CN116307650A - Novel power distribution network source network load coordination random optimization operation method oriented to flexibility - Google Patents

Novel power distribution network source network load coordination random optimization operation method oriented to flexibility Download PDF

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CN116307650A
CN116307650A CN202310588665.8A CN202310588665A CN116307650A CN 116307650 A CN116307650 A CN 116307650A CN 202310588665 A CN202310588665 A CN 202310588665A CN 116307650 A CN116307650 A CN 116307650A
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刘佳
唐早
汤奕
朱璇
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Southeast University
Liyang Research Institute of Southeast University
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Abstract

The invention discloses a novel power distribution network source network load coordination random optimization operation method oriented to flexibility, which comprises the following steps: based on a power distribution network safety domain theory, a novel power distribution network flexibility evaluation index system comprising flexible distance expectations, standard deviations and flexible distance variation coefficients is constructed; constructing a multi-objective coordination optimization operation model oriented to system flexibility improvement; providing constraint conditions of a multi-objective coordinated optimization operation model; and solving the multi-objective coordinated optimization operation model based on the forward boundary intersection point and the dynamic niche differential evolution algorithm until the pareto optimal scheme is output. The method provided by the invention realizes multidimensional optimization of the economical efficiency and the flexibility of the system operation, the safety margin of the obtained optimized operation method in each scene is considerable and measurable, the operation state of the system can be conveniently mastered by a dispatcher, the unsafe state can be prevented and controlled, and a theoretical basis is provided for the optimized scheduling of the novel power distribution network.

Description

Novel power distribution network source network load coordination random optimization operation method oriented to flexibility
Technical Field
The invention relates to the technical field of source network load coordination optimization, in particular to a novel power distribution network source network load coordination random optimization operation method oriented to flexibility.
Background
Economy, safety and reliability are three elements of traditional power system evaluation, but with the high-proportion large-scale access of new energy sources represented by wind power and photovoltaics to a power grid, the real-time electric power and electric quantity balance of the system is challenged. As a new element for evaluating source load response capability, flexibility is included in the evaluation category of a novel electric power system, and how to improve the system operation flexibility becomes a current hot research problem. For the power distribution network, the flexible resources are distributed, discretized, large-scale and other characteristics, including the adjustment means such as distributed power output control, flexible load response, structural form optimization and the like, and the system flexibility is improved by coordinating and optimizing the source network load resources in the novel power distribution network. On the other hand, the novel power distribution network has the characteristics of initiative, activity, feeder interconnection and the like, and when a system fails, the novel power distribution network is required to have the power supply capacity of recovering the power supply load without power supply, namely, the operation safety in the failure recovery mode is required to be ensured. Therefore, the system operation safety and flexibility can be effectively improved by excavating distributed flexible resources in the novel power distribution network, so that the multi-objective collaborative optimization of power distribution network scheduling is realized.
The power distribution network security domain theory becomes a new method for identifying the operation security of the system, and compared with the traditional point-by-point simulation method, the method has the characteristics of considerable and measurable working points, controllable safety margin and the like. By defining the outlet power vector of each feeder line, all working points which can meet safety constraints (including line capacity constraints and node voltage constraints) are searched, and then a closed convex set is formed, namely the safety domain of the power distribution network. The security domain boundary does not change along with the change of the system running state, the minimum Euclidean distance from the dynamic running working point to the security boundary can be calculated to evaluate the security margin of the system, and the security distance of the working point positioned inside the security domain is defined to be positive, and the security distance of the working point positioned outside the security domain is defined to be negative. When the working point is located outside the security domain, the orthogonal direction from the working point to the security boundary is the optimal restoration control direction. With the wide access of flexible resources such as distributed power sources and flexible loads to the power distribution network, the fluctuation of the system operation working points is more frequent, and the adoption of the power distribution network safety domain theory is helpful for rapidly researching and judging the operation safety, so that the economic optimization of the operation problem is realized by utilizing the distributed flexible source load resources on the premise of ensuring the system safety. Active management measures (such as on-load voltage regulating transformer tap adjustment, distributed power output/power factor adjustment, demand response, network reconstruction and the like) are utilized, so that the running state of the system can be effectively improved, and the elasticity and toughness of the novel power distribution network are enhanced.
In view of the above-mentioned problems and the shortcomings of the prior art, the following problems are needed to be solved:
1) Aiming at different feeder lines and different operation scenes, how to represent the average level and fluctuation condition of the distance from the working point to the safety boundary, a novel power distribution network flexibility evaluation index system based on a safety domain needs to be defined;
2) From the economical and flexible viewpoints, a novel multi-target optimizing operation model of the power distribution network is provided by using distributed flexible source network load resources, and a convex relaxation conversion method of a nonlinear mixed integer programming model is researched;
3) And researching a multi-target optimization algorithm comprising source network load running cost and flexibility evaluation indexes by combining model characteristics, and analyzing the calculation performance of a normal boundary intersection point and a meta heuristic algorithm on model solving.
Disclosure of Invention
Aiming at the problems, a novel power distribution network source network load coordination random optimization operation method oriented to flexibility is provided, and multi-objective coordination pareto optimizing of system economy and flexibility is realized by optimizing multi-element distributed flexible source network load resources in the novel power distribution network under the constraint conditions of network safety, active management and the like. In addition, the system flexibility is quantitatively evaluated based on the theory of the safety domain of the power distribution network, the flexible distance variation coefficient of each operation scene is calculated, the novel safety operation flexibility level of the power distribution network is comprehensively disclosed, so that the operation economy and flexibility of the power distribution network are comprehensively improved, the active supporting capacity of the distributed flexible source network load resource is excavated, and the system toughness and the resistance to fault risks are improved.
In order to achieve the above object, the present invention is realized by the following technical scheme:
novel power distribution network source network load coordination random optimization operation method oriented to flexibility, which comprises the following steps:
constructing a novel power distribution network flexibility evaluation index system based on a power distribution network safety domain theory, wherein the novel power distribution network flexibility evaluation index system comprises a flexible distance expectation, a standard deviation and a flexible distance variation coefficient;
constructing a multi-objective coordinated optimization operation model for improving system flexibility based on the novel power distribution network flexibility evaluation index system, wherein the multi-objective coordinated optimization operation model is double multi-objective optimization comprising a flexibility objective function and an economical objective function, the flexibility objective function is a minimum flexible distance variation coefficient, and the economical objective function is a minimum total operation cost;
providing constraint conditions of the multi-objective coordinated optimization operation model, wherein the constraint conditions comprise a node power balance equation, a power flow calculation equation, radial operation constraint, network security constraint and active management constraint; converting bilinear terms in a power flow calculation equation into a linear form by using a large M method;
and solving the multi-objective coordinated optimization operation model based on the forward boundary intersection point and the dynamic niche differential evolution algorithm, and outputting an operation simulation result.
As a preferable scheme of the invention, the distribution network security domain theory is defined as a feeder line outlet power working point set under the premise of ensuring the safe operation of a system, wherein the safe operation of the system comprises no overload of a line and no out-of-limit of node voltage;
the method for constructing the novel power distribution network flexibility evaluation index system specifically comprises the following steps: establishing a distribution network security domain model according to a distribution network security domain theory, analyzing the security boundary geometric characteristics of the distribution network security domain model, and calculating the minimum Euclidean distance from a feeder line outlet power working point to a security boundary to measure the system operation security margin; based on a scene analysis method, analyzing flexible distance expectations, standard deviations and flexible distance variation coefficients of all operation scenes, and further constructing a flexible evaluation index cluster based on the flexible distance expectations, the standard deviations and the flexible distance variation coefficients to form a novel power distribution network flexibility evaluation index system.
As a preferred scheme of the present invention, mathematical expressions corresponding to the flexible distance expectation, the standard deviation and the flexible distance variation coefficient are respectively:
Figure SMS_1
Figure SMS_2
；
Figure SMS_3
；
in the method, in the process of the invention,
Figure SMS_5
and->
Figure SMS_7
Scene +.>
Figure SMS_8
Flexible distance expectations and standard deviations; />
Figure SMS_10
Is a flexible distance variation coefficient; />
Figure SMS_12
For feed line->
Figure SMS_13
In scene->
Figure SMS_14
The medicine for treating the lower part of the bodyA living distance; />
Figure SMS_4
And->
Figure SMS_6
The number of feeder lines and the number of scenes are respectively; />
Figure SMS_9
And->
Figure SMS_11
Respectively a feeder set and a scene set;
Figure SMS_15
；
Figure SMS_16
；
in the method, in the process of the invention,
Figure SMS_17
for scene->
Figure SMS_19
Lower feeder outlet power operating point, +.>
Figure SMS_21
For critical operating point on safety boundary +.>
Figure SMS_23
For the safety boundary set, ++>
Figure SMS_24
Is a security domain; />
Figure SMS_26
For the outlet power of the feeder 1, < > for>
Figure SMS_28
For the outlet power of the feed line 2,
Figure SMS_29
for feed line->
Figure SMS_31
Outlet power of>
Figure SMS_33
For feed line->
Figure SMS_34
Is a power output of the engine; />
Figure SMS_37
And->
Figure SMS_38
Nodes +.>
Figure SMS_39
Voltage and feeder->
Figure SMS_40
Current (I)>
Figure SMS_18
And->
Figure SMS_20
Nodes +.>
Figure SMS_22
Voltage->
Figure SMS_25
Lower and upper limits of->
Figure SMS_27
And->
Figure SMS_30
Are respectively feeder lines->
Figure SMS_32
Current->
Figure SMS_35
Lower and upper limits of (2); />
Figure SMS_36
Is a set of nodes.
As a preferred solution of the present invention, the running total cost includes a switching action, a distributed power running, a distributed power active management, a transaction with a main network, a network loss and a demand response cost, and the corresponding calculation expression is as follows:
Figure SMS_41
；
Figure SMS_42
；
Figure SMS_43
；
Figure SMS_44
；
Figure SMS_45
；
Figure SMS_46
；
in the method, in the process of the invention,
Figure SMS_48
、/>
Figure SMS_49
、/>
Figure SMS_52
、/>
Figure SMS_54
、/>
Figure SMS_56
and->
Figure SMS_58
Respectively is scene->
Figure SMS_60
Time below->
Figure SMS_62
Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost; />
Figure SMS_64
And
Figure SMS_66
the unit switch action and demand response costs are respectively; />
Figure SMS_67
、/>
Figure SMS_70
And->
Figure SMS_72
Respectively at node->
Figure SMS_73
The unit operation cost of the micro gas turbine, wind power and photovoltaic; />
Figure SMS_76
、/>
Figure SMS_78
And->
Figure SMS_79
Respectively at the nodes
Figure SMS_81
The unit of micro gas turbine, wind power and photovoltaic actively manages the cost; />
Figure SMS_84
And->
Figure SMS_85
Respectively is scene->
Figure SMS_86
Time below->
Figure SMS_87
Unit transaction and loss costs of (a); />
Figure SMS_88
For the scene->
Figure SMS_89
Time below->
Figure SMS_90
Switch->
Figure SMS_91
Status (S)>
Figure SMS_92
For the scene->
Figure SMS_93
Time below->
Figure SMS_94
Switch->
Figure SMS_95
State, closed 1, open 0; />
Figure SMS_96
、/>
Figure SMS_47
And->
Figure SMS_50
Respectively at node->
Figure SMS_51
Micro gas turbine, wind power and photovoltaic in scene->
Figure SMS_53
Time below->
Figure SMS_55
Is an active force of (a); />
Figure SMS_57
For being located at node +.>
Figure SMS_59
Is in the scene of the load of (2)
Figure SMS_61
Time below->
Figure SMS_63
Is an active response of (a); />
Figure SMS_65
And->
Figure SMS_68
Respectively is scene->
Figure SMS_69
Time below->
Figure SMS_71
The interactive power and the network loss of the (a);
Figure SMS_74
for the scene->
Figure SMS_75
Time below->
Figure SMS_77
Is a span of (2); />
Figure SMS_80
、/>
Figure SMS_82
And->
Figure SMS_83
Respectively is a switch set and a switch partA set of distributed power supplies and a set of nodes.
As a preferred scheme of the invention, the objective function expression of the multi-objective coordination optimization operation model is as follows:
Figure SMS_97
；
wherein,,
Figure SMS_98
for the total cost of operation->
Figure SMS_99
Is a flexible distance variation coefficient;
Figure SMS_100
in the method, in the process of the invention,
Figure SMS_102
the number of running scenes; />
Figure SMS_104
、/>
Figure SMS_106
、/>
Figure SMS_107
、/>
Figure SMS_109
、/>
Figure SMS_110
And->
Figure SMS_111
Respectively, scenes
Figure SMS_101
Time below->
Figure SMS_103
Switch operation, distribution of (a)The method comprises the steps of power supply operation, distributed power supply active management, transaction with a main network, network loss and demand response cost; />
Figure SMS_105
And->
Figure SMS_108
A scene set and a time set, respectively.
As a preferred embodiment of the present invention, the expression of the node power balance equation is:
Figure SMS_112
；
Figure SMS_113
；
in the method, in the process of the invention,
Figure SMS_130
and->
Figure SMS_131
Lines are respectively->
Figure SMS_132
Active and reactive power of (a); />
Figure SMS_133
And->
Figure SMS_134
Lines are respectively->
Figure SMS_135
Resistance and reactance of (a); />
Figure SMS_136
For line->
Figure SMS_115
Square of the current; />
Figure SMS_117
And->
Figure SMS_119
Respectively at node->
Figure SMS_121
The active and reactive power outputs are predicted by the distributed power supply; />
Figure SMS_123
And->
Figure SMS_125
Respectively at node->
Figure SMS_127
Active and reactive power is removed from the distributed power supply; />
Figure SMS_129
And->
Figure SMS_114
Respectively at node->
Figure SMS_116
Active and reactive loads predicted by (a); />
Figure SMS_118
And->
Figure SMS_120
Respectively at node->
Figure SMS_122
Active and reactive load response amounts of (a); />
Figure SMS_124
And->
Figure SMS_126
Respectively is node set and node->
Figure SMS_128
A set of interconnected nodes;
the expression of the tide calculation equation is as follows:
Figure SMS_137
；
Figure SMS_138
；
Figure SMS_139
；
in the method, in the process of the invention,
Figure SMS_141
for node->
Figure SMS_142
Square of the voltage amplitude; />
Figure SMS_143
For node->
Figure SMS_144
Square of the voltage amplitude; />
Figure SMS_145
Is a sufficiently large positive number, taken as 10000; />
Figure SMS_146
For switch->
Figure SMS_147
State, closed 1, open 0; />
Figure SMS_140
Is vector transposition;
the flow calculation adopts a Distflow form after the large M method is relaxed.
The expression of the radial operation constraint is:
Figure SMS_148
；
Figure SMS_149
；
in the method, in the process of the invention,
Figure SMS_151
and->
Figure SMS_152
The number of nodes and the number of nodes of the transformer substation are respectively; />
Figure SMS_153
For line->
Figure SMS_154
Virtual active power of (a); />
Figure SMS_155
And->
Figure SMS_156
Switch set and node respectively->
Figure SMS_157
A set of interconnected nodes; />
Figure SMS_150
Is a substation node set.
As a preferred scheme of the present invention, the network security constraint includes a flexible distance constraint and a node voltage opportunity constraint, and the expression of the flexible distance constraint is:
Figure SMS_158
；
Figure SMS_159
；
in the method, in the process of the invention,
Figure SMS_160
for feed line->
Figure SMS_162
Flexible distance of (2); />
Figure SMS_164
For feed line->
Figure SMS_166
Is a minimum flexible distance of (2); />
Figure SMS_167
And
Figure SMS_169
are respectively feeder lines->
Figure SMS_171
And (ii) of the feeder line>
Figure SMS_161
The outlet power is in the super plane->
Figure SMS_163
Coefficients of (a); />
Figure SMS_165
The number of the feeder lines is the number; />
Figure SMS_168
For feed line->
Figure SMS_170
Is a power output of the engine; />
Figure SMS_172
And->
Figure SMS_173
Respectively a hyperplane set and a feeder line set;
the node voltage opportunity constraint expression is:
Figure SMS_174
；
in the method, in the process of the invention,
Figure SMS_176
representing probability->
Figure SMS_178
A safe confidence level for the node voltage; />
Figure SMS_179
For node->
Figure SMS_180
Voltage (V)>
Figure SMS_181
And->
Figure SMS_182
Nodes +.>
Figure SMS_183
Voltage->
Figure SMS_175
Lower and upper limits of (2); />
Figure SMS_177
Is a set of nodes.
As a preferred aspect of the present invention, the active management constraints include a switching action constraint, an on-load tap-changing transformer regulation constraint, a distributed power supply output constraint, a distributed power supply power factor constraint, and a demand response constraint;
the expression of the switch action constraint is as follows:
Figure SMS_184
in the method, in the process of the invention,
Figure SMS_186
、/>
Figure SMS_187
and->
Figure SMS_189
Respectively a switch set, a scene set and a time set; />
Figure SMS_191
For the scene->
Figure SMS_193
Time below->
Figure SMS_195
Switch->
Figure SMS_196
Status (S)>
Figure SMS_185
For the scene->
Figure SMS_188
Time below->
Figure SMS_190
Switch->
Figure SMS_192
State, closed 1, open 0; />
Figure SMS_194
For switch->
Figure SMS_197
An upper limit of the number of actions;
the expression of the on-load tap-changing voltage-regulating transformer tap-changing restriction is as follows:
Figure SMS_198
；
Figure SMS_199
；
Figure SMS_200
；
in the method, in the process of the invention,
Figure SMS_202
、/>
Figure SMS_204
nodes +.>
Figure SMS_206
Voltage and node->
Figure SMS_208
A voltage; />
Figure SMS_210
And->
Figure SMS_212
Lines are respectively->
Figure SMS_214
The on-load regulating transformer is at moment +.>
Figure SMS_201
And time->
Figure SMS_203
Tap positions of (2); />
Figure SMS_205
And->
Figure SMS_207
Lines are respectively->
Figure SMS_209
The upper limit and the lower limit of the tap position of the on-load voltage regulating transformer are set; />
Figure SMS_211
Step length is adjusted for the voltage of the on-load regulating transformer; />
Figure SMS_213
Is an on-load voltage regulating transformer set;
the expression of the distributed power supply output constraint is as follows:
Figure SMS_215
；
Figure SMS_216
；
in the method, in the process of the invention,
Figure SMS_218
the maximum cutting rate is output for the distributed power supply; />
Figure SMS_220
For being located at node +.>
Figure SMS_222
Is predictive of active power, +.>
Figure SMS_224
For being located at node +.>
Figure SMS_225
An upper active power output limit is predicted by the distributed power supply; />
Figure SMS_226
Is a distributed power supply set; />
Figure SMS_227
And->
Figure SMS_217
Respectively at node->
Figure SMS_219
Active and reactive power is removed from the distributed power supply; />
Figure SMS_221
For being located at node +.>
Figure SMS_223
A distributed power source output power factor angle;
the expression of the distributed power supply power factor constraint is as follows:
Figure SMS_228
；
in the method, in the process of the invention,
Figure SMS_229
and->
Figure SMS_230
Respectively at node->
Figure SMS_231
Upper and lower limits of the distributed power supply output power factor angle;
the expression of the demand response constraint is:
Figure SMS_232
；
Figure SMS_233
；
in the method, in the process of the invention,
Figure SMS_235
is a node set; />
Figure SMS_236
And->
Figure SMS_237
Respectively at node->
Figure SMS_238
Active and reactive load response amounts of (a);
Figure SMS_239
for being located at node +.>
Figure SMS_240
An upper active load response amount limit of (2); />
Figure SMS_241
For being located at node +.>
Figure SMS_234
Is a load power factor angle of (2).
As a preferred scheme of the present invention, the method for solving the multi-objective coordinated optimization operation model specifically includes: the method comprises the steps of inputting a wind-solar-load time sequence curve, a system topological structure and a dynamic niche differential evolution algorithm of a region where a power distribution network is located, randomly generating population individuals, defining fitness functions based on a flexibility objective function and an economy objective function respectively, calculating individual fitness values, retaining elite individuals, eliminating individuals after falling, and updating the population individuals through crossover, selection and mutation operations until a pareto optimal scheme is output.
Compared with the prior art, the invention has the beneficial effects that: the invention provides system flexibility by utilizing active management measures such as distributed power supply output adjustment, network reconstruction, demand response and the like, digs multi-element distributed flexible source network load resources, and improves the energy management level of the novel power distribution network in a multi-dimensional manner; the operation safety and margin of the feeder line outlet power working point are visually perceived, so that system schedulers can observe the operation situation of the power distribution network and control the power distribution network in a preventive manner, and the flexibility level of the system is quantitatively evaluated by counting the flexible distance of each operation scene and calculating the flexible distance expectation, standard deviation and flexible distance variation coefficient; the invention realizes the overall optimization of the system operation economy and flexibility, and is different from the traditional economy optimization, the invention meters the full link cost of the source network load including the distributed power supply operation, the switching action, the demand response and the like, and simultaneously explores the game equilibrium relation of the economy and flexibility optimization targets.
The method provided by the invention realizes multidimensional optimization of system operation economy and flexibility, and provides active supporting capability for system flexibility improvement by reasonably adjusting flexible source network load resources in the novel power distribution network, and improves operation economy on the premise of ensuring safe operation of the system; in addition, the safety margin of the obtained optimized operation method in each scene is considerable, so that a dispatcher can grasp the operation state of the system and prevent and control the unsafe state, and a theoretical basis is provided for the optimized dispatching of the novel power distribution network.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a diagram of a novel power distribution network multi-objective optimization operation technology;
FIG. 2 is a topology structure diagram of a simulation analysis system according to an embodiment of the present invention;
FIG. 3 is a graph of wind and light load timing for a region in which embodiments of the present invention are located;
FIG. 4 is a schematic diagram of a multi-target pareto front for both economy and flexibility of embodiments of the invention;
fig. 5 is a schematic diagram of flexible distance distribution of each feeder line in two schemes according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the invention, fall within the scope of protection of the invention.
As shown in fig. 1, the embodiment of the invention provides a novel power distribution network source network load coordination random optimization operation method oriented to flexibility, which specifically comprises the following steps:
step S1: constructing a novel power distribution network flexibility evaluation index system based on a power distribution network safety domain theory;
the distribution network safety domain theory is used in the field of system optimization, the quantitative influence of the spatial position distribution of the outlet power working points of all feeder lines on the system operation safety is analyzed, and a heuristic decision method is provided for improving the system flexibility.
The distribution network security domain theory is defined as a feeder line outlet power working point set under the premise of ensuring the safe operation of the system (the line is not overloaded and the node voltage is not out of limit), a distribution network security domain model is established according to the distribution network security domain theory, the security boundary geometric characteristics of the distribution network security domain model are analyzed, and the minimum Euclidean distance from the feeder line outlet power working point to the security boundary is calculated to measure the operation security margin of the system; based on a scene analysis method, analyzing flexible distance expectations, standard deviations and flexible distance variation coefficients of all operation scenes, and further constructing a flexible evaluation index cluster based on the flexible distance expectations, the standard deviations and the flexible distance variation coefficients to form a novel power distribution network flexibility evaluation index system.
The mathematical expressions corresponding to the flexible distance expectation, the standard deviation and the flexible distance variation coefficient are respectively as follows:
Figure SMS_242
；
Figure SMS_243
；
Figure SMS_244
；
in the method, in the process of the invention,
Figure SMS_246
and->
Figure SMS_248
Scene +.>
Figure SMS_249
Flexible distance expectations and standard deviations; />
Figure SMS_251
Is a flexible distance variation coefficient; />
Figure SMS_253
For feed line->
Figure SMS_254
In scene->
Figure SMS_255
A flexible distance below; />
Figure SMS_245
And->
Figure SMS_247
The number of feeder lines and the number of scenes are respectively; />
Figure SMS_250
And->
Figure SMS_252
Respectively a feeder set and a scene set;
Figure SMS_256
；
Figure SMS_257
；
in the method, in the process of the invention,
Figure SMS_259
for scene->
Figure SMS_261
Lower feeder outlet power operating point, +.>
Figure SMS_262
For critical operating point on safety boundary +.>
Figure SMS_264
For the safety boundary set, ++>
Figure SMS_266
Is a security domain; />
Figure SMS_268
For the outlet power of the feeder 1, < > for>
Figure SMS_270
For the outlet power of the feed line 2,
Figure SMS_272
for feed line->
Figure SMS_273
Outlet power of>
Figure SMS_275
For feed line->
Figure SMS_276
Is a power output of the engine; />
Figure SMS_278
And->
Figure SMS_279
Nodes +.>
Figure SMS_280
Voltage and feeder->
Figure SMS_281
Current (I)>
Figure SMS_258
And->
Figure SMS_260
Nodes +.>
Figure SMS_263
Voltage->
Figure SMS_265
Lower and upper limits of->
Figure SMS_267
And->
Figure SMS_269
Are respectively feeder lines->
Figure SMS_271
Current->
Figure SMS_274
Lower and upper limits of (2); />
Figure SMS_277
Is a set of nodes.
Step S2: constructing a multi-objective coordinated optimization operation model for improving system flexibility based on a novel power distribution network flexibility evaluation index system, wherein the multi-objective coordinated optimization operation model is double multi-objective optimization comprising a flexibility objective function and an economical objective function, the flexibility objective function is a minimum flexible distance variation coefficient, and the economical objective function is a minimum total operation cost;
the total running cost comprises switching action, distributed power supply running, distributed power supply active management, main network transaction, network loss and demand response cost, and the corresponding calculation expression is as follows:
Figure SMS_282
；
Figure SMS_283
；
Figure SMS_284
；
Figure SMS_285
；
Figure SMS_286
；
Figure SMS_287
；
in the method, in the process of the invention,
Figure SMS_289
、/>
Figure SMS_290
、/>
Figure SMS_291
、/>
Figure SMS_292
、/>
Figure SMS_293
and->
Figure SMS_294
Respectively is scene->
Figure SMS_295
Time below->
Figure SMS_288
Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost;
Figure SMS_296
and->
Figure SMS_298
The unit switch action and demand response costs are respectively; />
Figure SMS_299
、/>
Figure SMS_300
And->
Figure SMS_301
Respectively at node->
Figure SMS_302
The unit operation cost of the micro gas turbine, wind power and photovoltaic; />
Figure SMS_303
、/>
Figure SMS_297
And
Figure SMS_305
respectively at node->
Figure SMS_306
The unit of micro gas turbine, wind power and photovoltaic actively manages the cost; />
Figure SMS_308
And->
Figure SMS_310
Respectively is scene->
Figure SMS_312
Time below->
Figure SMS_314
Unit transaction and loss costs of (a); />
Figure SMS_316
For the scene->
Figure SMS_318
Time below->
Figure SMS_319
Switch->
Figure SMS_321
Status (S)>
Figure SMS_323
For the scene->
Figure SMS_325
Time below->
Figure SMS_327
Switch->
Figure SMS_329
State, closed 1, open 0; />
Figure SMS_331
、
Figure SMS_304
And->
Figure SMS_307
Respectively at node->
Figure SMS_309
Micro gas turbine, wind power and photovoltaic in scene->
Figure SMS_311
Time below->
Figure SMS_313
Is an active force of (a); />
Figure SMS_315
For being located at node +.>
Figure SMS_317
Is in scene->
Figure SMS_320
Time below->
Figure SMS_322
Is an active response of (a); />
Figure SMS_324
And->
Figure SMS_326
Respectively is scene->
Figure SMS_328
Time below->
Figure SMS_330
The interactive power and the network loss of the (a); />
Figure SMS_332
For the scene->
Figure SMS_333
The next moment
Figure SMS_334
Is a span of (2); />
Figure SMS_335
、/>
Figure SMS_336
And->
Figure SMS_337
Respectively a switch set, a distributed power supply set and a node set.
The objective function expression of the multi-objective coordination optimization operation model is as follows:
Figure SMS_338
wherein,,
Figure SMS_339
for the total cost of operation->
Figure SMS_340
Is a flexible distance variation coefficient;
Figure SMS_341
in the method, in the process of the invention,
Figure SMS_343
the number of running scenes; />
Figure SMS_344
、/>
Figure SMS_345
、/>
Figure SMS_346
、/>
Figure SMS_347
、/>
Figure SMS_349
And->
Figure SMS_351
Respectively, scenes
Figure SMS_342
Time below->
Figure SMS_348
Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost; />
Figure SMS_350
And->
Figure SMS_352
A scene set and a time set, respectively.
Step S3: providing constraint conditions of the multi-objective coordinated optimization operation model, wherein the constraint conditions comprise a node power balance equation, a power flow calculation equation, radial operation constraint, network security constraint and active management constraint; converting bilinear terms in a power flow calculation equation into a linear form by using a large M method;
the expression of the node power balance equation is:
Figure SMS_353
；
Figure SMS_354
；
in the method, in the process of the invention,
Figure SMS_371
and->
Figure SMS_372
Lines are respectively->
Figure SMS_373
Active and reactive power of (a); />
Figure SMS_374
And->
Figure SMS_375
Lines are respectively->
Figure SMS_376
Resistance and reactance of (a); />
Figure SMS_377
For line->
Figure SMS_356
Square of the current; />
Figure SMS_358
And->
Figure SMS_360
Respectively at node->
Figure SMS_362
The active and reactive power outputs are predicted by the distributed power supply; />
Figure SMS_364
And->
Figure SMS_366
Respectively at node->
Figure SMS_368
Active and reactive power is removed from the distributed power supply; />
Figure SMS_370
And
Figure SMS_355
respectively at node->
Figure SMS_357
Active and reactive loads predicted by (a); />
Figure SMS_359
And->
Figure SMS_361
Respectively at node->
Figure SMS_363
Active and reactive load response amounts of (a); />
Figure SMS_365
And->
Figure SMS_367
Respectively is node set and node->
Figure SMS_369
A set of interconnected nodes;
the expression of the flow calculation equation is:
Figure SMS_378
；
Figure SMS_379
；
Figure SMS_380
；
in the method, in the process of the invention,
Figure SMS_381
for node->
Figure SMS_383
Square of the voltage amplitude; />
Figure SMS_384
For node->
Figure SMS_385
Square of the voltage amplitude; />
Figure SMS_386
Is a sufficiently large positive number, taken as 10000; />
Figure SMS_387
For switch->
Figure SMS_388
State, closed 1, open 0; />
Figure SMS_382
Is vector transposition;
the flow calculation adopts a Distflow form after the large M method is relaxed.
The expression of the radial run constraint is:
Figure SMS_389
；
Figure SMS_390
；/>
in the method, in the process of the invention,
Figure SMS_392
and->
Figure SMS_393
The number of nodes and the number of nodes of the transformer substation are respectively; />
Figure SMS_394
For line->
Figure SMS_395
Virtual active power of (a); />
Figure SMS_396
And->
Figure SMS_397
Switch set and node respectively->
Figure SMS_398
A set of interconnected nodes; />
Figure SMS_391
Is a substation node set.
The network security constraint comprises a flexible distance constraint and a node voltage opportunity constraint, and the expression of the flexible distance constraint is as follows:
Figure SMS_399
；
Figure SMS_400
；
in the method, in the process of the invention,
Figure SMS_402
for feed line->
Figure SMS_404
Flexible distance of (2); />
Figure SMS_406
For feed line->
Figure SMS_408
Is a minimum flexible distance of (2); />
Figure SMS_410
And->
Figure SMS_411
Are respectively feeder lines->
Figure SMS_413
And (ii) of the feeder line>
Figure SMS_401
The outlet power is in the super plane->
Figure SMS_403
Coefficients of (a); />
Figure SMS_405
The number of the feeder lines is the number; />
Figure SMS_407
For feed line->
Figure SMS_409
Is a power output of the engine; />
Figure SMS_412
And->
Figure SMS_414
Respectively a hyperplane set and a feeder line set;
the node voltage opportunity constraint is expressed as:
Figure SMS_415
in the method, in the process of the invention,
Figure SMS_417
representing probability->
Figure SMS_418
A safe confidence level for the node voltage; />
Figure SMS_420
For node->
Figure SMS_421
Voltage (V)>
Figure SMS_422
And->
Figure SMS_423
Nodes +.>
Figure SMS_424
Voltage (V)/>
Figure SMS_416
Lower and upper limits of (2); />
Figure SMS_419
Is a set of nodes.
The active management constraints include a switching action constraint, an on-load tap-changing transformer regulation constraint, a distributed power supply output constraint, a distributed power supply power factor constraint and a demand response constraint;
the expression of the switch action constraint is:
Figure SMS_425
in the method, in the process of the invention,
Figure SMS_427
、/>
Figure SMS_428
and->
Figure SMS_430
Respectively a switch set, a scene set and a time set; />
Figure SMS_432
For the scene->
Figure SMS_434
Time below->
Figure SMS_436
Switch->
Figure SMS_438
Status (S)>
Figure SMS_426
For the scene->
Figure SMS_429
Time below->
Figure SMS_431
Switch->
Figure SMS_433
State, closed 1, open 0; />
Figure SMS_435
For switch->
Figure SMS_437
An upper limit of the number of actions;
the expression for the tap adjustment constraint of an on-load tap changer is:
Figure SMS_439
；
Figure SMS_440
；
Figure SMS_441
；
in the method, in the process of the invention,
Figure SMS_443
、/>
Figure SMS_444
nodes +.>
Figure SMS_446
Voltage and node->
Figure SMS_448
A voltage; />
Figure SMS_450
And->
Figure SMS_452
Lines are respectively->
Figure SMS_454
On-load regulating transformer at moment/>
Figure SMS_442
And time->
Figure SMS_445
Tap positions of (2); />
Figure SMS_447
And->
Figure SMS_449
Lines are respectively->
Figure SMS_451
The upper limit and the lower limit of the tap position of the on-load voltage regulating transformer are set; />
Figure SMS_453
Step length is adjusted for the voltage of the on-load regulating transformer; />
Figure SMS_455
Is an on-load voltage regulating transformer set;
the expression of the distributed power supply output constraint is:
Figure SMS_456
；
Figure SMS_457
；
in the method, in the process of the invention,
Figure SMS_459
the maximum cutting rate is output for the distributed power supply; />
Figure SMS_461
For being located at node +.>
Figure SMS_463
Is predictive of active power, +.>
Figure SMS_465
Is positioned atNode->
Figure SMS_466
An upper active power output limit is predicted by the distributed power supply; />
Figure SMS_467
Is a distributed power supply set;
Figure SMS_468
and->
Figure SMS_458
Respectively at node->
Figure SMS_460
Active and reactive power is removed from the distributed power supply; />
Figure SMS_462
To be located at the node
Figure SMS_464
A distributed power source output power factor angle;
the expression of the distributed power supply power factor constraint is:
Figure SMS_469
in the method, in the process of the invention,
Figure SMS_470
and->
Figure SMS_471
Respectively at node->
Figure SMS_472
Upper and lower limits of the distributed power supply output power factor angle;
the expression of the demand response constraint is:
Figure SMS_473
；
Figure SMS_474
；
in the method, in the process of the invention,
Figure SMS_476
is a node set; />
Figure SMS_477
And->
Figure SMS_478
Respectively at node->
Figure SMS_479
Active and reactive load response amounts of (a); />
Figure SMS_480
For being located at node +.>
Figure SMS_481
An upper active load response amount limit of (2); />
Figure SMS_482
For being located at node +.>
Figure SMS_475
Is a load power factor angle of (2).
Step S4: and solving the multi-objective coordinated optimization operation model based on the forward boundary intersection point and the dynamic niche differential evolution algorithm, and outputting an operation simulation result.
The method specifically comprises the following steps: the method comprises the steps of inputting a wind-solar-load time sequence curve, a system topological structure and a dynamic niche differential evolution algorithm of a region where a power distribution network is located, randomly generating population individuals, defining fitness functions based on a flexibility objective function and an economy objective function respectively, calculating individual fitness values, retaining elite individuals, eliminating individuals after falling, and updating the population individuals through crossover, selection and mutation operations until a pareto optimal scheme is output.
This example is described in further detail below in connection with specific embodiments.
In this embodiment, an improved 104-node novel power distribution network is adopted for simulation, and fig. 2 shows the topology structure of the system. The novel power distribution network is based on an IEEE RBTS Bus4 system, and the original system is expanded from 7 feeder lines to 20 feeder lines and from 38 nodes to 104 nodes on the premise of not changing the capacity and the load of a transformer. The novel power distribution network is newly provided with 7 interconnection switches, each of the nodes 7, 30, 54 and 84 is provided with a micro gas turbine, each of the nodes 7, 31, 56 and 85 is provided with a wind power, and each of the nodes 10 and 38 is provided with a photovoltaic. The maximum iteration number of the dynamic niche differential evolution algorithm is 50, the population size is 100, and the scaling factor and the crossing rate are linearly decreased from 0.9 to 0.1. The wind-solar-load time sequence curve of the region is shown in figure 3. The system transformer/line, load conditions and parameter settings are shown in tables 1-3, respectively.
Table 1 transformer/line data
Figure SMS_483
TABLE 2 load data
Figure SMS_484
TABLE 3 simulation parameters
Figure SMS_485
In this embodiment, 4 comparison schemes are set in total, namely, the flexible distance constraint lower limit is changed, and the specific design is as follows:
scheme 1: flexible distance constraints are not considered;
scheme 2: the flexible distance constraint lower limit is set to 0;
scheme 3: a medium flexible operation scheme, namely, the flexible distance constraint lower limit is set to be 0.15 MVA;
scheme 4: the higher flexible operating scheme, i.e. the flexible distance constraint lower limit, is set to 0.3 MVA.
Fig. 4 shows the overall cost of operation and the flexible distance coefficient of variation multi-objective optimized pareto front distribution for schemes 1 and 2. As can be seen from fig. 4, the system economy and flexibility index change trends are opposite, regardless of scheme 1 or scheme 2. For scheme 1, when the minimum total running cost is 19688 $, the flexible distance variation coefficient is the maximum value of 1.88, which means that the system has the best running economy but the worst flexibility; when the maximum total running cost is 122603 $, the flexible distance variation coefficient is the minimum value of 0.04, which means that the system has the worst running economy and the best flexibility. In addition, by comparing the schemes 1 and 2 and comprehensively considering the economy and flexibility of the system, the scheme 2 is better than the scheme 1, and the scheme 2 needs to consider the flexibility of the system to improve the comprehensive benefit of the scheme when the novel power distribution network is in optimal operation.
Table 4 shows the economic and flexibility index results for schemes 1-4.
Table 4 novel distribution network optimization run economics and flexibility results
Figure SMS_486
As can be seen from table 4, the overall cost of operation and the flexible distance coefficient of variation for scheme 2 were reduced by 3.75% and 19.15%, respectively, compared to scheme 1; the overall cost of operation and the flexible distance variation coefficient of scheme 3 were reduced by 1.35% and 31.91%, respectively. The solution-free scheme 4 is mainly characterized in that the lower limit of the flexible distance constraint is set to be higher, and the optimal solution is difficult to find in a feasible space by adjusting the distributed flexible source network load resource. While the distributed power supply of scheme 2 operates at a higher cost, the higher distributed power supply consumption helps to reduce system grid losses and power transactions with the main grid. In addition, power distribution network schedulers can autonomously set a flexible distance constraint lower limit according to system flexibility requirements.
The flexible distance situation for each feeder of schemes 1 and 2 is shown in fig. 5. It can be seen that the flexible distances of the feeder lines are different, mainly because the flexible distances depend on the transformer/line capacity, node load and distributed power source output condition, and the flexible distance level of the feeder lines can be influenced by the novel power distribution network source network load coordination operation scheme. For schemes 1 and 2, the flexible distance of the feed lines 2, 6 and 7 varies greatly while the feed lines 12, 14 and 15 remain substantially unchanged. In addition, the flexible distances of the feeder lines 2, 6 and 13 of the scheme 1 are negative, which means that the scheme 1 cannot guarantee the operation safety under the single fault of the system.
In summary, the invention provides system flexibility by utilizing active management measures such as distributed power supply output adjustment, network reconstruction, demand response and the like, digs multi-element distributed flexible source network load resources, and improves the energy management level of the novel power distribution network in a multi-dimensional manner; the operation safety and margin of the feeder line outlet power working point are visually perceived, so that system schedulers can observe the operation situation of the power distribution network and control the power distribution network in a preventive manner, and the flexibility level of the system is quantitatively evaluated by counting the flexible distance of each operation scene and calculating the flexible distance expectation, standard deviation and flexible distance variation coefficient; the invention realizes the overall optimization of the system operation economy and flexibility, and is different from the traditional economy optimization, the invention meters the full link cost of the source network load including the distributed power supply operation, the switching action, the demand response and the like, and simultaneously explores the game equilibrium relation of the economy and flexibility optimization targets.
The method provided by the invention realizes multidimensional optimization of system operation economy and flexibility, and provides active supporting capability for system flexibility improvement by reasonably adjusting flexible source network load resources in the novel power distribution network, and improves operation economy on the premise of ensuring safe operation of the system; in addition, the safety margin of the obtained optimized operation method in each scene is considerable, so that a dispatcher can grasp the operation state of the system and prevent and control the unsafe state, and a theoretical basis is provided for the optimized dispatching of the novel power distribution network.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of various changes or substitutions within the technical scope of the present application, and these should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (9)

1. The novel power distribution network source network load coordination random optimization operation method for flexibility is characterized by comprising the following steps of:
constructing a novel power distribution network flexibility evaluation index system based on a power distribution network safety domain theory, wherein the novel power distribution network flexibility evaluation index system comprises a flexible distance expectation, a standard deviation and a flexible distance variation coefficient;
constructing a multi-objective coordinated optimization operation model for improving system flexibility based on the novel power distribution network flexibility evaluation index system, wherein the multi-objective coordinated optimization operation model is double multi-objective optimization comprising a flexibility objective function and an economical objective function, the flexibility objective function is a minimum flexible distance variation coefficient, and the economical objective function is a minimum total operation cost;
providing constraint conditions of the multi-objective coordinated optimization operation model, wherein the constraint conditions comprise a node power balance equation, a power flow calculation equation, radial operation constraint, network security constraint and active management constraint; converting bilinear terms in a power flow calculation equation into a linear form by using a large M method;
and solving the multi-objective coordinated optimization operation model based on the forward boundary intersection point and the dynamic niche differential evolution algorithm, and outputting an operation simulation result.
2. The flexible-oriented novel power distribution network source network load coordination random optimization operation method is characterized in that the power distribution network security domain theory is defined as a feeder line outlet power working point set on the premise of ensuring the system security operation, and the system security operation comprises line overload prevention and node voltage non-out-of-limit;
the method for constructing the novel power distribution network flexibility evaluation index system specifically comprises the following steps: establishing a distribution network security domain model according to a distribution network security domain theory, analyzing the security boundary geometric characteristics of the distribution network security domain model, and calculating the minimum Euclidean distance from a feeder line outlet power working point to a security boundary to measure the system operation security margin; based on a scene analysis method, analyzing flexible distance expectations, standard deviations and flexible distance variation coefficients of all operation scenes, and further constructing a flexible evaluation index cluster based on the flexible distance expectations, the standard deviations and the flexible distance variation coefficients to form a novel power distribution network flexibility evaluation index system.
3. The flexible-oriented novel power distribution network source-network load coordination random optimization operation method according to claim 2, wherein mathematical expressions corresponding to flexible distance expectations, standard deviations and flexible distance variation coefficients are respectively as follows:
Figure QLYQS_1
；
Figure QLYQS_2
；
Figure QLYQS_3
；
in the method, in the process of the invention,
Figure QLYQS_5
and->
Figure QLYQS_6
Scene +.>
Figure QLYQS_8
Flexible distance expectations and standard deviations; />
Figure QLYQS_9
Is a flexible distance variation coefficient; />
Figure QLYQS_11
For feed line->
Figure QLYQS_13
In scene->
Figure QLYQS_14
A flexible distance below; />
Figure QLYQS_4
And->
Figure QLYQS_7
The number of feeder lines and the number of scenes are respectively;
Figure QLYQS_10
and->
Figure QLYQS_12
Respectively a feeder set and a scene set;
Figure QLYQS_15
；
Figure QLYQS_16
；
in the method, in the process of the invention,
Figure QLYQS_18
for scene->
Figure QLYQS_20
Lower feeder outlet power operating point, +.>
Figure QLYQS_22
For critical operating point on safety boundary +.>
Figure QLYQS_24
For the safety boundary set, ++>
Figure QLYQS_25
Is a security domain; />
Figure QLYQS_27
For the outlet power of the feeder 1, < > for>
Figure QLYQS_29
For the outlet power of the feed line 2, < > for>
Figure QLYQS_30
Is a feeder line
Figure QLYQS_32
Outlet power of>
Figure QLYQS_34
For feed line->
Figure QLYQS_35
Is a power output of the engine; />
Figure QLYQS_37
And->
Figure QLYQS_38
Nodes +.>
Figure QLYQS_39
Voltage and feeder->
Figure QLYQS_40
Current (I)>
Figure QLYQS_17
And
Figure QLYQS_19
nodes +.>
Figure QLYQS_21
Voltage->
Figure QLYQS_23
Lower and upper limits of->
Figure QLYQS_26
And->
Figure QLYQS_28
Are respectively feeder lines->
Figure QLYQS_31
Current->
Figure QLYQS_33
Lower and upper limits of (2); />
Figure QLYQS_36
Is a set of nodes.
4. The flexible-oriented novel power distribution network source-network load coordination random optimization operation method according to claim 1, wherein the total operation cost comprises switching actions, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost, and the corresponding calculation expression is as follows:
Figure QLYQS_41
；
Figure QLYQS_42
；
Figure QLYQS_43
；
Figure QLYQS_44
；
Figure QLYQS_45
；
Figure QLYQS_46
；
in the method, in the process of the invention,
Figure QLYQS_47
、/>
Figure QLYQS_49
、/>
Figure QLYQS_51
、/>
Figure QLYQS_52
、/>
Figure QLYQS_54
and->
Figure QLYQS_56
Respectively is scene->
Figure QLYQS_58
Time below->
Figure QLYQS_59
Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost; />
Figure QLYQS_60
And->
Figure QLYQS_61
The unit switch action and demand response costs are respectively; />
Figure QLYQS_63
、/>
Figure QLYQS_65
And->
Figure QLYQS_67
Respectively at node->
Figure QLYQS_69
The unit operation cost of the micro gas turbine, wind power and photovoltaic; />
Figure QLYQS_71
、/>
Figure QLYQS_72
And->
Figure QLYQS_74
Respectively at node->
Figure QLYQS_76
The unit of micro gas turbine, wind power and photovoltaic actively manages the cost; />
Figure QLYQS_78
And->
Figure QLYQS_80
Respectively is scene->
Figure QLYQS_82
Time below->
Figure QLYQS_84
Unit transaction and loss costs of (a); />
Figure QLYQS_85
For the scene->
Figure QLYQS_87
Time below->
Figure QLYQS_89
Switch->
Figure QLYQS_91
Status (S)>
Figure QLYQS_92
For the scene->
Figure QLYQS_93
The next moment
Figure QLYQS_94
Switch->
Figure QLYQS_95
State, closed 1, open 0; />
Figure QLYQS_96
、/>
Figure QLYQS_48
And->
Figure QLYQS_50
Respectively at node->
Figure QLYQS_53
Micro gas turbine, wind power and photovoltaic in scene->
Figure QLYQS_55
Time below->
Figure QLYQS_57
Is an active force of (a); />
Figure QLYQS_62
For being located at node +.>
Figure QLYQS_64
Is in scene->
Figure QLYQS_66
Time below->
Figure QLYQS_68
Is an active response of (a); />
Figure QLYQS_70
And->
Figure QLYQS_73
Respectively is scene->
Figure QLYQS_75
Time below->
Figure QLYQS_77
The interactive power and the network loss of the (a); />
Figure QLYQS_79
For the scene->
Figure QLYQS_81
Time below->
Figure QLYQS_83
Is a span of (2); />
Figure QLYQS_86
、/>
Figure QLYQS_88
And->
Figure QLYQS_90
Respectively a switch set, a distributed power supply set and a node set.
5. The flexible-oriented novel power distribution network source network load coordination random optimization operation method according to claim 1, wherein the objective function expression of the multi-objective coordination optimization operation model is as follows:
Figure QLYQS_97
；
wherein,,
Figure QLYQS_98
for the total cost of operation->
Figure QLYQS_99
Is a flexible distance variation coefficient;
Figure QLYQS_100
；
in the method, in the process of the invention,
Figure QLYQS_101
the number of running scenes; />
Figure QLYQS_103
、/>
Figure QLYQS_105
、/>
Figure QLYQS_107
、/>
Figure QLYQS_109
、/>
Figure QLYQS_110
And->
Figure QLYQS_111
Respectively is scene->
Figure QLYQS_102
Time below->
Figure QLYQS_104
Switching action, distributed power operation, distributed power active management, transaction with a main network, network loss and demand response cost; />
Figure QLYQS_106
And->
Figure QLYQS_108
A scene set and a time set, respectively.
6. The flexible-oriented novel power distribution network source-network load coordination random optimization operation method according to claim 1, wherein the expression of the node power balance equation is:
Figure QLYQS_112
；
Figure QLYQS_113
；
in the method, in the process of the invention,
Figure QLYQS_130
and->
Figure QLYQS_131
Lines are respectively->
Figure QLYQS_132
Active and reactive power of (a); />
Figure QLYQS_133
And->
Figure QLYQS_134
Lines are respectively->
Figure QLYQS_135
Resistance and reactance of (a);
Figure QLYQS_136
for line->
Figure QLYQS_114
Square of the current; />
Figure QLYQS_116
And->
Figure QLYQS_118
Respectively at node->
Figure QLYQS_121
The active and reactive power outputs are predicted by the distributed power supply; />
Figure QLYQS_123
And->
Figure QLYQS_125
Respectively at node->
Figure QLYQS_127
Active and reactive power is removed from the distributed power supply; />
Figure QLYQS_129
And->
Figure QLYQS_115
Respectively at node->
Figure QLYQS_117
Active and reactive loads predicted by (a); />
Figure QLYQS_119
And->
Figure QLYQS_120
Respectively at node->
Figure QLYQS_122
Active and reactive load response amounts of (a); />
Figure QLYQS_124
And->
Figure QLYQS_126
Respectively is node set and node->
Figure QLYQS_128
A set of interconnected nodes;
the expression of the tide calculation equation is as follows:
Figure QLYQS_137
；
Figure QLYQS_138
；
Figure QLYQS_139
；
in the method, in the process of the invention,
Figure QLYQS_141
for node->
Figure QLYQS_142
Square of the voltage amplitude; />
Figure QLYQS_143
For node->
Figure QLYQS_144
Square of the voltage amplitude; />
Figure QLYQS_145
Is a sufficiently large positive number, taken as 10000; />
Figure QLYQS_146
For switch->
Figure QLYQS_147
State, closed 1, open 0; />
Figure QLYQS_140
Is vector transposition;
the expression of the radial operation constraint is:
Figure QLYQS_148
；
Figure QLYQS_149
；
in the method, in the process of the invention,
Figure QLYQS_150
and->
Figure QLYQS_152
The number of nodes and the number of nodes of the transformer substation are respectively; />
Figure QLYQS_153
For line->
Figure QLYQS_154
Virtual active power of (a);
Figure QLYQS_155
and->
Figure QLYQS_156
Switch set and node respectively->
Figure QLYQS_157
A set of interconnected nodes; />
Figure QLYQS_151
Is a substation node set.
7. The flexible-oriented novel power distribution network source network load coordination random optimization operation method according to claim 1, wherein the network security constraint comprises a flexible distance constraint and a node voltage opportunity constraint, and the expression of the flexible distance constraint is as follows:
Figure QLYQS_158
；
Figure QLYQS_159
；
in the method, in the process of the invention,
Figure QLYQS_161
for feed line->
Figure QLYQS_162
Flexible distance of (2); />
Figure QLYQS_163
For feed line->
Figure QLYQS_165
Is a minimum flexible distance of (2); />
Figure QLYQS_167
And->
Figure QLYQS_169
Are respectively feeder lines->
Figure QLYQS_171
And (ii) of the feeder line>
Figure QLYQS_160
The outlet power is in the super plane->
Figure QLYQS_164
Coefficients of (a); />
Figure QLYQS_166
Is a feeder line stripA number; />
Figure QLYQS_168
For feed line->
Figure QLYQS_170
Is a power output of the engine;
Figure QLYQS_172
and->
Figure QLYQS_173
Respectively a hyperplane set and a feeder line set;
the node voltage opportunity constraint expression is:
Figure QLYQS_174
；
in the method, in the process of the invention,
Figure QLYQS_176
representing probability->
Figure QLYQS_178
A safe confidence level for the node voltage; />
Figure QLYQS_179
For node->
Figure QLYQS_180
Voltage (V)>
Figure QLYQS_181
And->
Figure QLYQS_182
Nodes +.>
Figure QLYQS_183
Voltage->
Figure QLYQS_175
Lower and upper limits of (2); />
Figure QLYQS_177
Is a set of nodes.
8. The flexible-oriented novel power distribution network source-network load coordination random optimization operation method according to claim 1, wherein the active management constraints comprise a switching action constraint, an on-load tap-changing transformer adjustment constraint, a distributed power supply output constraint, a distributed power supply power factor constraint and a demand response constraint;
the expression of the switch action constraint is as follows:
Figure QLYQS_184
；
in the method, in the process of the invention,
Figure QLYQS_186
、/>
Figure QLYQS_187
and->
Figure QLYQS_189
Respectively a switch set, a scene set and a time set; />
Figure QLYQS_191
For the scene->
Figure QLYQS_193
Time below->
Figure QLYQS_195
Switch->
Figure QLYQS_197
Status (S)>
Figure QLYQS_185
For the scene->
Figure QLYQS_188
Time below->
Figure QLYQS_190
Switch->
Figure QLYQS_192
State, closed 1, open 0; />
Figure QLYQS_194
For switch->
Figure QLYQS_196
An upper limit of the number of actions;
the expression of the on-load tap-changing voltage-regulating transformer tap-changing restriction is as follows:
Figure QLYQS_198
；
Figure QLYQS_199
；
Figure QLYQS_200
；
in the method, in the process of the invention,
Figure QLYQS_202
、/>
Figure QLYQS_203
nodes +.>
Figure QLYQS_205
Voltage and node->
Figure QLYQS_207
A voltage; />
Figure QLYQS_209
And->
Figure QLYQS_211
Lines are respectively->
Figure QLYQS_213
The on-load regulating transformer is at moment +.>
Figure QLYQS_201
And time->
Figure QLYQS_204
Tap positions of (2); />
Figure QLYQS_206
And->
Figure QLYQS_208
Lines are respectively->
Figure QLYQS_210
The upper limit and the lower limit of the tap position of the on-load voltage regulating transformer are set; />
Figure QLYQS_212
Step length is adjusted for the voltage of the on-load regulating transformer; />
Figure QLYQS_214
Is an on-load voltage regulating transformer set;
the expression of the distributed power supply output constraint is as follows:
Figure QLYQS_215
；
Figure QLYQS_216
；
in the method, in the process of the invention,
Figure QLYQS_218
the maximum cutting rate is output for the distributed power supply; />
Figure QLYQS_219
For being located at node +.>
Figure QLYQS_221
Is predictive of active power, +.>
Figure QLYQS_223
For being located at node +.>
Figure QLYQS_225
An upper active power output limit is predicted by the distributed power supply; />
Figure QLYQS_226
Is a distributed power supply set; />
Figure QLYQS_227
And->
Figure QLYQS_217
Respectively at node->
Figure QLYQS_220
Active and reactive power is removed from the distributed power supply; />
Figure QLYQS_222
For being located at node +.>
Figure QLYQS_224
A distributed power source output power factor angle;
the expression of the distributed power supply power factor constraint is as follows:
Figure QLYQS_228
；
in the method, in the process of the invention,
Figure QLYQS_229
and->
Figure QLYQS_230
Respectively at node->
Figure QLYQS_231
Upper and lower limits of the distributed power supply output power factor angle;
the expression of the demand response constraint is:
Figure QLYQS_232
；
Figure QLYQS_233
；
in the method, in the process of the invention,
Figure QLYQS_235
is a node set; />
Figure QLYQS_236
And->
Figure QLYQS_237
Respectively at node->
Figure QLYQS_238
Active and reactive load response amounts of (a); />
Figure QLYQS_239
For being located at node +.>
Figure QLYQS_240
An upper active load response amount limit of (2); />
Figure QLYQS_241
For being located at node +.>
Figure QLYQS_234
Is a load power factor angle of (2).
9. The flexible-oriented novel power distribution network source network load coordination random optimization operation method according to claim 1, wherein the method for solving the multi-objective coordination optimization operation model specifically comprises the following steps: the method comprises the steps of inputting a wind-solar-load time sequence curve, a system topological structure and a dynamic niche differential evolution algorithm of a region where a power distribution network is located, randomly generating population individuals, defining fitness functions based on a flexibility objective function and an economy objective function respectively, calculating individual fitness values, retaining elite individuals, eliminating individuals after falling, and updating the population individuals through crossover, selection and mutation operations until a pareto optimal scheme is output.
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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140330441A1 (en) * 2013-05-06 2014-11-06 Sas Institute Inc. Techniques to determine settings for an electrical distribution network
CN104866921A (en) * 2015-05-22 2015-08-26 天津大学 Power distribution system network reconstruction method based on safety domain
CN107358337A (en) * 2017-06-08 2017-11-17 上海电力学院 A kind of active power distribution network planing method based on network reconfiguration
CN108923427A (en) * 2018-08-13 2018-11-30 哈尔滨工程大学 A kind of reconstructing method of the ship power distribution network based on queue intelligent algorithm
CN109560547A (en) * 2019-01-15 2019-04-02 广东电网有限责任公司 A kind of active distribution network N-1 safety evaluation method considering transmission & distribution collaboration
CN110729765A (en) * 2019-08-30 2020-01-24 四川大学 Distribution network flexibility evaluation index system considering SOP and optimal scheduling method
CN111082451A (en) * 2019-09-18 2020-04-28 中国电建集团青海省电力设计院有限公司 Incremental distribution network multi-objective optimization scheduling model based on scene method
CN114117705A (en) * 2021-12-03 2022-03-01 国家电网有限公司 Power distribution information physical system optimization method and system, storage medium and computing equipment
CN115995845A (en) * 2023-02-14 2023-04-21 云南电网有限责任公司 Power distribution network planning method considering distributed power source network coordination control

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140330441A1 (en) * 2013-05-06 2014-11-06 Sas Institute Inc. Techniques to determine settings for an electrical distribution network
CN104866921A (en) * 2015-05-22 2015-08-26 天津大学 Power distribution system network reconstruction method based on safety domain
CN107358337A (en) * 2017-06-08 2017-11-17 上海电力学院 A kind of active power distribution network planing method based on network reconfiguration
CN108923427A (en) * 2018-08-13 2018-11-30 哈尔滨工程大学 A kind of reconstructing method of the ship power distribution network based on queue intelligent algorithm
CN109560547A (en) * 2019-01-15 2019-04-02 广东电网有限责任公司 A kind of active distribution network N-1 safety evaluation method considering transmission & distribution collaboration
CN110729765A (en) * 2019-08-30 2020-01-24 四川大学 Distribution network flexibility evaluation index system considering SOP and optimal scheduling method
CN111082451A (en) * 2019-09-18 2020-04-28 中国电建集团青海省电力设计院有限公司 Incremental distribution network multi-objective optimization scheduling model based on scene method
CN114117705A (en) * 2021-12-03 2022-03-01 国家电网有限公司 Power distribution information physical system optimization method and system, storage medium and computing equipment
CN115995845A (en) * 2023-02-14 2023-04-21 云南电网有限责任公司 Power distribution network planning method considering distributed power source network coordination control

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