CN116388183A - Designated time distributed economic scheduling method under directed unbalanced network - Google Patents

Designated time distributed economic scheduling method under directed unbalanced network Download PDF

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CN116388183A
CN116388183A CN202310647146.4A CN202310647146A CN116388183A CN 116388183 A CN116388183 A CN 116388183A CN 202310647146 A CN202310647146 A CN 202310647146A CN 116388183 A CN116388183 A CN 116388183A
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时侠圣
穆朝絮
孙长银
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Abstract

The invention discloses a distributed economic scheduling method for designated time under a directed unbalanced network, which comprises the following steps: setting system parameters including the number n of all generator sets participating in scheduling, and the expected generation power of each generator set
Figure ZY_1
Setting convergence time
Figure ZY_2
According to the communication network topological graph, a weighted adjacent matrix A is set, and each generator set selects initial decision behaviors according to the decision feasible region of each generator set
Figure ZY_3
And to error vector
Figure ZY_4
Penalty factor variable
Figure ZY_5
Sum weight gain variable
Figure ZY_6
Initializing to obtain initial values of all variables; and transferring the local constraint of the generator set to a cost function by using a penalty function method so as to convert the mathematical model. Compared with the current fixed time economic scheduling algorithm, the specified time economic scheduling algorithm provided by the invention can ensure that the convergence time is specified by a user; compared with the secondary economic dispatch problem, the economic dispatch algorithm provided by the invention is more flexible and practical, and can solve the more general economic dispatch problem.

Description

Designated time distributed economic scheduling method under directed unbalanced network
Technical Field
The invention relates to the field of intelligent power grid economic dispatch, in particular to a specified time distributed economic dispatch method under a directed unbalanced network.
Background
With the development of smart grids in recent years, economic dispatch problems of power systems are receiving more and more attention. Economic dispatch is one of the most fundamental problems in power grid systems. The economic dispatch problem aims to solve the problems of supply and demand balance of the generator set and the minimum total power generation cost in the power system. At present, thermal power generation is still one of the main modes of power production in China, and the running state of a generator set is reasonably regulated, so that the total power generation cost of a power system is reduced while the requirements of a user side are met, and the energy loss can be effectively reduced. As the scale of power systems continues to expand, traditional centralized approaches are unable to effectively address economic dispatch issues in large-scale systems. In addition, the existence of a total control center in the centralized method certainly reduces the robustness of the system, and the error of the information of a single sub-node can cause the deviation of the whole system.
In order to overcome the above-described drawbacks of the centralized approach, various distributed approaches are receiving a great deal of attention. The distributed method only requires local communication and local optimization among neighbors, not only enhances the robustness of the system, but also accords with the characteristics of plug and play in the future. A new set of distributed optimization algorithms, such as a consistency-based algorithm, an original dual algorithm, a nestrov acceleration algorithm, etc., are designed. These algorithms typically have an exponential convergence or a broad range of asymptotic convergence characteristics, and their convergence rate is often not shown, i.e., the algorithm cannot give the desired optimal allocation in a very short time. Therefore, it is of great practical significance to study distributed economic dispatch algorithms that consider fixed time convergence or specified time convergence characteristics.
Through retrieval, application publication number CN115310776, a smart grid economic dispatch method based on fixed time distributed optimization is disclosed, a distributed economic dispatch algorithm capable of converging at fixed time is designed by utilizing a symbol function, the patent can only solve the economic dispatch problem under an undirected network, and the designed algorithm converging time depends on global information such as the minimum positive characteristic root of a Laplace matrix of a communication network. Application publication number CN114881489, which is a smart grid economic dispatch method based on event triggering and fixed time, can only solve the problem of secondary cost function economic dispatch under the undirected network. Application publication number CN114925537, which is a non-initialized smart grid economic dispatch method based on specified time consistency, can only solve the problem of secondary cost function economic dispatch under a directed balanced network. The invention designs a distributed economic dispatch algorithm converging at a designated time based on the directed unbalanced network research intelligent power grid economic dispatch problem, and is suitable for solving the general economic dispatch problem.
Disclosure of Invention
The invention aims to provide a distributed economic scheduling method for designated time under a directed unbalanced network, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: a distributed economic scheduling method for appointed time under a directed unbalanced network comprises the following steps:
s1, establishing an economic dispatch problem mathematical model in a smart grid;
s2, setting system parameters including the number n of all generator sets participating in scheduling, and expected power generation of each generator set
Figure SMS_1
, wherein />
Figure SMS_2
Indicating the number of the generator set,/->
Figure SMS_3
Setting convergence time +.>
Figure SMS_4
；
S3, constructing a communication network topological graph between the generator sets according to a communication topological structure of the intelligent power grid system;
s4, setting a weighted adjacent matrix A according to the communication network topological graph;
s5, each generator set selects initial decision behaviors according to the decision feasible region of each generator set
Figure SMS_5
And to error vector
Figure SMS_6
Penalty factor variable->
Figure SMS_7
Sum weight gain variable->
Figure SMS_8
Initializing to obtain initial values of all variables;
s6, transferring the local constraint of the generator set to a cost function by using a penalty function method so as to convert the mathematical model;
s7, acquiring left characteristic roots of the weighted adjacent matrix A in a specified time by utilizing the consistency of the multi-agent system, and acquiring left characteristic roots of the directed unbalanced network Laplacian matrix in the specified time by utilizing an updating strategy so as to update the weight gain variable
Figure SMS_9
So that the weight gain variable +.>
Figure SMS_10
Converging to a stable value at a specified time;
s8, updating the error variable by utilizing the proportional-integral control idea
Figure SMS_11
So that the error variable +.>
Figure SMS_12
Converging to zero in a designated time to realize the supply and demand balance of economic dispatching problems;
and S9, acquiring the optimal output power of each generator set in the economic dispatch problem by utilizing the consistency of the multi-agent system, and acquiring the optimal output power of each generator set at the appointed time to obtain an optimal power distribution scheme.
Preferably, in step S1, the mathematical model of the economic dispatch problem is as follows:
Figure SMS_13
；
wherein ,
Figure SMS_14
for output power +.>
Figure SMS_15
Respectively +.>
Figure SMS_16
The lower and upper limits of the output power of the generator sets,
Figure SMS_17
is->
Figure SMS_18
Generating cost function of each generator set, +.>
Figure SMS_19
Is->
Figure SMS_20
Local constraint functions of the individual generator sets.
Preferably, in step S4, the acquiring manner of the weighted adjacency matrix a specifically includes:
if generating set in system
Figure SMS_21
Can receive neighbor generator set->
Figure SMS_22
Is set to->
Figure SMS_23
, wherein />
Figure SMS_24
Representing a generator set->
Figure SMS_25
The number of the neighbor nodes;
if the generator set
Figure SMS_26
Cannot receive neighbor genset->
Figure SMS_27
Is set to->
Figure SMS_28
The method comprises the steps of carrying out a first treatment on the surface of the Furthermore, due to the generator set->
Figure SMS_29
Self data are also available, so set +.>
Figure SMS_30
。
Preferably, each generator set in step S5 selects an initial decision action according to its own decision feasible region
Figure SMS_31
In the time of this, the generator set can be taken at will>
Figure SMS_32
, wherein />
Figure SMS_33
The set of all gensets is numbered.
Preferably, the method for obtaining the initial values of the variables in step S5 specifically includes:
Figure SMS_34
，
Figure SMS_35
the method comprises the steps of carrying out a first treatment on the surface of the Weight gain variable +.>
Figure SMS_36
The initial value is set to +.>
Figure SMS_37
I.e.>
Figure SMS_38
The individual elements are->
Figure SMS_39
Other elements are 0 +.>
Figure SMS_40
And (5) a dimension vector.
Preferably, the specific conversion mode of the conversion mathematical model in the step S6 is as follows:
Figure SMS_41
；
wherein ,
Figure SMS_42
for a new cost function->
Figure SMS_43
For the penalty factor, the gradient function corresponding to the new cost function is defined as +.>
Figure SMS_44
，/>
Figure SMS_45
Representing the function +.>
Figure SMS_46
Regarding variables->
Figure SMS_47
Is a gradient function of (a).
Preferably, the specific updating manner of the updating policy in step S7 is as follows:
Figure SMS_48
；
wherein the Laplace matrix is defined as
Figure SMS_50
，/>
Figure SMS_54
Is a unit array, and the non-periodic sampling time is defined as
Figure SMS_57
，/>
Figure SMS_51
Is a small positive number; />
Figure SMS_53
Respectively represent the generator set->
Figure SMS_56
and />
Figure SMS_59
At non-periodic sampling instants->
Figure SMS_52
Time gain variable +.>
Figure SMS_55
Value of->
Figure SMS_58
Representing a generator set->
Figure SMS_60
Weight gain variable of (2) at the current moment +.>
Figure SMS_49
Is a value of (2).
Preferably, proportional-integral control is used in step S8The idea is to update the error variable
Figure SMS_61
The specific updating mode of (a) is as follows: />
Figure SMS_62
；
wherein ,
Figure SMS_73
for aperiodic sampling instants +.>
Figure SMS_65
For a smaller positive number, the control parameter +.>
Figure SMS_69
；/>
Figure SMS_66
For the weight gain variable->
Figure SMS_67
Is>
Figure SMS_71
Steady state values obtained at the end of the first phase for the individual components; />
Figure SMS_75
Representing a generator set->
Figure SMS_74
At the present moment +.>
Figure SMS_78
Is>
Figure SMS_63
Respectively represent generator sets
Figure SMS_70
Error variable +.>
Figure SMS_77
At the present moment +.>
Figure SMS_80
And aperiodic sampling instant->
Figure SMS_79
Is a value of (2); />
Figure SMS_81
Respectively represent the generator set->
Figure SMS_64
and />
Figure SMS_68
At non-periodic sampling instants->
Figure SMS_72
A gradient value of time; />
Figure SMS_76
Representing the integral variable.
Preferably, the specific update manner of the optimal output power in step S9 is as follows:
Figure SMS_82
；
wherein ,
Figure SMS_84
for aperiodic sampling time, the control parameters are as follows
Figure SMS_88
Matrix->
Figure SMS_91
Is a diagonal matrix>
Figure SMS_85
Representation matrix->
Figure SMS_87
Is a second small feature root of (2); />
Figure SMS_90
Respectively represent the generator set->
Figure SMS_92
Penalty factor->
Figure SMS_83
At the current moment
Figure SMS_86
And aperiodic sampling instant->
Figure SMS_89
Values at that time.
Compared with the prior art, the invention has the beneficial effects that:
compared with the current fixed time economic scheduling algorithm, the specified time economic scheduling algorithm provided by the invention can ensure that the convergence time is specified by a user; compared with the secondary economic scheduling problem, the economic scheduling algorithm provided by the invention can solve the more general economic scheduling problem, so that the economic scheduling algorithm for the appointed time provided by the invention is more flexible and practical;
compared with the current economic scheduling algorithm of the designated time, the method can solve the more general economic scheduling problem, and the economic scheduling problem under the directed unbalanced network, and has wider application range.
Drawings
FIG. 1 is a main step diagram of a distributed economic dispatch method for a designated time in a directional unbalanced network according to an embodiment of the present invention;
FIG. 2 is a topology diagram of a communication network between generator sets of a specified time distributed economic dispatch method in a directed unbalanced network according to an embodiment of the present invention;
FIG. 3 is a graph of the weight gain variation of each generator set of the distributed economic dispatch method for a given time in a directed unbalanced network according to an embodiment of the present invention;
FIG. 4 is a diagram of the variation of the error variables of each generator set of a method for distributed economic dispatch of a given time in a directed unbalanced network according to an embodiment of the present invention;
fig. 5 is a graph of power generation change of each generator set according to another method for distributed economic dispatch of designated time in a directed unbalanced network according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The main execution body of the method in this embodiment is a terminal, and the terminal may be a device such as a mobile phone, a tablet computer, a PDA, a notebook or a desktop, or may be other devices with similar functions, which is not limited in this embodiment.
Referring to fig. 1 to 5, the present invention provides a distributed economic dispatch method for a designated time in a directional unbalanced network, which is applied to a distributed economic dispatch for a designated time of a smart grid, and includes the following steps:
s1, establishing an economic dispatch problem mathematical model in a smart grid; in step S1, the mathematical model of the economic dispatch problem of the smart grid is as follows:
Figure SMS_94
； wherein ,/>
Figure SMS_97
In order to output the power of the power supply,
Figure SMS_99
respectively +.>
Figure SMS_95
Lower and upper limits of the output power of the individual generator sets,/->
Figure SMS_96
Is->
Figure SMS_98
Generating cost function of each generator set, +.>
Figure SMS_100
Is->
Figure SMS_93
Local constraint functions of the individual generator sets.
S2, setting system parameters including the number n of all generator sets participating in scheduling, and expected power generation of each generator set
Figure SMS_101
, wherein />
Figure SMS_106
Indicating the number of the generator set,/->
Figure SMS_109
Setting convergence time +.>
Figure SMS_103
The method comprises the steps of carrying out a first treatment on the surface of the The system specifically comprises the system generator sets with the number of +.>
Figure SMS_107
The desired power of the generator set is set to +.>
Figure SMS_110
Specify convergence time to be set to +.>
Figure SMS_112
The method comprises the steps of carrying out a first treatment on the surface of the The cost function form of each generator set is +.>
Figure SMS_104
And has
Figure SMS_105
，
Figure SMS_108
The method comprises the steps of carrying out a first treatment on the surface of the To embody the wider applicability of the patent, an exponential term is added in the cost function of the generator set No. 1>
Figure SMS_111
The power generation power of all the generator sets is respectively as follows:
Figure SMS_102
。
s3, constructing a communication network topological graph between the generator sets according to a communication topological structure of the intelligent power grid system; the communication network between the generator sets is shown in fig. 1.
S4, setting a weighted adjacent matrix A according to the communication network topological graph; the acquiring manner of the weighted adjacency matrix a in step S4 specifically includes: if generating set in system
Figure SMS_114
Can receive neighbor generator set->
Figure SMS_117
Is set up
Figure SMS_120
, wherein />
Figure SMS_115
Representing a generator set->
Figure SMS_116
The number of the neighbor nodes; if the generator set is->
Figure SMS_119
Cannot receive neighbor genset->
Figure SMS_122
Is set to->
Figure SMS_113
The method comprises the steps of carrying out a first treatment on the surface of the Furthermore, due to the generator set->
Figure SMS_118
Self data is also available, so the arrangement
Figure SMS_121
. The weighted adjacency matrix a corresponding to fig. 2 is as follows: />
Figure SMS_123
。
S5, each generator set selects initial decision behaviors according to the decision feasible region of each generator set
Figure SMS_124
And to error vector
Figure SMS_125
Penalty factor variable->
Figure SMS_126
Sum weight gain variable->
Figure SMS_127
Initializing to obtain initial values of all variables; wherein each generator set selects an initial decision behavior according to its own decision feasible region>
Figure SMS_128
In the time of this, the generator set can be taken at will>
Figure SMS_129
, wherein />
Figure SMS_130
The set of all gensets is numbered.
Specifically, the mode of acquiring the initial values of the variables is specifically as follows:
Figure SMS_131
，/>
Figure SMS_132
the method comprises the steps of carrying out a first treatment on the surface of the Weight gain variable +.>
Figure SMS_133
The initial value is set to +.>
Figure SMS_134
I.e.>
Figure SMS_135
The individual elements are
Figure SMS_136
Other elements are 0 +.>
Figure SMS_137
And (5) a dimension vector.
S6, transferring the local constraint of the generator set to a cost function by using a penalty function method so as to convert the mathematical model; the specific conversion mode of the conversion mathematical model is as follows:
Figure SMS_138
； wherein ,/>
Figure SMS_139
For a new cost function->
Figure SMS_140
As penalty factors, the gradient function corresponding to the new cost function is defined as
Figure SMS_141
，/>
Figure SMS_142
Representing functions respectively
Figure SMS_143
Regarding variables->
Figure SMS_144
Is a gradient function of (a).
S7, acquiring left characteristic roots of the weighted adjacent matrix A in a specified time by utilizing the consistency of the multi-agent system, and acquiring left characteristic roots of the directed unbalanced network Laplacian matrix in the specified time by utilizing an updating strategy so as to update the weight gain variable
Figure SMS_156
So that the weight gain variable +.>
Figure SMS_147
Converging to a stable value at a specified time; the specific updating mode of the updating strategy is as follows: />
Figure SMS_152
The method comprises the steps of carrying out a first treatment on the surface of the Wherein the Laplace matrix is defined as
Figure SMS_160
，/>
Figure SMS_162
Is a unit array, and the aperiodic sampling time is defined as +.>
Figure SMS_161
，/>
Figure SMS_163
Is a small positive number; />
Figure SMS_153
Respectively represent the generator set->
Figure SMS_157
and />
Figure SMS_145
At non-periodic sampling instants->
Figure SMS_149
Time gain variable +.>
Figure SMS_148
Value of->
Figure SMS_151
Representing a generator set->
Figure SMS_155
Weight gain variable of (2) at the current moment +.>
Figure SMS_159
Is a value of (2). For example: set->
Figure SMS_146
，/>
Figure SMS_150
The weight gain variable is updated according to the iteration rule, and the change track of the weight gain variable is shown in fig. 2. It can be seen from fig. 2 that the weight gain variable converges to a stable value +.>
Figure SMS_154
And the steady state value satisfies
Figure SMS_158
。
S8, updating the error variable by utilizing the proportional-integral control idea
Figure SMS_164
So that the error variable +.>
Figure SMS_165
Converging to zero in a designated time to realize the supply and demand balance of economic dispatching problems; wherein the error variable +.>
Figure SMS_166
The specific updating mode of (a) is as follows: />
Figure SMS_167
； wherein ,
Figure SMS_178
for aperiodic sampling instants +.>
Figure SMS_169
For a smaller positive number, the control parameter +.>
Figure SMS_172
；/>
Figure SMS_183
For the weight gain variable->
Figure SMS_187
Is>
Figure SMS_185
Steady state values obtained at the end of the first phase for the individual components; />
Figure SMS_188
Representing a generator set->
Figure SMS_177
At the present moment +.>
Figure SMS_181
Is>
Figure SMS_168
Respectively represent the generator set->
Figure SMS_174
Error variable +.>
Figure SMS_171
At the present moment +.>
Figure SMS_175
And aperiodic sampling instant->
Figure SMS_179
Is a value of (2); />
Figure SMS_182
Respectively represent the generator set->
Figure SMS_180
and />
Figure SMS_184
At non-periodic sampling instants->
Figure SMS_186
A gradient value of time; />
Figure SMS_189
Representing the integral variable. For example: set->
Figure SMS_170
，
Figure SMS_173
According to the iteration rule, error variable +.>
Figure SMS_176
The trace over time is shown in figure 3. As can be seen from fig. 3, the error variable ie converges to zero in a specified time and is one-step convergence. This shows that the algorithm designed by the present invention allows the equality constraint to be established in the economic dispatch problem.
And S9, acquiring the optimal output power of each generator set in the economic dispatch problem by utilizing the consistency of the multi-agent system, and acquiring the optimal output power of each generator set at the appointed time to obtain an optimal power distribution scheme. The specific updating mode of the optimal output power is as follows:
Figure SMS_190
；
wherein ,
Figure SMS_193
for aperiodic sampling time, the control parameters are as follows
Figure SMS_197
Matrix->
Figure SMS_201
Is a diagonal matrix>
Figure SMS_192
Representation matrix->
Figure SMS_198
Is a second small feature root of (2); />
Figure SMS_202
Respectively represent the generator set->
Figure SMS_204
Penalty factor->
Figure SMS_191
At the current moment
Figure SMS_195
And aperiodic sampling instant->
Figure SMS_199
Values at that time. For example: set->
Figure SMS_203
，/>
Figure SMS_194
，/>
Figure SMS_196
The output power variation trace of each generator set is shown in fig. 4. As can be seen from FIG. 4, the algorithm designed by the invention can realize that each generator set obtains the optimal output power +.>
Figure SMS_200
I.e. the optimum output power of the generator set 6 is the upper limit value.
In this embodiment, compared with the current fixed time economic scheduling algorithm, the specified time economic scheduling algorithm provided by the invention can ensure that the convergence time is specified by the user; compared with the existing economic scheduling algorithm with the designated time, the economic scheduling algorithm provided by the invention can solve the more general economic scheduling problem, so that the economic scheduling algorithm with the designated time is more flexible and practical.
On the basis of the above embodiment, the present invention further provides a specified time distributed economic dispatch device under a directional unbalanced network, which is configured to support the specified time distributed economic dispatch method under the directional unbalanced network in the above embodiment, where the specified time distributed economic dispatch device under the directional unbalanced network includes:
a parameter presetting module for setting system parameters including the number n of all generator sets participating in scheduling, the expected generation power of each generator set
Figure SMS_205
, wherein />
Figure SMS_206
Indicating the number of the generator set,/->
Figure SMS_207
Setting convergence time
Figure SMS_208
；
A decision selection module for each generator set to select initial decision behavior according to its own decision feasible region
Figure SMS_209
And +.>
Figure SMS_210
Penalty factor variable->
Figure SMS_211
Sum weight gain variable->
Figure SMS_212
Initializing to obtain initial values of all variables;
the constraint transfer module is used for transferring the local constraint of the generator set into a cost function by using a penalty function method so as to convert the mathematical model;
the power distribution module is used for acquiring the left characteristic root of the weighted adjacent matrix in a specified time by utilizing the multi-agent system consistency and obtaining an optimal power generation power distribution scheme in the specified time by utilizing the multi-agent system consistency and the proportional integral control idea.
Those of ordinary skill in the art will appreciate that the modules and method steps of the examples described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or as a combination of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In addition, it should be noted that the combination of the technical features described in the present invention is not limited to the combination described in the claims or the combination described in the specific embodiments, and all the technical features described in the present invention may be freely combined or combined in any manner unless contradiction occurs between them. It should be noted that the above-mentioned embodiments are merely examples of the present invention, and it is obvious that the present invention is not limited to the above-mentioned embodiments, and many similar variations are possible. All modifications attainable or obvious from the present disclosure set forth herein should be deemed to be within the scope of the present disclosure.
The foregoing is merely illustrative of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A method for distributed economic dispatch of designated times in a directed unbalanced network, comprising the steps of:
s1, establishing an economic dispatch problem mathematical model in a smart grid;
s2, setting system parameters including the number n of all generator sets participating in scheduling, and expected power generation of each generator set
Figure QLYQS_1
, wherein />
Figure QLYQS_2
Indicating the number of the generator set,/->
Figure QLYQS_3
Setting convergence time +.>
Figure QLYQS_4
；
S3, constructing a communication network topological graph between the generator sets according to a communication topological structure of the intelligent power grid system;
s4, setting a weighted adjacent matrix A according to the communication network topological graph;
s5, each generator set selects initial decision behaviors according to the decision feasible region of each generator set
Figure QLYQS_5
And +.>
Figure QLYQS_6
Penalty factor variable->
Figure QLYQS_7
Sum weight gain variable->
Figure QLYQS_8
Initializing to obtain initial values of all variables;
s6, transferring the local constraint of the generator set to a cost function by using a penalty function method so as to convert the mathematical model;
s7, acquiring left characteristic roots of the weighted adjacent matrix A in a specified time by utilizing the consistency of the multi-agent system, and acquiring left characteristic roots of the directed unbalanced network Laplacian matrix in the specified time by utilizing an updating strategy so as to update the weight gain variable
Figure QLYQS_9
So that the weight gain variable +.>
Figure QLYQS_10
Converging to a stable value at a specified time;
s8, updating the error variable by utilizing the proportional-integral control idea
Figure QLYQS_11
So that the error variable +.>
Figure QLYQS_12
Converging to zero in a designated time to realize the supply and demand balance of economic dispatching problems;
and S9, acquiring the optimal output power of each generator set in the economic dispatch problem by utilizing the consistency of the multi-agent system, and acquiring the optimal output power of each generator set at the appointed time to obtain an optimal power distribution scheme.
2. The method for distributed economic dispatch of a specified time in a directed unbalanced network according to claim 1, wherein in step S1, the mathematical model of the economic dispatch problem is as follows:
Figure QLYQS_13
；
wherein ,
Figure QLYQS_14
for output power +.>
Figure QLYQS_15
Respectively +.>
Figure QLYQS_16
Lower and upper limits of the output power of the individual generator sets,/->
Figure QLYQS_17
Is->
Figure QLYQS_18
Generating cost function of each generator set, +.>
Figure QLYQS_19
Is->
Figure QLYQS_20
Local constraint functions of the individual generator sets.
3. The method for distributed economic dispatch of designated time in a directed unbalanced network according to claim 1, wherein the obtaining mode of the weighted adjacency matrix a in step S4 is specifically as follows:
if generating set in system
Figure QLYQS_21
Can receive neighbor generator set->
Figure QLYQS_22
Is set to->
Figure QLYQS_23
, wherein
Figure QLYQS_24
Representing a generator set->
Figure QLYQS_25
The number of the neighbor nodes;
if the generator set
Figure QLYQS_26
Cannot receive neighbor genset->
Figure QLYQS_27
Is set to->
Figure QLYQS_28
The method comprises the steps of carrying out a first treatment on the surface of the Furthermore, due to the generator set->
Figure QLYQS_29
Self data are also available, so set +.>
Figure QLYQS_30
。
4. The method for distributed economic dispatch of designated time in a directed unbalanced network according to claim 1 wherein each generating set in step S5 selects initial decision actions according to its own decision feasible region
Figure QLYQS_31
In the time of this, the generator set can be taken at will>
Figure QLYQS_32
, wherein />
Figure QLYQS_33
The set of all gensets is numbered.
5. The method for distributed economic dispatch of a specified time in a directional unbalanced network according to claim 1, wherein the method for obtaining the initial values of the variables in step S5 is specifically as follows:
Figure QLYQS_34
，
Figure QLYQS_35
；
the weight gain variable
Figure QLYQS_36
The initial value is set to +.>
Figure QLYQS_37
I.e.>
Figure QLYQS_38
The individual elements are->
Figure QLYQS_39
Other elements are 0 +.>
Figure QLYQS_40
And (5) a dimension vector.
6. The method for distributed economic dispatch of a specified time in a directed unbalanced network according to claim 1, wherein the specific transformation manner of the transformation mathematical model in step S6 is as follows:
Figure QLYQS_41
；
wherein ,
Figure QLYQS_42
for a new cost function->
Figure QLYQS_43
As penalty factors, the gradient function corresponding to the new cost function is defined as
Figure QLYQS_44
，/>
Figure QLYQS_45
Representing functions respectively
Figure QLYQS_46
Regarding variables->
Figure QLYQS_47
Is a gradient function of (a).
7. The method for distributed economic dispatch of a given time in a directed unbalanced network of claim 1 wherein the step ofThe specific updating mode of the updating strategy in the step S7 is as follows:
Figure QLYQS_48
；
wherein the Laplace matrix is defined as
Figure QLYQS_49
，/>
Figure QLYQS_53
Is a unit array, and the non-periodic sampling time is defined as
Figure QLYQS_56
，/>
Figure QLYQS_50
Is a small positive number; />
Figure QLYQS_55
Respectively represent the generator set->
Figure QLYQS_58
and />
Figure QLYQS_60
At non-periodic sampling instants->
Figure QLYQS_52
Time gain variable +.>
Figure QLYQS_54
Value of->
Figure QLYQS_57
Representing a generator set->
Figure QLYQS_59
Weight gain variable of (2) at the current moment +.>
Figure QLYQS_51
Is a value of (2).
8. The method for distributed economic dispatch of a specified time in a directed unbalanced network according to claim 1, wherein the step S8 updates the error variable by using a proportional-integral control concept
Figure QLYQS_61
The specific updating mode of (a) is as follows: />
Figure QLYQS_62
； wherein ,
Figure QLYQS_72
for aperiodic sampling instants +.>
Figure QLYQS_66
For a smaller positive number, the control parameter +.>
Figure QLYQS_69
；/>
Figure QLYQS_71
For the weight gain variable->
Figure QLYQS_73
Is>
Figure QLYQS_76
Steady state values obtained at the end of the first phase for the individual components;
Figure QLYQS_80
representing a generator set->
Figure QLYQS_75
At the present moment +.>
Figure QLYQS_79
Is>
Figure QLYQS_63
Respectively represent the generator set->
Figure QLYQS_68
Error variable +.>
Figure QLYQS_74
At the present moment +.>
Figure QLYQS_78
And aperiodic sampling instant->
Figure QLYQS_77
Is a value of (2); />
Figure QLYQS_81
Respectively represent the generator set->
Figure QLYQS_64
and />
Figure QLYQS_70
At non-periodic sampling instants->
Figure QLYQS_65
A gradient value of time; />
Figure QLYQS_67
Representing the integral variable.
9. The method for distributed economic dispatch of a specified time in a directed unbalanced network according to claim 1, wherein the specific update mode of the optimal output power in step S9 is as follows:
Figure QLYQS_82
；
wherein ,
Figure QLYQS_85
for aperiodic sampling time, the control parameters are as follows
Figure QLYQS_88
Matrix->
Figure QLYQS_91
Is a diagonal matrix>
Figure QLYQS_84
Representation matrix->
Figure QLYQS_86
Is a second small feature root of (2); />
Figure QLYQS_89
Respectively represent the generator set->
Figure QLYQS_92
Penalty factor->
Figure QLYQS_83
At the current moment
Figure QLYQS_87
And aperiodic sampling instant->
Figure QLYQS_90
Values at that time.
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