CN111786420A - Power system economic dispatching method considering renewable energy hybrid power generation - Google Patents

Power system economic dispatching method considering renewable energy hybrid power generation Download PDF

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CN111786420A
CN111786420A CN202010684087.4A CN202010684087A CN111786420A CN 111786420 A CN111786420 A CN 111786420A CN 202010684087 A CN202010684087 A CN 202010684087A CN 111786420 A CN111786420 A CN 111786420A
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cost
formula
wind
power generating
generating unit
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张岩
张晨
张全
韩新阳
靳晓凌
张钧
代贤忠
王旭斌
田鑫
曹子健
边海峰
神瑞宝
柴玉凤
谢光龙
张玥
邝燊琅
钟鑫祥
吴婷
查永星
王贵斌
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Shenzhen University
State Grid Tianjin Electric Power Co Ltd
State Grid Energy Research Institute Co Ltd
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Shenzhen University
State Grid Tianjin Electric Power Co Ltd
State Grid Energy Research Institute Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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Abstract

The invention discloses a method for economically scheduling a power system in consideration of renewable energy hybrid power generation, which comprises the following steps of: determining to obtain relevant data required by calculation; establishing a multi-objective power system economic optimization scheduling model by taking the minimum running cost, carbon dioxide emission and network loss cost of the system as a target; and (4) carrying out optimization solution on the scheduling model by an MOCE/D method, and determining and outputting an optimal scheduling result. The method effectively reduces the consumption of fossil energy and the emission of polluted gas by considering the renewable energy hybrid power generation to the economic dispatching of the power system, and has important significance for improving the operating efficiency of the power system.

Description

Power system economic dispatching method considering renewable energy hybrid power generation
Technical Field
The invention relates to the technical field of power system energy scheduling, in particular to a power system economic scheduling method considering renewable energy hybrid power generation.
Background
With the development of renewable energy supply technology, a traditional power grid system consisting of a thermal generator set is gradually uncomfortable to the appropriate environment, and a power generation mode of complementary utilization of various primary energy sources such as fossil fuel and renewable energy is gradually emerging. Therefore, coordinating and optimizing hydropower, heating power, wind energy and photovoltaic power generation dispatching has important significance for improving the operating efficiency of the power system.
In recent years, coupled economic emission scheduling has become a popular research area in the operation and control of power systems to meet economic and social demands due to the deterioration of global environment, depletion of fossil fuel reserves, and increasing demand for electrical energy.
In view of the above, it is desirable to provide a method for economically scheduling a power system considering renewable energy hybrid power generation, which can reduce consumption of fossil energy and emission of pollutant gas.
Disclosure of Invention
In order to solve the technical problem, the technical scheme adopted by the invention is to provide a method for economic dispatching of a power system considering renewable energy hybrid power generation, which comprises the following steps:
s1, determining and acquiring relevant data required by calculation;
s2, establishing a multi-objective power system economic optimization scheduling model by taking the minimum running cost, carbon dioxide emission penalty cost and network loss cost of the system as a target;
performing optimization solution on the scheduling model by an MOCE/D method, and determining and outputting an optimal scheduling result;
calculating the number of the required related data including thermal power generating units, wind power generating units, photovoltaic power generating units and hydroelectric generating units; actual output values and expected output values of each thermal power generating unit, each wind power generating unit, each photovoltaic power generating unit and each hydroelectric power generating unit; the upper and lower output limits of each unit; the maximum bearing capacity of each power transmission line; the voltage amplitude of the voltage node is limited.
In the method, the operation cost of the system is the fuel cost of the thermal power generating unit and the penalty cost of the renewable energy generating unit, and the system is used for controlling the operation of the thermal power generating unit and the renewable energy generating unit
(1) An objective function:
min f1+f2+f3
in the formula (f)1Is the running cost of the hybrid power generation system, f2Is the carbon dioxide emission penalty cost, f3Is the loss of network cost;
(2) the constraint conditions include: the system comprises a power balance constraint, a power system line transmission power constraint, a power system node voltage constraint, a generator set output constraint, a water flow and hydropower station output conversion constraint.
In the method, the specific solving steps of the MOCE/D method are as follows:
step 1, initializing setting;
is provided with
Figure BDA0002586860290000021
Initializing a uniformly distributed weight vector λ1,…,λN；
For any two weight vectors, finding out T vectors with the shortest distance by calculating the Euclidean distance between every two weight vectors; for each sub-question i 1.. times.c, let the set of vectors closest to it be the vector set
Figure BDA0002586860290000022
Is λiThe nearest T weight vectors and confirm the subgroup problem;
initializing a population x1，...，xN(ii) a For the jth sub-question, the sub-question,
Figure BDA0002586860290000023
let FVj＝F(xj)；
Randomly generating an initial population from a solution space omega, and calculating the initialF value of the population; initializing respective target values
Figure BDA0002586860290000024
Let zi＝min{fi(x1)，fi(x2)，...，fi(xN) }; selecting a distribution family, and initializing a parameter vector v for the sample density p (·; upsilon);
step 2, updating the search direction of the subproblems;
generating a sample which is subjected to normal distribution according to the parameter vector v; removing infeasible solutions; updating the optimal single target adaptive value; updating subgroup values and ER;
step 3, updating the parameter vector;
initial population was evaluated and (1-p) -fractional bits of the sample were calculated
Figure BDA0002586860290000031
Selecting
Figure BDA0002586860290000032
And find the best sample of
Figure BDA0002586860290000033
Minimizing the Kullback-Leibler distance; uniformly distributing the vector upsilon;
step 4, judging whether the judgment standard is met, and if the judgment standard is met, terminating and exporting ER; otherwise, returning to the step 2.
In the method, the operation cost of the hybrid power generation system is determined by the fuel cost F of the thermal power generating unitTRunning cost F of wind turbine generatorWThe running cost F of the photoelectric unitPAnd the waste cost of primary energy of the hydroelectric generating set FHComposition, the objective function is:
f1＝FT+FW+FP+FH
fuel cost of thermal power generating unit
Figure BDA0002586860290000034
In the formula, NGThe number of the thermal power generating units is; a isk、bk、ck、dk、ekThe cost coefficient is the kth thermal power generating unit;
Figure BDA0002586860290000035
the minimum output of the kth thermal power generating unit is obtained; the active output vector of the thermal power generating unit is as follows:
Figure BDA0002586860290000036
second running cost of wind and photoelectric unit
The operating cost of the wind power plant is as follows:
Figure BDA0002586860290000037
in the formula, NwNumber of wind turbines, Cw,l、
Figure BDA0002586860290000038
And
Figure BDA0002586860290000039
direct cost, overestimated penalty cost and underestimated penalty cost functions of the first wind generation set are respectively,
Figure BDA00025868602900000310
and Pw,lRespectively setting the actual output force and the expected output force of the first wind turbine generator set;
Cw,l(Pw,l)＝kw,dPw,l
Figure BDA00025868602900000311
Figure BDA0002586860290000041
in the formula, kw,d、kw,u、kw,oDirect cost, overestimated penalty cost and underestimated penalty cost coefficients of the wind turbine generator are respectively calculated; in addition, the wind speed distribution at a given position is closest to Weibull distribution, and the probability density function of wind power output is expressed as:
Figure BDA0002586860290000042
where γ and h are the scale factor and shape factor, respectively, of the probability distribution function,
Figure BDA0002586860290000043
l＝(vr-vin)/vin，vrindicating rated wind speed, vinRepresenting a cut-in wind speed;
the operating cost of a photovoltaic power plant is as follows:
Figure BDA0002586860290000044
in the formula, NPVThe number of the photoelectric units is the number of the photoelectric units,
Figure BDA0002586860290000045
and
Figure BDA0002586860290000046
respectively an overestimation penalty cost function and an underestimation penalty cost function of the mth photoelectric unit,
Figure BDA0002586860290000047
and Ppv,mRespectively setting the actual output force and the expected output force of the mth wind turbine generator set;
Figure BDA0002586860290000048
Figure BDA0002586860290000049
in the formula, kpv,uAnd kpv,oRespectively representing the overestimation penalty cost coefficient and the underestimation penalty cost coefficient of the photoelectric generator set; in addition, the light energy irradiation distribution at a given position is closest to the Beta distribution, so the probability density function of the wind power output can be expressed as:
Figure BDA00025868602900000410
B(α,β)＝((α)(β))/(α+β)；
in the formula, alpha and Beta are scale factors of Beta distribution; (. cndot.) is a gamma function;
thirdly, the waste cost of primary energy of the hydroelectric generating set is as follows:
Figure BDA0002586860290000051
in the formula, NhNumber of hydroelectric generating sets, kh,wA penalty factor, Q, for primary energy wastenAnd
Figure BDA0002586860290000052
the actual water flow and the maximum available water flow of the nth hydroelectric generating set are respectively.
In the above method, the carbon dioxide emission objective function is as follows:
Figure BDA0002586860290000053
in the formula, αk、βk、γk、kAnd λkAnd the emission coefficient of the kth thermal power generating unit.
In the above method, the network loss cost objective function is specifically as follows:
Figure BDA0002586860290000054
in the formula, NLIs the total number of transmission lines, ViAnd VjIs the voltage magnitude of bus i and j, respectively, thetaiAnd thetajRespectively their voltage phase angle.
According to the invention, the renewable energy hybrid power generation is considered to the economic dispatching of the power system, so that the consumption of fossil energy and the emission of polluted gas are effectively reduced by introducing the renewable energy; due to the influence of meteorological factors such as wind speed, radiation intensity and temperature, the output of renewable energy sources always shows variability, intermittency and low predictability.
Drawings
FIG. 1 is a flow chart provided by the present invention;
FIG. 2 is a flow chart of a MOCE/D method solution model provided in the present invention.
Detailed Description
According to the invention, the renewable energy hybrid power generation is considered to the economic dispatching of the power system, so that the consumption of fossil energy and the emission of polluted gas are effectively reduced by introducing the renewable energy; due to the influence of meteorological factors such as wind speed, radiation intensity and temperature, the output of renewable energy sources always shows variability, intermittency and low predictability. The invention is described in detail below with reference to specific embodiments and the accompanying drawings.
As shown in fig. 1, the present invention provides a method for economic dispatch of a power system considering renewable energy hybrid power generation, comprising the following steps:
s1, determining relevant data required by calculation;
the number of the thermal power generating units, the wind power generating units, the photovoltaic power generating units and the hydroelectric generating units is included; actual output values and expected output values of each thermal power generating unit, each wind power generating unit, each photovoltaic power generating unit and each hydroelectric power generating unit; the upper and lower output limits of each unit; the maximum bearing capacity of each power transmission line; the voltage amplitude of the voltage node is limited.
S2, establishing a multi-objective power system economic optimization scheduling model by taking the minimum running cost, carbon dioxide emission penalty cost and network loss cost of the system as a target; the method comprises the following specific steps:
in the embodiment, (one), hybrid power generation system includes traditional thermal power generating unit, wind turbine generator system, photoelectricity unit and hydroelectric generator set, and the consumption of fossil energy and the emission of gaseous pollutants can be reduced in the joining of renewable energy, selects running cost, the punishment cost of carbon dioxide emission and the net loss cost of this system as the optimization objective, and wherein, system's running cost includes thermal power generating unit's fuel cost and renewable energy generating unit's punishment cost. The objective function is specifically as follows:
min f1+f2+f3
in the formula (f)1Is the running cost of the hybrid power generation system, f2Is the carbon dioxide emission penalty cost, f3Is the loss of network cost. Wherein f is1、f2、f3The calculation is specifically as follows:
1. hybrid power generation system operating cost f1
In this embodiment, the first objective is to optimize the operating cost of the hybrid power generation system by the fuel cost F of the thermal power generating unitTRunning cost F of wind turbine generatorWThe running cost F of the photoelectric unitPAnd the waste cost of primary energy of the hydroelectric generating set FHThe objective function is:
f1＝FT+FW+FP+FH
fuel cost of thermal power generating unit
The valve point effect which is not considered by the traditional modeling of the cost function of the thermal power generating unit is considered, and the effect is added into the cost function in the form of a sine function, and is shown as the following formula:
Figure BDA0002586860290000071
in the formula, NGThe number of the thermal power generating units is; a isk、bk、ck、dk、ekThe cost coefficient is the kth thermal power generating unit;
Figure BDA0002586860290000072
the minimum output of the kth thermal power generating unit is obtained; the active output vector of the thermal power generating unit is as follows:
Figure BDA0002586860290000073
second running cost of wind and photoelectric unit
Due to uncertainty of wind speed and illumination radiation, actual output of the intermittent power supply cannot always be the same as planned output, and uncertainty is processed by adopting an estimation error punishment method in the embodiment. Specifically, the objective function takes into account the underestimated cost of wasting available wind, generating electricity from light, and the overestimated cost of requiring additional reserves, the operating costs of wind and photovoltaic power plants being respectively represented by the following equations:
Figure BDA0002586860290000074
in the formula, NwNumber of wind turbines, Cw,l、
Figure BDA0002586860290000075
And
Figure BDA0002586860290000076
direct cost, overestimated penalty cost and underestimated penalty cost functions of the first wind generation set are respectively,
Figure BDA0002586860290000077
and Pw,lThe actual output force and the expected output force of the first wind turbine generator set are respectively.
Cw,l(Pw,l)＝kw,dPw,l
Figure BDA0002586860290000078
Figure BDA0002586860290000079
In the formula, kw,d、kw,u、kw,oDirect cost, overestimated penalty cost and underestimated penalty cost coefficients of the wind turbine generator are respectively calculated; in addition, the wind speed distribution at a given position is closest to Weibull distribution, and the probability density function of wind power output is expressed as:
Figure BDA0002586860290000081
where γ and h are the scale factor and shape factor, respectively, of the probability distribution function,
Figure BDA0002586860290000082
l＝(vr-vin)/vin，vrindicating rated wind speed, vinIndicating the cut-in wind speed.
Compared with the direct cost of wind power, the direct cost of the photoelectricity can be generally ignored, so only considering the high and low estimation penalty costs of the photoelectricity is as follows:
Figure BDA0002586860290000083
in the formula, NPVThe number of the photoelectric units is the number of the photoelectric units,
Figure BDA0002586860290000084
and
Figure BDA0002586860290000085
respectively an overestimation penalty cost function and an underestimation penalty cost function of the mth photoelectric unit,
Figure BDA0002586860290000086
and Ppv,mThe actual output force and the expected output force of the mth wind turbine generator set are respectively.
Figure BDA0002586860290000087
Figure BDA0002586860290000088
In the formula, kpv,uAnd kpv,oRespectively are the overestimation penalty cost coefficient and the underestimation penalty cost coefficient of the photoelectric unit. In addition, the light energy irradiation distribution at a given position is closest to the Beta distribution, so the probability density function of the wind power output can be expressed as:
Figure BDA0002586860290000089
where α and β are the scale factors of the Beta distribution, and B (α, β) ═ ((α) (β))/(α + β) (. cndot.) is a gamma function.
Cost of waste of primary energy of hydroelectric generating set
In the operation process of the hydroelectric generating set, because the water flow does not reach the expected maximum water flow, the generated primary energy waste cost is expressed as:
Figure BDA00025868602900000810
in the formula, NhNumber of hydroelectric generating sets, kh,wA penalty factor, Q, for primary energy wastenAnd
Figure BDA0002586860290000091
the actual water flow and the maximum available water flow of the nth hydroelectric generating set are respectively.
2. Carbon dioxide emission penalty cost f2
The second objective is to optimize the carbon dioxide emission penalty cost of the hybrid power generation system, and the objective function is represented by the following formula:
Figure BDA0002586860290000092
in the formula, αk、βk、γk、kAnd λkFor kth thermal power generating unitsThe discharge coefficient.
3. Loss on grid cost f3
The final optimization objective is to minimize the network loss of the hybrid power system, whose objective function is expressed as follows:
Figure BDA0002586860290000093
in the formula, NLIs the total number of transmission lines, ViAnd VjIs the voltage magnitude of bus i and j, respectively, thetaiAnd thetajRespectively their voltage phase angle.
(II) determining constraint conditions of the multi-objective optimization model of the power system:
power balance constraint
In the power system, the total power of the generator is equal to the sum of the load and the loss of the transmission line, and the expression is as follows:
Figure BDA0002586860290000094
② power system line transmission power constraint
The apparent power of each transmission line should be within its maximum load capacity to avoid overload, and the expression is as follows:
max[|Sij|,|Sji|]≤Ss,max,s＝1,2,...,NL
in the formula, SijRepresenting the apparent power from node i to node j.
Thirdly, the node voltage constraint of the power system has the following expression:
Figure BDA0002586860290000101
in the formula (I), the compound is shown in the specification,
Figure BDA0002586860290000102
and
Figure BDA0002586860290000103
respectively, the upper and lower voltage limits of node i.
Output restraint of generator set
The output constraints of the thermal generator set, the wind generating set, the photovoltaic generator set and the hydroelectric generating set are respectively as follows:
Figure BDA0002586860290000104
in the formula (I), the compound is shown in the specification,
Figure BDA0002586860290000105
the lower limit and the upper limit of the output of the thermal power generating unit are set;
Figure BDA0002586860290000106
the lower limit and the upper limit of the output of the wind turbine generator are set;
Figure BDA0002586860290000107
the lower limit and the upper limit of the output of the photovoltaic generator set are set;
Figure BDA0002586860290000108
the lower limit and the upper limit of the output of the hydroelectric generating set.
Converting and constraining water flow and hydropower station output, wherein a relational expression is as follows:
Figure BDA0002586860290000109
Figure BDA00025868602900001010
in the formula, an、bn、cnAnd the coefficient is the relation coefficient between the output value and the water flow of the nth hydroelectric generating set.
S3, carrying out optimization solution on the scheduling model through an MOCE/D method, and determining to output an optimal scheduling result, wherein the method specifically comprises the following steps:
in the embodiment, a multi-objective optimization problem is converted into a series of single-objective optimization sub-problems based on a decomposed multi-objective evolutionary algorithm (MOEA/D), and then the sub-problems are simultaneously optimized by using a certain amount of information of adjacent problems and an evolutionary algorithm. MOEA/D has great advantages in maintaining solution distribution and computational complexity due to the decomposition operation. The cross entropy algorithm continuously increases the probability that the search solution appears near the global optimal solution by a reconstruction probability sampling method, and gradually obtains the global optimal solution. Compared with other algorithms, the cross entropy algorithm has stronger searching capability.
A multi-target cross entropy algorithm (MOCE/D) based on decomposition is obtained by replacing the cross entropy algorithm with the evolutionary algorithm in MOEA/D, and then the scheduling model is optimized and solved to determine and output the optimal scheduling result. The method integrates the advantages of low MOEA/D calculation complexity and strong search capability of a cross entropy algorithm, so that when the MOCE/D is used for solving the multi-objective optimization model, the pareto frontier with more uniform distribution, higher convergence rate and more diversity can be obtained.
The idea of solving the center by the MOCE/D method is shown in FIG. 2, and specifically comprises the following steps:
step 1, initializing setting; the method comprises the following steps:
s311, setting
Figure BDA0002586860290000111
ER is a set used for storing and obtaining non-dominated solutions;
s312, initializing uniformly distributed weight vector lambda1,…,λNRespectively corresponding to each sub-problem in MOCE/D; in this embodiment, a multi-objective problem to be optimized may be decomposed into C sub-problems.
S313, for any two weight vectors, finding out T vectors with the shortest distance by calculating the Euclidean distance between every two weight vectors; for each sub-question i 1.. times.c, let the set of vectors closest to it be the vector set
Figure BDA0002586860290000112
Is λiThe nearest T weight vectors and confirm the subgroup problem;
s314, initializing the populationx1，...，xN(ii) a For the jth sub-question, the sub-question,
Figure BDA0002586860290000113
let FVj＝F(xj)；
S315, randomly generating an initial population x from a solution space omega1，x2，..xcCalculating x1，x2，..xcF value of (i.e. FV)1，FV2...FVi；
S316, initializing each target value
Figure BDA0002586860290000114
Let zi＝min{fi(x1)，fi(x2)，...，fi(xN)}；fi(xN) The running cost, the carbon dioxide emission penalty cost or the network loss cost of the hybrid power generation system are calculated;
s317, selecting a distribution family, and initializing a parameter vector v for the sample density p (·; upsilon).
Step 2, updating the search direction of the subproblems;
s321, generating a sample y which follows normal distribution according to the parameter vector v;
s322, removing infeasible solutions: in order to eliminate infeasible solutions and improve the solution efficiency, a greedy algorithm is used for the sample y to generate y';
s323, updating an optimal single target adaptive value (z): for each target, j 1objIf Z isj＞fj(y'), then Zj＞fj(y’)；
S324, updating subgroup values, for each target, j ∈ NE (i), if gte(y′|λj，z)≤gte(xj|λjZ), then xjY' and FVj＝F(y’)；
S325, updating ER: removing vectors dominated by F (y') in ER; if F (y ') has a complementary dominant relationship with the vector in ER, then F (y') is added to ER.
Step 3, updating the parameter vector;
s331, evaluating the initial population x1，x2，..xcAnd (1-p) -fraction bits of the sample are calculated
Figure BDA0002586860290000121
S332, selecting
Figure BDA0002586860290000124
And find the best sample of
Figure BDA0002586860290000122
Minimizing the Kullback-Leibler distance;
and S333, uniformly distributing the vector upsilon.
And 4, judging whether the judgment standard is met or not, and if the judgment standard is met, terminating and exporting the ER. Otherwise, returning to the step 2.
The following describes specifically solving the model of this embodiment through the above steps, specifically as follows:
first, a multi-objective optimization problem based on Pareto (Pareto) theory is described as follows:
Minimize fm(x),m＝1,2,...,Nobj
Figure BDA0002586860290000123
in the formula: x is a decision variable; f. ofm(x) Is the mth objective function; gj(x) Is the jth inequality constraint; h isk(x) Is the kth equality constraint; n is a radical ofobj，Nineq，NeqRespectively expressing the number of the objective function, the inequality constraint and the equality constraint; x is the number ofi (L)And xi (U)Lower and upper limits of the decision variables, respectively. If the decision vector x satisfies all constraints and variable ranges, x is called a feasible solution, and the feasible domain is composed of all feasible solutions.
The embodiment combines the group search algorithm with the Chebyshev decomposition method to provideThe multi-objective group search algorithm based on the Chebyshev decomposition method is firstly utilized to obtain a group of weight vectors lambda which are uniformly distributed by using the step S3121,…,λN(ii) a z is a reference point, and as can be seen from the above, the chebyshev decomposition method can decompose a multi-objective problem to be optimized into C sub-problems, and the expression formula is shown as follows:
Figure BDA0002586860290000131
wherein f isi(x) Expressed as a value of the optimization objective function. m is the number of the optimization objective functions; n is expressed as the number of subproblems, the weight vector lambdaiIs selected as the same as lambdaiThe T vectors with the nearest euclidean distance. The T sub-questions closest to the current sub-question are found using step S313. In each optimization process, the optimization of each sub-problem uses the information of the sub-problem adjacent to the sub-problem. The multi-target is decomposed into a plurality of sub-problems, the Euclidean distance is sequentially applied to the problems to form sub-group problems, one problem in the sub-group problems is solved by using a group search algorithm through the steps S32-S34, and then other sub-problems are solved through the similarity of information among the sub-problems.
This embodiment transforms the optimization Problem into a related random Problem (where u is a certain parameter and u ∈ V) (Associated storage protocol, ASP) by using Cross-Entropy (CE) algorithm, i.e., "find the optimal solution from feasible domain" as a small probability event:
m(γ)＝Pu(J(X)≥γ)＝EuI{J(X)≥γ}
wherein X is a random state vector corresponding to the variable X, I { J (X) ≧ gamma } is a set of indicator functions defined for different gammas, PuIs a probability measure of a random state variable X having a probability density function of f (X, u), EuIs the expectation of that event, m (y) is an estimate of the small probability event, wherein,
Figure BDA0002586860290000132
in the formula: xi∈ X, are elements in the random state vector X.
In this embodiment, a large number of samples need to be generated to obtain satisfactory accuracy, and importance sampling (importation function g (x)) is used, that is, the significance of function g (x), g (x) in the above formula is to increase the probability of generating random samples to reduce the sampling times, and the calculation is as follows:
Figure BDA0002586860290000133
in the formula, EgIs the expectation of a small probability event with g (x) as a function of probability density.
The estimate of m becomes:
Figure BDA0002586860290000141
in the formula, XiIs an element in X.
It can be shown that the best way to estimate m is to scale the density as shown below, i.e. to obtain a mean value of the density
Figure BDA0002586860290000142
The unbiased estimate of m can be obtained by sampling again, and the difficulty of the above process lies in gbest(x) Depending on the unknown m, an appropriate g is typically selected within the f (x; v) family. The appropriate parameter v is chosen below such that gbest(x) And f (x; v)best) Is minimum, the distance between two probability distribution density functions is measured by adopting cross entropy, and the definition of the cross entropy is as follows:
Figure BDA0002586860290000143
from the above equation, when the cross entropy is the smallest,
Figure BDA0002586860290000144
is equivalent to
Figure BDA0002586860290000145
The expected value can be estimated approximately by the average value, and the expected value in the formula can be estimated approximately by the average value:
Figure BDA0002586860290000146
wherein, XiIs a random variable generated from a probability density function f (x; u) when gamma approaches gammabestWhen f (x; v)best) There is a considerable probability that the generated samples are very close to the optimal solution xbestThereby obtaining the value of the above formula.
The performance of each candidate solution is evaluated according to a fitness function, and these fitness values are sorted in descending or ascending order, respectively, for maximizing or minimizing the problem, and the (1- ρ) -quantile can be calculated with the following formula:
Figure BDA0002586860290000151
then from
Figure BDA0002586860290000152
The best candidate point is selected as the optimal population for estimating the distribution parameters according to the minimization of the Kullback-Leibler distance, and in addition, the reconstructed distribution p (x; v) is utilized1) The next population is generated. The above process of evaluating the performance of each candidate solution according to the fitness function is repeated until a stopping criterion is met.
The present invention is not limited to the above-mentioned preferred embodiments, and any structural changes made under the teaching of the present invention shall fall within the protection scope of the present invention, which has the same or similar technical solutions as the present invention.

Claims (6)

1. A method for economic dispatch of an electric power system considering hybrid generation of renewable energy, comprising the steps of:
s1, determining and acquiring relevant data required by calculation;
s2, establishing a multi-objective power system economic optimization scheduling model by taking the minimum running cost, carbon dioxide emission penalty cost and network loss cost of the system as a target;
s3, carrying out optimization solution on the scheduling model through an MOCE/D method, and determining to output an optimal scheduling result;
calculating the number of the required related data including thermal power generating units, wind power generating units, photovoltaic power generating units and hydroelectric generating units; actual output values and expected output values of each thermal power generating unit, each wind power generating unit, each photovoltaic power generating unit and each hydroelectric power generating unit; the upper and lower output limits of each unit; the maximum bearing capacity of each power transmission line; the voltage amplitude of the voltage node is limited.
2. The method of claim 1, wherein the operating cost of the system is a fuel cost of a thermal power generating unit and a penalty cost of a renewable energy generating unit, the penalty cost
(1) An objective function:
min f1+f2+f3
in the formula (f)1Is the running cost of the hybrid power generation system, f2Is the carbon dioxide emission penalty cost, f3Is the loss of network cost;
(2) the constraint conditions include: the system comprises a power balance constraint, a power system line transmission power constraint, a power system node voltage constraint, a generator set output constraint, a water flow and hydropower station output conversion constraint.
3. The method of claim 1, wherein the MOCE/D method specifically comprises the following steps:
step 1, initializing setting;
is provided with
Figure FDA0002586860280000011
Initializing a uniformly distributed weight vector λ1，...，λN；
For any two weight vectors, finding out T vectors with the shortest distance by calculating the Euclidean distance between every two weight vectors; for each sub-question i 1.. times.c, let the set of vectors closest to it be the vector set
Figure FDA0002586860280000028
Is λiThe nearest T weight vectors and confirm the subgroup problem;
initializing a population x1，...，xN(ii) a For the jth sub-question, the sub-question,
Figure FDA0002586860280000022
let FVj＝F(xj)；
Randomly generating an initial population from a solution space omega, and calculating an F value of the initial population; initializing respective target values
Figure FDA0002586860280000023
Let zi＝min{fi(x1)，fi(x2)，...，fi(xN) }; selecting a distribution family, and initializing a parameter vector v for the sample density p (·; upsilon);
step 2, updating the search direction of the subproblems;
generating a sample which is subjected to normal distribution according to the parameter vector v; removing infeasible solutions; updating the optimal single target adaptive value; updating subgroup values and ER;
step 3, updating the parameter vector;
initial population was evaluated and (1-p) -fractional bits of the sample were calculated
Figure FDA0002586860280000024
Selecting
Figure FDA0002586860280000025
And find the best sample of
Figure FDA0002586860280000026
Minimizing the Kullback-Leibler distance; uniformly distributing the vector upsilon;
step 4, judging whether the judgment standard is met, and if the judgment standard is met, terminating and exporting ER; otherwise, returning to the step 2.
4. The method of claim 1, wherein the hybrid power system operating cost is derived from a thermal power unit fuel cost FTRunning cost F of wind turbine generatorWThe running cost F of the photoelectric unitPAnd the waste cost of primary energy of the hydroelectric generating set FHComposition, the objective function is:
f1＝FT+FW+FP+FH
fuel cost of thermal power generating unit
Figure FDA0002586860280000027
In the formula, NGThe number of the thermal power generating units is; a isk、bk、ck、dk、ekThe cost coefficient is the kth thermal power generating unit;
Figure FDA0002586860280000031
the minimum output of the kth thermal power generating unit is obtained; the active output vector of the thermal power generating unit is as follows:
Figure FDA0002586860280000032
second running cost of wind and photoelectric unit
The operating cost of the wind power plant is as follows:
Figure FDA0002586860280000033
in the formula, NwNumber of wind turbines, Cw,l、
Figure FDA0002586860280000034
And
Figure FDA0002586860280000035
direct cost, overestimated penalty cost and underestimated penalty cost functions of the first wind generation set are respectively,
Figure FDA0002586860280000036
and Pw,lRespectively setting the actual output force and the expected output force of the first wind turbine generator set;
Cw,l(Pw,l)＝kw,dPw,l
Figure FDA0002586860280000037
Figure FDA0002586860280000038
in the formula, kw,d、kw,u、kw,oDirect cost, overestimated penalty cost and underestimated penalty cost coefficients of the wind turbine generator are respectively calculated; in addition, the wind speed distribution at a given position is closest to Weibull distribution, and the probability density function of wind power output is expressed as:
Figure FDA0002586860280000039
where γ and h are the scale factor and shape factor, respectively, of the probability distribution function,
Figure FDA00025868602800000310
l＝(vr-vin)/vin，vrindicating rated wind speed, vinRepresenting a cut-in wind speed;
the operating cost of a photovoltaic power plant is as follows:
Figure FDA00025868602800000311
in the formula, NPVThe number of the photoelectric units is the number of the photoelectric units,
Figure FDA00025868602800000312
and
Figure FDA00025868602800000313
respectively an overestimation penalty cost function and an underestimation penalty cost function of the mth photoelectric unit,
Figure FDA0002586860280000041
and Ppv,mRespectively setting the actual output force and the expected output force of the mth wind turbine generator set;
Figure FDA0002586860280000042
Figure FDA0002586860280000043
in the formula, kpv,uAnd kpv,oRespectively representing the overestimation penalty cost coefficient and the underestimation penalty cost coefficient of the photoelectric generator set; in addition, the light energy irradiation distribution at a given position is closest to the Beta distribution, so the probability density function of the wind power output can be expressed as:
Figure FDA0002586860280000044
B(α,β)＝((α)(β))/(α+β)；
in the formula, alpha and Beta are scale factors of Beta distribution; (. cndot.) is a gamma function;
thirdly, the waste cost of primary energy of the hydroelectric generating set is as follows:
Figure FDA0002586860280000045
in the formula, NhNumber of hydroelectric generating sets, kh,wA penalty factor, Q, for primary energy wastenAnd
Figure FDA0002586860280000046
the actual water flow and the maximum available water flow of the nth hydroelectric generating set are respectively.
5. The method of claim 2, wherein the carbon dioxide emissions objective function is as follows:
Figure FDA0002586860280000047
in the formula, αk、βk、γk、kAnd λkAnd the emission coefficient of the kth thermal power generating unit.
6. The method of claim 2, wherein the network loss cost objective function is specified as follows:
Figure FDA0002586860280000048
in the formula, NLIs the total number of transmission lines, ViAnd VjIs the voltage magnitude of bus i and j, respectively, thetaiAnd thetajRespectively their voltage phase angle.
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