CN108345998B - Hybrid rice algorithm-based water, fire and electricity economic dispatching method and system - Google Patents

Hybrid rice algorithm-based water, fire and electricity economic dispatching method and system Download PDF

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CN108345998B
CN108345998B CN201810132242.4A CN201810132242A CN108345998B CN 108345998 B CN108345998 B CN 108345998B CN 201810132242 A CN201810132242 A CN 201810132242A CN 108345998 B CN108345998 B CN 108345998B
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叶志伟
金灿
苏军
王春枝
孙爽
任紫扉
陈凤
杨娟
张旭
郑逍
孙一恒
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Abstract

The invention discloses a hybrid rice algorithm-based water, fire and electricity economic dispatching method and system, wherein the method comprises the following steps: determining the power generation range of a hydraulic power plant and the power generation range of a thermal power plant; randomly selecting any numerical value in the power generation range of the hydraulic power plant as the initial power generation of the hydraulic power plant, and selecting any numerical value in the power generation range of the thermal power plant as the initial power generation of the thermal power plant; constructing a first objective function and a second objective function; carrying out optimal value solution on the first objective function to obtain a first optimal value; carrying out optimal value solution on the second objective function to obtain a second optimal value; determining the corresponding optimal power generation capacity of the thermal power plant and the optimal power generation capacity of the hydraulic power plant; determining an optimal distribution ratio according to the optimal power generation capacity of the thermal power plant and the optimal power generation capacity of the hydraulic power plant; and determining an optimal scheduling scheme according to the optimal distribution ratio. The method or the system of the invention is adopted to carry out multi-target reactive power optimization based on the hybrid rice algorithm on the water, fire and electricity dispatching, thereby realizing the optimal dispatching of water, fire and electricity economy.

Description

Hybrid rice algorithm-based water, fire and electricity economic dispatching method and system
Technical Field
The invention relates to the field of water-fire-electricity economic dispatching, in particular to a water-fire-electricity economic dispatching method and system based on a hybrid rice algorithm.
Background
With the surge of electricity demand and the increasing exhaustion of fossil energy, the efficient utilization of non-renewable energy becomes more and more important, and it is necessary to research the efficient utilization of fuel in thermal power plants. The hydraulic power dispatching means that the output share of the hydraulic power plant is fully exerted under the condition that a series of constraint conditions are met through a corresponding decision criterion in a certain operation period, so that the aim of minimizing the fuel cost of the thermal power plant is fulfilled. In the dispatching process, not only the self constraints of the hydraulic power plant and the thermal power plant are considered, but also the network loss in the transmission process of the power grid, the valve point effect of the fuel cost function of the thermal power plant, the forbidden discharge area of the reservoir and other factors are considered. The complexity of the problem is increased, and meanwhile, the research on the problem of the scheduling decision of the hydroelectric power has important significance.
The method for solving the water, fire and electricity optimization scheduling problem mainly comprises two main categories of a traditional numerical analysis method and an artificial intelligence algorithm, and the traditional method has low solving quality when facing a complex nonlinear optimization problem due to the self-precision problem or the dimension disaster problem. However, when facing a high-dimensionality, large-scale and multi-constraint water-fire-electricity system, the traditional particle swarm algorithm is easy to fall into a local optimal solution and cannot ensure global convergence, and the constraint conditions of the water-fire-electricity system are relatively complex. For the processing of the equality constraint, the existing processing method mainly increases the penalty coefficient to inhibit the possibility of violating the constraint, but the method cannot completely ensure that the particles in the iterative process do not violate the constraint, so that a lot of infeasible solutions are generated, the simulation time is consumed, and the efficiency of the algorithm is reduced.
Disclosure of Invention
The invention aims to provide a hybrid rice algorithm-based water, fire and electricity economic dispatching method and system, which are used for performing multi-target reactive power optimization based on a hybrid rice algorithm on water, fire and electricity dispatching and solve the problem that global search cannot be performed in the optimal dispatching solving process and local optimization is easy to fall into.
In order to achieve the purpose, the invention provides the following scheme:
a water, fire and electricity economic dispatching method based on a hybrid rice algorithm comprises the following steps:
determining the power generation range of the hydraulic power plant and the power generation range of the thermal power plant according to the minimum power generation amount and the maximum power generation amount of the hydraulic power plant and the minimum power generation amount and the maximum power generation amount of the thermal power plant in a set time period;
randomly selecting any numerical value in the power generation range of the hydraulic power plant as initial power generation of the hydraulic power plant, and selecting any numerical value in the power generation range of the thermal power plant as initial power generation of the thermal power plant;
constructing a first objective function according to the fuel cost of the thermal power plant;
constructing a second objective function according to the hydropower plant cost;
performing optimal value solving on the first objective function according to a hybrid rice algorithm and the initial power generation amount of the thermal power plant to obtain a first optimal value;
performing optimal value solving on the second objective function according to a hybrid rice algorithm and the initial power generation amount of the hydropower plant to obtain a second optimal value;
determining the corresponding optimal power generation amount of the thermal power plant according to the first optimal value;
determining the corresponding optimal power generation amount of the hydraulic power plant according to the second optimal value;
determining an optimal distribution ratio according to the optimal power generation capacity of the thermal power plant and the optimal power generation capacity of the hydraulic power plant;
and determining an optimal scheduling scheme according to the optimal distribution ratio.
Optionally, the first objective function is specifically:
Figure GDA0002984388720000021
wherein F represents the total cost of the thermal power plant; t represents a total scheduling period; n is a radical ofsRepresenting the total number of thermal power plants; psi,tIndicating the power generation of a thermal power plant；fi(Psi,t) Representing the power generation amount P of the ith thermal power plant during the period tsi,tAs a function of fuel cost.
Optionally, the fuel cost function is specifically: f. ofi(Psi,t)＝asi+bsiPsi,t+csiP2 si,tWherein a issi，bsi，csiCoefficients of said fuel cost function for the ith thermal power plant.
Optionally, the determining an optimal distribution ratio according to the optimal power generation capacity of the thermal power plant and the optimal power generation capacity of the hydraulic power plant specifically includes: according to the system load balance constraint formula
Figure GDA0002984388720000022
Determining the optimal distribution ratio, wherein Psi,tRepresenting the power generation amount, P, of the i-th thermal power plant during a period thj,tRepresenting the power generation amount of the jth hydropower plant in the t period; pD，tRequired for load of t period, NsIndicates the total number of thermal power plants, NhRepresenting the total number of hydraulic power plants.
Optionally, the obtaining of the first optimal value by solving the optimal value of the first objective function according to the hybrid rice algorithm in combination with the initial power generation amount of the thermal power plant specifically includes:
initializing a first rice population, a first maximum breeding time and a first maximum selfing time, wherein the first rice population is composed of a plurality of first rice individuals, the first rice individuals represent values of the first objective function, and the initial power generation amount of the thermal power plant is selected as the first rice individuals;
respectively calculating a first fitness value of the first rice individuals in the first rice population; the first fitness value represents the quality of the first rice individual in the first rice population;
sequencing the first rice individuals according to the first fitness value to obtain a first individual fitness sequence;
dividing the first individual fitness sequence into a first maintenance line, a first sterile line and a first recovery line;
crossing the first maintainer line and the first sterile line to generate a new first sterile line individual;
selfing the first restoring line to generate a new first restoring line individual;
and obtaining a first optimal individual according to the new first sterile line individual and the new first restorer line individual, wherein the first optimal individual is the first optimal value.
Optionally, the solving of the optimal value of the second objective function according to the hybrid rice algorithm in combination with the initial power generation of the hydropower plant to obtain the second optimal value specifically includes:
initializing a second rice population, a second maximum breeding frequency and a second maximum selfing frequency, wherein the second rice population is composed of a plurality of second rice individuals, the second rice individuals represent values of the second objective function, and the initial power generation amount of the hydraulic power plant is selected as the second rice individuals;
respectively calculating a second fitness value of the second rice individuals in the second rice population; the second fitness value represents the quality of the second rice individual in the second rice population;
sorting the second rice individuals according to the second fitness value to obtain a second individual fitness sequence;
dividing the second fitness sequence into a second maintainer line, a second sterile line and a second restorer line;
crossing the second maintainer line with the second sterile line to generate a new second sterile line individual;
selfing the second restorer line to generate a new second restorer line individual;
and obtaining a second optimal individual according to the new second sterile line individual and the new second restorer line individual, wherein the second optimal individual is the second optimal value.
A water, fire and electricity economic dispatching system based on a hybrid rice algorithm comprises:
the data determining module is used for determining the generating capacity range of the hydraulic power plant and the generating capacity range of the thermal power plant according to the minimum generating capacity and the maximum generating capacity of the hydraulic power plant and the minimum generating capacity and the maximum generating capacity of the thermal power plant in a set time period;
the initial value determining module is used for randomly selecting any numerical value in the power generation range of the hydraulic power plant as the initial power generation of the hydraulic power plant and any numerical value in the power generation range of the thermal power plant as the initial power generation of the thermal power plant;
the first objective function construction module is used for constructing a first objective function according to the fuel cost of the thermal power plant;
the second objective function construction module is used for constructing a second objective function according to the cost of the hydraulic power plant;
the first optimal value acquisition module is used for carrying out optimal value solution on the first objective function according to a hybrid rice algorithm and the initial power generation amount of the thermal power plant to obtain a first optimal value;
the second optimal value acquisition module is used for carrying out optimal value solution on the second objective function according to a hybrid rice algorithm and the initial power generation amount of the hydropower plant to obtain a second optimal value;
the thermal power plant power generation determining module is used for determining the corresponding optimal thermal power plant power generation amount according to the first optimal value;
the hydraulic power plant power generation determining module is used for determining the corresponding optimal hydraulic power plant power generation amount according to the second optimal value;
the optimal distribution ratio determining module is used for determining an optimal distribution ratio according to the optimal power generation capacity of the thermal power plant and the optimal power generation capacity of the hydraulic power plant;
and the optimal scheme determining module is used for determining an optimal scheduling scheme according to the optimal distribution ratio.
Optionally, the first objective function constructing module is: according to the formula
Figure GDA0002984388720000041
Constructing a first objective function; wherein F represents the total cost of the thermal power plant; t represents a total scheduling period; n is a radical ofsRepresenting the total number of thermal power plants; psi,tTo representThe generated energy of the thermal power plant; f. ofi(Psi,t) Representing the power generation amount P of the ith thermal power plant during the period tsi,tAs a function of fuel cost.
Optionally, the first objective function further includes: a fuel cost function determination unit for determining the cost of fuel according to the formula fi(Psi,t)＝asi+bsiPsi,t+csiP2 si,tDetermining a fuel cost function, wherein asi，bsi，csiCoefficients of said fuel cost function for the ith thermal power plant.
Optionally, the optimal distribution ratio determining module specifically includes: according to the system load balance constraint formula
Figure GDA0002984388720000051
Determining the optimal distribution ratio, wherein Psi,tRepresenting the power generation amount, P, of the i-th thermal power plant during a period thj,tRepresenting the power generation amount of the jth hydropower plant in the t period; pD，tN required for load of t periodsIndicates the total number of thermal power plants, NhRepresenting the total number of hydraulic power plants.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the method, the maximum and minimum generated energy of the hydropower station and the thermal power station are collected, the optimal value of the cost of the hydropower station and the thermal power station is solved by adopting a rice algorithm, the corresponding optimal generated energy of the hydropower station and the thermal power station is obtained according to the optimal value of the cost, the optimal distribution ratio is formed, and the water, the fire and the electricity are further dispatched. The rice algorithm is adopted for scheduling, so that global search can be performed, and local optimization is not easy to fall into. But does not give up local optimization, gives consideration to both local and global, and the principle is simple, easily realizes, and the commonality is strong. The method has the advantages of strong optimization capability, low calculation complexity and high calculation speed, is suitable for various optimization problems, and is suitable for solving the multi-objective optimization problem.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a water-fire-electricity economic dispatching method according to an embodiment of the invention;
fig. 2 is a structural diagram of a water-fire-electricity economic dispatching system according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a hybrid rice algorithm-based water, fire and electricity economic dispatching method and system, which are used for performing multi-target reactive power optimization based on a hybrid rice algorithm on water, fire and electricity dispatching and solve the problem that global search cannot be performed in the optimal dispatching solving process and local optimization is easy to fall into.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flow chart of a water-fire-electricity economic dispatching method according to an embodiment of the invention. Referring to fig. 1, a hybrid rice algorithm-based water-fire-electricity economic dispatching method includes:
step 101: determining the power generation range of the hydraulic power plant and the power generation range of the thermal power plant according to the minimum power generation amount and the maximum power generation amount of the hydraulic power plant and the minimum power generation amount and the maximum power generation amount of the thermal power plant in a set time period;
step 102: randomly selecting any numerical value in the power generation range of the hydraulic power plant as initial power generation of the hydraulic power plant, and selecting any numerical value in the power generation range of the thermal power plant as initial power generation of the thermal power plant;
step 103: constructing a first objective function according to the fuel cost of the thermal power plant;
step 104: constructing a second objective function according to the hydropower plant cost;
step 105: performing optimal value solving on the first objective function according to a hybrid rice algorithm and the initial power generation amount of the thermal power plant to obtain a first optimal value;
step 106: performing optimal value solving on the second objective function according to a hybrid rice algorithm and the initial power generation amount of the hydropower plant to obtain a second optimal value;
step 107: determining the corresponding optimal power generation amount of the thermal power plant according to the first optimal value;
step 108: determining the corresponding optimal power generation amount of the hydraulic power plant according to the second optimal value;
step 109: determining an optimal distribution ratio according to the optimal power generation capacity of the thermal power plant and the optimal power generation capacity of the hydraulic power plant;
step 110: and determining an optimal scheduling scheme according to the optimal distribution ratio.
By adopting the method to schedule the water-gas power plant, global search can be realized, and local optimization is not easy to fall into. But does not give up local optimization, gives consideration to both local and global, and the principle is simple, easily realizes, and the commonality is strong.
The first objective function in step 103 is specifically:
Figure GDA0002984388720000061
wherein F represents the total cost of the thermal power plant; t represents a total scheduling period; n is a radical ofsRepresenting the total number of thermal power plants; psi,tRepresenting the power generation of the thermal power plant; f. ofi(Psi,t) Representing the power generation amount P of the ith thermal power plant during the period tsi,tAs a function of fuel cost.
The fuel cost function is specifically: f. ofi(Psi,t)＝asi+bsiPsi,t+csiP2 si,tWherein a issi，bsi，csiIs as followsCoefficients of the fuel cost function for i thermal power plants.
Step 109 specifically includes: according to the system load balance constraint formula
Figure GDA0002984388720000071
Determining the optimal distribution ratio, wherein Psi,tRepresenting the power generation amount, P, of the i-th thermal power plant during a period thj,tRepresenting the power generation amount of the jth hydropower plant in the t period; pD，tN required for load of t periodsIndicates the total number of thermal power plants, NhRepresenting the total number of hydraulic power plants.
Step 105 specifically includes: initializing a first rice population, a first maximum breeding time and a first maximum selfing time, wherein the first rice population is composed of a plurality of first rice individuals, the first rice individuals represent values of the first objective function, and the initial power generation amount of the thermal power plant is selected as the first rice individuals;
respectively calculating a first fitness value of the first rice individuals in the first rice population; the first fitness value represents the quality of the first rice individual in the first rice population;
sequencing the first rice individuals according to the first fitness value to obtain a first individual fitness sequence;
dividing the first individual fitness sequence into a first maintenance line, a first sterile line and a first recovery line;
crossing the first maintainer line and the first sterile line to generate a new first sterile line individual;
selfing the first restoring line to generate a new first restoring line individual;
and obtaining a first optimal individual according to the new first sterile line individual and the new first restorer line individual, wherein the first optimal individual is the first optimal value.
Step 106 specifically includes: initializing a second rice population, a second maximum breeding frequency and a second maximum selfing frequency, wherein the second rice population is composed of a plurality of second rice individuals, the second rice individuals represent values of the second objective function, and the initial power generation amount of the hydraulic power plant is selected as the second rice individuals;
respectively calculating a second fitness value of the second rice individuals in the second rice population; the second fitness value represents the quality of the second rice individual in the second rice population;
sorting the second rice individuals according to the second fitness value to obtain a second individual fitness sequence;
dividing the second fitness sequence into a second maintainer line, a second sterile line and a second restorer line;
crossing the second maintainer line with the second sterile line to generate a new second sterile line individual;
selfing the second restorer line to generate a new second restorer line individual;
and obtaining a second optimal individual according to the new second sterile line individual and the new second restorer line individual, wherein the second optimal individual is the second optimal value.
Fig. 2 is a structural diagram of a water-fire-electricity economic dispatching system according to an embodiment of the invention. Referring to fig. 2, a water, fire and electricity economic dispatching system based on hybrid rice algorithm includes:
the data determining module 201 is configured to determine a power generation range of a hydroelectric power plant and a power generation range of a thermal power plant according to a minimum power generation amount and a maximum power generation amount of the hydroelectric power plant and a minimum power generation amount and a maximum power generation amount of the thermal power plant within a set time period;
the initial value determining module 202 is configured to randomly select any value within the power generation range of the hydraulic power plant as the initial power generation of the hydraulic power plant, and select any value within the power generation range of the thermal power plant as the initial power generation of the thermal power plant;
a first objective function construction module 203, configured to construct a first objective function according to the fuel cost of the thermal power plant;
a second objective function constructing module 204, configured to construct a second objective function according to the cost of the hydraulic power plant;
the first optimal value obtaining module 205 is configured to perform optimal value solution on the first objective function according to a hybrid rice algorithm and in combination with the initial power generation amount of the thermal power plant to obtain a first optimal value;
a second optimal value obtaining module 206, configured to perform optimal value solution on the second objective function according to a hybrid rice algorithm in combination with the initial power generation amount of the hydropower plant to obtain a second optimal value;
the thermal power plant power generation determining module 207 is configured to determine a corresponding optimal thermal power plant power generation amount according to the first optimal value;
the hydraulic power plant power generation determining module 208 is configured to determine a corresponding optimal hydraulic power plant power generation amount according to the second optimal value;
an optimal distribution ratio determining module 209, configured to determine an optimal distribution ratio according to the optimal power generation amount of the thermal power plant and the optimal power generation amount of the hydraulic power plant;
and an optimal scheme determining module 210, configured to determine an optimal scheduling scheme according to the optimal allocation ratio.
By adopting the system, the problem of optimal scheduling of the hybrid economy of water, fire and electricity can be solved, and the optimal scheduling of the water, fire and electricity can be realized. The system has the advantages of strong optimizing capability, low calculation complexity, high calculation speed and capability of jumping out of a local optimal solution. The convergence rate is fast. Global search can be performed, and local optimization is not easy to fall into.
The first objective function constructing module 203 specifically includes: according to the formula
Figure GDA0002984388720000091
Constructing a first objective function; wherein F represents the total cost of the thermal power plant; t represents a total scheduling period; n is a radical ofsRepresenting the total number of thermal power plants; psi,tRepresenting the power generation of the thermal power plant; f. ofi(Psi，t) Representing the power generation amount P of the ith thermal power plant during the period tsi，tAs a function of fuel cost.
Further comprising: a fuel cost function determination unit for determining the cost of fuel according to the formula fi(Psi,t)＝asi+bsiPsi,t+csiP2 si,tDetermining a fuel cost function, wherein asi，bsi，csiCoefficients of said fuel cost function for the ith thermal power plant.
The optimal distribution ratio determining module 209 specifically includes: according to the system load balance constraint formula
Figure GDA0002984388720000092
Determining the optimal distribution ratio, wherein Psi,tRepresenting the power generation amount, P, of the i-th thermal power plant during a period thj,tRepresenting the power generation amount of the jth hydropower plant in the t period; pD，tN required for load of t periodsIndicates the total number of thermal power plants, NhRepresenting the total number of hydraulic power plants.
The hybrid rice algorithm is adopted for solving, and the specific hybrid rice algorithm is as follows:
step1 initializes: setting the total number of the rice population as N, wherein the rice population consists of a plurality of rice individuals, the proportion of the maintainer line and the sterile line in the population is a%, the quantity of the maintainer line and the sterile line is N multiplied by a/100, the proportion of the restorer line in the population is (100-2 a)%, and the dimensionality of the gene of each individual is D.
Figure GDA0002984388720000093
Represents the gene of the i-th individual in the population at the time of t-th breeding,
Figure GDA0002984388720000094
when t is 0, i.e. the initial time, N solutions are randomly generated in the solution space
Figure GDA0002984388720000095
The specific generation formula is
Figure GDA0002984388720000101
Where j is in {1,2, D-1, D }, minxj，maxxjRespectively representing the maximum value and the minimum value of the j-th dimension component of the search space.
Aiming at the characteristics of the water-fire combined optimization scheduling problem, the original parameters of the hybrid rice algorithm are set, and the initial genes of all the rice are set according to the constraint conditions. In the rice setting, the output of each time period of a thermal power generating unit and the power generation water flow of each time period of a hydropower station are selected as individual variables.
The following parameters are determined at initialization:
the number N of rice populations;
② the maximum breeding times maxIteration;
③ the maximum selfing times maxTime.
Each individual rice is a candidate solution of the combined power generation flow of the thermal power station and the hydropower station.
Step2 fitness value calculation: respectively calculating the fitness value of each individual in the population, and sorting the rice according to the quality of the rice, wherein the number of the maintainer line, the sterile line and the restorer line is A, A, N-2A respectively.
Step3 hybridization: the maintainer line and the sterile line are crossed to generate a new sterile line individual.
For each breeding, the crossing process is performed as many times as the number of individuals of the sterile line. In each hybridization, an individual is selected from the sterile line and the maintainer line as a male parent and a female parent respectively, and the selection mode can be random selection or one-to-one mapping mode. The hybridization mode is that the genes at the corresponding positions of the male parent and the female parent are added according to random weight for recombination to obtain an individual with a new gene. Calculating the fitness of the new individual, comparing the fitness with the sterile line individuals in the male parent and the female parent by taking a greedy algorithm as a criterion, and keeping the individual with better fitness to the next generation.
(ii) random hybridization
Figure GDA0002984388720000102
Wherein
Figure GDA0002984388720000103
J-dimensional Gene, r, representing a New Individual produced by the kth Cross in this round of Breeding1,r2Is [ -1,1 [ ]]A random number in between, and r1+r2Not equal to 0. a, b are randomly selected from {1,2, A }, XAaDenotes the a-th individual in the sterile line, XBbThe b-th individual in the holding system is shown. Each dimension of the gene of the new individual generated is obtained by crossing random individuals in the sterile line and the maintainer line in a random ratio.
(ii) enantiomeric hybridization
Figure GDA0002984388720000111
Wherein a, b, k, XAaDenotes the a-th individual in the sterile line, XBbThe b-th individual in the holding system is shown. Each dimension of the genes of the new individuals generated was crossed in random proportions by the kth individual in the kth sports maintainer line of the sterile line.
The newly generated individuals are greedy algorithmically selected after hybridization.
If f (new _ X)k)＞f(XBk) Will new _ XkSubstituted XBkRetained to the next generation if f (new _ X)k)≤f(XBk) Then X will beBkAnd retained to the next generation.
Step4 selfing process: the restorer line is selfed to generate new restorer line individuals.
In the breeding process, the times of selfing are the same as the individual number of the restorer line. Each selfing, the genes on each position of the individual restorer lines participating in selfing are close to a random quantity towards the current optimal solution. Calculating the fitness of the new individual and selecting the optimal individual to be stored into the next generation according to the greedy algorithm compared with the individual of the recovery line before selfing. If the individuals stored in the next generation are individuals before selfing, the selfing times of the individuals are increased by 1. If the individuals stored in the next generation are new individuals generated by selfing, if the new individuals are superior to the current optimal individuals, the selfing times are set to be 0, otherwise, the selfing times are kept unchanged. If the selfing frequency of a restorer individual reaches the limit frequency maxTime, the restorer individual does not participate in the selfing process in the next breeding round and is replaced by a resetting process.
new_Xk＝XSk+rand(0,1)(Xbest-XSr) (4)
In the formula, new _ XkRepresents a new individual generated by the kth selfing in the breeding process of the round, XsDenotes the s-th individual in the restorer line, XbestRepresenting the currently found optimal individual, XSrThe sr individual in the restorer line, wherein sr takes the random value of {1,2, N-2A }.
And carrying out greedy algorithm selection on newly generated individuals after selfing.
If f (new _ X)k)＞f(XSk) Will new _ XkSubstituted XSrKeeping the selfing frequency unchanged until the next generation, if f (new _ X)k)≤f(XSk) Then X will beSkKeeping the generation to the next generation, adding 1 to the selfing frequency, namely timeSk＝timeSk+1。
If f (new _ X)k)＞f(Xbest) Then new _ X will bekReplace the current record of the optimal individual and set the selfing times to 0, timeSk0. If timeSkAnd the time is more than or equal to maxTime, the individual does not carry out the selfing process but carries out the resetting process in the next-generation breeding.
Step5 reset procedure:
the resetting process is actually a sub-process of the selfing process, and is used for treating individual restorer lines reaching the upper limit of selfing times. The reset process will randomly generate a set of genes within the solution space and add the set of genes to the genes of the individuals involved in the reset, while their selfing times will be set to 0.
Figure GDA0002984388720000121
Step6 records the genes of the currently obtained optimal individuals:
and (3) skipping to the step (2) if the maximum breeding algebra maxIteration is not reached or the error is smaller than the optimization error, otherwise, outputting the gene of the current optimal individual as a result. The output result is the final result.
Wherein, variable constraint problem is also involved in the reactive power optimization process. The reactive optimization constraints are divided into control variable constraints and state variable constraints.
Constraints of the water-fire-electricity system:
system load balancing constraints
Figure GDA0002984388720000122
Wherein: phj,tThe power generation amount in the t period of the jth hydropower plant is a quadratic function related to the water discharge amount and the storage capacity; pD，tRequired for load for time period t; pL，tFor the network loss of the period t, the corresponding expression is as follows (the network loss problem here is not considered in the present invention):
Figure GDA0002984388720000123
Phj,t＝0.00981×Qj,t×Hj,t×ηj
wherein Q isj,tAnd the electricity generation flow rate of the j-th hydropower station in unit time is shown, and eta is the electricity generation efficiency of the j-th hydropower station.
Figure GDA0002984388720000124
Wherein Z isui,tAnd Zdi,tRespectively an upstream water level and a downstream water level at the moment of the ith-stage hydropower station.
The calculation formula of the generated energy of the hydraulic power plant is as follows: phj,t＝f(Vj,t,Qj,t) (8)
Qj,tRepresents the generating reference flow rate V of the j-th hydropower station in unit timej,tIndicating the storage capacity of the jth reservoir at time t.
The following are constraints in the present invention:
and (3) water dynamic balance constraint:
Figure GDA0002984388720000131
wherein: vj，tThe storage capacity of the jth reservoir at the moment t; n is a radical ofjThe number of the upstream reservoirs directly connected with the reservoir.
Output power constraint of the hydraulic power plant:
Phj,min≤Phj,t≤Phj,max (10)
output power constraint of the thermal power plant:
Psi,min≤Psi,t≤Psi,max (11)
reservoir capacity constraint:
Vj,min≤Vj,t≤Vj,max (12)
reservoir water discharge capacity constraint:
Qj,min≤Qj,t≤Qj,max (13)
and (3) limiting the climbing rate of the thermal power plant:
Figure GDA0002984388720000132
wherein: phj，minAnd Phj，maxThe lower limit value and the upper limit value of the output power of the jth hydropower plant are respectively set; psi，minAnd Psi，maxThe lower limit value and the upper limit value of the output power of the ith thermal power plant are respectively set; vj，minAnd Vj，maxThe lower limit value and the upper limit value of the jth reservoir capacity are respectively set; qj，minAnd Qj，maxThe water discharge of the jth reservoir is respectively the lower limit value and the upper limit value; URiAnd DRiRespectively an upper limit value and a lower limit value of the ramp rate limit of the thermal power plant.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (8)

1. A water, fire and electricity economic dispatching method based on a hybrid rice algorithm is characterized by comprising the following steps:
determining the power generation range of the hydraulic power plant and the power generation range of the thermal power plant according to the minimum power generation amount and the maximum power generation amount of the hydraulic power plant and the minimum power generation amount and the maximum power generation amount of the thermal power plant in a set time period;
randomly selecting any numerical value in the power generation range of the hydraulic power plant as initial power generation of the hydraulic power plant, and selecting any numerical value in the power generation range of the thermal power plant as initial power generation of the thermal power plant;
constructing a first objective function according to the fuel cost of the thermal power plant;
constructing a second objective function according to the hydropower plant cost;
performing optimal value solving on the first objective function according to a hybrid rice algorithm and the initial power generation amount of the thermal power plant to obtain a first optimal value;
performing optimal value solving on the second objective function according to a hybrid rice algorithm and the initial power generation amount of the hydropower plant to obtain a second optimal value;
determining the corresponding optimal power generation amount of the thermal power plant according to the first optimal value;
determining the corresponding optimal power generation amount of the hydraulic power plant according to the second optimal value;
determining an optimal distribution ratio according to the optimal power generation capacity of the thermal power plant and the optimal power generation capacity of the hydraulic power plant;
the power generation amount according to the optimal thermal power plantAnd determining an optimal distribution ratio of the optimal hydraulic power plant generated energy, specifically: according to the system load balance constraint formula
Figure FDA0002984388710000011
Determining the optimal distribution ratio, wherein Psi,tRepresenting the power generation amount, P, of the i-th thermal power plant during a period thj,tRepresenting the power generation amount of the jth hydropower plant in the t period; pD,tRequired for load in t period, Ns represents total number of thermal power plants, NhRepresenting the total number of the hydraulic power plants;
carrying out optimal value solution on the first objective function based on constraint conditions;
carrying out optimal value solution on the second objective function based on constraint conditions;
the constraint conditions include:
and (3) water dynamic balance constraint:
Figure FDA0002984388710000012
wherein: vj,tThe storage capacity of the jth reservoir at the moment t; qj,tRepresenting the generating reference flow of the j-th hydropower station in unit time; n is a radical ofjThe number of the upstream reservoirs directly connected with the jth reservoir is;
output power constraint of the hydraulic power plant:
Phj,min≤Phj,t≤Phj,max
output power constraint of the thermal power plant:
Psi,min≤Psi,t≤Psi,max
reservoir capacity constraint:
Vj,min≤Vj,t≤Vj,max
reservoir water discharge capacity constraint:
Qj,min≤Qj,t≤Qj,max
and (3) limiting the climbing rate of the thermal power plant:
Figure FDA0002984388710000021
wherein: phj,minAnd Phj,maxThe lower limit value and the upper limit value of the output power of the jth hydropower plant are respectively set; psi,minAnd Psi,maxThe lower limit value and the upper limit value of the output power of the ith thermal power plant are respectively set; vj,minAnd Vj,maxThe lower limit value and the upper limit value of the jth reservoir capacity are respectively set; qj,minAnd Qj,maxThe water discharge of the jth reservoir is respectively the lower limit value and the upper limit value; URiAnd DRiRespectively an upper limit value and a lower limit value of the ramp rate limit of the thermal power plant;
determining an optimal scheduling scheme according to the optimal distribution ratio;
the hybrid rice algorithm comprises the following steps:
initializing the number of rice populations, the maximum breeding times and the maximum selfing times; the rice population comprises a plurality of rice individuals, and each rice individual is a candidate solution of the combined power generation flow of the thermal power plant and the hydraulic power plant;
respectively calculating the fitness value of each rice individual in the rice population, sequencing the rice individuals according to the fitness value of the rice individual to obtain an individual fitness sequence, and dividing the individual fitness sequence into a maintainer line, a sterile line and a restorer line; the number of rice individuals in the maintainer line is A, the number of rice individuals in the sterile line is A, the number of rice individuals in the restorer line is N-2A, and N is the total number of rice individuals in the rice population;
hybridizing the maintainer line and the sterile line to generate a new sterile line individual;
the hybridization of the maintainer line and the sterile line to generate a new sterile line individual specifically comprises the following steps: selecting a rice individual from the sterile line and the maintainer line as a male parent and a female parent respectively in each hybridization in a random selection mode or an antipodal selection mode; the randomly selected hybridization mode is that the genes at the corresponding positions of the male parent and the female parent are added according to random weights to be recombined to obtain a rice individual with a new gene; calculating the fitness value of the rice individual with the new gene, carrying out fitness comparison on the rice individual with the new gene and rice individuals of sterile lines in male parents and female parents of the rice individual with the new gene by taking a greedy algorithm as a criterion, and determining the rice individual with the new gene and the rice individual with better fitness in the rice individuals of sterile lines in the male parents and female parents of the rice individual with the new gene as the new sterile line individual to be reserved to the next generation;
the formula of the randomly chosen hybridization pattern is:
Figure FDA0002984388710000031
wherein,
Figure FDA0002984388710000032
representing the j dimension gene of the rice individual with the new gene generated by the k-th hybridization in the breeding process, wherein j belongs to {1,2, …, D-1, D }, and D represents the dimension total number of the gene of the rice individual; r is1,r2Is [ -1,1 [ ]]A random number in between, and r1+r2≠0；
Figure FDA0002984388710000033
The j-th gene of the a-th rice individual in the sterile line is shown, and a belongs to {1,2, A };
Figure FDA0002984388710000034
represents the b th individual rice in the maintainer line, b ∈ {1,2, a };
the formula for the enantioselectivity hybridization mode is:
Figure FDA0002984388710000041
wherein a ═ b ═ k;
selfing the rice individuals of the restorer line to generate new restorer line individuals;
selfing the rice individuals of the restorer line to generate new restorer line individuals, which specifically comprises the following steps: selfing the rice individuals of the restorer line to generate rice individuals with new genes; calculating the fitness value of the rice individual with the new gene generated by selfing, comparing the fitness of the rice individual with the new gene generated by selfing with the fitness of the rice individual of the restoring line before selfing according to a greedy algorithm, and determining the rice individual with the better fitness among the rice individual with the new gene generated by selfing and the rice individual of the restoring line before selfing as a new restoring line individual and storing the new restoring line individual into the next generation;
the selfing formula is as follows: new _ Xk＝XSk+rand(0,1)(Xbest-XSr)
In the formula, new _ XkDenotes rice individuals having a novel gene produced by the k-th selfing in the breeding process, XSkIndicates the Sk rice individual in the restorer line, XbestRepresenting the current best individual, XSrThe Sr of the Sr rice individual in the restorer line is randomly selected from {1,2, N-2A };
if the new restorer line individual is the rice individual of the restorer line before selfing, adding 1 to the selfing times of the rice individual of the restorer line;
if the new restoring line individual is the selfed rice individual with the new gene, and the fitness of the selfed rice individual with the new gene is superior to that of the current optimal individual, setting the selfing frequency of the selfed rice individual with the new gene as 0;
if the new restoring line individual is the rice individual with the new gene generated by the self-crossing and the fitness of the current optimal individual is superior to the fitness of the rice individual with the new gene generated by the self-crossing, keeping the self-crossing times of the rice individual with the new gene generated by the self-crossing unchanged;
if the selfing times of the rice individuals of any one of the restorer lines reach the preset limit times maxTime, resetting the rice individuals of the restorer line of which the selfing times reach the preset limit times maxTime in the next round of breeding;
the reset formula is:
Figure FDA0002984388710000051
in the formula
Figure FDA0002984388710000052
J-dimensional gene of rice individual with new gene produced by k-th self-cross in breeding process;
Figure FDA0002984388710000053
a j-th dimension gene representing a Sk-th individual rice in the restorer line; minxjRepresenting the minimum value of the j-th dimension component of the search space; maxxjRepresenting the maximum value of the j-th dimension component of the search space;
obtaining an optimal individual according to the new sterile line individual and the new restorer line individual;
if the maximum breeding algebra maxIteration is not reached or is smaller than a preset optimization error, returning to the step of respectively calculating the fitness value of each rice individual in the rice population and sequencing the rice individuals according to the fitness value of the rice individual to obtain an individual fitness sequence; and otherwise, taking the optimal individual as the optimal value of the objective function.
2. The scheduling method according to claim 1, wherein the first objective function is specifically:
Figure FDA0002984388710000054
wherein F represents the total cost of the thermal power plant; t represents a total scheduling period; n is a radical ofsRepresenting the total number of thermal power plants; psi,tRepresenting the power generation of the thermal power plant; f. ofi(Psi,t) Representing the power generation amount P of the ith thermal power plant during the period tsi,tAs a function of fuel cost.
3. The schedule of claim 2The method is characterized in that the fuel cost function is embodied as: f. ofi(Psi,t)＝asi+bsiPsi,t+csiP2 si,tWherein a issi，bsi，csiRespectively coefficients of the fuel cost function of the ith thermal power plant.
4. The scheduling method according to claim 1, wherein the performing an optimal value solution on the first objective function according to a hybrid rice algorithm in combination with the initial power generation amount of the thermal power plant to obtain a first optimal value specifically comprises:
initializing a first rice population, a first maximum breeding time and a first maximum selfing time, wherein the first rice population is composed of a plurality of first rice individuals, the first rice individuals represent values of the first objective function, and the initial power generation amount of the thermal power plant is selected as the first rice individuals;
respectively calculating a first fitness value of the first rice individuals in the first rice population; the first fitness value represents the quality of the first rice individual in the first rice population;
sequencing the first rice individuals according to the first fitness value to obtain a first individual fitness sequence;
dividing the first individual fitness sequence into a first maintenance line, a first sterile line and a first recovery line;
crossing the first maintainer line and the first sterile line to generate a new first sterile line individual;
selfing the first restoring line to generate a new first restoring line individual;
and obtaining a first optimal individual according to the new first sterile line individual and the new first restorer line individual, wherein the first optimal individual is the first optimal value.
5. The scheduling method according to claim 1, wherein the performing an optimal solution on the second objective function according to the hybrid rice algorithm in combination with the initial power generation amount of the hydroelectric power plant to obtain a second optimal value specifically comprises:
initializing a second rice population, a second maximum breeding frequency and a second maximum selfing frequency, wherein the second rice population is composed of a plurality of second rice individuals, the second rice individuals represent values of the second objective function, and the initial power generation amount of the hydraulic power plant is selected as the second rice individuals;
respectively calculating a second fitness value of the second rice individuals in the second rice population; the second fitness value represents the quality of the second rice individual in the second rice population;
sorting the second rice individuals according to the second fitness value to obtain a second individual fitness sequence;
dividing the second fitness sequence into a second maintainer line, a second sterile line and a second restorer line;
crossing the second maintainer line with the second sterile line to generate a new second sterile line individual;
selfing the second restorer line to generate a new second restorer line individual;
and obtaining a second optimal individual according to the new second sterile line individual and the new second restorer line individual, wherein the second optimal individual is the second optimal value.
6. A water, fire and electricity economic dispatching system based on hybrid rice algorithm is characterized by comprising:
the data determining module is used for determining the generating capacity range of the hydraulic power plant and the generating capacity range of the thermal power plant according to the minimum generating capacity and the maximum generating capacity of the hydraulic power plant and the minimum generating capacity and the maximum generating capacity of the thermal power plant in a set time period;
the initial value determining module is used for randomly selecting any numerical value in the power generation range of the hydraulic power plant as the initial power generation of the hydraulic power plant and any numerical value in the power generation range of the thermal power plant as the initial power generation of the thermal power plant;
the first objective function construction module is used for constructing a first objective function according to the fuel cost of the thermal power plant;
the second objective function construction module is used for constructing a second objective function according to the cost of the hydraulic power plant;
the first optimal value acquisition module is used for carrying out optimal value solution on the first objective function according to a hybrid rice algorithm and the initial power generation amount of the thermal power plant to obtain a first optimal value;
the second optimal value acquisition module is used for carrying out optimal value solution on the second objective function according to a hybrid rice algorithm and the initial power generation amount of the hydropower plant to obtain a second optimal value;
the thermal power plant power generation determining module is used for determining the corresponding optimal thermal power plant power generation amount according to the first optimal value;
the hydraulic power plant power generation determining module is used for determining the corresponding optimal hydraulic power plant power generation amount according to the second optimal value;
the optimal distribution ratio determining module is used for determining an optimal distribution ratio according to the optimal power generation capacity of the thermal power plant and the optimal power generation capacity of the hydraulic power plant;
the optimal distribution ratio determining module specifically comprises: according to the system load balance constraint formula
Figure FDA0002984388710000081
Determining the optimal distribution ratio, wherein Psi,tRepresenting the power generation amount, P, of the i-th thermal power plant during a period thj,tRepresenting the power generation amount of the jth hydropower plant in the t period; pD,tRequired for load in t period, Ns represents total number of thermal power plants, NhRepresenting the total number of the hydraulic power plants;
the first optimal value obtaining module specifically comprises: carrying out optimal value solution on the first objective function based on constraint conditions;
the second optimal value obtaining module specifically comprises: carrying out optimal value solution on the second objective function based on constraint conditions;
the constraint conditions include:
and (3) water dynamic balance constraint:
Figure FDA0002984388710000091
wherein: vj,tThe storage capacity of the jth reservoir at the moment t; qj,tRepresenting the generating reference flow of the j-th hydropower station in unit time; n is a radical ofjThe number of the upstream reservoirs directly connected with the jth reservoir is;
output power constraint of the hydraulic power plant:
Phj,min≤Phj,t≤Phj,max
output power constraint of the thermal power plant:
Psi,min≤Psi,t≤Psi,max
reservoir capacity constraint:
Vj,min≤Vj,t≤Vj,max
reservoir water discharge capacity constraint:
Qj,min≤Qj,t≤Qj,max
and (3) limiting the climbing rate of the thermal power plant:
Figure FDA0002984388710000092
wherein: phj,minAnd Phj,maxThe lower limit value and the upper limit value of the output power of the jth hydropower plant are respectively set; psi,minAnd Psi,maxThe lower limit value and the upper limit value of the output power of the ith thermal power plant are respectively set; vj,minAnd Vj,maxThe lower limit value and the upper limit value of the jth reservoir capacity are respectively set; qj,minAnd Qj,maxThe water discharge of the jth reservoir is respectively the lower limit value and the upper limit value; URiAnd DRiRespectively an upper limit value and a lower limit value of the ramp rate limit of the thermal power plant;
the optimal scheme determining module is used for determining an optimal scheduling scheme according to the optimal distribution ratio;
the hybrid rice algorithm comprises the following steps:
initializing the number of rice populations, the maximum breeding times and the maximum selfing times; the rice population comprises a plurality of rice individuals, and each rice individual is a candidate solution of the combined power generation flow of the thermal power plant and the hydraulic power plant;
respectively calculating the fitness value of each rice individual in the rice population, sequencing the rice individuals according to the fitness value of the rice individual to obtain an individual fitness sequence, and dividing the individual fitness sequence into a maintainer line, a sterile line and a restorer line; the number of rice individuals in the maintainer line is A, the number of rice individuals in the sterile line is A, the number of rice individuals in the restorer line is N-2A, and N is the total number of rice individuals in the rice population;
hybridizing the maintainer line and the sterile line to generate a new sterile line individual;
the hybridization of the maintainer line and the sterile line to generate a new sterile line individual specifically comprises the following steps: selecting a rice individual from the sterile line and the maintainer line as a male parent and a female parent respectively in each hybridization in a random selection mode or an antipodal selection mode; the randomly selected hybridization mode is that the genes at the corresponding positions of the male parent and the female parent are added according to random weights to be recombined to obtain a rice individual with a new gene; calculating the fitness value of the rice individual with the new gene, carrying out fitness comparison on the rice individual with the new gene and rice individuals of sterile lines in male parents and female parents of the rice individual with the new gene by taking a greedy algorithm as a criterion, and determining the rice individual with the new gene and the rice individual with better fitness in the rice individuals of sterile lines in the male parents and female parents of the rice individual with the new gene as the new sterile line individual to be reserved to the next generation;
the formula of the randomly chosen hybridization pattern is:
Figure FDA0002984388710000101
wherein,
Figure FDA0002984388710000102
representing the j dimension gene of the rice individual with the new gene generated by the k-th hybridization in the breeding process, wherein j belongs to {1,2, …, D-1, D }, and D represents the dimension total number of the gene of the rice individual; r is1,r2Is [ -1,1 [ ]]A random number in between, and r1+r2≠0；
Figure FDA0002984388710000111
The j-th gene of the a-th rice individual in the sterile line is shown, and a belongs to {1,2, A };
Figure FDA0002984388710000112
represents the b th individual rice in the maintainer line, b ∈ {1,2, a };
the formula for the enantioselectivity hybridization mode is:
Figure FDA0002984388710000113
wherein a ═ b ═ k;
selfing the rice individuals of the restorer line to generate new restorer line individuals;
selfing the rice individuals of the restorer line to generate new restorer line individuals, which specifically comprises the following steps: selfing the rice individuals of the restorer line to generate rice individuals with new genes; calculating the fitness value of the rice individual with the new gene generated by selfing, comparing the fitness of the rice individual with the new gene generated by selfing with the fitness of the rice individual of the restoring line before selfing according to a greedy algorithm, and determining the rice individual with the better fitness among the rice individual with the new gene generated by selfing and the rice individual of the restoring line before selfing as a new restoring line individual and storing the new restoring line individual into the next generation;
the selfing formula is as follows: new _ Xk＝XSk+rand(0,1)(Xbest-XSr)
In the formula, new _ XkDenotes rice individuals having a novel gene produced by the k-th selfing in the breeding process, XSkIndicates the Sk rice individual in the restorer line, XbestRepresenting the current best individual, XSrThe Sr of the Sr rice individual in the restorer line is randomly selected from {1,2, N-2A };
if the new restorer line individual is the rice individual of the restorer line before selfing, adding 1 to the selfing times of the rice individual of the restorer line;
if the new restoring line individual is the selfed rice individual with the new gene, and the fitness of the selfed rice individual with the new gene is superior to that of the current optimal individual, setting the selfing frequency of the selfed rice individual with the new gene as 0;
if the new restoring line individual is the rice individual with the new gene generated by the self-crossing and the fitness of the current optimal individual is superior to the fitness of the rice individual with the new gene generated by the self-crossing, keeping the self-crossing times of the rice individual with the new gene generated by the self-crossing unchanged;
if the selfing times of the rice individuals of any one of the restorer lines reach the preset limit times maxTime, resetting the rice individuals of the restorer line of which the selfing times reach the preset limit times maxTime in the next round of breeding;
the reset formula is:
Figure FDA0002984388710000121
in the formula
Figure FDA0002984388710000122
J-dimensional gene of rice individual with new gene produced by k-th self-cross in breeding process;
Figure FDA0002984388710000123
a j-th dimension gene representing a Sk-th individual rice in the restorer line; minxjRepresenting the minimum value of the j-th dimension component of the search space; maxxjRepresenting the maximum value of the j-th dimension component of the search space;
obtaining an optimal individual according to the new sterile line individual and the new restorer line individual;
if the maximum breeding algebra maxIteration is not reached or is smaller than a preset optimization error, returning to the step of respectively calculating the fitness value of each rice individual in the rice population and sequencing the rice individuals according to the fitness value of the rice individual to obtain an individual fitness sequence; and otherwise, taking the optimal individual as the optimal value of the objective function.
7. The scheduling system of claim 6 wherein the first objective function construction module is: according to the formula
Figure FDA0002984388710000124
Constructing a first objective function; wherein F represents the total cost of the thermal power plant; t represents a total scheduling period; n is a radical ofsRepresenting the total number of thermal power plants; psi,tRepresenting the power generation of the thermal power plant; f. ofi(Psi,t) Representing the power generation amount P of the ith thermal power plant during the period tsi,tAs a function of fuel cost.
8. The scheduling system of claim 6 wherein the first objective function further comprises: a fuel cost function determination unit for determining the cost of fuel according to the formula fi(Psi,t)＝asi+bsiPsi,t+csiP2 si,tDetermining a fuel cost function, wherein asi，bsi，csiCoefficients of said fuel cost function for the ith thermal power plant.
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