CN117439190A - Water, fire and wind system dispatching method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a water, fire and wind system dispatching method, a device, equipment and a storage medium, and belongs to the technical field of power system dispatching. Firstly initializing a multi-element universe algorithm parameter, and determining a variable to be optimized based on a water, fire and wind system model; then, taking the variable to be optimized as any individual position of the multi-element universe population, and randomly initializing the multi-element universe population; then introducing corrected universe expansion probability, an individual history optimal individual exploration mechanism and a full-opposite learning mechanism into a multi-universe algorithm to carry out population iteration; and finally, after the iteration is finished, taking the global optimal position of the variable to be optimized as the optimal scheduling output of the water-fire-wind system model. The method solves the defects of insufficient diversity performance, low convergence speed and low solving precision of the conventional scheduling method in the HTW problem.
Description
Technical Field
The invention belongs to the technical field of power system dispatching, and particularly relates to a water-fire-wind system dispatching method, a device, equipment and a storage medium.
Background
In recent years, with the increasing of the exhaustion crisis of fossil energy and the emergence of extreme climates, the development of new energy sources represented by wind energy has become an inevitable choice in the current society. Wind energy is widely distributed and has large reserve, but the characteristics of intermittence, large regional difference, low conversion efficiency and the like make the short-term economic dispatching of a water-electricity-thermal power-wind power (HTW) hybrid power system more difficult. Generally, short-term economic dispatch is an important research component of power system dispatch, and a dispatching scheme with optimal total economic cost or pollutant emission target is required to be formed in a day or shorter, and meanwhile, various complex and strong coupling constraint conditions of water, fire and wind turbines are also required to be met.
In recent years, scheduling problems involving hydroelectric, thermal and wind electronic systems have been widely studied in electrical systems. The HTW problem accepted by researchers mainly comprises four hydropower stations, three thermal power stations and two wind turbines. Although the prior literature has conducted intensive researches on the HTW problem by adopting a plurality of methods, the traditional linear programming method is difficult to directly model and solve due to the strong coupling, multi-period and multi-constraint characteristics of HTW variables, and the prior heuristic algorithm has the problems of low solving efficiency, insufficient precision and even difficulty in obtaining an unconstrained damage scheme. Based on the above analysis, there is a need to find an efficient solution to get a reasonably reliable solution to the HTW problem.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a water-fire-wind system dispatching method, a device, equipment and a storage medium, which aim to solve the defects of insufficient diversity performance, low convergence speed and low solving precision of the traditional dispatching method in the HTW problem.
To achieve the above object, in a first aspect, the present invention provides a method for scheduling a water, fire and wind system, the method comprising:
initializing a multi-element universe algorithm parameter, and determining a variable to be optimized based on a water, fire and wind system model;
taking the variable to be optimized as any individual position of the multi-element universe population, and randomly initializing the multi-element universe population;
introducing corrected universe expansion probability, an individual history optimal individual exploration mechanism and a full-opposite learning mechanism into a multi-universe algorithm to carry out population iteration;
and after the iteration is finished, taking the global optimal position of the variable to be optimized as the optimal scheduling output of the water-fire-wind system model.
Preferably, the updated formula of the corrected cosmic expansion probability is as follows:
wherein,and->The universe expansion rate and the fitness value of the ith individual are respectively the kth iteration; />And->Respectively the optimal fitness and the worst fitness in the kth iteration population; />Shuttle position in the j-th dimension for the ith individual of the kth iteration; />Randomly selecting the position of an individual with index ind in the j-th dimension in the kth iterative population by a roulette manner; r is (r) 1 Is [0,1]Random numbers uniformly distributed in the interval; />The position of the ith individual in the jth dimension for the kth iteration.
Preferably, the updating formula of the individual history optimal individual exploration mechanism is as follows:
wherein,the position of the ith individual in the jth dimension for the kth iteration; />Is the firstIterating the historical optimal position of the ith individual in the j-th dimension k times; TDR is the cosmic operation speed; />Temporary locations in the j-th dimension for the ith individual of the kth iteration; />Shuttle position in the j-th dimension for the ith individual of the kth iteration; WEP is the existence probability of worm holes; r is (r) 3 And r 4 Is [0,1]Random numbers uniformly distributed in the interval.
Preferably, the kth iteration is the temporary location of the ith individual in the jth dimensionThe method comprises the following steps:
wherein C is 1 As the location-learning factor(s),UB j and LB j The upper limit and the lower limit of the j-th dimension of the variable to be optimized are respectively; r is (r) 2 Is [0,1]Random numbers uniformly distributed in the interval; />The maximum iteration number of the population; />A globally optimal position in a j-th dimension for a kth iteration;
the existence probability WEP of the worm holes and the universe running speed TDR are as follows:
wherein WEP is as follows max And WEP min The maximum value and the minimum value of the existence probability of the worm holes are respectively.
Preferably, the update formula of the full opposite learning mechanism is as follows:
wherein,full oppositional position in the j-th dimension for the ith individual of the kth iteration; /> Andreflection expansion, quasi-reflection, quasi-opposition and expansion opposition positions of the ith individual in the jth dimension for the kth iteration, respectively; p (P) Re 、P Qr 、P Qo And P Eo The occurrence probabilities of reflection expansion, quasi-reflection, quasi-opponent and expansion opponent respectively; r is (r) 5 Is [0,1]Random numbers uniformly distributed in the interval.
Preferably, the method comprises the steps of,and->The method comprises the following steps of:
wherein rand (delta) 1 ,Δ 2 ) Representing the variable delta 1 And delta 2 Random numbers uniformly distributed among the two;the position of the ith individual in the jth dimension for the kth iteration; UB (UB) j And LB j The upper limit and the lower limit of the j-th dimension of the variable to be optimized are respectively; />Representing the standard oppositional position of the ith individual in the jth dimension of the kth iteration, +.> The position of the ith individual in the jth dimension for the kth iteration; in the next iteration, _> The position vector of the ith individual for the k+1th iteration; f () represents the fitness value of the individual, < ->A position vector representing an ith individual of the kth iteration; />Representing the full of the ith individual of the kth iterationThe position vector of the opposite position, D, represents the total dimension.
Preferably, the variable X to be optimized is:wherein (1)>Indicating the delivery flow of the jth hydropower station in the t period; />Representing the output of the ith thermal power plant in the t period; />Representing the output of the first wind power plant in the t period; n, N s And N l Respectively the number of hydropower stations, thermal power plants and wind farms.
In a second aspect, the present invention provides a water-fire-wind system scheduling system, the system comprising:
the parameter initialization unit is used for initializing the parameters of the multi-element universe algorithm and determining variables to be optimized based on the water, fire and wind system model;
the population initializing unit is used for randomly initializing the multi-universe population by taking the variable to be optimized as any individual position of the multi-universe population;
the population iteration unit is used for introducing corrected universe expansion probability, an individual history optimal individual exploration mechanism and a full-opposite learning mechanism into the multi-universe algorithm to carry out population iteration;
and the output unit is used for taking the global optimal position of the variable to be optimized as the optimal scheduling output of the water-fire-wind system model after the iteration is finished.
In a third aspect, the present invention provides an electronic device comprising: a memory for storing a program; a processor for executing a memory-stored program, the processor being for performing any of the methods described in the first aspect when the memory-stored program is executed.
In a fourth aspect, the present invention provides a storage medium storing a computer program which, when run on a processor, causes the processor to perform any of the methods described in the first aspect.
In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:
according to the method, the variable to be optimized is determined based on a water, fire and wind system model, the variable to be optimized is used as any individual position of a multi-element universe population, population iteration is carried out by adopting an improved multi-element universe algorithm, individual selection pressure is increased by using corrected universe expansion probability in the improved multi-element universe algorithm, so that the probability of selecting individuals with poor performance is improved, population diversity is improved by adopting an individual history optimal individual exploration mechanism, population convergence speed and precision are improved by using a full opposite learning mechanism, and therefore the technical defects that the multi-element universe algorithm is insufficient in individual selection pressure, diversity is reduced and solving precision is insufficient in solving the water, fire and wind system problem are overcome.
Drawings
FIG. 1 is a flow chart of a water, fire and wind system dispatching method provided in an embodiment of the invention;
FIG. 2 is a graph of an optimal output process of the water, fire and wind system after optimal scheduling in an embodiment of the invention;
FIG. 3 is a diagram of the process of optimizing the traffic flow of each hydropower station after scheduling in an embodiment of the 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.
The terms "first" and "second" and the like in the description and in the claims are used for distinguishing between different objects and not for describing a particular sequential order of objects. For example, the first response message and the second response message, etc. are used to distinguish between different response messages, and are not used to describe a particular order of response messages.
In embodiments of the invention, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the description of the embodiments of the present invention, unless otherwise specified, the meaning of "plurality" means two or more, for example, the meaning of a plurality of processing units means two or more, or the like; the plurality of elements means two or more elements and the like.
Next, the technical scheme provided in the embodiment of the present invention is described.
As shown in fig. 1, the embodiment of the invention discloses a water, fire and wind system dispatching method, which comprises the following steps:
(1) Setting a multi-universe algorithm parameter and a water, fire and wind system model type, wherein the multi-universe algorithm parameter comprises the following components: cosmic operation speed TDR, worm hole existence probability WEP, population scale N and maximum iteration numberAnd an fitness function F () and upper and lower limits of variables to be optimized; after the model type of the water, fire and wind system is determined, the variable parameter composition to be optimized and the number D of the total dimensions of any individuals can be determined; in the embodiment, the adaptability function F () adopts a water, fire and wind system with minimum total cost;
(2) Randomly initializing a population in a problem space and calculating individual fitness values of the initial population;
the location of any individual in the multi-universe population is generated as follows:
wherein,the position of the ith individual in the jth dimension is the kth iteration, namely the specific parameter value of the variable to be optimized of the model; rand (0, 1) is [0,1]Random numbers uniformly distributed in intervals; UB (UB) j An upper bound for the j-th dimension of the problem space; LB (LB) j Is the lower bound of the j-th dimension of the problem space. N and D represent the population size number and the total number of variables to be optimized in the problem, respectively.
(3) Individuals in the multi-universe population are ordered according to the order of fitness from small to large.
(4) And updating the global optimal individual position and the individual history optimal position in the current multi-universe population according to the fitness.
The global optimal individual position is the individual position with small adaptability in the current population, and the individual history optimal position is the individual position when any individual in the population is stopped to the current occurrence with the minimum adaptability.
The global optimal position and the individual history optimal position are expressed as follows:
gBest is the global optimal individual position vector in the current population of the kth iteration; pBest is the current individual of the kth iterationAnd corresponding to the individual historical optimal position vector.
(5) And calculating the universe expansion probability according to the fitness in the current population.
The universe expansion probability is expressed as follows:
wherein,and->The universe expansion rate and the fitness value of the ith individual are respectively the kth iteration; />And->The optimal fitness and the worst fitness in the kth population, respectively. />Shuttle positions of the ith individual for the kth iteration of the population; />Randomly selecting the j-th dimension position of the individual with index ind in the kth iterative population by a roulette mode; r is (r) 1 Is [0,1]Random numbers with uniformly distributed intervals. />The position of the ith individual in the jth dimension is iterated for the kth iteration of the population.
(6) And introducing an individual history optimal individual exploration mechanism to promote population diversity.
The optimal individual exploration mechanism of the individual history is expressed as follows:
wherein,historical optimal positions of the ith individuals in the j-th dimension for the kth iteration of the population; />Temporary locations in the j-th dimension for the ith individual of the kth iteration; c (C) 1 For the position learning factor, ++>UB j And LB j The upper limit and the lower limit of the j-th dimension of the variable to be optimized of the water, fire and wind model are respectively; />The global optimal position in the j-th dimension for the kth iteration of the population; TDR is the cosmic operation speed; WEP is the existence probability of worm holes; WEP (web-defined Power line) max And WEP min The maximum value and the minimum value of the probability of existence of the worm holes; />Is the maximum iteration number of the population. r is (r) 2 ,r 3 ,r 4 Respectively [0,1 ]]Random numbers with uniform and separate intervals;
(7) And a full-opposite learning mechanism is introduced to improve the population convergence speed and accuracy.
The full opponent learning mechanism updates the formula as follows:
wherein,full oppositional position in the j-th dimension for the ith individual of the kth iteration; /> Andreflection expansion, quasi-reflection, quasi-opposition and expansion opposition positions of the ith individual in the jth dimension for the kth iteration, respectively; p (P) Re 、P Qr 、P Qo And P Eo The occurrence probabilities of reflection expansion, quasi-reflection, quasi-opponent and expansion opponent respectively; rand (delta) 1 ,Δ 2 ) Representing the variable delta 1 And delta 2 Random numbers uniformly distributed among the two; />Representing the standard oppositional position of the ith individual in the jth dimension for the kth iteration. />The position vector of the ith individual for the k+1th iteration; />Representing individual->Is a fitness value of (a);a position vector representing an ith individual of the kth iteration;a position vector representing the fully opposite position of the ith individual of the kth iteration.
(8) Judging whether the population searching stopping condition is reached, if the maximum iteration number is reached, considering that the global optimal position is satisfied and output as a final scheme, otherwise, turning to the step (3) to carry out repeated iteration calculation.
In order to verify the effect of the model provided by the invention, the embodiment uses a water-fire-wind system model formed by four hydropower stations, three thermal power plants and two wind power plants for experiments, and the objective function is expressed as follows:
the economic objective of the water-fire-wind system problem is to minimize the total power generation cost of thermal power and wind power in the dispatching range, which can be expressed as:
in the method, in the process of the invention,the total power generation cost of the ith thermal power plant in the t period; />The output of the ith thermal power plant in the t-th period; />Total power generation cost of the first wind farm in the t period, < >>The output of the first wind power plant in the t time period; n (N) s And N w The total number of the thermal power plants and the wind power plants are respectively; />And->Overestimated, underestimated and direct costs for the t-th period of the first individual wind farm, ++>And->Corresponding overestimated, underestimated, and direct cost coefficients, respectively; a, a i 、b i 、c i 、d i And e i Respectively the cost coefficients of the ith thermal power plant; />The minimum output of the ith thermal power plant in the t-th period.
To demonstrate the feasibility of the method of the invention, the population size of the method of the invention was set to 30. The maximum number of iterations was set at 1000 and 20 independent replicates were performed.
The methods and systems provided by the present invention were analyzed in conjunction with the relevant data, see table 1:
TABLE 1
As shown in table 1, the total output of the water, fire and wind system in each period can meet the load requirement of the power system, and the delivery flow of the water and electricity, the output of the thermal power and the output of the wind power all operate in the constraint range, which fully illustrates that the proposed strategy can be effectively combined with the original algorithm so as to improve the optimizing capability of the algorithm on the water, fire and wind system.
As shown in fig. 2 and 3, it can be seen from table 2 that the water, fire and wind system can meet the load demand by reasonably distributing the output process of each period, wherein the output processes of the thermal power plant 2 and the thermal power plant 3 are complex, which has a great relation with the self-adjusting capability. From table 3, it can be seen that each reservoir capacity can fluctuate within a reasonable range and reach a given hydropower station capacity requirement at the end of the scheduling period. The above analysis again illustrates the search capabilities and engineering utility of the proposed method.
The invention also realizes a water, fire and wind system dispatching system;
it should be understood that, the system is used to perform the method in the foregoing embodiment, and the corresponding units in the system implement principles and technical effects similar to those described in the foregoing method, and the working process of the system may refer to the corresponding processes in the foregoing method, which are not repeated herein.
Based on the method in the above embodiment, the embodiment of the invention provides an electronic device. The apparatus may include: at least one memory for storing programs and at least one processor for executing the programs stored by the memory. Wherein the processor is adapted to perform the method described in the above embodiments when the program stored in the memory is executed.
Based on the method in the above embodiment, the embodiment of the present invention provides a storage medium storing a computer program, which when executed on a processor causes the processor to perform the method in the above embodiment.
It is to be appreciated that the processor in embodiments of the invention may be a central processing unit (centralprocessing unit, CPU), other general purpose processor, digital signal processor (digital signalprocessor, DSP), application specific integrated circuit (application specific integrated circuit, ASIC), field programmable gate array (field programmable gate array, FPGA) or other programmable logic device, transistor logic device, hardware components, or any combination thereof. The general purpose processor may be a microprocessor, but in the alternative, it may be any conventional processor.
The method steps in the embodiments of the present invention may be implemented by hardware, or may be implemented by executing software instructions by a processor. The software instructions may be comprised of corresponding software modules that may be stored in random access memory (random access memory, RAM), flash memory, read-only memory (ROM), programmable ROM (PROM), erasable programmable PROM (EPROM), electrically erasable programmable EPROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a storage medium or transmitted over the storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The storage media may be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
It will be appreciated that the various numerical numbers referred to in the embodiments of the present invention are merely for ease of description and are not intended to limit the scope of the embodiments of the present invention.
It will be readily appreciated by those skilled in the art that the foregoing 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 (10)
1. A method of scheduling a water and fire wind system, the method comprising:
initializing a multi-element universe algorithm parameter, and determining a variable to be optimized based on a water, fire and wind system model;
taking the variable to be optimized as any individual position of the multi-element universe population, and randomly initializing the multi-element universe population;
introducing corrected universe expansion probability, an individual history optimal individual exploration mechanism and a full-opposite learning mechanism into a multi-universe algorithm to carry out population iteration;
and after the iteration is finished, taking the global optimal position of the variable to be optimized as the optimal scheduling output of the water-fire-wind system model.
2. The method of claim 1, wherein the updated formula for the corrected cosmic expansion probability is as follows:
wherein,and->The universe expansion rate and the fitness value of the ith individual are respectively the kth iteration; />Andrespectively the optimal fitness and the worst fitness in the kth iteration population; />Shuttle position in the j-th dimension for the ith individual of the kth iteration; />Randomly selecting the position of an individual with index ind in the j-th dimension in the kth iterative population by a roulette manner; r is (r) 1 Is [0,1]Random numbers uniformly distributed in the interval; />The position of the ith individual in the jth dimension for the kth iteration.
3. The method of claim 1, wherein the updated formula for the individual history optimal individual exploration mechanism is as follows:
wherein,the position of the ith individual in the jth dimension for the kth iteration; />Historical optimal positions of the ith individual in the jth dimension for the kth iteration; TDR is the cosmic operation speed; />Temporary locations in the j-th dimension for the ith individual of the kth iteration; />Shuttle position in the j-th dimension for the ith individual of the kth iteration; WEP is the existence probability of worm holes; r is (r) 3 And r 4 Is [0,1]Random numbers uniformly distributed in the interval.
4. A method according to claim 3, wherein the kth iteration is the temporary location of the ith individual in the jth dimensionThe method comprises the following steps:
wherein C is 1 As the location-learning factor(s),UB j and LB j The upper limit and the lower limit of the j-th dimension of the variable to be optimized are respectively; r is (r) 2 Is [0,1]Random numbers uniformly distributed in the interval; />The maximum iteration number of the population; />A globally optimal position in a j-th dimension for a kth iteration;
the existence probability WEP of the worm holes and the universe running speed TDR are as follows:
wherein WEP is as follows max And WEP min The maximum value and the minimum value of the existence probability of the worm holes are respectively.
5. The method of claim 1, wherein the updated formula of the fully contradictory learning mechanism is as follows:
wherein,full oppositional position in the j-th dimension for the ith individual of the kth iteration; /> And->Reflection expansion, quasi-reflection, quasi-opposition and expansion opposition positions of the ith individual in the jth dimension for the kth iteration, respectively; p (P) Re 、P Qr 、P Qo And P Eo The occurrence probabilities of reflection expansion, quasi-reflection, quasi-opponent and expansion opponent respectively; r is (r) 5 Is [0,1]Random numbers uniformly distributed in the interval.
6. The method of claim 5, wherein the step of determining the position of the probe is performed,and->The method comprises the following steps of:
wherein rand (delta) 1 ,Δ 2 ) Representing the variable delta 1 And delta 2 Random numbers uniformly distributed among the two;the position of the ith individual in the jth dimension for the kth iteration; UB (UB) j And LB j The upper limit and the lower limit of the j-th dimension of the variable to be optimized are respectively; />Representing the standard oppositional position of the ith individual in the jth dimension of the kth iteration, +.>The position of the ith individual in the jth dimension for the kth iteration; in the next iteration, _>The position vector of the ith individual for the k+1th iteration; f () represents the fitness value of the individual, < ->A position vector representing an ith individual of the kth iteration; />A position vector representing the full oppositional position of the ith individual of the kth iteration, D representing the total dimension.
7. The method according to claim 1, characterized in that the variable to be optimized X is:wherein (1)>Indicating the delivery flow of the jth hydropower station in the t period; />Representing the output of the ith thermal power plant in the t period; w (W) l t Representing the output of the first wind power plant in the t period; n, N s And N l Respectively the number of hydropower stations, thermal power plants and wind farms.
8. A water and fire wind system scheduling system, the system comprising:
the parameter initialization unit is used for initializing the parameters of the multi-element universe algorithm and determining variables to be optimized based on the water, fire and wind system model;
the population initializing unit is used for randomly initializing the multi-universe population by taking the variable to be optimized as any individual position of the multi-universe population;
the population iteration unit is used for introducing corrected universe expansion probability, an individual history optimal individual exploration mechanism and a full-opposite learning mechanism into the multi-universe algorithm to carry out population iteration;
and the output unit is used for taking the global optimal position of the variable to be optimized as the optimal scheduling output of the water-fire-wind system model after the iteration is finished.
9. An electronic device, comprising:
a memory for storing a program;
a processor for executing the program stored in the memory, the processor being for performing the method of any one of claims 1-7 when the program stored in the memory is executed.
10. A storage medium storing a computer program, characterized in that the computer program, when run on a processor, causes the processor to perform the method of any one of claims 1-7.
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