CN109241630A - The method for optimizing scheduling and device of electric system - Google Patents
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Abstract
The invention discloses a kind of electric power system dispatching optimization method and devices.Pass through the pre-set Model for Multi-Objective Optimization of determination, and based on NSGA-II algorithm in Model for Multi-Objective Optimization first object function and the second objective function optimize, obtain meeting the Pareto optimal solution set of the constraint condition in Model for Multi-Objective Optimization, Pareto optimal solution set includes the noninferior solution in the first object function and second objective function.Multiple-objection optimization is carried out by above-mentioned dispatch using NSGA-II algorithm to network system, on solving the problems, such as multiple-objection optimization, confirmation meets the Pareto optimal solution set of the constraint condition in the Model for Multi-Objective Optimization, that is the noninferior solution during multiple objective optimizations is chosen, the optimal solution for the optimization multiple target compromised.It is implemented without and sacrifices other targets as cost, complete the purpose of the economic environment Optimized Operation of the electric system of current multiple-objection optimization demand.
Description
Technical field
The present invention relates to energy technology field, specially a kind of electric power system dispatching optimization method and device.
Background technique
Electric system be include power supply, transmission of electricity, power transformation, distribution, supply network a complicated system, electric system
On the basis of Optimized Operation generally refers to economic load dispatching, that is, the economic performance and run-limiting of consideration generating set, optimization
It formulates the system of sening as an envoy to and meets service requirement and the minimum generation schedule of operating cost.Currently, thermoelectricity Thermal generation unit is as electricity
The main force of power production, the discharge amount of polluted gas increasingly become focal issue concerned by people.
In the prior art, environmental economy optimizing scheduling has been carried out for the electric system of thermoelectricity Thermal generation unit, has been
Single goal is optimized.It is embodied in the start and stop and power output that how to control each unit, so that total coal consumption amount reaches most
It is small.But as the new energy of the other forms such as wind-powered electricity generation is connected to the grid, in the case where considering the grid-connected situations of new energy form such as wind-power electricity generation
Power system environment economic load dispatching also put on schedule.
Since economic environment Optimal Scheduling under new-energy grid-connected is a multi-objective optimization question, it is clear that tradition
Single object optimization method and be not suitable for.Therefore, a kind of economic environment of electric system for multiple-objection optimization is needed at present
Optimized Operation mode.
Summary of the invention
In view of this, the embodiment of the invention provides a kind of method for optimizing scheduling of electric system and device, to solve to adopt
With existing single object optimization method, the economic environment that can not be suitable for the electric system of current multiple-objection optimization demand, which optimizes, to be adjusted
Degree problem.
To achieve the above object, the embodiment of the present invention provides the following technical solutions::
A kind of method for optimizing scheduling of electric system, comprising:
Determine that pre-set Model for Multi-Objective Optimization, the Model for Multi-Objective Optimization areIts
In, F (PG, Pw)=F (PG)+F(Pw) it is first object function corresponding to the cost of electricity-generating of grid-connected unit,ai、bi、ciFor the fuel cost system of the thermal power generation unit in i-th grid-connected unit
Number, PGiFor the active power output of i-th thermal power generation unit, F (Pw)=Cw×Pw, PwFor the power generation function of grid connected wind power unit
Rate, Cw indicate averagely to run required for the every electric energy for generating certain unit of wind power plant that the Wind turbines in grid-connected unit are constituted
Maintenance cost,For the firepower hair in the grid-connected unit
Second objective function corresponding to the polluted gas discharge amount of motor group, αi、βi、γi、ζi、λiFor the institute in i-th grid-connected unit
State the pollutant emission coefficient of discharge of thermal power generation unit, E (PG, Pw) be the equation of first object function and the second objective function about
Beam, I (PG, Pw) it is first object function and the second objective function inequality constraints；
Based on the genetic algorithm NSGA-II of the non-dominated ranking with elitism strategy in the Model for Multi-Objective Optimization
One objective function and second objective function optimize, and obtain meeting the constraint condition in the Model for Multi-Objective Optimization
Pareto optimal solution set, the Pareto optimal solution set include non-in the first object function and second objective function
Inferior solution.
Preferably, the pre-set process of the Model for Multi-Objective Optimization includes:
It calculates and the cost of electricity-generating F (P for the unit that obtains generating electricity by way of merging two or more grid systemsG, Pw)=F (PG)+F(Pw), the cost of electricity-generating is made
For first object function, wherein the unit that generates electricity by way of merging two or more grid systems includes at least thermal power generation unit and wind power generating set,ai、bi、ciFor the fuel cost system of the thermal power generation unit in i-th grid-connected unit
Number, PGiFor the active power output of i-th thermal power generation unit, F (Pw)=Cw×Pw, PwFor the wind-power electricity generation in grid-connected unit
The generated output of unit, Cw indicate the every electric energy for generating certain unit of wind power plant that the wind power generating set in grid-connected unit is constituted
Required average operation and maintenance cost；
It calculates and obtains the polluted gas discharge amount of the unit that generates electricity by way of merging two or more grid systemsUsing the polluted gas discharge amount as the second target
Function, wherein αi、βi、γi、ζi、λiFor the pollutant emission coefficient of discharge of the thermal power generation unit in i-th grid-connected unit；
Determine the equality constraint E (P of the first object function, the second objective functionG, Pw), and determine first mesh
Inequality constraints I (the P of scalar functions, the second objective functionG, Pw)；
Based on the first object function, the second objective function and the first object function, the second objective function
Equality constraint and inequality constraints construct Model for Multi-Objective Optimization
Preferably, the equality constraint includes: system power Constraints of EquilibriumWherein, PDIt is total for system
Workload demand；
The inequality constraints includes at least: thermal power generation unit units limits, wind power generating set units limits, wind-powered electricity generation
Penetration constraint and the constraint of system spinning reserve；
The thermal power generation unit units limits are PGi,min≤PGi≤PGi,max, wherein PGi,minAnd PGi,maxRespectively i-th
Platform fired power generating unit minimum load and maximum output；
The wind power generating set units limits are 0≤Pw≤Wav, wherein WavGo out for the wind power generating set maximum
Power；
The wind power penetration limit is constrained to 0≤Pw≤δw,maxPD；
The system spinning reserve is constrained toWherein, PSRIt is system spinning reserve capacity.
Preferably, the genetic algorithm NSGA-II based on the non-dominated ranking with elitism strategy is excellent to the multiple target
The first object function and the second objective function changed in model optimize, and obtain meeting the pact in the Model for Multi-Objective Optimization
The Pareto optimal solution set of beam condition, comprising:
Obtain the firepower hair in the power output and the grid-connected unit of the wind power generating set in grid-connected unit to be optimized
The active power output of motor group；
The multiple target is arranged in the active power output of power output and the thermal power generation unit based on the wind power generating set
The relevant parameter of Optimized model, the relevant parameter include at least optimum individual coefficient, maximum evolutionary generation, stop algebraical sum/
Or fitness function deviation；
The grid-connected unit to be optimized is arranged in genetic algorithm NSGA-II based on the non-dominated ranking with elitism strategy
Population scale, and according to the equality constraint and inequality constraints generation first generation initial population in the Model for Multi-Objective Optimization
P0, wind power generating set and thermal power generation unit in the grid-connected unit to be optimized are the individual in the population scale；
Based on the first object function and the second objective function in the Model for Multi-Objective Optimization, to contemporary parent population Pt
Quick non-dominated ranking is carried out, and crowded according to range information calculating pseudo range of the vector of the individual in the variable space
Degree；
Based on the genetic manipulation in the NSGA-II, by the first generation initial population P0Starting executes genetic manipulation, obtains
To sub- population Qt；
Merge the contemporary population PtWith the sub- population Qt, obtain merging population Rt；
To the merging population RtIn individual carry out the calculating of quick non-dominated ranking and pseudo range crowding, and base
Next-generation parent population P is generated in sequence and calculated result selection individualt+1
Judge the next-generation parent population Pt+1Algebra whether reach maximum value；
If it is not, then by the next-generation parent population Pt+1As contemporary parent population PtIt returns and executes to contemporary parent kind
Group PtQuick non-dominated ranking is carried out, and pseudo range is calculated according to range information of the vector of the individual in the variable space
The step for crowding；
If so, the confirmation next-generation parent population Pt+1In the corresponding first object function of individual and the second target
The calculated value of function constitutes the Pareto optimal solution set for meeting the constraint condition in the Model for Multi-Objective Optimization.
Preferably, described to contemporary parent population PtCarry out quick non-dominated ranking, comprising:
Calculate the contemporary parent population PtIn each individual fitness function value；
Based on the corresponding fitness function value of each individual, individual x is determinedjWith individual xnBetween dominance relation, wherein j
=1,2 ..., N, and j ≠ n；
It judges whether there is better than the individual xjIndividual xn；
If it does not exist, then confirm the individual xjFor non-dominant individual；
If it exists, then continue to determine whether exist better than the individual xj+1Individual xn, until confirming the contemporary parent
Population PtIn all non-dominant individuals.
Second aspect of the present invention discloses a kind of optimizing scheduling device of electric system, which is characterized in that the scheduling dress
It sets and includes:
Confirmation unit, for determining that pre-set Model for Multi-Objective Optimization, the Model for Multi-Objective Optimization beWherein, F (PG, Pw)=F (PG)+F(Pw) it is the first mesh corresponding to the cost of electricity-generating of grid-connected unit
Scalar functions,ai、bi、ciFor the fuel of the thermal power generation unit in i-th grid-connected unit
Cost coefficient, PGiFor the active power output of i-th thermal power generation unit, F (Pw)=Cw×Pw, PwFor grid connected wind power unit
Generated output, Cw indicate to put down required for the every electric energy for generating certain unit of wind power plant that the Wind turbines in grid-connected unit are constituted
Equal operation and maintenance cost,For in the grid-connected unit
Second objective function corresponding to the polluted gas discharge amount of thermal power generation unit, αi、βi、γi、ζi、λiFor i-th grid-connected unit
In the thermal power generation unit pollutant emission coefficient of discharge, E (PG, Pw) it is first object function and the second objective function
Equality constraint, I (PG, Pw) it is first object function and the second objective function inequality constraints；
Optimizing scheduling unit, for the genetic algorithm NSGA-II based on the non-dominated ranking with elitism strategy to described more
The first object function and second objective function in objective optimization model optimize, and obtain meeting the multiple target
The Pareto optimal solution set of constraint condition in Optimized model, the Pareto optimal solution set include the first object function and
Noninferior solution in second objective function.
Preferably, further includes: default unit, the default unit include wind power cost computing module, polluted gas discharge
Measure computing module, confirmation module and building module；
The wind power cost computing module, the cost of electricity-generating F (P for the unit that calculates and obtain to generate electricity by way of merging two or more grid systemsG, Pw)=F
(PG)+F(Pw), using the cost of electricity-generating as first object function, wherein the unit that generates electricity by way of merging two or more grid systems is sent out including at least firepower
Motor group and wind power generating set,ai、bi、ciFor the firepower in i-th grid-connected unit
The fuel cost coefficient of generating set, PGiFor the active power output of i-th thermal power generation unit, F (Pw)=Cw×Pw, PwFor
The generated output of grid connected wind power unit, Cw indicate the every certain unit of generation of wind power plant that the Wind turbines in grid-connected unit are constituted
Average operation and maintenance cost required for electric energy；
The polluted gas Emission amount calculation module, for calculating and obtaining the polluted gas row of the unit that generates electricity by way of merging two or more grid systems
High-volumeUsing the polluted gas discharge amount as second
Objective function, wherein αi、βi、γi、ζi、λiFor the pollutant discharge amount of the thermal power generation unit in i-th grid-connected unit
Coefficient；
The confirmation module, for determining the equality constraint E (P of the first object function, the second objective functionG, Pw),
And determine the inequality constraints I (P of the first object function, the second objective functionG, Pw)；
The building module, for being based on the first object function, the second objective function and the first object letter
The equality constraint and inequality constraints of number, the second objective function construct Model for Multi-Objective Optimization
Preferably, described for determining the equality constraint E (P of the first object function, the second objective functionG, Pw), with
And determine the inequality constraints I (P of the first object function, the second objective functionG, Pw) confirmation module, for confirmation etc.
Formula constraint includes system power Constraints of EquilibriumWherein, PDFor system total capacity requirement；The inequality is about
Beam includes at least: thermal power generation unit units limits, wind power generating set units limits, wind power penetration limit constrain and are
Spinning reserve of uniting constrains；The thermal power generation unit units limits are PGi,min≤PGi≤PGi,max, wherein PGi,minAnd PGi,maxPoint
It Wei not i-th fired power generating unit minimum load and maximum output；The wind power generating set units limits are 0≤Pw≤Wav, wherein
WavFor the wind power generating set maximum output；The wind power penetration limit is constrained to 0≤Pw≤δw,maxPD；The system
Spinning reserve is constrained toWherein, PSRIt is system spinning reserve capacity.
Preferably, the optimizing scheduling unit includes:
Module is obtained, for obtaining the power output of the wind power generating set in grid-connected unit to be optimized and described grid-connected
The active power output of thermal power generation unit in unit；
First setup module, for based on the wind power generating set power output and the thermal power generation unit it is active go out
The relevant parameter of the Model for Multi-Objective Optimization is arranged in power, and the relevant parameter includes at least optimum individual coefficient, and maximum is evolved
Algebra stops algebra and/or fitness function deviation；
Second setup module, for described in the genetic algorithm NSGA-II setting based on the non-dominated ranking with elitism strategy
The population scale of grid-connected unit to be optimized, and according to the equality constraint and inequality constraints life in the Model for Multi-Objective Optimization
At first generation initial population P0, wind power generating set and thermal power generation unit in the grid-connected unit to be optimized are described kind
Individual in group's scale；
Sequence and computing module, for based on the first object function and the second target letter in the Model for Multi-Objective Optimization
Number, to contemporary parent population PtCarry out quick non-dominated ranking, and the distance according to the vector of the individual in the variable space
Information calculates pseudo range crowding；
Genetic manipulation module, for based on the genetic manipulation in the NSGA-II, by the first generation initial population P0It rises
Begin to execute genetic manipulation, obtains sub- population Qt；
Merging module, for merging the contemporary population PtWith the sub- population Qt, obtain merging population Rt；
Generation module, for the merging population RtIn individual carry out quick non-dominated ranking and pseudo range is crowded
The calculating of degree, and next-generation parent population P is generated based on sequence and calculated result selection individualt+1
Pareto optimal solution set obtains module, for judging the next-generation parent population Pt+1Algebra whether reach most
Big value, if it is not, then by the next-generation parent population Pt+1As contemporary parent population PtIt returns and executes the sequence and calculating mould
Block, if so, the confirmation next-generation parent population Pt+1In individual corresponding first object function and the second objective function
Calculated value constitutes the Pareto optimal solution set for meeting the constraint condition in the Model for Multi-Objective Optimization.
Preferably, described for contemporary parent population PtSequence and the computing module for carrying out quick non-dominated ranking, are used for
Calculate the contemporary parent population PtIn each individual fitness function value, be based on the corresponding fitness function of each individual
Value determines individual xjWith individual xnBetween dominance relation, wherein j=1,2 ..., N, and j ≠ n；It judges whether there is better than institute
State individual xjIndividual xn；If it does not exist, then confirm the individual xjFor non-dominant individual；If it exists, then it continues to determine whether to deposit
It is being better than the individual xj+1Individual xn, until confirming the contemporary parent population PtIn all non-dominant individuals.
As shown in the above, the embodiment of the invention discloses a kind of method for optimizing scheduling of electric system and devices.It is logical
It crosses and determines pre-set Model for Multi-Objective Optimization, and the genetic algorithm NSGA-II based on the non-dominated ranking with elitism strategy
To in the Model for Multi-Objective Optimization first object function and second objective function optimize, obtain meeting described more
The Pareto optimal solution set of constraint condition in objective optimization model, the Pareto optimal solution set include the first object letter
Noninferior solution in several and second objective function.More mesh are carried out to network system scheduling using NSGA-II algorithm by above-mentioned
Mark optimization, on solving the problems, such as multiple-objection optimization, confirmation meets the constraint condition in the Model for Multi-Objective Optimization
Pareto optimal solution set, that is to say, that the noninferior solution during multiple objective optimizations is chosen, the optimization multiple target compromised
Optimal solution.It is implemented without and sacrifices other targets as cost, complete the economy of the electric system of current multiple-objection optimization demand
The purpose of environment optimization scheduling.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of electric power system dispatching optimization method flow chart provided in an embodiment of the present invention；
Fig. 2 is another electric power system dispatching optimization method flow chart provided in an embodiment of the present invention；
Fig. 3 is another electric power system dispatching optimization method flow chart provided in an embodiment of the present invention；
Fig. 4 is another electric power system dispatching optimization method flow chart provided in an embodiment of the present invention；
Fig. 5 is that a kind of electric power system dispatching provided in an embodiment of the present invention optimizes apparatus structure schematic diagram.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
In this application, the terms "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion,
So that the process, method, article or equipment for including a series of elements not only includes those elements, but also including not having
The other element being expressly recited, or further include for elements inherent to such a process, method, article, or device.Do not having
There is the element limited in the case where more limiting by sentence "including a ...", it is not excluded that in the mistake including the element
There is also other identical elements in journey, method, article or equipment.
As shown in Figure 1, a kind of flow chart of the method for optimizing scheduling of electric system is provided for the embodiment of the present invention, the tune
Spend optimization method the following steps are included:
S101: pre-set Model for Multi-Objective Optimization is determined.
In S101, pre-set Model for Multi-Objective Optimization is specifically as shown in formula (1).
Wherein, F (PG, Pw) it is first object function corresponding to the cost of electricity-generating of grid-connected unit, F (PG) it is grid-connected unit
Generating set polluted gas discharge amount corresponding to the second objective function.E(PG, Pw) it is first object function and the second mesh
The equality constraint of scalar functions.I(PG, Pw) it is first object function and the second objective function inequality constraints.
F(PG, Pw)=F (PG)+F(Pw) (2)
In the concrete realization, grid-connected electric system first has to consider that its economy, i.e. cost of electricity-generating are minimum.Currently, meter
The cost of electricity-generating for entering electric system includes: the fuel cost and new energy equipment of conventional thermal power generation unit, such as wind-power electricity generation
The operation expense of unit.
The statistical analysis of fuel cost based on wide variety of conventional thermal power generation unit obtains going out for its fuel cost and unit
Power is in the relationship of quadratic function.For example, for the electric system being made of the thermal power generation unit of n platform routine, fuel
Shown in cost such as formula (3).
Wherein, ai、bi、ciFor the fuel cost coefficient of the thermal power generation unit in i-th grid-connected unit, PGiFor i-th institute
State the active power output of thermal power generation unit.
For new energy equipment, i.e. wind power generating set, the cost of electricity-generating of the wind power plant constituted is primarily directed to wind-powered electricity generation
The operation expense of field.Shown in its operation expense such as formula (4).
F(Pw)=Cw×Pw (4)
Wherein, PwFor the generated output of the Wind turbines in grid-connected unit, CwIndicate that the Wind turbines in grid-connected unit are constituted
The every electric energy for generating certain unit of wind power plant required for average operation and maintenance cost.
It should be noted that the generated output of wind power generating set is easy to be influenced by more multifactor.Such as wind-power electricity generation
The factors such as engine efficiency, wind-driven generator drive system structure and inverter.On the other hand, wind speed is also to influence wind-driven generator
Generated output principal element.Based on this, the embodiment of the present invention is given, the letter of the generated output and wind speed of wind-driven generator
Single empirical equation, as shown in formula (5).
Wherein, PrFor wind-driven generator rated power, vrFor wind energy conversion system rated wind speed, vinAnd voutRespectively cut wind speed
And cut-out wind speed, v are wind speed.V in some specific time period can use Weibull distribution and be calculated.
It can be seen that one of optimization aim as Model for Multi-Objective Optimization based on the above-mentioned analysis for grid-connected unit
The cost of electricity-generating of grid-connected unit, objective function, that is to say, that the first object function in Model for Multi-Objective Optimization can use formula
(2) it indicates.
During grid-connected unit generation, conventional thermal power generation unit inevitably discharges a large amount of contamination gas
Body, environment are dispatched generally with polluted gas discharge amount at least for regulation goal.Polluted gas discharge amount and thermal power generation unit
Active power output has individual functional relation, calculates routine using polluted gas comprehensive discharge model in embodiments of the present invention
The polluted gas discharge amount of thermal power generation unit, shown in the polluted gas discharge amount such as formula (6).
Wherein, αi、βi、γi、ζi、λiFor the pollutant emission coefficient of discharge of the thermal power generation unit in i-th grid-connected unit.
S102: based on the genetic algorithm NSGA-II of the non-dominated ranking with elitism strategy to the Model for Multi-Objective Optimization
In first object function and second objective function optimize, obtain meeting the constraint in the Model for Multi-Objective Optimization
The Pareto optimal solution set of condition.
In S102, the Pareto optimal solution set includes in the first object function and second objective function
Noninferior solution.
The multiple-objection optimization being related in embodiments of the present invention is to control decision variable in given feasible zone, so that more
A target is optimal.Therefore, it can be defined as under one group of constraint condition, so that multiple objective functions all tend to be optimal.By
Not unique in the solution of multi-objective optimization question, i.e., there is no the feelings for making all objective functions all while being optimal
Condition, it is therefore desirable to by the optimal optimal solution set being deconstructed into of one group of Pareto.
Therefore, in S102, it is based on NSGA-II algorithm, the case where for multiple-objection optimization, to the multiple-objection optimization mould
First object function and second objective function in type optimize, and obtain meeting the pact in the Model for Multi-Objective Optimization
The Pareto optimal solution set of beam condition.
The NSGA-II algorithm refers to the genetic algorithm of the non-dominated ranking with elitism strategy.I.e. by introducing quickly non-branch
Retain technology, using crowding and crowding comparison operator with ordering techniques, elite, by the computation complexity of algorithm from O (mM3)
It is down to O (mM2)。
For the above-mentioned Pareto optimal solution or Pareto optimal solution set being related to, below to the relevant content of Pareto into
Row illustrates.
Pareto domination is illustrated below:
Assuming that X is the set of feasible solution of multi-goal optimizing function, xa, xb ∈ X are two solutions of multi-objective optimization question, that
:
1, and if only ifThere is fi (xa) < fi (xb), then claims xa to dominate xb, be denoted as xa < xb.Wherein, xa is known as non-branch
With solution, xb is known as dominating solution.
2, and if only ifThere is fi (xa)≤fi (xb), then claims xa is non-to be inferior to xb.
Pareto optimal solution or Pareto optimal solution set are illustrated below:
A feasible solution x* ∈ X is given, whenThere is f (x*) < f (x), then x* is known as the absolute of Multiobjective Programming
Optimal solution.X ∈ X if it does not exist, so that f (x) < f (x*), then x* is known as effective solution to Goal Programming Problem, i.e. Pareto is most
Excellent solution.On this basis, it can define to obtain Pareto optimal solution set P*.
The optimal forward position Pareto is illustrated below:
The song that the corresponding aiming field of vector for all Pareto optimal solutions composition that Pareto optimal solution set P* includes is constituted
Line or curved surface are known as the optimal forward position Pareto.
The method for optimizing scheduling of electric system disclosed by the embodiments of the present invention.Pass through the pre-set multiple-objection optimization of determination
Model, and based on the genetic algorithm NSGA-II of the non-dominated ranking with elitism strategy in the Model for Multi-Objective Optimization
One objective function and second objective function optimize, and obtain meeting the constraint condition in the Model for Multi-Objective Optimization
Pareto optimal solution set, the Pareto optimal solution set include non-in the first object function and second objective function
Inferior solution.Multiple-objection optimization is carried out by above-mentioned dispatch using NSGA-II algorithm to network system, is solving asking for multiple-objection optimization
In topic, confirmation meets the Pareto optimal solution set of the constraint condition in the Model for Multi-Objective Optimization, that is to say, that chooses multiple
Noninferior solution during objective optimization, the optimal solution for the optimization multiple target compromised.It is implemented without and sacrifices other targets work
For cost, the purpose of the economic environment Optimized Operation of the electric system of current multiple-objection optimization demand is completed.
Based on the method for optimizing scheduling of electric system disclosed in the embodiments of the present invention, the multiple target being directed to is excellent
The pre-set process for changing model, as shown in Fig. 2, mainly including the following steps:
S201: being calculated based on formula (2) and the cost of electricity-generating for the unit that obtains generating electricity by way of merging two or more grid systems, using the cost of electricity-generating as first
Objective function.
In S201, specific calculating process and the calculation being related to be can be found in disclosed in the embodiments of the present invention
The explanation of cost of electricity-generating in relation to the unit that generates electricity by way of merging two or more grid systems in S101, is not discussed here.
S202: being calculated based on formula (6) and obtains the polluted gas discharge amount of the unit that generates electricity by way of merging two or more grid systems.
In S201, specific calculating process and the calculation being related to be can be found in disclosed in the embodiments of the present invention
The explanation of polluted gas discharge amount in S101 in relation to the unit that generates electricity by way of merging two or more grid systems, is not discussed here.
S203: it determines the equality constraint of the first object function, the second objective function, and determines the first object
The inequality constraints of function, the second objective function.
In S203, in actual moving process, in order to enable Model for Multi-Objective Optimization is more nearly actual conditions, therefore,
The constraint that more multifactor constraint increases variable can be added.It can specifically include but be not limited to following constraint condition.
Equality constraint includes: system power Constraints of Equilibrium:
Wherein, PDFor system total capacity requirement.
Inequality constraints includes at least: thermal power generation unit units limits, wind power generating set units limits, wind-powered electricity generation penetrate
Power limit constraint and the constraint of system spinning reserve.
Thermal power generation unit units limits:
PGi,min≤PGi≤PGi,max (8)
Wherein, PGi,minAnd PGi,maxRespectively i-th fired power generating unit minimum load and maximum output.
Wind power generating set units limits:
0≤Pw≤Wav (9)
Wherein, WavFor the wind power generating set maximum output.
Wind power penetration limit constraint:
0≤Pw≤δw,maxPD (10)
The constraint of system spinning reserve:
Wherein, PSRIt is system spinning reserve capacity.
S204: based on the first object function, the second objective function and the first object function, the second target
The equality constraint and inequality constraints of function construct Model for Multi-Objective Optimization.
In S204, shown in constructed Model for Multi-Objective Optimization such as formula (1).For details, reference can be made to the explanations for being directed to formula (1).
The embodiment of the present invention passes through the above-mentioned process for presetting Model for Multi-Objective Optimization.In setting Model for Multi-Objective Optimization
When, by cost of electricity-generating F (PG, Pw) it is used as power system economy index, by polluted gas discharge amount F (PG) it is used as electric system
Environmental pollution index, and be based on power system economy index and power system environment contamination index, constitute multiple-objection optimization
Two objective functions of model.And utilize E (PG, Pw) and I (PG, Pw) respectively indicate the equality constraint and inequality of objective function
Constraint.It is to meet various equality constraints and inequality to be minimised as target that final building, which generates Model for Multi-Objective Optimization,
Optimizing is carried out under constraint condition.
Based on the method for optimizing scheduling of electric system disclosed in the embodiments of the present invention, step S102's was specifically executed
Journey, as shown in figure 3, including the following steps:
S301: it obtains in the power output and the grid-connected unit of the wind power generating set in grid-connected unit to be optimized
The active power output of thermal power generation unit.
S302: the active power output setting of power output and the thermal power generation unit based on the wind power generating set is described more
The relevant parameter of objective optimization model, the relevant parameter include at least optimum individual coefficient, and maximum evolutionary generation stops algebra
And/or fitness function deviation.
S303: the genetic algorithm NSGA-II based on the non-dominated ranking with elitism strategy be arranged it is described to be optimized grid-connected
The population scale of unit, and according in the Model for Multi-Objective Optimization equality constraint and inequality constraints generate the first generation it is initial
Population P0, wind power generating set and thermal power generation unit in the grid-connected unit to be optimized are in the population scale
Body.
S304: based on the first object function and the second objective function in the Model for Multi-Objective Optimization, to contemporary parent
Population PtCarry out quick non-dominated ranking, and according to range information of the vector of the individual in the variable space calculate virtually away from
From crowding.
In S304, for contemporary parent population PtThe process of quick non-dominated ranking is carried out, as shown in Figure 4, comprising:
S401: the contemporary parent population P is calculatedtIn each individual fitness function value.
S402: being based on the corresponding fitness function value of each individual, originated by j=1, determines individual xjWith individual xnBetween
Dominance relation.
In S402, j=1,2 ..., N, and j ≠ n.
S403: it judges whether there is better than the individual xjIndividual xn, if it does not exist, then execute S404, and if it exists, then
Execute S405.
S404: the confirmation individual xjFor non-dominant individual.
S405: so that j=j+1, returns and execute in S403, execute judgement, until confirming the contemporary parent population PtIn
All non-dominant individuals.
S305: based on the genetic manipulation in the NSGA-II, by the first generation initial population P0Starting executes heredity behaviour
Make, obtains sub- population Qt。
S306: merge the contemporary population PtWith the sub- population Qt, obtain merging population Rt。
S307: to the merging population RtIn individual carry out the meter of quick non-dominated ranking and pseudo range crowding
It calculates, and next-generation parent population P is generated based on sequence and calculated result selection individualt+1。
S308: judge the next-generation parent population Pt+1Algebra whether reach maximum value, if it is not, then execute S309, if
It is then to execute S310.
S309: by the next-generation parent population Pt+1As contemporary parent population PtIt returns and executes S304.
S310: the confirmation next-generation parent population Pt+1In the corresponding first object function of individual and the second target letter
Several calculated values constitutes the Pareto optimal solution set for meeting the constraint condition in the Model for Multi-Objective Optimization.
The method for optimizing scheduling of electric system disclosed by the embodiments of the present invention.Pass through the pre-set multiple-objection optimization of determination
Model, and based on the genetic algorithm NSGA-II of the non-dominated ranking with elitism strategy in the Model for Multi-Objective Optimization
One objective function and second objective function optimize, and obtain meeting the constraint condition in the Model for Multi-Objective Optimization
Pareto optimal solution set, the Pareto optimal solution set include non-in the first object function and second objective function
Inferior solution.Multiple-objection optimization is carried out by above-mentioned dispatch using NSGA-II algorithm to network system, is solving asking for multiple-objection optimization
In topic, confirmation meets the Pareto optimal solution set of the constraint condition in the Model for Multi-Objective Optimization, that is to say, that chooses multiple
Noninferior solution during objective optimization, the optimal solution for the optimization multiple target compromised.It is implemented without and sacrifices other targets work
For cost, the purpose of the economic environment Optimized Operation of the electric system of current multiple-objection optimization demand is completed.
Based on a kind of method for optimizing scheduling of electric system disclosed in the embodiments of the present invention, the embodiment of the present invention is also public
A kind of dispatching device of electric system is opened, as shown in figure 5, the dispatching device includes:
Confirmation unit 501, for determining pre-set Model for Multi-Objective Optimization
Wherein, the Model for Multi-Objective Optimization is that formula (1) is shown.Specific implementation principle can be found in above-mentioned saying to formula (1)
It is bright.
Optimizing scheduling unit 502, for the genetic algorithm NSGA-II based on the non-dominated ranking with elitism strategy to described
The first object function and second objective function in Model for Multi-Objective Optimization optimize, and obtain meeting more mesh
Mark the Pareto optimal solution set of the constraint condition in Optimized model.
Wherein, the Pareto optimal solution set includes the noninferior solution in the first object function and the second objective function.
In the dispatching device further include: default unit.
The default unit includes wind power cost computing module, polluted gas Emission amount calculation module, confirmation module and building
Module.
The wind power cost computing module, for the cost of electricity-generating for the unit that calculates and obtain to generate electricity by way of merging two or more grid systems, by the power generation
Cost is as first object function.
The polluted gas Emission amount calculation module, for calculating and obtaining the polluted gas row of the unit that generates electricity by way of merging two or more grid systems
High-volume, using the polluted gas discharge amount as the second objective function.
The confirmation module, for determining the equality constraint of the first object function, the second objective function, and determination
The inequality constraints of the first object function, the second objective function.
The building module, for being based on the first object function, the second objective function and the first object letter
The equality constraint and inequality constraints of number, the second objective function construct Model for Multi-Objective Optimization.
Optionally, confirmation module, the equality constraint for confirmation include system power Constraints of Equilibrium, for differing for confirmation
Formula constraint at least includes: thermal power generation unit units limits, wind power generating set units limits, wind power penetration limit constraint
It is constrained with system spinning reserve.
Optionally, the optimizing scheduling unit includes:
Module is obtained, for obtaining the power output of the wind power generating set in grid-connected unit to be optimized and described grid-connected
The active power output of thermal power generation unit in unit.
First setup module, for based on the wind power generating set power output and the thermal power generation unit it is active go out
The relevant parameter of the Model for Multi-Objective Optimization is arranged in power, and the relevant parameter includes at least optimum individual coefficient, and maximum is evolved
Algebra stops algebra and/or fitness function deviation.
Second setup module, for described in the genetic algorithm NSGA-II setting based on the non-dominated ranking with elitism strategy
The population scale of grid-connected unit to be optimized, and according to the equality constraint and inequality constraints life in the Model for Multi-Objective Optimization
At first generation initial population P0, wind power generating set and thermal power generation unit in the grid-connected unit to be optimized are described kind
Individual in group's scale.
Sequence and computing module, for based on the first object function and the second target letter in the Model for Multi-Objective Optimization
Number, to contemporary parent population PtCarry out quick non-dominated ranking, and the distance according to the vector of the individual in the variable space
Information calculates pseudo range crowding.
Genetic manipulation module, for based on the genetic manipulation in the NSGA-II, by the first generation initial population P0It rises
Begin to execute genetic manipulation, obtains sub- population Qt。
Merging module, for merging the contemporary population PtWith the sub- population Qt, obtain merging population Rt。
Generation module, for the merging population RtIn individual carry out quick non-dominated ranking and pseudo range is crowded
The calculating of degree, and next-generation parent population P is generated based on sequence and calculated result selection individualt+1。
Pareto optimal solution set obtains module, for judging the next-generation parent population Pt+1Algebra whether reach most
Big value, if it is not, then by the next-generation parent population Pt+1As contemporary parent population PtIt returns and executes the sequence and calculating mould
Block, if so, the confirmation next-generation parent population Pt+1In individual corresponding first object function and the second objective function
Calculated value constitutes the Pareto optimal solution set for meeting the constraint condition in the Model for Multi-Objective Optimization.
Optionally, sequence and computing module, for calculating the contemporary parent population PtIn each individual fitness letter
Numerical value is based on the corresponding fitness function value of each individual, determines individual xjWith individual xnBetween dominance relation, wherein j=
1,2 ..., N, and j ≠ n；It judges whether there is better than the individual xjIndividual xn；If it does not exist, then confirm the individual xjFor
Non-dominant individual；If it exists, then continue to determine whether exist better than the individual xj+1Individual xn, until confirming the present age
Parent population PtIn all non-dominant individuals.
In conclusion the method for optimizing scheduling device of electric system disclosed by the embodiments of the present invention.It is set in advance by determination
The Model for Multi-Objective Optimization set, and based on the genetic algorithm NSGA-II of the non-dominated ranking with elitism strategy to the multiple target
First object function and second objective function in Optimized model optimize, and obtain meeting the Model for Multi-Objective Optimization
In constraint condition Pareto optimal solution set, the Pareto optimal solution set includes the first object function and described second
Noninferior solution in objective function.Multiple-objection optimization is carried out by above-mentioned dispatch using NSGA-II algorithm to network system, is being solved
On the problem of multiple-objection optimization, confirmation meets the Pareto optimal solution set of the constraint condition in the Model for Multi-Objective Optimization,
That is the noninferior solution during multiple objective optimizations is chosen, the optimal solution for the optimization multiple target compromised.It is implemented without
Other targets are sacrificed as cost, complete the mesh of the economic environment Optimized Operation of the electric system of current multiple-objection optimization demand
's.
All the embodiments in this specification are described in a progressive manner, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for system or
For system embodiment, since it is substantially similar to the method embodiment, so describing fairly simple, related place is referring to method
The part of embodiment illustrates.System and system embodiment described above is only schematical, wherein the conduct
The unit of separate part description may or may not be physically separated, component shown as a unit can be or
Person may not be physical unit, it can and it is in one place, or may be distributed over multiple network units.It can root
According to actual need that some or all of the modules therein is selected to achieve the purpose of the solution of this embodiment.Ordinary skill
Personnel can understand and implement without creative efforts.
Professional further appreciates that, unit described in conjunction with the examples disclosed in the embodiments of the present disclosure
And algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These
Function is implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Profession
Technical staff can use different methods to achieve the described function each specific application, but this realization is not answered
Think beyond the scope of this invention.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (10)
1. a kind of method for optimizing scheduling of electric system characterized by comprising
Determine that pre-set Model for Multi-Objective Optimization, the Model for Multi-Objective Optimization areWherein, F
(PG, Pw)=F (PG)+F(Pw) it is first object function corresponding to the cost of electricity-generating of grid-connected unit,ai、bi、ciFor the fuel cost system of the thermal power generation unit in i-th grid-connected unit
Number, PGiFor the active power output of i-th thermal power generation unit, F (Pw)=Cw×Pw, PwFor the power generation function of grid connected wind power unit
Rate, Cw indicate averagely to run required for the every electric energy for generating certain unit of wind power plant that the Wind turbines in grid-connected unit are constituted
Maintenance cost,For the firepower hair in the grid-connected unit
Second objective function corresponding to the polluted gas discharge amount of motor group, αi、βi、γi、ζi、λiFor the institute in i-th grid-connected unit
State the pollutant emission coefficient of discharge of thermal power generation unit, E (PG, Pw) be the equation of first object function and the second objective function about
Beam, I (PG, Pw) it is first object function and the second objective function inequality constraints；
Based on the genetic algorithm NSGA-II of the non-dominated ranking with elitism strategy to the first mesh in the Model for Multi-Objective Optimization
Scalar functions and second objective function optimize, and obtain meeting the constraint condition in the Model for Multi-Objective Optimization
Pareto optimal solution set, the Pareto optimal solution set include non-in the first object function and second objective function
Inferior solution.
2. the method according to claim 1, wherein the pre-set process packet of the Model for Multi-Objective Optimization
It includes:
It calculates and the cost of electricity-generating F (P for the unit that obtains generating electricity by way of merging two or more grid systemsG, Pw)=F (PG)+F(Pw), using the cost of electricity-generating as
One objective function, wherein the unit that generates electricity by way of merging two or more grid systems includes at least thermal power generation unit and wind power generating set,ai、bi、ciFor the fuel cost system of the thermal power generation unit in i-th grid-connected unit
Number, PGiFor the active power output of i-th thermal power generation unit, F (Pw)=Cw×Pw, PwFor the wind-power electricity generation in grid-connected unit
The generated output of unit, Cw indicate the every electric energy for generating certain unit of wind power plant that the wind power generating set in grid-connected unit is constituted
Required average operation and maintenance cost；
It calculates and obtains the polluted gas discharge amount of the unit that generates electricity by way of merging two or more grid systemsUsing the polluted gas discharge amount as the second target
Function, wherein αi、βi、γi、ζi、λiFor the pollutant emission coefficient of discharge of the thermal power generation unit in i-th grid-connected unit；
Determine the equality constraint E (P of the first object function, the second objective functionG, Pw), and determine the first object letter
Inequality constraints I (the P of number, the second objective functionG, Pw)；
Equation based on the first object function, the second objective function and the first object function, the second objective function
Constraint and inequality constraints construct Model for Multi-Objective Optimization
3. according to the method described in claim 2, it is characterized in that, the equality constraint includes: system power Constraints of EquilibriumWherein, PDFor system total capacity requirement；
The inequality constraints includes at least: thermal power generation unit units limits, wind power generating set units limits, wind-powered electricity generation penetrate
Power limit constraint and the constraint of system spinning reserve；
The thermal power generation unit units limits are PGi,min≤PGi≤PGi,max, wherein PGi,minAnd PGi,maxRespectively i-th fire
Motor group minimum load and maximum output；
The wind power generating set units limits are 0≤Pw≤Wav, wherein WavFor the wind power generating set maximum output；
The wind power penetration limit is constrained to 0≤Pw≤δw,maxPD；
The system spinning reserve is constrained toWherein, PSRIt is system spinning reserve capacity.
4. method described in any one of -3 according to claim 1, which is characterized in that described based on the non-branch with elitism strategy
Genetic algorithm NSGA-II with sequence in the Model for Multi-Objective Optimization first object function and the second objective function carry out
Optimization, obtains meeting the Pareto optimal solution set of the constraint condition in the Model for Multi-Objective Optimization, comprising:
Obtain the thermoelectric generator in the power output and the grid-connected unit of the wind power generating set in grid-connected unit to be optimized
The active power output of group；
The multiple-objection optimization is arranged in the active power output of power output and the thermal power generation unit based on the wind power generating set
The relevant parameter of model, the relevant parameter include at least optimum individual coefficient, and maximum evolutionary generation stops algebra and/or fits
Response function deviation；
The population of the grid-connected unit to be optimized is arranged in genetic algorithm NSGA-II based on the non-dominated ranking with elitism strategy
Scale, and according to the equality constraint and inequality constraints generation first generation initial population P in the Model for Multi-Objective Optimization0, institute
Stating wind power generating set and thermal power generation unit in grid-connected unit to be optimized is the individual in the population scale；
Based on the first object function and the second objective function in the Model for Multi-Objective Optimization, to contemporary parent population PtIt carries out
Quick non-dominated ranking, and pseudo range crowding is calculated according to range information of the vector of the individual in the variable space；
Based on the genetic manipulation in the NSGA-II, by the first generation initial population P0Starting executes genetic manipulation, obtains son
Population Qt；
Merge the contemporary population PtWith the sub- population Qt, obtain merging population Rt；
To the merging population RtIn individual carry out the calculating of quick non-dominated ranking and pseudo range crowding, and based on row
Sequence and calculated result selection individual generate next-generation parent population Pt+1
Judge the next-generation parent population Pt+1Algebra whether reach maximum value；
If it is not, then by the next-generation parent population Pt+1As contemporary parent population PtIt returns and executes to contemporary parent population PtInto
The quick non-dominated ranking of row, and pseudo range crowding is calculated according to range information of the vector of the individual in the variable space
The step for；
If so, the confirmation next-generation parent population Pt+1In the corresponding first object function of individual and the second objective function
Calculated value constitute the Pareto optimal solution set for meeting constraint condition in the Model for Multi-Objective Optimization.
5. according to the method described in claim 4, it is characterized in that, described to contemporary parent population PtCarry out quickly non-dominant row
Sequence, comprising:
Calculate the contemporary parent population PtIn each individual fitness function value；
Based on the corresponding fitness function value of each individual, individual x is determinedjWith individual xnBetween dominance relation, wherein j=1,
2 ..., N, and j ≠ n；
It judges whether there is better than the individual xjIndividual xn；
If it does not exist, then confirm the individual xjFor non-dominant individual；
If it exists, then continue to determine whether exist better than the individual xj+1Individual xn, until confirming the contemporary parent population
PtIn all non-dominant individuals.
6. a kind of optimizing scheduling device of electric system, which is characterized in that the dispatching device includes:
Confirmation unit, for determining that pre-set Model for Multi-Objective Optimization, the Model for Multi-Objective Optimization beWherein, F (PG, Pw)=F (PG)+F(Pw) it is the first mesh corresponding to the cost of electricity-generating of grid-connected unit
Scalar functions,ai、bi、ciFor the fuel of the thermal power generation unit in i-th grid-connected unit
Cost coefficient, PGiFor the active power output of i-th thermal power generation unit, F (Pw)=Cw×Pw, PwFor grid connected wind power unit
Generated output, Cw indicate to put down required for the every electric energy for generating certain unit of wind power plant that the Wind turbines in grid-connected unit are constituted
Equal operation and maintenance cost,For in the grid-connected unit
Second objective function corresponding to the polluted gas discharge amount of thermal power generation unit, αi、βi、γi、ζi、λiFor i-th grid-connected unit
In the thermal power generation unit pollutant emission coefficient of discharge, E (PG, Pw) it is first object function and the second objective function
Equality constraint, I (PG, Pw) it is first object function and the second objective function inequality constraints；
Optimizing scheduling unit, for the genetic algorithm NSGA-II based on the non-dominated ranking with elitism strategy to the multiple target
The first object function and second objective function in Optimized model optimize, and obtain meeting the multiple-objection optimization
The Pareto optimal solution set of constraint condition in model, the Pareto optimal solution set include the first object function and second
Noninferior solution in objective function.
7. device according to claim 6, which is characterized in that further include: default unit, the default unit includes wind-powered electricity generation
Cost calculation module, polluted gas Emission amount calculation module, confirmation module and building module；
The wind power cost computing module, the cost of electricity-generating F (P for the unit that calculates and obtain to generate electricity by way of merging two or more grid systemsG, Pw)=F (PG)+F
(Pw), using the cost of electricity-generating as first object function, wherein the unit that generates electricity by way of merging two or more grid systems is including at least thermal power generation unit
And wind power generating set,ai、bi、ciFor the thermoelectric generator in i-th grid-connected unit
The fuel cost coefficient of group, PGiFor the active power output of i-th thermal power generation unit, F (Pw)=Cw×Pw, PwFor grid-connected wind
The generated output of motor group, Cw indicate the every electric energy institute for generating certain unit of wind power plant that the Wind turbines in grid-connected unit are constituted
The average operation and maintenance cost needed；
The polluted gas Emission amount calculation module, for calculating and obtaining the polluted gas discharge amount of the unit that generates electricity by way of merging two or more grid systemsUsing the polluted gas discharge amount as the second target
Function, wherein αi、βi、γi、ζi、λiFor the pollutant emission coefficient of discharge of the thermal power generation unit in i-th grid-connected unit；
The confirmation module, for determining the equality constraint E (P of the first object function, the second objective functionG, Pw), and
Determine the inequality constraints I (P of the first object function, the second objective functionG, Pw)；
The building module, for based on the first object function, the second objective function and the first object function,
The equality constraint and inequality constraints of second objective function construct Model for Multi-Objective Optimization
8. device according to claim 7, which is characterized in that described for determining the first object function, the second mesh
Equality constraint E (the P of scalar functionsG, Pw), and determine the inequality constraints I of the first object function, the second objective function
(PG, Pw) confirmation module, the equality constraint for confirmation includes system power Constraints of EquilibriumWherein, PD
For system total capacity requirement；The inequality constraints includes at least: thermal power generation unit units limits, wind power generating set power output
Constraint, wind power penetration limit constraint and the constraint of system spinning reserve；The thermal power generation unit units limits are PGi,min≤
PGi≤PGi,max, wherein PGi,minAnd PGi,maxRespectively i-th fired power generating unit minimum load and maximum output；The wind-power electricity generation
Unit output is constrained to 0≤Pw≤Wav, wherein WavFor the wind power generating set maximum output；The wind-powered electricity generation penetrates power pole
Limit is constrained to 0≤Pw≤δw,maxPD；The system spinning reserve is constrained toWherein, PSRIt is system rotation
Turn spare capacity.
9. device a method according to any one of claims 6-8, which is characterized in that the optimizing scheduling unit includes:
Obtain module, for obtain the wind power generating set in grid-connected unit to be optimized power output and the grid-connected unit
In thermal power generation unit active power output；
First setup module, the active power output for power output and the thermal power generation unit based on the wind power generating set are set
Set the relevant parameter of the Model for Multi-Objective Optimization, the relevant parameter includes at least optimum individual coefficient, maximum evolutionary generation,
Stop algebra and/or fitness function deviation；
Second setup module, it is described to excellent for the genetic algorithm NSGA-II setting based on the non-dominated ranking with elitism strategy
The population scale for the grid-connected unit changed, and according in the Model for Multi-Objective Optimization equality constraint and inequality constraints generate the
Generation initial population P0, wind power generating set and thermal power generation unit in the grid-connected unit to be optimized are population rule
Individual in mould；
Sequence and computing module, for based on the first object function and the second objective function in the Model for Multi-Objective Optimization,
To contemporary parent population PtCarry out quick non-dominated ranking, and the range information according to the vector of the individual in the variable space
Calculate pseudo range crowding；
Genetic manipulation module, for based on the genetic manipulation in the NSGA-II, by the first generation initial population P0Starting is held
Row genetic manipulation obtains sub- population Qt；
Merging module, for merging the contemporary population PtWith the sub- population Qt, obtain merging population Rt；
Generation module, for the merging population RtIn individual carry out quick non-dominated ranking and pseudo range crowding
It calculates, and next-generation parent population P is generated based on sequence and calculated result selection individualt+1
Pareto optimal solution set obtains module, for judging the next-generation parent population Pt+1Algebra whether reach maximum value,
If it is not, then by the next-generation parent population Pt+1As contemporary parent population PtIt returns and executes the sequence and computing module, if
It is then to confirm the next-generation parent population Pt+1In individual corresponding first object function and the second objective function calculating
Value constitutes the Pareto optimal solution set for meeting the constraint condition in the Model for Multi-Objective Optimization.
10. device according to claim 9, which is characterized in that described for contemporary parent population PtIt carries out quickly non-dominant
The sequence of sequence and computing module, for calculating the contemporary parent population PtIn each individual fitness function value, be based on
The corresponding fitness function value of each individual, determines individual xjWith individual xnBetween dominance relation, wherein j=1,2 ..., N,
And j ≠ n；It judges whether there is better than the individual xjIndividual xn；If it does not exist, then confirm the individual xjIt is non-dominant
Body；If it exists, then continue to determine whether exist better than the individual xj+1Individual xn, until confirming the contemporary parent population Pt
In all non-dominant individuals.
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| CN201811056562.2A CN109241630A (en) | 2018-09-11 | 2018-09-11 | The method for optimizing scheduling and device of electric system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110033118A (en) * | 2019-02-25 | 2019-07-19 | 浙江工业大学 | Elastomeric network modeling and the blower multiobjective optimization control method based on genetic algorithm |
| CN110245799A (en) * | 2019-06-18 | 2019-09-17 | 国网江西省电力有限公司经济技术研究院 | Consider the multi-objective planning method of the Distribution Network Frame structural transition of load flexible demand |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103580020A (en) * | 2013-03-07 | 2014-02-12 | 长沙理工大学 | Method for multi-target dynamic optimal dispatching of power system with wind farm based on NSGA-II and Look-ahead |
| CN107958424A (en) * | 2016-10-14 | 2018-04-24 | 华北电力大学（保定） | A kind of Economic Dispatch strategy based on improvement gravitation search algorithm |
| CN108448649A (en) * | 2018-03-21 | 2018-08-24 | 广东电网有限责任公司电力科学研究院 | A kind of combined scheduling method and system based on autonomous learning group hunting algorithm |
-
2018
- 2018-09-11 CN CN201811056562.2A patent/CN109241630A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103580020A (en) * | 2013-03-07 | 2014-02-12 | 长沙理工大学 | Method for multi-target dynamic optimal dispatching of power system with wind farm based on NSGA-II and Look-ahead |
| CN107958424A (en) * | 2016-10-14 | 2018-04-24 | 华北电力大学（保定） | A kind of Economic Dispatch strategy based on improvement gravitation search algorithm |
| CN108448649A (en) * | 2018-03-21 | 2018-08-24 | 广东电网有限责任公司电力科学研究院 | A kind of combined scheduling method and system based on autonomous learning group hunting algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 王贺: "风电短期功率预测与并网多目标调度优化研究", 《中国博士学位论文全文数据库 工程科技II辑》 * |
| 罗斌: "基于NSGA-II的含风电场电力系统多目标调度计划研究", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
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