CN109635999A - A kind of power station dispatching method looked for food based on population-bacterium and system - Google Patents
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Abstract
The invention discloses a kind of power station dispatching method looked for food based on population-bacterium and systems, applied to Optimized Scheduling of Hydroelectric Power field, the present invention has the characteristics that high dimensional nonlinear and dynamic for Optimized Scheduling of Hydroelectric Power, a particle in particle swarm algorithm is regarded as the operation reserve in a power station, optimal result is found out with hybrid algorithm, first by the calculating of PSO algorithm and the position and speed of more new particle, to complete the search in entire space, remember the lower individual of particle and the optimal information of group, then particle a part of in population is all regarded as a bacterium, the function of local search is completed by the trend operation of BFO algorithm again, the advantage of two kinds of algorithms is given full play to, improve precision and efficiency that Optimized Scheduling of Hydroelectric Power solves.
Description
Technical field
The invention belongs to Optimized Scheduling of Hydroelectric Power field, looked for food optimization more particularly, to one kind based on population-bacterium
The power station dispatching method and system of algorithm.
Background technique
Power station scheduling is about a beam intensity, non-linear and multistage combinatorial optimization problem.Traditional particle group optimizing
Algorithm (Particle Swarm Optimization, PSO) is applied to power station scheduling the inside there are fast convergence rate but is easy
It falls into local extremum and relies on parameter strong problem, and bacterial optimization algorithm (Bacteria Foraging
Optimization, BFO) have the characteristics that ability of searching optimum is strong but low efficiency.
Therefore, the precision and efficiency that how to improve Optimized Scheduling of Hydroelectric Power solution are the technical problems of current urgent need to resolve.
Summary of the invention
Aiming at the above defects or improvement requirements of the prior art, the present invention provides one kind is looked for food based on population-bacterium
Power station dispatching method and system, thus solve that traditional particle swarm optimization algorithm and bacterial optimization algorithm are existing to solve essence
Degree and the lower technical problem of efficiency.
To achieve the above object, according to one aspect of the present invention, a kind of water looked for food based on population-bacterium is provided
Power station dispatching method, comprising:
(1) for single power station, with power station, total power generation is up to target in schedule periods, with hydroelectric station operation mistake
Restrictive condition in journey is that constraint condition determines fitness function, in the case where meeting the constraint condition, if random generate
Initial position vector of the dry group period end SEA LEVEL VARIATION sequence respectively as each particle a, wherein particle indicates power station
A kind of operation reserve；
(2) for each particle, the current location information of each particle is obtained, and respectively by the current location information of each particle
It substitutes into the fitness function and obtains the current fitness value of each particle, and using fitness value maximum when corresponding particle is as working as
For global optimum's particle of population；
(3) position and speed for updating each particle obtains next-generation population, in the next-generation population particle it is suitable
It answers angle value to be less than the intended particle for corresponding to the fitness value of particle in previous generation population, bacterium is carried out to the intended particle and is looked for food
Chemotactic operates to obtain the location information of the intended particle, then works as fitness value maximum particle in the next-generation population
Global optimum position of the front position as the next-generation population；
(4) if current iteration number does not reach maximum number of iterations, (2) are returned to step, if current iteration time
Number reaches the maximum number of iterations, then using global optimum's location information obtained in last time iterative process as power station
Final operation reserve.
Preferably, the fitness function are as follows:Wherein, N is that power station generates electricity within total period
Amount, T are the when number of segment divided in total period, and A is comprehensive power factor, and Q is hydropower station flow, and Δ H is head, described suitable
The constraint condition that response function meets is hydropower station water level constraint, units limits, traffic constraints and water balance constraint.
Preferably, it is described it is random generate several groups period end SEA LEVEL VARIATION sequence respectively as each particle initial position to
Amount, comprising:
It is random to generate m group period end SEA LEVEL VARIATION sequenceMake respectively
For the initial position vector of m particle, wherein the information of particle indicates that the individual extreme value of particle i is P with D dimensional vectori, by Pi
CoordinateCurrent location as particle.
Preferably, the position and speed for updating each particle obtains next-generation population, comprising:
ByThe speed of more new particle, by
The position of more new particle, wherein (1,2 ..., m), d=(1,2 ..., D), ω are inertial factor, c to i=1With c2For study because
Son, r1With r2For random number,For update before particle speed,For update after particle speed,Indicate local optimum
The position of particle,Indicate the position of particle before updating,Indicate the position of global optimum's particle,Indicate grain after updating
The position of son.
It is another aspect of this invention to provide that providing a kind of power station scheduling system looked for food based on population-bacterium, packet
It includes:
Model construction module is used for for single power station, and with power station, total power generation is up to target in schedule periods,
Fitness function is determined as constraint condition using the restrictive condition during hydroelectric station operation；
Initialization module, in the case where meeting the constraint condition, the random several groups period end water level that generates to become
Change sequence respectively as the initial position vector of each particle, wherein a particle indicates a kind of operation reserve in power station；
Fitness value determining module, for obtaining the current location information of each particle, and respectively will be each for each particle
The current location information of particle substitutes into the fitness function and obtains the current fitness value of each particle, and fitness value is maximum
When global optimum's particle as contemporary population of corresponding particle；
Update module, the position and speed for updating each particle obtain next-generation population, for the next-generation population
The fitness value of middle particle be less than previous generation population in correspond to particle fitness value intended particle, to the intended particle into
Row bacterium chemotactic of looking for food operates to obtain the location information of the intended particle, then most by fitness value in the next-generation population
Global optimum position of the current location of big particle as the next-generation population；
Judge execution module, executes described fit for returning when current iteration number does not reach maximum number of iterations
The operation for answering angle value determining module, when current iteration number reaches the maximum number of iterations, by last time iterative process
Obtained in final operation reserve of global optimum's location information as power station.
In general, through the invention it is contemplated above technical scheme is compared with the prior art, can obtain down and show
Beneficial effect: it is proposed by the present invention to be looked for food the Optimized Scheduling of Hydroelectric Power method of optimization algorithm based on hybrid particle swarm-bacterium, and it is single
Optimized Scheduling of Hydroelectric Power method based on particle swarm optimization algorithm and bacterium look for food the Optimized Scheduling of Hydroelectric Power side of optimization algorithm
The advantages of method is compared, and two kinds of algorithms are combined overcomes the disadvantage of two kinds of algorithms respectively, not only there is good ability of searching optimum,
There are also the abilities of good local search, provide more preferably scheduling strategy for power station.
Detailed description of the invention
Fig. 1 is a kind of method flow schematic diagram provided in an embodiment of the present invention；
Fig. 2 is provided in an embodiment of the present invention a kind of with variation of water level process when PSO-BFO algorithm and PSO algorithm
Line chart；
Fig. 3 is provided in an embodiment of the present invention a kind of with PSO-BFO algorithm and power output change procedure accumulative when PSO algorithm
Figure.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
Not constituting a conflict with each other can be combined with each other.
The characteristics of present invention introduces BFO algorithms, particle of the fitness value less than previous generation uses after updating in PSO algorithm
The chemotactic step of BFO algorithm updates particle position.A kind of mixed population-bacterium is proposed to look for food optimization algorithm (PSO-
BFO), determine the water level of day part, generating flow and power output in the dispatching cycle of power station, overcome two kinds of algorithms of BFO and PSO
Limitation, thus significantly more efficient solution Optimized Scheduling of Hydroelectric Power model.
Basic thought of the invention are as follows: have the characteristics that high dimensional nonlinear and dynamic for Optimized Scheduling of Hydroelectric Power,
A particle in particle swarm algorithm regards the operation reserve in a power station as, with hybrid algorithm find out it is optimal as a result,
First calculated by PSO algorithm and the position and speed of more new particle, to complete the search in entire space, remember the individual of lower particle with
Then the optimal information of group regards each particle in population as a bacterium, then by the trend of BFO algorithm and
Aggregation operator completes the function of local search, has given full play to the advantage of two kinds of algorithms, improves what Optimized Scheduling of Hydroelectric Power solved
Precision and efficiency.
It is as shown in Figure 1 a kind of method flow schematic diagram provided in an embodiment of the present invention, comprising:
S1: collecting the data in power station, including water level-storage capacity, the level of tail water-letdown flow, unit envision out the line of force, by
Moon average flow graph, normal pool level, level of dead water, power factor, installed capacity, guarantee power output, most greater than machine flow and
Flood control etc.；
S2: Optimal Operation Model is established
For single power station, with power station, total power generation is up to target in schedule periods, and other conditions are as constraint
Condition determines fitness function are as follows:
Wherein, N is power station generated energy within total period；T is the when number of segment divided in total period；A is comprehensive power output system
Number, is generally taken as 8.5；Q is hydropower station flow, according to the first last water level Z of corresponding period tJust、ZEnd, Lai Shuiliang I and on
Swimming position storage capacity relation table determines, detailed process are as follows: inquiry upstream water level storage capacity relation table obtains just storage capacity and last storage capacity VJust、
VEnd, then Q=I- (VEnd-VJust)/t；Δ H is head, subtracts downstream mean water, upstream average water for this period upstream mean water
Position is the average value of upstream first water level and last water level, and downstream mean water is determined according to the level of tail water-discharge relation.
S3: hydropower station water level constraint, units limits, traffic constraints and water balance constraint are determined:
Zt,min≤Zt≤Zt,max
Nbt≤Nt≤Nyt
Qt,min≤Qt≤Qt,max
Qt=I- (VEnd-VJust)/t
Wherein, Zt,minFor the minimum emerging sharp water level of reservoir；Zt,maxFor normal pool level；Nbt、NytThe respectively guarantee in power station
Power output and anticipation power output；Qt,min、Qt,maxThe respectively minimum water flow of hydraulic turbine requirements of comprehensive utilization and most serious offense water flow
Amount.
S4: it is assumed that a particle is exactly a kind of operation reserve in power station, the element of particle position vector is that power station is each
The variation of period end water level Z, reservoir day part end water level must satisfy the various constraint conditions in model；
S5: in meeting step S3 under the conditions of various restrict, it is randomly generated m group period end SEA LEVEL VARIATION sequenceThat is m particle of random initializtion, the information D dimensional vector table of particle
Show, wherein the individual extreme value of particle i, is denoted as Pi, its coordinate It is set as particle
Current location；Current evolutionary generation k=1 is initialized, K is setmaxFor maximum evolutionary generation；
S6: the current location information of particle is obtained, and is substituted into the size that fitness function calculates current adaptive value；
S7: more each interparticle fitness value size of traversal, taking the maximum particle of adaptive value is global optimum's particle；
S8: the position and speed of particle, which updates, uses PSO algorithm, constantly calculates the position and speed of each particle, obtains
The information of next-generation population X (k+1)；
Particle rapidity and location update formula:
Wherein, (1,2 ..., m) i=；D=(1,2 ..., D)；ω is nonnegative constant, referred to as inertial factor, and ω can also be with
With iterative linear reduce, value is generally between [0.8,1.2]；c1、c2For Studying factors nonnegative constant, generally 2；r1、
r2For the random number between [0,1]；Vid∈(-Vmax,Vmax)；VmaxFor constant；
S9: the fitness value of particle and the adaptive value of previous generation particle after the more each update of traversal will adapt to after update
The increased particle of angle value carries out step S10；The particle that fitness value after update reduces first is carried out to the chemotactic operation of BFO, then
Step S10 is carried out again；
BFO chemotactic formula are as follows:
Wherein, Δ indicates that a unit vector in random direction, C (i) are a step-length, θi(j+1, k, l) is particle
Position.
S10: the maximum particle of global optimum's particle fitness value is substituted in entire updated group；
S11: when the number of iterations reaches maximum value i.e. k=KmaxWhen, terminate program, at this time particle information be it is global compared with
Excellent solution；Otherwise, k=k+1, return step S6 are enabled.
To verify superiority of the PSO-BFO algorithm proposed by the present invention in Optimized Scheduling of Hydroelectric Power model solution method,
The present embodiment using every the power station He Yan as research object explanation.
It chooses every the one month daily mean flow in the power station He Yan and first last water level as input condition, with PSO-
BFO algorithm solves target Optimized Scheduling of Hydroelectric Power model, while with PSO algorithm to target Optimized Scheduling of Hydroelectric Power model
Solved the SEA LEVEL VARIATION process for obtaining power station under two kinds of algorithms as a comparison, day part power output and accumulative power output process.
Choose every peak level of the power station He Yan in this schedule periods and lowest water level be respectively Zt,max=
200m, Zt,min；The first last water level of schedule periods is respectively 193.000m and 193.005m；Minimum generating flow is 73m3/ s, it is maximum
Generating flow is 1292m3/s；The installed capacity in power station is 1,200,000 KW；Upstream water level-the storage-capacity curve and flow-in power station
The level of tail water it is also known that.
When calculating, the relative parameters setting of PSO-BFO algorithm is as follows: inertial factor ω=0.8；Studying factors c1=c2
=2；Population quantity m=50；Population algebraic maximum Kmax=100.
By the calculated result of PSO-BFO algorithm of the invention in instances and PSO algorithm, calculated result is compared in instances
It is as follows:
Table 1
Table 1 lists accumulative power output and day maximum, minimum load of two kinds of algorithms in schedule periods.As can be seen that in water
Under the constraint condition of power station, it is greater than PSO algorithm with PSO-BFO algorithm gross capability, total power output improves 1.8%, and explanation is compared
PSO algorithm, PSO-BFO algorithm can seek the more figure of merit.And the day minimum load of two kinds of algorithms is close, day maximum output PSO algorithm
Greater than PSO-BFO algorithm, illustrate that the variation that PSO-BFO algorithm is contributed in power station is dispatched is smaller, more compared to PSO algorithm
Add steady.
Fig. 2 is provided in an embodiment of the present invention a kind of with variation of water level process when PSO-BFO algorithm and PSO algorithm
Line chart；Fig. 3 is provided in an embodiment of the present invention a kind of with PSO-BFO algorithm and power output change procedure accumulative when PSO algorithm
Figure.As shown in Figure 2, the water level reached in schedule periods with PSO-BFO algorithm is higher, and the variation range of water level is wider, compares
The algorithm middle and later periods search range PSO is big, from the figure 3, it may be seen that the accumulative power output of two kinds of algorithms is close in most of period, illustrates BFO-
There is no break PSO convergence, or the fine advantage for having played PSO algorithm completely for PSO algorithm.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should all include
Within protection scope of the present invention.
Claims (5)
1. a kind of power station dispatching method looked for food based on population-bacterium characterized by comprising
(1) for any power station, with power station, total power generation is up to target in schedule periods, during hydroelectric station operation
Restrictive condition be constraint condition determine fitness function, in the case where meeting the constraint condition, generate several groups at random
Initial position vector of the period end SEA LEVEL VARIATION sequence respectively as each particle a, wherein particle indicates the one kind in power station
Operation reserve；
(2) for each particle, the current location information of each particle is obtained, and respectively substitutes into the current location information of each particle
The fitness function obtains the current fitness value of each particle, and using fitness value maximum when corresponding particle is as the present age kind
Global optimum's particle of group；
(3) position and speed for updating each particle obtains next-generation population, for the fitness of particle in the next-generation population
Value is less than the intended particle for correspond to fitness value of particle in previous generation population, looks for food chemotactic to intended particle progress bacterium
Operation obtains the location information of the intended particle, then by the present bit of fitness value maximum particle in the next-generation population
Set the global optimum position as the next-generation population；
(4) if current iteration number does not reach maximum number of iterations, (2) are returned to step, if current iteration number reaches
To the maximum number of iterations, then most using global optimum's location information obtained in last time iterative process as power station
Whole operation reserve.
2. the method according to claim 1, wherein the fitness function are as follows:Its
In, N is power station generated energy within total period, and T is the when number of segment divided in total period, and A is comprehensive power factor, and Q is water power
Power plant discharge, Δ H are head, and the constraint condition that the fitness function meets is hydropower station water level constraint, units limits, stream
Amount constraint and water balance constraint.
3. method according to claim 1 or 2, which is characterized in that the random generation several groups period end SEA LEVEL VARIATION
Initial position vector of the sequence respectively as each particle, comprising:
It is random to generate m group period end SEA LEVEL VARIATION sequenceRespectively as m
The initial position vector of particle, wherein the information of particle indicates that the individual extreme value of particle i is P with D dimensional vectori, by PiCoordinateCurrent location as particle.
4. according to the method described in claim 3, it is characterized in that, the position and speed for updating each particle obtains the next generation
Population, comprising:
ByThe speed of more new particle, byUpdate grain
The position of son, wherein (1,2 ..., m), d=(1,2 ..., D), ω are inertial factor, c to i=1With c2For Studying factors, r1With
r2For random number,For update before particle speed,For update after particle speed,Indicate the position of local optimum particle
It sets,Indicate the position of particle before updating,Indicate the position of global optimum's particle,Indicate the position of particle after updating.
5. a kind of power station scheduling system looked for food based on population-bacterium characterized by comprising
Model construction module, for for single power station, total power generation to be up to target in schedule periods with power station, with water
Restrictive condition in the operational process of power station is that constraint condition determines fitness function；
Initialization module, for generating several groups period end SEA LEVEL VARIATION sequence at random in the case where meeting the constraint condition
Arrange the initial position vector respectively as each particle, wherein a particle indicates a kind of operation reserve in power station；
Fitness value determining module, for obtaining the current location information of each particle, and respectively by each particle for each particle
Current location information substitute into the fitness function and obtain the current fitness value of each particle, and by when fitness value maximum pairs
Global optimum particle of the particle answered as contemporary population；
Update module, the position and speed for updating each particle obtain next-generation population, for grain in the next-generation population
The fitness value of son is less than the intended particle that the fitness value of particle is corresponded in previous generation population, carries out to the intended particle thin
Bacterium chemotactic of looking for food operates to obtain the location information of the intended particle, then by fitness value maximum grain in the next-generation population
Global optimum position of the current location of son as the next-generation population；
Execution module is judged, for returning and executing the fitness when current iteration number does not reach maximum number of iterations
It is worth the operation of determining module, when current iteration number reaches the maximum number of iterations, will be obtained in last time iterative process
Final operation reserve of the global optimum's location information arrived as power station.
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| CN110569958A (en) * | 2019-09-04 | 2019-12-13 | 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 |
| CN111142387A (en) * | 2020-01-09 | 2020-05-12 | 东南大学 | Limited generalized predictive control method based on improved particle swarm optimization |
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