CN109635999B - Hydropower station scheduling method and system based on particle swarm-bacterial foraging - Google Patents
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Abstract
The invention discloses a hydropower station dispatching method and a hydropower station dispatching system based on particle swarm-bacterial foraging, which are applied to the field of hydropower station dispatching optimization.
Description
Technical Field
The invention belongs to the field of hydropower station optimal scheduling, and particularly relates to a hydropower station scheduling method and system based on a particle swarm-bacterial foraging optimization algorithm.
Background
Hydropower station scheduling is a constrained, nonlinear and multi-stage combined optimization problem. The traditional particle swarm optimization algorithm (Particle Swarm Optimization, PSO) has the problems of high convergence speed, easy sinking into local extremum and strong dependence on parameters when applied to hydropower station scheduling, and the bacterial optimization algorithm (Bacteria Foraging Optimization, BFO) has the characteristics of strong global searching capability and low efficiency.
Therefore, how to improve the precision and efficiency of the optimal scheduling solution of the hydropower station is a technical problem to be solved at present.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a hydropower station scheduling method and system based on particle swarm-bacterial foraging, thereby solving the technical problems of lower solving precision and efficiency of the traditional particle swarm optimization algorithm and bacterial optimization algorithm.
To achieve the above object, according to one aspect of the present invention, there is provided a hydropower station scheduling method based on particle swarm-bacterial foraging, comprising:
(1) For a single hydropower station, determining a fitness function by taking the maximum total power generation amount of the hydropower station in a dispatching period as a target and taking a limiting condition in the operation process of the hydropower station as a constraint condition, and randomly generating a plurality of groups of time period end water level change sequences to be respectively used as initial position vectors of particles under the condition that the constraint condition is met, wherein one particle represents one operation strategy of the hydropower station;
(2) For each particle, acquiring current position information of each particle, substituting the current position information of each particle into the fitness function to obtain a current fitness value of each particle, and taking the particle corresponding to the maximum fitness value as a global optimal particle of the contemporary population;
(3) Updating the position and speed of each particle to obtain a next generation population, for target particles with the fitness value of the particles in the next generation population being smaller than that of the corresponding particles in the previous generation population, performing bacterial foraging chemotaxis operation on the target particles to obtain the position information of the target particles, and then taking the current position of the particle with the largest fitness value in the next generation population as the global optimal position of the next generation population;
(4) And (3) if the current iteration number does not reach the maximum iteration number, returning to the step (2), and if the current iteration number reaches the maximum iteration number, taking the global optimal position information obtained in the last iteration process as a final operation strategy of the hydropower station.
Preferably, the fitness function is:
the method comprises the steps of generating energy of a hydropower station in a total period, dividing the total period into a period number T, integrating an output coefficient A, generating flow of the hydropower station Q, and water head delta H, wherein the constraint conditions met by the fitness function are hydropower station water level constraint, output constraint, flow constraint and water balance constraint.
Preferably, the randomly generating a plurality of groups of time period end water level change sequences respectively as initial position vectors of the particles includes:
randomly generating m groups of time period end water level change sequences
Respectively as initial position vectors of m particles, wherein the information of the particles is represented by a D-dimensional vector, and the individual extremum of the particles i is P i Will P i Coordinates of->
As the current position of the particle.
Preferably, the updating the position and velocity of each particle results in a next generation population, comprising:
from the following components
Update the speed of the particles by ∈>
The position of the particles is updated, wherein, i= (1, 2,) m, d= (1, 2,., D), ω is an inertial factor, c 1 And c 2 R is the learning factor 1 And r 2 Is a random number +.>
For updating the speed of the pre-particle +.>
For the speed of the updated particles +.>
Representing the position of locally optimal particles->
Indicating the position of the particle before updating->
Representing the position of globally optimal particles->
Indicating the location of the updated particles.
According to another aspect of the present invention there is provided a hydropower station scheduling system based on particle swarm-bacterial foraging, comprising:
the model building module is used for determining a fitness function for a single hydropower station by taking the maximum total power generation amount of the hydropower station in a dispatching period as a target and taking a limiting condition in the running process of the hydropower station as a constraint condition;
the initialization module is used for randomly generating a plurality of groups of time period end water level change sequences to be respectively used as initial position vectors of particles under the condition that the constraint condition is met, wherein one particle represents one operation strategy of the hydropower station;
the fitness value determining module is used for obtaining the current position information of each particle for each particle, substituting the current position information of each particle into the fitness function to obtain the current fitness value of each particle, and taking the particle corresponding to the maximum fitness value as the global optimal particle of the contemporary population;
the updating module is used for updating the position and the speed of each particle to obtain a next generation population, for target particles with the fitness value of the particles in the next generation population being smaller than that of the corresponding particles in the previous generation population, performing bacterial foraging chemotaxis operation on the target particles to obtain the position information of the target particles, and then taking the current position of the particle with the largest fitness value in the next generation population as the global optimal position of the next generation population;
and the judging and executing module is used for returning to execute the operation of the fitness value determining module when the current iteration number does not reach the maximum iteration number, and taking the global optimal position information obtained in the last iteration process as the final operation strategy of the hydropower station when the current iteration number reaches the maximum iteration number.
In general, the above technical solutions conceived by the present invention, compared with the prior art, enable the following beneficial effects to be obtained: compared with the single hydropower station optimal scheduling method based on the particle swarm optimization algorithm and the single hydropower station optimal scheduling method based on the bacterial foraging optimization algorithm, the hydropower station optimal scheduling method based on the mixed particle swarm-bacterial foraging optimization algorithm provided by the invention combines the advantages of the two algorithms, overcomes the respective defects of the two algorithms, has good global searching capability and good local searching capability, and provides a better scheduling strategy for the hydropower station.
Drawings
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a line diagram of the upstream water level change process when the PSO-BFO algorithm and the PSO algorithm are applied, according to the embodiment of the present invention;
FIG. 3 is a graph showing the cumulative force variation process when the PSO-BFO algorithm and the PSO algorithm are applied according to the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The invention introduces the characteristic of BFO algorithm, and uses chemotactic step of BFO algorithm to update particle position of particles with the updated adaptability value smaller than the previous generation in PSO algorithm. The mixed particle swarm-bacterial foraging optimization algorithm (PSO-BFO) is provided, the water level, the power generation flow and the power output of each period in the hydropower station scheduling period are determined, and the limitations of the BFO algorithm and the PSO algorithm are overcome, so that the hydropower station optimal scheduling model is solved more effectively.
The basic idea of the invention is as follows: aiming at the characteristics of high-dimensional nonlinearity and dynamic performance of the hydropower station optimal scheduling, one particle in the particle swarm algorithm is regarded as an operation strategy of the hydropower station, the optimal result is obtained by using a hybrid algorithm, the position and the speed of the particle are calculated and updated by a PSO algorithm to finish the searching of the whole space, the optimal information of the individual and the group of the particle is memorized, then each particle in the particle swarm is regarded as a bacterium, and the function of local searching is finished by the trend and the aggregation operation of the BFO algorithm, so that the advantages of the two algorithms are fully exerted, and the precision and the efficiency of the hydropower station optimal scheduling solution are improved.
Fig. 1 is a schematic flow chart of a method according to an embodiment of the present invention, including:
s1: collecting data of a hydropower station, including water level-reservoir capacity, downstream water level-discharging flow, unit pre-thought flow line, month-by-month average flow process line, normal water storage level, dead water level, power output coefficient, installed capacity, guaranteed output, maximum machine-passing flow, flood control limiting water level and the like;
s2: establishing an optimized scheduling model
For a single hydropower station, the maximum total power generation of the hydropower station in the dispatching period is taken as a target, other conditions are taken as constraint conditions,the fitness function is determined as:
wherein N is the generated energy of the hydropower station in the total period; t is the number of divided time periods in the total time period; a is the comprehensive output coefficient, and is generally 8.5; q is the power generation flow of the hydropower station, and the initial and final water levels Z of the corresponding period t are used for Initially, the method comprises 、Z Powder (D) The water inflow I and the upstream water level reservoir capacity relation table are determined, and the specific process is as follows: inquiring the upstream water level storage capacity relation table to obtain the primary storage capacity and the final storage capacity V Initially, the method comprises 、V Powder (D) Q=i- (V) Powder (D) -V Initially, the method comprises ) T; Δh is the head, the upstream mean water level is the average of the upstream initial water level and the last water level, and the downstream mean water level is determined according to the downstream water level-flow relationship.
S3: determining hydropower station water level constraint, output constraint, flow constraint and water balance constraint:
Z t,min ≤Z t ≤Z t,max
N bt ≤N t ≤N yt
Q t,min ≤Q t ≤Q t,max
Q t ＝I-(V powder (D) -V Initially, the method comprises )/t
Wherein Z is t,min The lowest profit level of the reservoir; z is Z t,max Is the normal water storage level; n (N) bt 、N yt The guaranteed output and the expected output of the hydropower station are respectively; q (Q) t,min 、Q t,max The minimum water flow and the maximum water flow required by the comprehensive utilization of the water turbine are respectively obtained.
S4: assuming that a particle is an operation strategy of the hydropower station, the element of the particle position vector is the end water level Z of each period of the hydropower station, and the change of the end water level of each period of the reservoir must meet various constraint conditions in a model;
s5: randomly generating m groups of period end water level change sequences under the condition of meeting various constraint conditions in the step S3
I.e. randomly initializing m particles, the information of which is represented by a D-dimensional vector, wherein the individual extremum of particle i, denoted P i Coordinates thereof are +.>
Setting the current position of the particles; initializing the current evolution algebra k=1, setting K max Is the maximum evolution algebra;
s6: obtaining current position information of particles, substituting the current position information into a fitness function to calculate the current fitness value;
s7: traversing and comparing the fitness value among the particles, and taking the particle with the largest fitness value as the global optimal particle;
s8: the position and the speed of the particles are updated by using a PSO algorithm, and the position and the speed of each particle are continuously calculated to obtain the information of the next generation population X (k+1);
particle velocity and location update formula:
wherein i= (1, 2,) m; d= (1, 2,., D); omega is a non-negative constant, called inertia factor, and omega can also linearly decrease with iteration, and the value is generally [0.8,1.2 ]]Between them; c 1 、c 2 Is a learning factor non-negative constant, typically 2; r is (r) 1 、r 2 Is between [0,1 ]]Random numbers in between; v (V) id ∈(-V max ,V max )；V max Is a constant;
s9: step S10, traversing and comparing the fitness value of each updated particle with the fitness value of the particle of the previous generation, and carrying out step S10 on the particle with the increased fitness value after updating; the particle with the reduced fitness value after updating is subjected to chemotactic operation of BFO, and then step S10 is performed;
the BFO chemotactic formula is:
wherein Δ represents a unit vector in a random direction, C (i) is a step size, θ i (j+1, k, l) is the position of the particle.
S10: replacing the global optimal particles with the maximum fitness value in the whole updated group;
s11: when the number of iterations reaches a maximum, i.e. k=k max Ending the program, wherein the particle information is the global preferred solution; otherwise, let k=k+1, return to step S6.
In order to verify the superiority of the PSO-BFO algorithm provided by the invention on the solving method of the optimal scheduling model of the hydropower station, the embodiment takes the marine rock hydropower station as a research object for explanation.
And (3) selecting the daily average flow and the initial and final water level of the marine-rock hydropower station for one month as input conditions, solving the target hydropower station optimal scheduling model by using a PSO-BFO algorithm, and meanwhile, solving the target hydropower station optimal scheduling model by using the PSO algorithm as comparison to obtain the water level change process, the output process and the accumulated output process of the hydropower station under the two algorithms.
The highest water level and the lowest water level of the selected marine rock hydropower station in the scheduling period are respectively Z t , max ＝200m，Z t,min The method comprises the steps of carrying out a first treatment on the surface of the The initial and final water levels of the scheduling period are 193.000m and 193.005m respectively; the minimum power generation flow is 73m 3 Per second, the maximum power generation flow is 1292m 3 S; the installed capacity of the hydropower station is 120 ten thousand KW; the upstream water level-reservoir capacity curve and the flow-downstream water level of the hydropower station are also known.
In the calculation, the relevant parameters of the PSO-BFO algorithm are set as follows: inertia factor ω=0.8; learning factor c 1 ＝c 2 =2; population number m=50; maximum value K of population algebra max ＝100。
The calculation result of the PSO-BFO algorithm in the example is compared with the calculation result of the PSO algorithm in the example as follows:
TABLE 1
Table 1 lists the cumulative contribution and daily maximum and minimum contribution of the two algorithms over the schedule period. It can be seen that under the constraint condition of the hydropower station, the total output is improved by 1.8% by applying the PSO-BFO algorithm and is greater than that of the PSO algorithm, so that the PSO-BFO algorithm can find a better value compared with the PSO algorithm. The daily minimum output of the two algorithms is close, the daily maximum output PSO algorithm is larger than the PSO-BFO algorithm, and the PSO-BFO algorithm has smaller output change in hydropower station scheduling and is more stable compared with the PSO algorithm.
FIG. 2 is a line diagram of the upstream water level change process when the PSO-BFO algorithm and the PSO algorithm are applied, according to the embodiment of the present invention; FIG. 3 is a graph showing the cumulative force variation process when the PSO-BFO algorithm and the PSO algorithm are applied according to the embodiment of the present invention. As can be seen from fig. 2, the water level reached by using the PSO-BFO algorithm in the scheduling period is higher, the range of variation of the water level is wider, and compared with the later search range in the PSO algorithm, the cumulative output of the two algorithms is close in most time periods as can be seen from fig. 3, which indicates that the BFO-PSO algorithm does not completely break the convergence of the PSO algorithm, or the advantages of the PSO algorithm are well exerted.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (5)
1. A hydropower station scheduling method based on particle swarm-bacterial foraging, comprising:
(1) For any hydropower station, determining a fitness function by taking the maximum total power generation amount of the hydropower station in a dispatching period as a target and taking a limiting condition in the operation process of the hydropower station as a constraint condition, and randomly generating a plurality of groups of time period end water level change sequences to be respectively used as initial position vectors of particles under the condition that the constraint condition is met, wherein one particle represents one operation strategy of the hydropower station;
(2) For each particle, acquiring current position information of each particle, substituting the current position information of each particle into the fitness function to obtain a current fitness value of each particle, and taking the particle corresponding to the maximum fitness value as a global optimal particle of the contemporary population;
(3) Updating the position and speed of each particle to obtain a next generation population, carrying out the step (4) on the updated particles with the increased moderate values, carrying out bacterial foraging chemotactic operation on the updated particles with the reduced fitness values, and then carrying out the step (4);
(4) Replacing the global optimal particles with the maximum fitness value in the whole updated group;
(5) If the current iteration number does not reach the maximum iteration number, returning to the execution step (3), and if the current iteration number reaches the maximum iteration number, taking global optimal position information obtained in the last iteration process as a final operation strategy of the hydropower station;
under the combination of a particle swarm optimization algorithm and a bacterial foraging algorithm, setting parameters in the particle swarm optimization algorithm: the value of the inertia factor omega is [0.8,1.2 ]]Between them; learning factor c 1 ＝c 2 =2; population number m=50; maximum value K of population algebra max ＝100。
2. The method of claim 1, wherein the fitness function is:
wherein N is the power generation amount of the hydropower station in the total period, T is the time period divided in the total period, A is the comprehensive output coefficient, Q is the power generation flow of the hydropower station, deltaH is the water head, and the constraint condition satisfied by the fitness function is the water level constraint and the output of the hydropower stationConstraint, flow constraint, and water balance constraint.
3. The method according to claim 1 or 2, wherein the randomly generating sets of end-of-period water level change sequences as initial position vectors for each particle, respectively, comprises:
randomly generating m groups of time period end water level change sequences
Respectively as initial position vectors of m particles, wherein the information of the particles is represented by a D-dimensional vector, and the individual extremum of the particles i is P i Will P i Coordinates of (c)
As the current position of the particle.
4. A method according to claim 3, wherein said updating the position and velocity of each particle results in a next generation population, comprising:
from the following components
Update the speed of the particles by ∈>
The position of the particles is updated, wherein, i= (1, 2,) m, d= (1, 2,., D), ω is an inertial factor, c 1 And c 2 R is the learning factor 1 And r 2 Is a random number +.>
For updating the speed of the pre-particle +.>
For the speed of the updated particles +.>
Representing the position of locally optimal particles->
Indicating the position of the particle before updating->
Representing the position of globally optimal particles->
Indicating the location of the updated particles.
5. A hydropower station scheduling system based on particle swarm-bacterial foraging, comprising:
the model building module is used for determining a fitness function for a single hydropower station by taking the maximum total power generation amount of the hydropower station in a dispatching period as a target and taking a limiting condition in the running process of the hydropower station as a constraint condition;
the initialization module is used for randomly generating a plurality of groups of time period end water level change sequences to be respectively used as initial position vectors of particles under the condition that the constraint condition is met, wherein one particle represents one operation strategy of the hydropower station;
the fitness value determining module is used for obtaining the current position information of each particle for each particle, substituting the current position information of each particle into the fitness function to obtain the current fitness value of each particle, and taking the particle corresponding to the maximum fitness value as the global optimal particle of the contemporary population;
the updating module is used for updating the position and the speed of each particle to obtain a next generation population, for target particles with the fitness value of the particles in the next generation population being smaller than that of the corresponding particles in the previous generation population, performing bacterial foraging chemotaxis operation on the target particles to obtain the position information of the target particles, and then taking the current position of the particle with the largest fitness value in the next generation population as the global optimal position of the next generation population;
the judging and executing module is used for returning to execute the operation of the fitness value determining module when the current iteration number does not reach the maximum iteration number, and taking the global optimal position information obtained in the last iteration process as the final operation strategy of the hydropower station when the current iteration number reaches the maximum iteration number;
under the combination of a particle swarm optimization algorithm and a bacterial foraging algorithm, setting parameters in the particle swarm optimization algorithm: inertia factor ω=0.8; learning factor c 1 ＝c 2 =2; population number m=50; maximum value K of population algebra max ＝100。
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| CN103530709A (en) * | 2013-11-04 | 2014-01-22 | 上海海事大学 | Container quay berth and quay crane distribution method based on bacterial foraging optimization method |
| CN105373183A (en) * | 2015-10-20 | 2016-03-02 | 同济大学 | Method for tracking whole-situation maximum power point in photovoltaic array |
| CN106169109A (en) * | 2016-08-17 | 2016-11-30 | 国网江西省电力公司柘林水电厂 | A kind of Optimized Scheduling of Hydroelectric Power method based on chaos difference particle cluster algorithm |
| WO2018072351A1 (en) * | 2016-10-20 | 2018-04-26 | 北京工业大学 | Method for optimizing support vector machine on basis of particle swarm optimization algorithm |
| CN108537370A (en) * | 2018-03-23 | 2018-09-14 | 华中科技大学 | Especially big basin water station group Optimization Scheduling based on hybrid intelligent dimension-reduction algorithm |
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