein M is the number of the optimal individuals;
4.2) selecting corresponding honey sources according to the probability by the following bees, and further becoming leading bees.
And 5: judging whether the position of the selected honey source can be further improved within the preset mining limit, if not, leading the bees at the position to abandon the honey source to become scout bees, and turning to the step 2 to search a new honey source again, wherein the abandoned honey source is replaced by the new honey source found by the scout bees; otherwise, go to step 6;
step 6: judging a cycle termination condition, if the position of the optimal honey source is acceptable or reaches the maximum cycle number, stopping calculating and outputting the best honey source position, namely the optimal scheduling scheme of the system; otherwise, go to step 3 to recalculate, that is, the new leading bee starts to search for new bee source.
Has the advantages that: by adopting the technical scheme, the invention has the following effects:
(1) comprehensive benefits of power generation, estuary ecology and water supply can be comprehensively considered in optimal scheduling of the cascade reservoir system;
(2) the improvement of the artificial bee colony algorithm can be realized, and the calculation speed and precision are improved compared with the basic artificial bee colony algorithm;
(3) determining an initial solution by using chaotic Tent mapping, wherein the fitness value obtained by the first evolution is generally superior to the result calculated by a basic artificial bee colony algorithm;
(4) the index distribution scale factor is introduced to improve the search strategy, so that the blindness of the basic artificial bee colony algorithm in the stage of searching a new bee source is reduced, and the optimization speed is accelerated;
(5) a roulette mode in a basic artificial bee colony algorithm is replaced by a self-adaptive proportion selection strategy, so that the colony keeps high diversity in the early stage, the early trapping of local optimality is avoided, the search space is reduced in the later stage, and the optimization speed is ensured.
(6) The multi-target optimized dispatching scheme for solving the cascade reservoir system based on the improved artificial bee colony algorithm can be realized, and a new way is provided for solving the multi-target optimized dispatching problem of the cascade reservoir system.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow diagram of an improved artificial bee colony algorithm of the present invention;
FIG. 3 is a three-dimensional image of the Sphere function;
FIG. 4 is a three-dimensional image of the Rosenbrock function;
FIG. 5 is a three-dimensional image of the Rastrigrin function;
FIG. 6 is a comparison graph of evolution curves of a Sphere function;
FIG. 7 is a comparison graph of the Rosenbrock function evolution curve;
FIG. 8 is a comparison graph of the evolution curve of the Rastrigrin function.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
The performance of the artificial bee colony algorithm before and after improvement is tested through experimental design, and for convenience, the improved artificial bee colony algorithm is referred to as an IABC algorithm for short. In the simulation experiments we selected the following three test functions, the Sphere function, the rosenblock function and the rasrigin function, which are defined as follows:
(1)Sphere Function
Figure GDA0003021399920000081
min(f1)＝f1(0,…,0)＝0
the Sphere function is a nonlinear symmetrical unimodal spherical function, is simple, can be separated from different dimensions, has only a unique minimum value point, is easy to apply to all algorithm tests capable of performing numerical optimization calculation, and is mainly used for verifying the optimization accuracy of the algorithm. A three-dimensional image of this function is shown in fig. 3.
(2)Rosenbrock Function
Figure GDA0003021399920000082
min(f2)＝f2(1,…,1)＝0
Although the Rosenbrock function is a unimodal function, the function is difficult to solve due to strong coupling among variables, and the Rosenbrock function is a typical ill-conditioned quadratic function which is difficult to minimize. The optimal solution of the function is in a narrow parabolic valley area, and the function can only provide little information, so that the algorithm is easy to fall into local optimization due to difficulty in judging the search direction in the calculation process, and the probability of finding the minimum value is low. The rosenblock function is often used when evaluating the performance of an algorithm. The three-dimensional image of the function is shown in FIG. 4:
(3)Rastrigin Function
Figure GDA0003021399920000091
min(f3)＝f3(0,…,0)＝0
as a typical complex multi-peak function, the Rastrigrin function is constructed based on a Sphere function, and a plurality of local extreme points are manufactured by utilizing the periodicity of a cosine function. Therefore, the function is very difficult to reach the global optimum point, so that the algorithm to be tested is often trapped in a certain local minimum point in the search calculation process. The three-dimensional image of the function is shown in FIG. 5:
in the experiment, the termination condition of the algorithm is that the maximum iteration number Loopnum is 2000, the number of swarms (such as the size of a honey bee colony) SN is 25, the dimension D is 30, the mining Limit of the same honey source is 100, and the like. The number of runs of each algorithm was set to 30, and the optimization ability of each algorithm was evaluated by the 30-run results, as shown in table 1. From the data listed in the table, the operation conditions of the three functions of Sphere, Rosenbrock and Rastrigrin are shown, the results obtained by adopting the improved artificial bee colony algorithm are superior to those obtained by adopting the standard artificial bee colony algorithm, the variance calculated by adopting the improved bee colony algorithm is smaller, and the improved algorithm is more stable and has stronger robustness. The Sphere function is a nonlinear symmetrical unimodal function, so that the obtained result is closer to the optimal solution; the latter two functions have a large error between the obtained result and the theoretical optimal solution due to the difficulty of searching.
TABLE 1 results comparison of solving debug functions by different methods
Figure GDA0003021399920000092
Fig. 6, 7 and 8 show evolution graphs of three typical function tests using the modified artificial bee colony algorithm and the artificial bee colony algorithm, respectively. The figure shows that the speed of function convergence can be accelerated by adopting the improved artificial bee colony algorithm, and the searched minimum value is more superior to the artificial bee colony algorithm; because the improved algorithm adopts the Tent mapping model to improve the initial solution of the bee colony, under the test of three typical functions, the function value of the first time of evolution by using the improved artificial bee colony algorithm is superior to the result calculated by using the standard artificial bee colony algorithm.
The effectiveness and the rationality of the method are explained by taking the multi-target joint optimization scheduling of the reservoir with the steps of Xinanjiang and Fuchunjiang as an example.
The Qiantangjiang river mouth is a typical strong tide river mouth, and the Xinanjiang and Fuchungjiang step reservoirs on the Qiantangjiang river basin are separated by about 60km and have close hydraulic connection, wherein the Xinanjiang reservoir is a perennial regulation reservoir, and the Fuchungjiang reservoir is a reverse regulation reservoir of the Xinanjiang reservoir. The average flow of a dam site of a Xinan river power station for many years is 321m3/s (runoff data statistics in 1961-2008), the normal water storage level is 108.0m, the corresponding storage capacity is 178.4 hundred million m3, the flood control limit water level is 106.5m, the dead water level is 86m, the effective storage capacity is 102.7 hundred million m3, the comprehensive output coefficient is 8.5, the output is 15.99 ten thousand kW, the installed capacity is 81 ten thousand kW, and the multi-year regulation performance is realized. The Fuchunjiang reservoir has a total reservoir capacity of 9.2 hundred million m3, has daily regulation performance, has a power station installed capacity of 297.2MW, is provided with 4 rotating blade type water-turbine generator sets with single machine capacity of 60MW and one capacity of 57.2MW, and is merged into a power grid in east China by 110 kilovolt and 220 kilovolt power transmission lines; the design flow of the generator set is 500m3/s, the design water head is 14.3m, and the total length of the hydropower station hub is 554.35 m. The comparison of important parameters of reservoir in the hydropower station of Xinanjiang-Fuchunjiang is shown in Table 2.
TABLE 2 reservoir parameters of the Xinanjiang-Fuchunjiang hydropower station
Figure GDA0003021399920000101
In the example, the design series selects runoff data of 15 hydrologic years in 1968-1982, respectively adopts a conventional scheduling graph, a basic artificial bee colony algorithm and an improved artificial bee colony algorithm, comprehensively considers the comprehensive benefits of power generation, estuary ecology and water supply, establishes a cascade reservoir power generation optimization scheduling model meeting the estuary ecological environment water demand for the step reservoir of Xinanjiang-Fuchunjiang, and can be expressed as follows:
(1) objective function
The method comprises the following steps of (1) optimizing an objective with the maximum cascade power station power generation amount as a model, wherein the objective function is as follows:
Figure GDA0003021399920000111
in the formula, E is the generated energy (kW.h) of the cascade hydropower station; 1,2, wherein 1 represents a Xinanjiang power station, and 2 represents a Fuchunjiang power station; e (i, t) is the power generation amount (kW.h) of the ith power station in the t-th period; t is a scheduling period; a. theiIs the comprehensive output coefficient of two power stations, wherein Xinanjiang A1The weight is taken as 8.5, Fuchunjiang A2Taking the value as 8.3; q (i, t) is the power generation flow (m3/s) of the ith power station in the t period;
Figure GDA0003021399920000112
average upstream water level (m) for the ith reservoir during the t-th period;Z(i, t) the average downstream water level (m) of the ith reservoir in the t period can be obtained through the downstream water level flow relation of the reservoirs; Δ t is the number of hours (h) of the t-th period.
(2) Constraint conditions
1) Estuary ecological environment water demand restriction
River mouth base stream ecological water demand
Tennant method: and in the period from 9 months to 6 months in the next year, 10% of the average monthly flow of the plurality of years is selected as the minimum ecological water demand in the river channel, and in the period from 7 months to 8 months, 30% of the average monthly flow of the plurality of years is selected as the minimum ecological water demand in the river channel.
7Q10 method: a typical hydrologic year with 90% guarantee rate is 1962, the most withered month of the year is December, the average flow rate is 158.7m3/s, and the minimum ecological water demand of the mouth of the river in the Qiantangjiang river is selected.
The specific results are shown in Table 3.
Table 3 estuary base stream ecological environment water demand unit: m is3/s
Figure GDA0003021399920000113
② maintaining the water demand for river mouth scouring and silting balance
According to the analysis of the actual measurement data, the average sand content of Hangzhou gulf below Ph is generally 1-3 kg/m 3; the sand content of seven-castle or more is 1 to 3kg/m3 in most cases. According to the calculation, the amount of the sand in the land area above a Weir is about 340 million t, \28553andthe amount of the sand in the land area above Ph is about 422 million t since 1980. The silt at the river mouth of the Qiantangjiang river mainly comes from a sea area, \28553, the average tidal range of the cross section of a river rises and the silt conveying amount is about 500 ten thousand tons, and the silt conveying amount can reach 1000 thousand tons when the tidal range is large (the tidal range is about 8 m). Qiantang river estuary Cao E river is collected into the Yanguan river segment, and the tidal bore is strong and rapid. In tidal bore river, the maximum flow velocity of tidal bore generally appears in several minutes to tens of minutes after tidal bore, the maximum vertical average flow velocity is 28553, the cross section of the river is about 4m/s, and the maximum flow velocity reaches 5m/s from the point mountain to the Yanguan river. And the flow velocity is gradually reduced towards the upstream, and the maximum flow velocity of the seven forts is reduced to about 2.5 m/s.
Table 4 units of sand content for different stations: kg/m3
Figure GDA0003021399920000121
Under certain silt scouring conditions, the maximum sand content corresponds to the minimum silt transportation water demand. The method is characterized in that parameters are selected correspondingly according to the actual conditions of the qian tang river mouth, the maximum sand content, the average value of the maximum monthly average sand content and the average sand content over years are respectively used as selection bases of parameters of sand contents of different levels in the water demand for transporting sediment, and the calculation result is that the minimum flow of 240.4-721.2 m3/s in the flood season from 4 months to 6 months flushes the river mouth silted at the early stage.
③ river mouth preventing salt water from invading water demand
The water intake of Qiantang river estuary is 35 hundred million m3 (containing Fuyang, Fuyang and Zhuge) per year. Wherein the domestic and industrial water is 8 hundred million m3, which is an uninterruptible water supply; 23 hundred million m3 of water is used for rural life and seasonal agricultural irrigation, wherein the number of water supply days for the agricultural irrigation is 60-100 days; the environment, traffic and other water use 4 hundred million m3, and the water supply time is uncertain. In the water intake ports, about 80% of water intake time except for cottage and rich yang is influenced by the upward movement of the salt water, the chlorine content of the river water exceeds the standards of life, industry (the chlorine content is 250mg/l) and agricultural water (the chlorine content is 1200mg/l), and water supply has to be stopped or reduced, so that great inconvenience is brought to industrial and agricultural production and people's life.
In various water uses, the domestic water has the highest requirement on the chlorine content of the qian tang river water, the highest requirement on the safety of water taking, and the agricultural water also meets properly, so the condition for calculating the salt water demand of the qian tang river mouth is that the domestic water with qualified salinity can be obtained in the Hangzhou city. Average salinity of 31-32 ‰, VestuaryThe average tidal volume 24000 km 3 of the hill station is taken. Through calculation, the fresh water flow required by the qiantang river mouth for preventing the salt water from invading is 269.8m 3/s.
2) Water balance constraint of reservoir
In time: v (I, t +1) ═ V (I, t) + (I, t) -Q (I, t) -S (I, t)) Δ t (22)
Spatially: i (2, t) ═ Q (1, t) + S (1, t) + Q1,2(t) (23)
In the formula, V (i, t) and V (i, t +1) are respectively the reservoir water storage capacity (m3) of the ith power station at the beginning and the end of the t time period; i (I, t) is the average warehousing flow (m3/s) of the ith power station in the t-th time period; q (i, t) is the power generation flow (m3/s) of the ith power station in the t period; s (1, t) is the water abandoning flow (m3/S) of the Xinanjiang power station in the t time period; q. q.s1,2(t) is the interval flow (m3/s) between the Xinanjiang and the Fuchunjiang in the t-th time period; Δ t is the t-th period duration(s); t is 1, …, T.
3) Reservoir capacity restriction
Vmin(i,t)<V(i,t)<Vmax(i,t) (24)
In the formula, Vmin(i, t) is the minimum allowable storage capacity (m3) of the ith reservoir in the tth period; vmax(i, t) is the maximum allowable storage capacity of the ith reservoir in the tth period (m 3).
According to the operation mode of the reservoir in the Xinan river and the Fuchun river, and in order to correspond to the scheduling time of the conventional scheduling chart in the Xinan river, the beginning of the third month is taken as the scheduling starting time. In addition, the Xinanjiang reservoir (namely the Qiandao lake) belongs to the first-class water body of the country and is a national-class key landscape tourist attraction area, so in order to meet the requirements of ecological landscape, the lowest water level of the Xinanjiang reservoir is generally not allowed to be lower than 89.0 m. The lowest and highest requirements of each reservoir to the water level at the beginning of each month are shown in table 5.
Table 5 upper and lower limit units of initial water levels of the new anjiang reservoir and the Fuchunjiang reservoir at each time period: m is
Figure GDA0003021399920000131
3) Power station generated flow constraint
Maximum power generation flow Q of step reservoir of Xinanjiang-Fuchunjiangmax(i, t) mainly refers to the maximum flow (m) of the water turbine in the t period of the ith power station3The flow rate of the reservoir discharge exceeds the value, namely water abandon occurs, and the water abandon flow rate is S (i, t); minimum generated current Qmin(i, t) refers to the minimum flow (m) required during the t period to meet the ecological water demand, shipping requirements, irrigation, etc. of the riverway downstream of the ith reservoir3/s), the reservoir discharge constraint is expressed as:
Figure GDA0003021399920000141
4) output constraints for hydropower stations
The output constraint of the power station mainly considers the limit of the guaranteed output and the installed capacity of the power station, and the guaranteed output of the power station under the current condition is 159.9MW and the guaranteed output of the power station under the current condition is 39.4MW by calculating the warehousing runoff data of 47 hydrologic years in Xinanjiang. The installed capacity is shown in table 2.
5) Variable boundary constraints
Vi,(K+1,1)＝Vi,(K,1) (26)
In the formula, Vi,(K,1)、Vi,(K+1,1)The initial storage capacity (m) of the i-th reservoir after K, K +1 th iteration respectively3)。
6) Non-negative constraint
All decision making physical parameters involved in the model are non-negative.
And solving the established Xinanjiang-Fuchunjiang step reservoir combined optimization scheduling model by using runoff data of the Anjiang reservoir warehousing flow and the new-rich interval runoff flow of a design series center respectively by adopting three methods of a conventional scheduling graph, an artificial bee colony algorithm and an improved artificial bee colony algorithm. The parameters of the artificial bee colony algorithm are set as follows: the population number is 25, the termination condition of the algorithm is that the maximum iteration number is 2000, the mining limit of the same honey source is 100, and the like. The initial water level of the Xinanjiang reservoir is the average value of the normal water storage level 108m and the lowest ecological water level 89m, namely 98.50 m.
The parameters of the artificial bee colony algorithm in the model solving are set as follows: the population number is 25, the termination condition of the algorithm is that the maximum iteration number is 2000, the mining limit of the same honey source is 100, and the like.
And (3) calculating the result: the average power generation of the power station system of the Xin ' anjiang-Fuchunjiang obtained by the conventional dispatch diagram is 26.59 hundred million kWh, the average power generation of the Xin ' anjiang-Fuchunjiang power station system of the Xin ' anjiang is 6.00 hundred million kWh more than that of the power station system of the conventional dispatch diagram, and the average power generation of the Xin ' anjiang-Fuchunjiang power station system of the Xin ' anjiang. Therefore, the cascade hydropower station reservoir optimal scheduling method based on the improved artificial bee colony algorithm is feasible, the improved artificial bee colony algorithm is applied to the combined optimal scheduling of the cascade hydropower station reservoir from the aspects of theoretical research and production practice, and the method has a wide space. The calculation result of the new-rich cascade power station combined dispatching under the condition of solving the current situation by using the improved artificial bee colony algorithm is shown in a table 6.
Table 6 optimal scheduling results of steps reservoir in xinanjiang-fuchunian river based on improved artificial bee colony algorithm
Figure GDA0003021399920000151

Claims (5)

1. A cascade reservoir multi-objective optimization scheduling method based on an improved artificial bee colony algorithm is characterized by comprising the following steps:
step 1: acquiring basic information data of a cascade reservoir system, and establishing a multi-target scheduling model including power generation, estuary ecology and water supply, wherein the multi-target scheduling model includes a scheduling period, the number of reservoirs, the size of a bee colony, the size of a bee collecting and following bee colony, population individual vectors, the mining limit of the same bee source and the maximum cycle number;
step 2: the detection bees generate an initial solution of an optimal honey source by adopting chaotic search, namely, an optimal power generation flow sequence is initialized;
and step 3: leading bees to search nearby the currently reserved optimal honey source by adopting a search strategy of introducing index distribution scale factors;
and 4, step 4: selecting the follower bees from the searched honey sources by adopting a self-adaptive proportion selection strategy, and changing the follower bees into leading bees;
and 5: judging whether the position of the selected honey source can be further improved within the preset mining limit, if not, leading the bees at the position to abandon the honey source to become scout bees, and turning to the step 2 to search a new honey source again, wherein the abandoned honey source is replaced by the new honey source found by the scout bees; otherwise, go to step 6;
step 6: judging a cycle termination condition, if the position of the optimal honey source is acceptable or reaches the maximum cycle number, stopping calculating and outputting the best honey source position, namely the optimal scheduling scheme of the system; otherwise, turning to the step 3 to recalculate, namely, the new leading bee starts to search a new bee source;
the objective functions of the multi-objective scheduling model comprise a power generation objective function with the largest generated energy in a scheduling period, an ecological objective function with the smallest water demand of a estuary ecological environment, and a reservoir group water supply objective function with the smallest reservoir water supply relative to the minimum water shortage degree in the scheduling period, and the specific objective functions are as follows:
the objective function of power generation is:
Figure FDA0003021399910000011
in the formula, M is the number of step reservoirs, T is the number of divided time intervals in a dispatching cycle, delta T is the duration of each time interval in the dispatching cycle, and N (i, T) is the output of the ith reservoir in the T-th time interval;
the ecological objective function is:
Figure FDA0003021399910000012
in the formula, QEcological environment(i, t) is the ecological environment water demand flow of the ith downstream riverway of the reservoir in the t-th period, and Q (i, t) is the discharge flow of the ith reservoir in the t-th period;
reservoir group water supply objective function, namely:
Figure FDA0003021399910000021
in the formula, Wk(i, t) is the amount of water supplied by the ith reservoir to the kth water supply section during the t-th period, Wk(t) local water supply amount of kth water supply section in the t-th period, Xk(T) is the water demand of the kth water supply zone in the tth period, i is 1,2, …, M, T is 1,2, …, T, K is 1,2, …, K and K is the number of water supply zones;
the method comprises the following steps of taking a maximum generated energy target in an objective function as a main objective function of a scheduling problem, converting a minimum water shortage target in a estuary ecological environment into a constraint condition for processing, and preferentially deducting a diversion flow from a reservoir warehousing runoff in solving a water supply target to enable the target to naturally meet the requirement, wherein the specific constraint condition is as follows:
(1) water balance constraint
The water balance condition of a certain hydropower station of the cascade is linked with the upstream and downstream hydropower stations in time and space, the water balance in time means that the water quantity of the reservoir at different moments of the reservoir must meet a continuity equation, and the water balance in space means that the warehousing flow of the downstream reservoir is the sum of the ex-warehouse flow of the upstream reservoir and the interval flow between the ex-warehouse flow and the upstream reservoir, specifically:
in time:
Figure FDA0003021399910000022
spatially:
I(i+1,t)＝Qdrain device(i,t)+qi-1,i(t) (6)
Wherein V (I, t) and V (I, t +1) are the storage capacity of the ith reservoir at the t th and t +1 th moments, I (I, t) and QDrain device(i, t) is the inlet runoff and the discharge flow of the ith reservoir in the t time period, Wk(I, t) is the water supply amount of the ith reservoir to the kth water supply area in the t period, I (I +1, t) is the warehousing runoff of the (I +1) th reservoir in the t period, and q is the warehousing runoff of the ith reservoir in the t periodi-1,i(t) is the interval confluence of the i-1 th reservoir and the i-th reservoir in the t-th time period;
(2) reservoir discharge restriction
The minimum flow that the reservoir must let down is used for satisfying the many-sided requirement of shipping basic flow, irrigation water of the river course of reservoir low reaches, and in flood season, the flow that lets down of reservoir must control the safety discharge within range of river course of low reaches to guarantee the flood control safety of low reaches cities and towns, dyke, it is specific:
Qmin(i,t)<Qdrain device(i,t)<Qmax(i,t) (7)
In the formula, Qmin(i,t)、Qmax(i, t) are respectively the minimum flow and the maximum flow allowed to be discharged by the ith reservoir in the tth time period, and the two values are generally comprehensively determined by the minimum flow and the flow fluctuation range of shipping, the limit of the overflow capacity of the hydraulic turbine set and the maximum discharge flow under the limit of flood control;
(3) power station output constraints
The power station output constraint comprises the guaranteed output of the power station, the maximum installed capacity and the output requirement of a power system to the power station, and specifically comprises the following steps:
Nmin(i,t)<N(i,t)<Nmax(i,t) (8)
wherein N (i, t) is the power station output of the ith reservoir in the t period, Nmin(i,t)、Nmax(i, t) are respectively the minimum and maximum output allowed by the power station in the t time period of the ith reservoir;
(4) water level or reservoir capacity constraints
The water level constraint or the reservoir capacity constraint is actually a constraint condition, the two are mutually converted according to a 'reservoir capacity-water level relation curve', the constraint is freely selected according to actual conditions in specific application, and the constraint comprises the dead water level, the normal water storage level or the flood control limit water level of the reservoir and the special limitation of the reservoir capacity in a dispatching period, and is specific:
Zmin(i,t)<Z(i,t)<Zmax(i, t) or Vmin(i,t)<V(i,t)<Vmax(i,t) (9)
In the formula: z (i, t) and V (i, t) are respectively the water level and the storage capacity of the ith reservoir in the tth period, and Zmin(i,t)、Zmax(i,t)，Vmin(i,t)、Vmax(i, t) are respectively the lowest highest water level and the minimum maximum storage capacity allowed by the ith reservoir at the tth moment;
(5) non-negative constraint
All decision physical parameters in the model are not negative:
X≥0 (10)
in the formula, X is a vector formed by decision variables;
the search strategy for introducing the index distribution scale factor specifically comprises the following steps:
3.1) each honey source corresponds to a leading bee, and the fitness value W of each honey source at present is calculatediI.e. the comprehensive benefit value corresponding to the power generation flow sequence, the bee colony evolved to the kth step is searched near the currently reserved optimal honey source, and an exponential distribution scale factor SF is introducedi jImproving the search strategy, wherein the search formula is as follows:
Figure FDA0003021399910000041
in the formula (I), the compound is shown in the specification,
Figure FDA0003021399910000042
that is, a new feasible solution is obtained for the neighborhood search if
Figure FDA0003021399910000043
If the value exceeds a certain boundary value of the solution space, the value is taken as the boundary value,
Figure FDA0003021399910000044
is [ -1,1 [ ]]D is the solution space dimension, SFi jThe method is generated from exponentially distributed random numbers, and specifically comprises the following steps:
Figure FDA0003021399910000045
in the formula, beta is [0,1 ]]The value of m is 1 or 2, rm]Is two random numbers, r 1]∈[α1,0),r[2]∈(0,α2]，α1<0,α2>0，SFi jIs taken as
Figure FDA0003021399910000046
The corresponding fitness value is more optimal
Figure FDA0003021399910000047
Or
Figure FDA0003021399910000048
3.2) if
Figure FDA0003021399910000049
Corresponding fitness value
Figure FDA00030213999100000410
Is superior to
Figure FDA00030213999100000411
Is a fitness value WiThen use
Figure FDA00030213999100000412
Instead of the former
Figure FDA00030213999100000413
Otherwise, the original state is maintained
Figure FDA00030213999100000414
And is not changed.
2. The cascade reservoir multi-objective optimization scheduling method based on the improved artificial bee colony algorithm according to claim 1, wherein the basic information data comprises: the system comprises overflow capacity values Q of a reservoir, a pump and a gate, initial and final reservoir capacity limits V of the reservoir, normal water storage level Z of the reservoir, flood control limit water level Z, dead water level Z, relationship curves S-Z of lake and reservoir capacity-water level, relationship curves Z-Q of downstream water level-discharge flow of the reservoir, a reservoir generator set output constraint value N and inflow water W.
3. The cascade reservoir multi-objective optimization scheduling method based on the improved artificial bee colony algorithm as claimed in claim 1, wherein the estuary ecological water demand is determined according to a functional demand principle, a time-sharing consideration principle, a multifunctional coordination principle and a river-sharing consideration principle, and comprises an ecological base flow f of an estuary areaBasic flowSand transportation water demand fSand conveying deviceAnd maintaining the water-salt balance water demand f of the estuary systemAnti-saltBecause the ecological water demands are mutually compatible and overlapped, the ecological environment water demand f at the river mouth is obtained according to the additivity and the maximum value principleEcological environmentThe specific calculation formula is as follows:
fecological environment＝max(fBasic flow,fSand conveying device,fAnti-salt) (3)。
4. The cascade reservoir multi-objective optimization scheduling method based on the improved artificial bee colony algorithm according to claim 1, characterized in that the specific method for generating the initial solution by the chaotic search is as follows:
2.1) generating a generating flow sequence (X) of honey sources with individual number SN at the initial moment randomly in a solution space1,X2,…,XSN) As a decision variable requiring chaotic mapping, where XiIs a D-dimensional vector;
2.2) by
Figure FDA0003021399910000051
Represents XiA component in the D-dimensional solution space is to be
Figure FDA0003021399910000052
It is mapped to [0,1 ] according to equation (11)]Chaotic variable in between
Figure FDA0003021399910000053
Figure FDA0003021399910000054
2.3) updating the chaos variable according to equation (12)
Figure FDA0003021399910000055
Figure FDA0003021399910000056
2.4) obtaining decision variables according to the formula (13)
Figure FDA0003021399910000057
Figure FDA0003021399910000058
2.5) New Current Generation sequence (X) thus obtained after chaotic Tent mapping1,X2,…,XSN) I.e. the initial solution.
5. The cascade reservoir multi-objective optimization scheduling method based on the improved artificial bee colony algorithm as claimed in claim 1, wherein the selection strategy of the adaptive proportion is specifically as follows:
4.1) leading the bee with following the bee sharing honey source information in waving dancing honeycomb, following the bee and according to the fitness of each honey source, adopting the probability that self-adaptation proportion selection mechanism calculated honey source and selecting, specifically do:
Figure FDA0003021399910000059
in the formula, WiFor the fitness value of each current honey source, SN is the individual number of the honey source, and the power exponent lambda is obtained by applying the formula (17):
Figure FDA00030213999100000510
wherein q is a coefficient when
Figure FDA00030213999100000511
When λ is 1; when in use
Figure FDA00030213999100000512
When is lambda<1, where λ → 0, Pi1/SN; when in use
Figure FDA0003021399910000061
When is lambda>1, wherein λ → ∞ and Wi<Wmax，Pi0; if W isi＝Wmax，Pi1/M, wherein M is the number of the optimal individuals;
4.2) selecting corresponding honey sources according to the probability by the following bees, and further becoming leading bees.
CN201710126746.0A 2017-03-06 2017-03-06 Multi-objective optimal scheduling method for cascade reservoir based on improved artificial bee colony algorithm Active CN106951985B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710126746.0A CN106951985B (en) 2017-03-06 2017-03-06 Multi-objective optimal scheduling method for cascade reservoir based on improved artificial bee colony algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710126746.0A CN106951985B (en) 2017-03-06 2017-03-06 Multi-objective optimal scheduling method for cascade reservoir based on improved artificial bee colony algorithm

Publications (2)

Publication Number Publication Date
CN106951985A CN106951985A (en) 2017-07-14
CN106951985B true CN106951985B (en) 2021-06-25

Family

ID=59467790

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710126746.0A Active CN106951985B (en) 2017-03-06 2017-03-06 Multi-objective optimal scheduling method for cascade reservoir based on improved artificial bee colony algorithm

Country Status (1)

Country Link
CN (1) CN106951985B (en)

Families Citing this family (37)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107491635B (en) * 2017-07-26 2020-12-18 华北电力大学 Cascade reservoir water-sand combined optimization scheduling method based on nested dimension reduction algorithm
CN107274113B (en) * 2017-07-26 2020-10-30 武汉理工大学 Novel dam maintenance decision evaluation method
CN108005016B (en) * 2017-10-31 2023-11-17 江苏省水利勘测设计研究院有限公司 Water resource allocation device and allocation method for south-to-north water diversion system
CN107808253B (en) * 2017-11-08 2021-12-17 四创科技有限公司 Irrigation area channel system real-time water distribution method based on coordination algorithm
CN107992980B (en) * 2017-12-22 2018-12-21 大连理工大学 A kind of step power station Multiobjective Optimal Operation method coupling relatively objective adjacent scale and marginal analysis principle
CN108494016B (en) * 2018-02-09 2021-12-10 华北水利水电大学 Gravitational acceleration-based bee colony algorithm optimization microgrid generator economic operation method
CN108695902B (en) * 2018-05-08 2020-02-21 华中科技大学 Ecological-power generation dynamic mutual feedback regulation and control method for cascade reservoir group
CN108487176B (en) * 2018-05-18 2024-03-01 北京水沃新华科技有限公司 Water management model
CN109325610B (en) * 2018-08-15 2020-04-03 东莞理工学院 Piecewise linear scheduling method for multi-water-source cascade pump station water transfer system
CN109272245B (en) * 2018-09-30 2021-09-07 国能大渡河流域水电开发有限公司 Method and system for automatically distributing opening of flood discharge gate of cascade hydropower station in real time
CN109583638A (en) * 2018-11-16 2019-04-05 新疆大学 A kind of multistage reservoir optimizing and dispatching method based on mixing cuckoo optimization algorithm
CN109670650B (en) * 2018-12-27 2020-08-04 华中科技大学 Multi-objective optimization algorithm-based solving method for cascade reservoir group scheduling model
CN109858173B (en) * 2019-02-18 2020-11-13 华中科技大学 Multi-target tangent method for joint scheduling of wind, light and water complementary power generation system
CN110188912B (en) * 2019-02-28 2023-01-24 西安理工大学 Improved pollen algorithm-based surface water and underground water combined scheduling optimization method
CN110163420B (en) * 2019-04-28 2021-07-27 华中科技大学 Multi-target ecological scheduling method and system based on decomposition culture evolution algorithm
CN110082717A (en) * 2019-04-30 2019-08-02 上海海事大学 A kind of underwater wireless sensor node positioning method
CN110428099B (en) * 2019-07-30 2022-09-27 中国水利水电科学研究院 Reservoir multi-target water supply capacity optimization method based on particle swarm algorithm
CN110533244B (en) * 2019-08-28 2023-04-18 重庆大学 Optimal scheduling method and system for cascade dam and computer readable storage medium
CN110569958B (en) * 2019-09-04 2022-02-08 长江水利委员会长江科学院 High-dimensional complex water distribution model solving method based on hybrid artificial bee colony algorithm
CN110717838B (en) * 2019-09-06 2021-03-16 四川大学 Multi-target scheduling evaluation system index construction and grade division method for cascade power station
CN110851977B (en) * 2019-11-06 2023-01-31 武汉大学 Water supply-power generation-ecological multi-target scheduling graph optimization method based on ecological flow
CN111005346B (en) * 2019-12-06 2021-03-19 河海大学 Reservoir group multi-objective action mechanism and optimization scheduling scheme analysis method
CN111325400B (en) * 2020-02-20 2023-06-02 内蒙古自治区水利水电勘测设计院 High-altitude long-distance water delivery positioning method and positioning system thereof
CN111353653B (en) * 2020-03-13 2020-12-11 大连理工大学 Photovoltaic output short-term interval prediction method
CN111461421B (en) * 2020-03-28 2021-02-09 华中科技大学 Cascade reservoir risk assessment method and system based on mutual feedback relationship analysis
CN111861137A (en) * 2020-06-28 2020-10-30 中国电建集团昆明勘测设计研究院有限公司 Parallel multi-target scheduling method for cascade reservoir groups
CN112036632B (en) * 2020-08-26 2021-06-04 三峡大学 Optimal scheduling method based on cascade reservoir ecological power generation multi-target medium and long term random scheduling model
CN112926880A (en) * 2021-03-27 2021-06-08 西安理工大学 Basin ecological scheduling method based on two-layer structure
CN113205250B (en) * 2021-04-28 2022-08-19 河海大学 Wind-solar-water complementary power generation plan compilation method considering reservoir scheduling risk
CN113738566B (en) * 2021-09-14 2023-09-19 国网新源控股有限公司 Over-speed protection and flow control method based on pumped storage power station
CN114548609B (en) * 2022-04-27 2022-08-05 中国水利水电科学研究院 Water resource collaborative allocation method for ecological power generation and water supply
CN114819753B (en) * 2022-06-24 2022-10-18 长江水利委员会水文局 Cascade reservoir ecological scheduling method for promoting fish spawning
CN114839943B (en) * 2022-07-04 2022-10-25 国能大渡河流域水电开发有限公司 Cascade power station gate control strategy generation and rolling optimization method and system
CN115409387B (en) * 2022-08-30 2023-04-18 华中科技大学 Reservoir optimal scheduling method and system based on improved differential evolution
CN115952958B (en) * 2023-03-14 2023-07-11 珠江水利委员会珠江水利科学研究院 Reservoir group joint optimization scheduling method based on MADDPG reinforcement learning
CN117196019B (en) * 2023-07-27 2024-03-08 南京中禹智慧水利研究院有限公司 New Anjiang model parameter calibration method based on improved self-adaptive genetic algorithm
CN116960988B (en) * 2023-09-12 2023-12-15 浙江贵仁信息科技股份有限公司 Reservoir optimal scheduling method, system, equipment and medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
流域梯级水电站群及其互联电力系统联合优化运行;廖想;《中国博士学位论文全文数据库，工程科技II辑 》;20160229(第2期);C037-7 *

Also Published As

Publication number Publication date
CN106951985A (en) 2017-07-14

Similar Documents

Publication Publication Date Title
CN106951985B (en) Multi-objective optimal scheduling method for cascade reservoir based on improved artificial bee colony algorithm
CN107506909B (en) Cascade reservoir hydropower station group scheduling control system and method for fish habitat protection
Bagher et al. Hydroelectric energy advantages and disadvantages
WO2019006733A1 (en) Long-term joint peak regulation dispatching method for trans-provincial interconnected hydropower station cluster
CN105956714B (en) Novel group search method for optimal scheduling of cascade reservoir group
CN106682810B (en) Long-term operation method of cross-basin cascade hydropower station group under dynamic production of giant hydropower station
CN103088784B (en) Cascade reservoir flood control water level real-time dynamic control method
CN104063808B (en) Trans-provincial power transmission cascade hydropower station group peak-shaving dispatching two-phase search method
CN103942728A (en) Cascade hydropower station group daily power generation plan making method
CN110739726B (en) Multi-type power supply capacity long-term planning method considering offshore wind power access
Harby et al. Pumped storage hydropower
CN109447405A (en) A kind of library multi-stag step library group&#39;s short-term plan formulating method undertaking peak regulation task
CN105427017A (en) Water power concentration power grid extra large scale power station group short period plan compiling method
CN112700080A (en) Multistage optimal scheduling method for cascade hydropower
CN105552971A (en) Regional absorption method for coordinative optimization of small hydropower station and large- and medium-sized hydropower station group
CN108425349A (en) A kind of method of construction of the big library pumped-storage power station of high dam
CN103088783B (en) Reservoir regulation scheme generating method facing ecology
CN106712060A (en) Multi-agent-based hundred-megawatt level battery energy storage system control method and system
CN111476477A (en) Power generation benefit target-based medium and long term optimization scheduling method for cascade hydropower station
Zhang et al. Self-optimization simulation model of short-term cascaded hydroelectric system dispatching based on the daily load curve
CN110826806B (en) Cascade hydropower station optimal scheduling rule making method combining aggregation reservoir and simulation optimization
Huang et al. Optimal Allocation Model of Water Resources in Tidal Flat Development of Coastal Areas
Suvitha et al. Effective hydropower renewable energy source selection using fuzzy neutrosophic boundary approximate area
Yang et al. Analysis of the Impact of the Yangtze-Huai River Diversion Project on the Hydrodynamics and Water Quality of the Xi and Zhao Rivers
CN110378805A (en) Step power station power generation dispatching is classified peak regulating method

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