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CN113595080B - Double-layer optimal scheduling method for active distribution network based on improved satin blue gardener bird algorithm - Google Patents

Double-layer optimal scheduling method for active distribution network based on improved satin blue gardener bird algorithm Download PDF

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CN113595080B
CN113595080B CN202111139215.8A CN202111139215A CN113595080B CN 113595080 B CN113595080 B CN 113595080B CN 202111139215 A CN202111139215 A CN 202111139215A CN 113595080 B CN113595080 B CN 113595080B
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distribution network
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bird
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CN113595080A (en
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陈湘
徐敏
刘涛
陈伟
张亮
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Nanchang University
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    • 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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    • H—ELECTRICITY
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    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
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    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
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    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
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    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
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    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
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Abstract

The invention provides an active power distribution network double-layer optimization scheduling method based on an improved satin blue gardener algorithm, which comprises the following steps of: and establishing a double-layer optimization scheduling model considering the overall economy of the active power distribution network under the condition of demand response, wherein the model is a double-layer nonlinear programming mathematical model aiming at the lowest operation cost of the power distribution network system. The upper layer takes the time-of-use electricity price of the power distribution network as a variable and the objective function as the total operation cost, and the lower layer takes the output of the schedulable distributed power generator set of the active power distribution network as a variable and the objective function as the scheduling operation cost. The invention improves the global optimization capability of the satin blue gardener algorithm to the complicated double-layer nonlinear programming problem by introducing a nonlinear self-adaptive mechanism, can effectively improve the economical efficiency of the active power distribution network operation, and has practical value.

Description

Active power distribution network double-layer optimization scheduling method based on improved satin blue gardener algorithm
Technical Field
The invention relates to the technical field of economic operation of an active power distribution network, in particular to a double-layer optimized scheduling method of the active power distribution network based on an improved satin blue gardener algorithm.
Background
Compared with the traditional power Distribution Network (ADN), an Active Distribution Network (ADN) having a schedulable unit has the capability of Active control and management, and is considered as a development direction of a future power Distribution Network. With the advance of the electricity change policy, the user side can also participate in the distribution network scheduling, so that the effect of demand response in the active distribution network is gradually highlighted. Meanwhile, Distributed energy (DG) such as Wind Turbine (WT), Photovoltaic Power Generation (PV) and gas Turbine (MGT) are added to the Power distribution network, so that the electricity purchasing cost of the system can be effectively reduced, and meanwhile, the day-ahead scheduling of the output of the schedulable unit is of great significance for reducing the network loss, improving the Power supply reliability of the system and reducing the operation cost of the Power distribution network.
Demand responses are currently divided into two categories, one being incentive type responses and the other being price type responses. The price type demand response borrows the price elasticity of the power demand, adjusts the electricity price, and enables the user side to actively participate in the scheduling process.
The Satin blue gardener algorithm (SBO) is a novel swarm intelligence algorithm which is provided by Seyyed H.S.M. equal to 2017 and simulates the doll behavior of adult male Satin blue gardeners in nature, is inspired by the nesting habit of the Satin blue gardener, and finds the optimal solution of the Optimization problem through the mechanisms of competition, variation and elimination. Compared with other optimization algorithms, the optimization effect of the SBO optimization algorithm is good, but the problems of low convergence speed, low convergence precision and the like exist.
Disclosure of Invention
The invention aims to provide a double-layer optimized dispatching method for an active power distribution network based on an improved satin blue gardener algorithm. Aiming at the problems of low overall search speed and low convergence precision of the original satin blue gardener optimization algorithm, the nonlinear adaptive search and variation idea is introduced for improvement, the search speed of the algorithm is improved, the optimization capability of the algorithm in different search stages is enhanced, and the optimal solution with higher precision is sought.
In order to achieve the purpose, the invention provides the following technical scheme: an active power distribution network double-layer optimization scheduling method based on an improved satin blue gardener algorithm comprises the following steps:
step 1: initializing parameters and establishing an improved power distribution network node system;
step 2: constructing a mathematical model of double-layer optimization scheduling of the active power distribution network containing demand response, wherein the upper layer is demand response optimization, the lower layer is active power distribution network operation optimization scheduling, and a target function with the minimum sum of the total cost of the upper layer of the active power distribution network optimization scheduling and the minimum cost of the lower layer of the active power distribution network scheduling operation is given, and corresponding constraint conditions of the upper layer and the lower layer are given;
and step 3: and (2) performing optimization solution on the model of the active power distribution network double-layer optimization scheduling in the step (2) by adopting a satin blue gardener algorithm based on nonlinear self-adaption improvement, inputting variables generated by an upper layer into a lower layer, inputting optimal adaptive values generated by a lower layer into the upper layer, updating the variables generated by iteration after the upper layer calculates the optimal values, inputting the variables into the lower layer, and outputting the optimal solution of time-of-use electricity price and schedulable unit output and corresponding objective function values if the upper layer meets the iteration termination condition.
Further, the step 1 specifically comprises: initializing parameters, establishing an active power distribution network topological structure containing various distributed power supplies, numbering nodes and branches, and adding loads and distributed data at designated nodes to realize grid connection of the active power distribution network distributed power supplies.
Further, the mathematical model of the active power distribution network double-layer optimization scheduling established in the step 2 is as follows:
an objective function:
Figure 545483DEST_PATH_IMAGE001
(1)
in the formula (I), the compound is shown in the specification,
Figure 603569DEST_PATH_IMAGE002
for the sake of the total cost,
Figure 930776DEST_PATH_IMAGE003
in order to schedule the cost of the operation,
Figure 122723DEST_PATH_IMAGE004
cost for demand response; wherein:
Figure 343620DEST_PATH_IMAGE005
(2)
in the formula:
Figure 979132DEST_PATH_IMAGE006
and
Figure 42903DEST_PATH_IMAGE007
respectively the electricity prices and loads before the demand response,
Figure 523694DEST_PATH_IMAGE008
and
Figure 864676DEST_PATH_IMAGE009
respectively the price of electricity and the load after response; simultaneously, the constraint conditions are met: (a) the method comprises the following steps of (a) user total electric quantity constraint, (b) user average electricity price constraint, (c) user demand response constraint, and (d) user participation constraint.
Wherein the user demand response constraint is:
Figure 920357DEST_PATH_IMAGE010
(3)
in the formula:
Figure 877949DEST_PATH_IMAGE011
and
Figure 693589DEST_PATH_IMAGE012
the loads before and after the demand response at time t,
Figure 279291DEST_PATH_IMAGE013
for user engagement, T is the total number of scheduled time periods,
Figure 522185DEST_PATH_IMAGE014
in order to obtain a high elastic coefficient of price,
Figure 91706DEST_PATH_IMAGE015
and
Figure 179879DEST_PATH_IMAGE016
respectively the electricity prices before and after the demand response at the moment j.
The user engagement constraint is:
Figure 151246DEST_PATH_IMAGE017
(4)
in the formula:
Figure 299462DEST_PATH_IMAGE018
for demand response user engagement, a value range of [0,1 ]]，
Figure 231646DEST_PATH_IMAGE019
The upper limit of the participation degree is,
Figure 372777DEST_PATH_IMAGE020
for the purpose of the intensity of the price information,
Figure 683804DEST_PATH_IMAGE021
and
Figure 783347DEST_PATH_IMAGE022
respectively, the upper and lower limits of the intensity of the price information.
Scheduling operation cost:
Figure 547035DEST_PATH_IMAGE023
(5)
Figure 757436DEST_PATH_IMAGE024
(6)
in the formula:
Figure 922970DEST_PATH_IMAGE025
、
Figure 662255DEST_PATH_IMAGE026
、
Figure 913239DEST_PATH_IMAGE027
、
Figure 661752DEST_PATH_IMAGE028
respectively calculating the power generation cost of the gas turbine, the wind power generation cost, the photovoltaic power generation cost and the power grid electricity purchasing cost;
Figure 947371DEST_PATH_IMAGE029
、
Figure 388717DEST_PATH_IMAGE030
and
Figure 126997DEST_PATH_IMAGE031
respectively the number of the gas turbines, the wind power stations and the photovoltaic power stations which participate in scheduling, a, b and c are the power generation cost coefficients of the gas turbines,
Figure 413622DEST_PATH_IMAGE032
、
Figure 678381DEST_PATH_IMAGE033
、
Figure 41360DEST_PATH_IMAGE034
、
Figure 516204DEST_PATH_IMAGE035
、
Figure 357252DEST_PATH_IMAGE036
respectively wind power maintenance, wind power compensation, photoelectric maintenance, photoelectric compensation and main network electricity purchasing cost parameters,
Figure 601152DEST_PATH_IMAGE037
、
Figure 603874DEST_PATH_IMAGE038
、
Figure 831593DEST_PATH_IMAGE039
are respectively as
Figure 945174DEST_PATH_IMAGE040
The generated energy of the gas turbine, the wind power station and the photovoltaic power station at any moment,
Figure 574738DEST_PATH_IMAGE041
and purchasing electric quantity from the main network for the distribution network.
Simultaneously, the constraint conditions are met: (a) power balance constraint, (b) distributed power supply output constraint, (c) climbing constraint, and (d) power distribution network power flow constraint.
Further, in the step 3, an optimized solution of the optimized scheduling of the active power distribution network is optimized by adopting an improved satin blue gardener optimization algorithm, and the steps are as follows:
s1: setting the total number of population individuals as N and the maximum iteration number as maximum;
s2: randomly generating initial satin blue gardener individuals, wherein the position information of each individual represents a time-sharing electricity price information set or a scheduling unit output set;
s3, calculating the adaptive value of the initial population, and calculating the proportion of the adaptive value of each individual in the total of the adaptive values of the population; the competition of the satin blue gardener takes the advantages and disadvantages of the puppet pavilion as a standard, and the individual adaptive value, namely the advantages and disadvantages of the puppet pavilion, is taken as the probability of the individual being selected in the natural selection process, so that the objective function value of each iteration can be ensured to be reduced or unchanged;
s4: updating the satin blue garden cub population; the male birds continuously adjust the parameters of the coupling pavilion by means of experience and information sharing, namely continuously updating individual position information, representing that time-of-use electricity price information or dispatching unit output is continuously adjusted, and an updating formula is as follows:
Figure 748362DEST_PATH_IMAGE042
(7)
in the formula (I), the compound is shown in the specification,
Figure 932218DEST_PATH_IMAGE043
is the kth dimension variable of the ith generation of ith individuals,
Figure 115069DEST_PATH_IMAGE044
for the purpose of the step-size factor,
Figure 67982DEST_PATH_IMAGE045
is the k-dimension variable of the selected j-th individual,
Figure 412506DEST_PATH_IMAGE046
a k-dimension variable which is a global optimal individual; wherein
Figure 83659DEST_PATH_IMAGE047
Selecting through a roulette mechanism;
s5: introducing a self-adaptive mechanism into a satin blue gardener algorithm; changing the original fixed step size factor into a nonlinear adaptive factor:
Figure 70201DEST_PATH_IMAGE048
(8)
in the formula: a is the step size maximum threshold value and,
Figure 408778DEST_PATH_IMAGE049
to select the probability, the probability is obtained by roulette,itin order to be able to perform the number of iterations,
Figure 455363DEST_PATH_IMAGE050
is the maximum value of the iteration times;
Figure 613812DEST_PATH_IMAGE051
、
Figure 404044DEST_PATH_IMAGE052
respectively, an adaptive upper and lower limiting factor.
S6: individual variation; the algorithm has certain probability variation, and the variation process follows normal distribution; the closer the fitness is to the global optimal solution, the greater the mutation probability is, and then the mutation probability under self-adaptation is:
Figure 154654DEST_PATH_IMAGE053
(9)
in the formula:
Figure 637719DEST_PATH_IMAGE054
based on the probability of the variation,
Figure 893251DEST_PATH_IMAGE055
is the adapted value of the individual i,
Figure 205284DEST_PATH_IMAGE056
is the maximum adaptation value of the population.
S7: calculating the updated adaptive value of the satin blue gardener population, combining the new population with the old population, rearranging all individuals in the combined population according to the adaptive value, reserving a part of individuals with smaller adaptive values, eliminating the rest of individuals, and updating the global optimal adaptive value and the optimal individuals; judging whether a termination condition is met, if so, terminating iteration, and outputting an optimal adaptive value and a corresponding optimal individual, otherwise, continuing the next cycle from S4; the operation optimization scheduling process is nested in the demand response optimization process, and the optimal value calculated each time at the operation optimization scheduling layer is used as a part of the adaptive value of the demand response optimization to participate in the demand response optimization process.
Further, in the improved satin blue gardener algorithm, each dimension variable of each satin blue gardener represents power price information at one time or schedulable unit output at one time of a unit, each satin blue gardener represents power price information of one day or output state of all units of one day, and the improved satin blue gardener algorithm is to find a time-of-use power price and schedule unit output set under the optimal demand response meeting constraint conditions.
Compared with the prior art, the invention has the beneficial effects that:
the method of the invention adopts a double-layer optimization scheduling mode by introducing demand response, namely, the demand response is used for adjusting the load demand of the user, and meanwhile, the output of the schedulable unit is adjusted, thereby reducing the total operation cost of the active power distribution network. And an adaptive mechanism is adopted to improve the original satin blue gardener algorithm so as to enhance the global search capability of the algorithm, and the improved satin blue gardener algorithm is used for double-layer optimized scheduling of the active power distribution network, so that the convergence speed of the original problem is improved, the obtained optimal solution adaptive value is better, and the economic operation problem of the active power distribution network is effectively solved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of a modified satin blue gardener algorithm;
FIG. 3 is a modified power distribution network;
FIG. 4 is a daily load curve;
FIG. 5 is a wind and light power generation prediction curve;
FIG. 6 is a total cost convergence curve for the satin blue gardener algorithm;
FIG. 7 is a total cost convergence curve for the modified satin blue gardener algorithm;
FIG. 8 is the time of use electricity price under the satin blue gardener algorithm;
FIG. 9 is a time of use electricity price under the modified satin blue gardener algorithm;
FIG. 10 is a dispatch effort under the satin blue gardener algorithm;
fig. 11 is a scheduling effort under the improved satin blue gardener algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. The embodiments described herein are only for explaining the technical solution of the present invention and are not limited to the present invention.
According to the active power distribution network double-layer optimization scheduling method based on the improved satin blue gardener algorithm, after a photovoltaic generator set, a wind turbine generator set and a gas turbine are added into a 30-node power distribution network system, active power and reactive power of nodes change, the structure of the power distribution network changes from passive to active, the power transmission direction changes, and challenges are brought to the economy and reliability of the power distribution network. In addition, considering the effect of demand response on load can reduce the operating cost of the active power distribution network, but also increases the complexity of the problem. The dynamic adaptive capacity of the step factor is increased by depending on a self-adaptive mechanism, so that the dynamic adaptive capacity of the step factor can follow the change of a search stage, the characteristics of high convergence speed and strong optimization capability are achieved, the search strength near the optimal solution is increased, and the operation of the active power distribution network is more economic and reliable. As shown in fig. 1, the method comprises the following steps:
step 1: initializing parameters and establishing an improved power distribution network node system;
initializing parameters, establishing an active power distribution network topological structure containing various distributed power supplies, numbering nodes and branches, and adding loads and distributed data at designated nodes to realize grid connection of the active power distribution network distributed power supplies.
Step 2: and constructing a mathematical model of double-layer optimization scheduling of the active power distribution network containing demand response, wherein the upper layer is demand response optimization, the lower layer is active power distribution network operation optimization scheduling, and a target function with the minimum sum of the total cost of the upper layer active power distribution network optimization scheduling and the minimum cost of the lower layer active power distribution network scheduling operation is given, and corresponding constraint conditions of the upper layer and the lower layer are given.
The established mathematical model of the double-layer optimized scheduling of the active power distribution network is as follows:
an objective function:
Figure 738027DEST_PATH_IMAGE057
(1)
in the formula (I), the compound is shown in the specification,
Figure 110103DEST_PATH_IMAGE058
for the sake of the total cost,
Figure 259456DEST_PATH_IMAGE059
in order to schedule the cost of the operation,
Figure 375179DEST_PATH_IMAGE060
cost for demand response; wherein:
Figure 28008DEST_PATH_IMAGE061
(2)
in the formula:
Figure 570985DEST_PATH_IMAGE062
and
Figure 66689DEST_PATH_IMAGE063
respectively the electricity prices and loads before the demand response,
Figure 736835DEST_PATH_IMAGE064
and
Figure 759018DEST_PATH_IMAGE065
respectively the price of electricity and the load after response; simultaneously, the constraint conditions are met: (a) the method comprises the following steps of (a) user total electric quantity constraint, (b) user average electricity price constraint, (c) user demand response constraint, and (d) user participation constraint.
Wherein the user demand response constraint is:
Figure 223629DEST_PATH_IMAGE066
(3)
in the formula:
Figure 331262DEST_PATH_IMAGE067
and
Figure 805100DEST_PATH_IMAGE068
the loads before and after the demand response are respectively,
Figure 416210DEST_PATH_IMAGE069
for user engagement, T is the total number of scheduled time periods,
Figure 582880DEST_PATH_IMAGE070
in order to obtain the coefficient of mutual elasticity,
Figure 912230DEST_PATH_IMAGE071
and
Figure 658600DEST_PATH_IMAGE072
respectively the electricity prices before and after the demand response at the moment j.
The user engagement constraint is:
Figure 124217DEST_PATH_IMAGE073
(4)
in the formula:
Figure 196209DEST_PATH_IMAGE074
for demand response user engagement, a value range of [0,1 ]]，
Figure 12855DEST_PATH_IMAGE075
The upper limit of the participation degree is,
Figure 828496DEST_PATH_IMAGE076
for the purpose of the intensity of the price information,
Figure 148619DEST_PATH_IMAGE077
and
Figure 391512DEST_PATH_IMAGE078
respectively, the upper and lower limits of the intensity of the price information.
Scheduling operation cost:
Figure 429875DEST_PATH_IMAGE079
(5)
Figure 49207DEST_PATH_IMAGE080
(6)
in the formula:
Figure 754995DEST_PATH_IMAGE081
、
Figure 903210DEST_PATH_IMAGE082
、
Figure 694449DEST_PATH_IMAGE083
、
Figure 586313DEST_PATH_IMAGE084
respectively calculating the power generation cost of the gas turbine, the wind power generation cost, the photovoltaic power generation cost and the power grid electricity purchasing cost;
Figure 146607DEST_PATH_IMAGE085
、
Figure 465724DEST_PATH_IMAGE086
and
Figure 744258DEST_PATH_IMAGE087
respectively the number of the gas turbines, the wind power stations and the photovoltaic power stations which participate in scheduling, a, b and c are the power generation cost coefficients of the gas turbines,
Figure 174234DEST_PATH_IMAGE088
、
Figure 854614DEST_PATH_IMAGE089
、
Figure 610211DEST_PATH_IMAGE090
、
Figure 110463DEST_PATH_IMAGE091
、
Figure 344129DEST_PATH_IMAGE092
respectively wind power maintenance, wind power compensation, photoelectric maintenance, photoelectric compensation and main network electricity purchasing cost parameters,
Figure 879016DEST_PATH_IMAGE093
、
Figure 539935DEST_PATH_IMAGE094
、
Figure 527483DEST_PATH_IMAGE095
are respectively as
Figure 564840DEST_PATH_IMAGE096
The generated energy of the gas turbine, the wind power station and the photovoltaic power station at any moment,
Figure 954233DEST_PATH_IMAGE097
and purchasing electric quantity from the main network for the distribution network.
Simultaneously, the constraint conditions are met: (a) power balance constraint, (b) distributed power supply output constraint, (c) climbing constraint, and (d) power distribution network power flow constraint.
And step 3: and (2) performing optimization solution on the model of the active power distribution network double-layer optimization scheduling in the step (2) by adopting a satin blue gardener algorithm based on nonlinear self-adaption improvement, inputting variables generated by an upper layer into a lower layer, inputting optimal adaptive values generated by a lower layer into the upper layer, updating the variables generated by iteration after the upper layer calculates the optimal values, inputting the variables into the lower layer, and outputting the optimal solution of time-of-use electricity price and schedulable unit output and corresponding objective function values if the upper layer meets the iteration termination condition.
A flow chart of the improved satin blue gardener algorithm is illustrated in connection with fig. 2.
S1: setting the total number of population individuals as N and the maximum iteration number as maximum;
s2: randomly generating initial satin blue gardener individuals, wherein the position information of each individual represents a time-sharing electricity price information set or a scheduling unit output set;
s3: calculating the adaptive value of the initial population, and calculating the proportion of the adaptive value of each individual in the total of the adaptive values of the population; the competition of the satin blue gardener takes the advantages and disadvantages of the puppet pavilion as a standard, and the individual adaptive value, namely the advantages and disadvantages of the puppet pavilion, is taken as the probability of the individual being selected in the natural selection process, so that the objective function value of each iteration can be ensured to be reduced or unchanged;
s4: updating the satin blue garden cub population; the male birds continuously adjust the parameters of the coupling pavilion by means of experience and information sharing, namely continuously updating individual position information, representing that time-of-use electricity price information or dispatching unit output is continuously adjusted, and an updating formula is as follows:
Figure 51633DEST_PATH_IMAGE098
(7)
in the formula (I), the compound is shown in the specification,
Figure 526477DEST_PATH_IMAGE099
is the kth dimension variable of the ith generation of ith individuals,
Figure 101946DEST_PATH_IMAGE100
for the purpose of the step-size factor,
Figure 345846DEST_PATH_IMAGE101
is the k-dimension variable of the selected j-th individual,
Figure 614147DEST_PATH_IMAGE102
a k-dimension variable which is a global optimal individual; wherein
Figure 576287DEST_PATH_IMAGE103
Selecting through a roulette mechanism;
s5: introducing a self-adaptive mechanism into a satin blue gardener algorithm; because the convergence rate of the original algorithm is low, the convergence precision is not high, the application of the original algorithm in the active power distribution network double-layer optimization scheduling under the demand response is not facilitated, the convergence rate of the algorithm can be improved by the self-adaptive improvement, and the algorithm has a good elastic optimization range, so that the original fixed step size factor is changed into a nonlinear self-adaptive factor:
Figure 955447DEST_PATH_IMAGE104
(8)
in the formula: a is the step size maximum threshold value and,
Figure 53853DEST_PATH_IMAGE105
to select the probability, the probability is obtained by roulette,itin order to be able to perform the number of iterations,
Figure 493055DEST_PATH_IMAGE106
is the maximum value of the iteration times;
Figure 942491DEST_PATH_IMAGE107
、
Figure 859763DEST_PATH_IMAGE108
respectively are self-adaptive upper and lower limit factors;
s6: individual variation; in nature, part of the males steal materials from the coupling kiosks of other males, so that the algorithm has a certain probability variation, and the variation process follows normal distribution. The survival of suitable persons, the elimination of uncomfortable persons and the death are rules of the nature, the probability that the even pavilion of a stronger male bird is damaged is smaller, namely the closer the fitness is to the global optimal solution, the higher the mutation probability is, and the mutation probability under self-adaptation is as follows:
Figure 78255DEST_PATH_IMAGE109
(9)
in the formula:
Figure 688359DEST_PATH_IMAGE110
based on the probability of the variation,
Figure 745528DEST_PATH_IMAGE111
is the adapted value of the individual i,
Figure 981338DEST_PATH_IMAGE112
the maximum adaptive value of the population;
s7: calculating the updated adaptive value of the satin blue gardener population, combining the new population with the old population, rearranging all individuals in the combined population according to the adaptive value, reserving a part of individuals with smaller adaptive values, eliminating the rest of individuals, and updating the global optimal adaptive value and the optimal individuals; judging whether a termination condition is met, if so, terminating iteration, and outputting an optimal adaptive value and a corresponding optimal individual, otherwise, continuing the next cycle from S4; it should be noted that the operation optimization scheduling process is nested in the demand response optimization process, and the optimal value calculated each time at the operation optimization scheduling layer is used as a part of the adaptive value of the demand response optimization to participate in the demand response optimization process.
In the improved satin blue gardener algorithm, each dimension variable of each satin blue gardener represents power price information at one moment or schedulable unit output at one moment of a unit, each satin blue gardener represents power price information of one day or output state of all units of one day, and the improved satin blue gardener algorithm is to search a time-sharing power price and schedule unit output set under the optimal demand response meeting constraint conditions.
Example (b): in order to verify the effectiveness of the improved satin blue gardener algorithm in the double-layer optimization scheduling, a photovoltaic generator, a wind driven generator and a gas turbine are configured in an IEEE30 node power distribution network system to serve as distributed energy, so that an original power distribution network is changed into an active power distribution network, as shown in figure 3, wherein the numbers 1-30 are node numbers. Wherein G is a main network connected to the distribution network, and the access positions, i.e., information, of other DGs are as follows in table 1:
TABLE 1 Power Access location and parameters of each DG
Figure 805068DEST_PATH_IMAGE113
And taking 24 as the total scheduling time period number T, wherein a load power prediction curve is shown in figure 4, and the output prediction of the wind and light within 24 hours is shown in figure 5. The scheduling strategy is to preferentially consume wind power and photoelectricity, when the wind power and the photoelectricity cannot meet the system requirements, the small gas turbine is scheduled to generate electricity, and when the output of the gas turbine reaches the limit, electricity is purchased from the main network; when the wind power and the photoelectricity are too much, wind and light are abandoned.
The optimization method of the improved satin blue gardener optimization algorithm comprises the following steps:
s1: the total number N of the upper layer population is 30, the total number N of the lower layer population is 30, the maximum iteration time Maxiter of the upper layer is 100, and the lower layer is 20.
S2: initial satin blue bouillon individuals were randomly generated.
S3: calculating the adaptive value of the initial population, and calculating the proportion of the adaptive value of each individual in the total adaptive value of the population.
S4: and updating the satin blue gardener population. The male birds rely on experience and information sharing modes to continuously adjust parameters of the coupling kiosk, namely, continuously update individual vectors to achieve the optimal effect.
S5: introducing a self-adaptive mechanism into a satin blue gardener algorithm, setting a step factor into a non-linear self-adaptive factor, and changing an original updating formula (7) into:
Figure 835341DEST_PATH_IMAGE115
wherein g isuAnd g d2 and 0.5 respectively, and alpha is 1.
S6: and (5) individual variation. In nature, part of the males steal materials from the coupling kiosks of other males, so that the algorithm has a certain probability variation, and the variation process follows normal distribution.
Survival of suitable persons, elimination of unsuitable persons and death are rules of the nature, the probability that the even pavilion of a stronger male bird is damaged is smaller, namely the fitness is closer to the global optimal solution variation probability, and the variation probability is shown in an expression (9), wherein the variation probability under self-adaptive improvement is as follows:
Figure 484803DEST_PATH_IMAGE116
wherein p ismBased on the probability of variation, let pm=0.05。
The above steps were programmed and simulated with matlab2016a platform. In the invention, the active power distribution network reaches the optimal economic operation state by formulating reasonable time-of-use electricity price and scheduling the output of the gas turbine set in the active power distribution network. Fig. 6 is a graph of the convergence of the optimal total operating cost of the original satin blue gardener algorithm, and fig. 7 is a graph of the convergence of the optimal total operating cost of the improved satin blue gardener algorithm. The result proves that the improved satin blue gardener algorithm can find the optimal time-of-use electricity price and the optimal unit output, the convergence speed is high, and the effect is expected.
The calculation results are shown in Table 2
TABLE 2 simulation results
Figure 993144DEST_PATH_IMAGE117
Compared with the original satin blue gardener algorithm, as shown in fig. 8 and fig. 10, the PMG1, the PMG2 and the PMG3 in the improved satin blue gardener algorithm are respectively the output power of the schedulable gas turbine generator sets MT1, MT2 and MT3, and the Pgrid is the input power of the main grid G, so that the electricity purchasing quantity of the main grid is reduced after optimization, the operation cost is reduced, the grid loss is reduced, and the operation of the power distribution network is more economic.
The foregoing merely represents preferred embodiments of the invention, which are described in some detail and detail, and therefore should not be construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, various changes, modifications and substitutions can be made without departing from the spirit of the present invention, and these are all within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (4)

1.基于改进缎蓝园丁鸟算法的主动配电网双层优化调度方法，其特征在于：包括以下步骤：1. the active distribution network double-layer optimization scheduling method based on the improved satin blue gardener bird algorithm, is characterized in that: comprise the following steps: 步骤1：参数初始化，建立改进的配电网节点系统；Step 1: Initialize parameters and establish an improved distribution network node system; 步骤2：构建出含需求响应的主动配电网双层优化调度的数学模型；上层为需求响应优化，下层为主动配电网运行优化调度，并给出上层的主动配电网优化调度总成本之和最小的目标函数与下层的主动配电网调度运行成本最小的目标函数，以及上下层相应的约束条件；Step 2: Construct a mathematical model of active distribution network double-layer optimization scheduling with demand response; the upper layer is demand response optimization, the lower layer is active distribution network operation optimization scheduling, and the total cost of active distribution network optimization scheduling in the upper layer is given The objective function with the smallest sum and the objective function with the smallest operation cost of active distribution network dispatching in the lower layer, and the corresponding constraints of the upper and lower layers; 所述步骤2建立的主动配电网双层优化调度的数学模型为：The mathematical model of the active distribution network double-layer optimal scheduling established in the step 2 is: 目标函数：Objective function:
Figure 553692DEST_PATH_IMAGE002
Figure 553692DEST_PATH_IMAGE002
 式中，
Figure 64308DEST_PATH_IMAGE004
为总成本，
Figure 578466DEST_PATH_IMAGE006
为调度运行成本，
Figure 567150DEST_PATH_IMAGE008
为需求响应成本；其中：In the formula,
Figure 64308DEST_PATH_IMAGE004
is the total cost,
Figure 578466DEST_PATH_IMAGE006
for the scheduling running cost,
Figure 567150DEST_PATH_IMAGE008
is the demand response cost; where:
Figure 178260DEST_PATH_IMAGE010
Figure 178260DEST_PATH_IMAGE010
 式中：
Figure 469564DEST_PATH_IMAGE012
和
Figure 330073DEST_PATH_IMAGE014
分别为需求响应前的电价和负荷，
Figure 997815DEST_PATH_IMAGE016
和
Figure 525748DEST_PATH_IMAGE018
分别为响应后的电价和负荷；同时满足约束条件：用户总电量约束，用户平均电价约束，用户需求响应约束，用户参与度约束；where:
Figure 469564DEST_PATH_IMAGE012
and
Figure 330073DEST_PATH_IMAGE014
are the electricity price and load before demand response, respectively,
Figure 997815DEST_PATH_IMAGE016
and
Figure 525748DEST_PATH_IMAGE018
Respectively, the electricity price and load after the response; at the same time, the constraints are satisfied: the user's total electricity constraint, the user's average electricity price constraint, the user's demand response constraint, and the user's participation degree constraint; 其中用户需求响应约束为：The user demand response constraints are:
Figure 987953DEST_PATH_IMAGE020
Figure 987953DEST_PATH_IMAGE020
 式中：
Figure 539020DEST_PATH_IMAGE022
和
Figure 869508DEST_PATH_IMAGE024
分别为t时刻需求响应前后的负荷，
Figure 861734DEST_PATH_IMAGE026
为用户参与度，T为总调度的时段数，
Figure 153563DEST_PATH_IMAGE028
为价格弹性系数，
Figure 864030DEST_PATH_IMAGE030
和
Figure 732629DEST_PATH_IMAGE032
分别为j时刻需求响应前后的电价；where:
Figure 539020DEST_PATH_IMAGE022
and
Figure 869508DEST_PATH_IMAGE024
are the loads before and after the demand response at time t, respectively,
Figure 861734DEST_PATH_IMAGE026
is the user participation, T is the total number of scheduled time periods,
Figure 153563DEST_PATH_IMAGE028
is the price elasticity coefficient,
Figure 864030DEST_PATH_IMAGE030
and
Figure 732629DEST_PATH_IMAGE032
are the electricity prices before and after the demand response at time j; 用户参与度约束为：User engagement constraints are:
Figure 172837DEST_PATH_IMAGE034
Figure 172837DEST_PATH_IMAGE034
 式中：
Figure 976845DEST_PATH_IMAGE036
为需求响应用户参与度，取值范围[0,1]，
Figure 564822DEST_PATH_IMAGE038
为参与度上限，
Figure 112478DEST_PATH_IMAGE040
为价格信息强度，
Figure 203930DEST_PATH_IMAGE042
和
Figure 444419DEST_PATH_IMAGE044
分别为价格信息强度上下限；where:
Figure 976845DEST_PATH_IMAGE036
is the user participation degree of demand response, the value range is [0,1],
Figure 564822DEST_PATH_IMAGE038
is the participation cap,
Figure 112478DEST_PATH_IMAGE040
is the price information strength,
Figure 203930DEST_PATH_IMAGE042
and
Figure 444419DEST_PATH_IMAGE044
are the upper and lower limits of price information strength, respectively; 调度运行成本：Scheduling running costs:
Figure 191795DEST_PATH_IMAGE046
Figure 191795DEST_PATH_IMAGE046
Figure 667776DEST_PATH_IMAGE048
Figure 667776DEST_PATH_IMAGE048
 式中：
Figure 489101DEST_PATH_IMAGE050
、
Figure 290704DEST_PATH_IMAGE052
、
Figure 197480DEST_PATH_IMAGE054
、
Figure 411905DEST_PATH_IMAGE056
分别为燃气轮机发电成本、风力发电成本、光伏发电成本和电网购电成本；
Figure 477950DEST_PATH_IMAGE058
、
Figure 60241DEST_PATH_IMAGE060
和
Figure 844526DEST_PATH_IMAGE062
分别为参与调度的燃气轮机、风电站和光伏电站的数量，a、b、c为燃气轮机发电成本系数，
Figure 537676DEST_PATH_IMAGE064
、
Figure 192648DEST_PATH_IMAGE066
、
Figure 273737DEST_PATH_IMAGE068
、
Figure 155105DEST_PATH_IMAGE070
、
Figure 776579DEST_PATH_IMAGE072
分别为风电维护、风电补偿、光电维护、光电补偿、主网购电成本参数，
Figure 692583DEST_PATH_IMAGE074
、
Figure 475731DEST_PATH_IMAGE076
、
Figure 844395DEST_PATH_IMAGE078
分别为时刻燃气轮机、风电站和光伏电站的发电量，
Figure 472823DEST_PATH_IMAGE080
为配网向主网购电量；where:
Figure 489101DEST_PATH_IMAGE050
,
Figure 290704DEST_PATH_IMAGE052
,
Figure 197480DEST_PATH_IMAGE054
,
Figure 411905DEST_PATH_IMAGE056
are gas turbine power generation cost, wind power generation cost, photovoltaic power generation cost and grid power purchase cost;
Figure 477950DEST_PATH_IMAGE058
,
Figure 60241DEST_PATH_IMAGE060
and
Figure 844526DEST_PATH_IMAGE062
are the number of gas turbines, wind power plants and photovoltaic power plants participating in dispatching, respectively, a, b, and c are the gas turbine power generation cost coefficients,
Figure 537676DEST_PATH_IMAGE064
,
Figure 192648DEST_PATH_IMAGE066
,
Figure 273737DEST_PATH_IMAGE068
,
Figure 155105DEST_PATH_IMAGE070
,
Figure 776579DEST_PATH_IMAGE072
are wind power maintenance, wind power compensation, photovoltaic maintenance, photovoltaic compensation, and main network power purchase cost parameters,
Figure 692583DEST_PATH_IMAGE074
,
Figure 475731DEST_PATH_IMAGE076
,
Figure 844395DEST_PATH_IMAGE078
are the power generation of the gas turbine, wind power station and photovoltaic power station at the moment, respectively,
Figure 472823DEST_PATH_IMAGE080
Purchase electricity from the main network for the distribution network; 同时满足约束条件：功率平衡约束；分布式电源的出力约束；爬坡约束；配电网潮流约束；Constraints are met at the same time: power balance constraints; output constraints of distributed power sources; ramp constraints; power flow constraints of distribution networks; 步骤3：采用基于非线性自适应改进的缎蓝园丁鸟算法，对步骤2的主动配电网双层优化调度的模型寻优求解；上层产生的变量输入到下层，下层产生的最优适应值输入到上层，上层计算出最优值后更新迭代产生的变量输入到下层，若上层满足迭代终止条件，则输出分时电价和可调度机组出力最优解和相应的目标函数值。Step 3: Use the satin blue gardener bird algorithm based on nonlinear adaptive improvement to optimize the model for the double-layer optimal scheduling of the active distribution network in step 2; the variables generated by the upper layer are input to the lower layer, and the optimal fitness value generated by the lower layer Input to the upper layer, the upper layer calculates the optimal value and updates the variables generated by the iteration and inputs it to the lower layer. If the upper layer satisfies the iteration termination conditions, the optimal solution of the time-of-use electricity price and the output of dispatchable units and the corresponding objective function value are output. 2.根据权利要求1所述的基于改进缎蓝园丁鸟算法的主动配电网双层优化调度方法，其特征在于：所述步骤1具体为：对参数进行初始化，建立含多种分布式电源的主动配电网拓扑结构，首先对节点和支路进行编号，然后在指定节点添加负荷、分布式数据，实现主动配电网分布式电源并网。2. the active distribution network double-layer optimization scheduling method based on improved satin blue gardener bird algorithm according to claim 1, it is characterized in that: described step 1 is specifically: initialize parameters, establish a plurality of distributed power sources The topology of the active distribution network is based on the number of nodes and branches, and then the load and distributed data are added to the designated nodes to realize the grid-connected distributed power supply of the active distribution network. 3.根据权利要求1所述的基于改进缎蓝园丁鸟算法的主动配电网双层优化调度方法，其特征在于：所述步骤3采用改进缎蓝园丁鸟优化算法对主动配电网优化调度最优解进行寻优，步骤如下：3. the active distribution network double-layer optimization scheduling method based on the improved satin blue gardener bird algorithm according to claim 1, is characterized in that: described step 3 adopts the improved satin blue gardener bird optimization algorithm to optimize the scheduling of the active distribution network To find the optimal solution, the steps are as follows: S1：设种群个体总数为N，最大迭代次数为Maxiter；S1: Let the total number of individuals in the population be N, and the maximum number of iterations is Maxiter; S2：随机产生初始缎蓝园丁鸟个体，其中每个个体的位置信息代表一种分时电价信息集合或调度机组出力集合；S2: Randomly generate initial satin-blue gardener bird individuals, where the location information of each individual represents a time-of-use electricity price information set or dispatch unit output set; S3:计算初始种群的适应值，将每个个体的适应值在群体适应值总和中所占的比例计算出来；缎蓝园丁鸟的竞争以求偶亭的优劣为标准，个体适应值即求偶亭的优劣，作为该个体在自然选择过程中被选中的概率，确保每次迭代目标函数值只能减小或不变；S3: Calculate the fitness value of the initial population, and calculate the proportion of the fitness value of each individual in the total fitness value of the group; the competition of the satin blue gardener bird is based on the pros and cons of the courtship pavilion, and the individual fitness value is the courtship pavilion. The pros and cons of , as the probability of the individual being selected in the natural selection process, ensure that the value of the objective function can only be reduced or unchanged in each iteration; S4：更新缎蓝园丁鸟种群；雄性鸟依靠经验以及信息共享的方式，不停调整求偶亭的参数，即不断更新个体位置信息，代表不断调整分时电价信息或调度机组出力，其更新的公式如下：S4: Update the satin-blue gardener bird population; the male bird constantly adjusts the parameters of the courtship booth by means of experience and information sharing, that is, the individual location information is continuously updated, which means that the time-of-use electricity price information is continuously adjusted or the unit output is dispatched. The updated formula as follows:
Figure 370896DEST_PATH_IMAGE082
Figure 370896DEST_PATH_IMAGE082
 式中，
Figure 528208DEST_PATH_IMAGE084
为第t代第i个体的第k维变量，
Figure 384168DEST_PATH_IMAGE086
为步长因子，
Figure 347445DEST_PATH_IMAGE088
为被选中的第j个体的第k维变量，
Figure 972462DEST_PATH_IMAGE090
为全局最优个体的第k维变量；其中
Figure 362992DEST_PATH_IMAGE092
通过轮盘赌的机制选出；In the formula,
Figure 528208DEST_PATH_IMAGE084
is the k-th dimension variable of the i-th individual of the t-th generation,
Figure 384168DEST_PATH_IMAGE086
is the step factor,
Figure 347445DEST_PATH_IMAGE088
is the kth dimension variable of the selected jth individual,
Figure 972462DEST_PATH_IMAGE090
is the k-th dimension variable of the global optimal individual; where
Figure 362992DEST_PATH_IMAGE092
Selected by the mechanism of roulette; S5：将自适应的机制引入缎蓝园丁鸟算法；将原固定步长因子改为非线性的自适应因子：S5: The adaptive mechanism is introduced into the satin blue gardener bird algorithm; the original fixed step factor is changed to a nonlinear adaptive factor:
Figure 440669DEST_PATH_IMAGE094
Figure 440669DEST_PATH_IMAGE094
 式中：α为步长最大阈值，
Figure 410899DEST_PATH_IMAGE096
为被选中的概率，由轮盘赌的方式得到，it为迭代次数，
Figure 15056DEST_PATH_IMAGE098
为迭代次数最大值；
Figure 717433DEST_PATH_IMAGE100
、
Figure 407040DEST_PATH_IMAGE102
分别为自适应上下限因子；In the formula: α is the maximum threshold of step size,
Figure 410899DEST_PATH_IMAGE096
is the probability of being selected, obtained by roulette, it is the number of iterations,
Figure 15056DEST_PATH_IMAGE098
is the maximum number of iterations;
Figure 717433DEST_PATH_IMAGE100
,
Figure 407040DEST_PATH_IMAGE102
are the adaptive upper and lower limit factors, respectively; S6：个体变异；算法会有一定几率变异，变异过程服从正态分布；适应度越靠近全局最优解变异概率就越大，则自适应下的变异概率为：S6: Individual mutation; the algorithm has a certain probability of mutation, and the mutation process obeys the normal distribution; the closer the fitness is to the global optimal solution, the greater the mutation probability, then the mutation probability under self-adaptation is:
Figure 587486DEST_PATH_IMAGE104
Figure 587486DEST_PATH_IMAGE104
 式中：
Figure 46149DEST_PATH_IMAGE106
为基础变异概率，
Figure 2252DEST_PATH_IMAGE108
为个体i的适应值，
Figure 54521DEST_PATH_IMAGE110
为种群最大适应值；where:
Figure 46149DEST_PATH_IMAGE106
is the base mutation probability,
Figure 2252DEST_PATH_IMAGE108
is the fitness value of individual i,
Figure 54521DEST_PATH_IMAGE110
is the maximum fitness value of the population; S7：计算更新后的缎蓝园丁鸟种群的适应值，组合新种群与旧种群，并对组合种群中的所有个体根据适应值大小重新排列，保留适应值较小的部分个体，淘汰掉剩下部分的个体，并更新全局最优适应值和最佳个体；判断是否满足终止条件，若满足则终止迭代，输出最优适应值和所对应的最佳个体，否则，从S4继续下一轮循环；运行优化调度过程是嵌套在需求响应优化过程中的，在运行优化调度层每次计算出的最优值作为需求响应的适应值的一部分参与需求响应优化过程。S7: Calculate the fitness value of the updated satin blue gardener bird population, combine the new population and the old population, and rearrange all individuals in the combined population according to the fitness value, retain some individuals with smaller fitness values, and eliminate the remaining ones Part of the individuals, and update the global optimal fitness value and the best individual; judge whether the termination condition is satisfied, if it is satisfied, terminate the iteration, output the optimal fitness value and the corresponding best individual, otherwise, continue from S4 to the next cycle ; The operation optimization scheduling process is nested in the demand response optimization process, and the optimal value calculated each time at the operation optimization scheduling layer participates in the demand response optimization process as a part of the adaptive value of the demand response. 4.根据权利要求3所述的基于改进缎蓝园丁鸟算法的主动配电网双层优化调度方法，其特征在于：在所述改进的缎蓝园丁鸟算法中，缎蓝园丁鸟个体的每一维变量代表一个时刻的电价信息或一个机组一个时刻的可调度机组出力，每一个缎蓝园丁鸟个体代表一天的电价信息或全部机组一天的出力状态，改进缎蓝园丁鸟算法就是寻找满足约束条件的最佳需求响应下的分时电价和调度机组出力集合。4. the active distribution network double-layer optimization scheduling method based on improved satin blue gardener bird algorithm according to claim 3, it is characterized in that: in described improved satin blue gardener bird algorithm, each satin blue gardener bird individual The one-dimensional variable represents the electricity price information at a moment or the dispatchable unit output of a unit at a moment, and each satin blue gardener bird individual represents the electricity price information for one day or the output status of all the units in one day. The improved satin blue gardener bird algorithm is to find and satisfy the constraints The time-of-use electricity price and dispatch unit output set under the optimal demand response conditions.
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