CN116029532A - Energy storage planning method for lifting bearing capacity of power distribution network - Google Patents

Energy storage planning method for lifting bearing capacity of power distribution network Download PDF

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CN116029532A
CN116029532A CN202310154969.3A CN202310154969A CN116029532A CN 116029532 A CN116029532 A CN 116029532A CN 202310154969 A CN202310154969 A CN 202310154969A CN 116029532 A CN116029532 A CN 116029532A
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energy storage
distribution network
cost
capacity
planning
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CN116029532B (en
Inventor
李映雪
朱文广
彭怀德
钟士元
刘念
罗路平
郭泉辉
王敏
张雪婷
吴浩
戴奇奇
熊云
王伟
熊宁
宫嘉炜
郑春
孔强
周威
黄晓伟
黄玉晶
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Jiangxi Tengda Electric Power Design Institute Co ltd
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Jiangxi Electric Power Co Ltd
Jiangxi Ganfeng Lienergy Technology Co Ltd
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Jiangxi Tengda Electric Power Design Institute Co ltd
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Jiangxi Electric Power Co Ltd
Jiangxi Ganfeng Lienergy Technology Co Ltd
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    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract

The invention discloses an energy storage planning method for improving the bearing capacity of a power distribution network, which comprises the following steps: calculating the load capacity of the distribution network according to the multidimensional index, calculating the energy storage planning cost according to the investment cost, the running cost and the energy storage degradation cost, and constructing a multi-objective optimization model for the energy storage planning of the distribution network by taking the maximum load capacity and the minimum energy storage planning cost of the distribution network as objective functions, and solving the model to obtain an energy storage planning scheme. The method solves the problems that the operation characteristics of the original distribution network are changed, and the energy storage of the distribution network cannot be planned by the distributed optical storage access distribution network, and establishes the mathematical relationship between the healthy state of the energy storage and the degradation cost coefficient of the energy storage, thereby realizing that the healthy state of the energy storage is considered when the degradation cost of the energy storage of the battery is calculated.

Description

Energy storage planning method for lifting bearing capacity of power distribution network
Technical Field
The invention belongs to the technical field of power automation, and particularly relates to an energy storage planning method for improving the bearing capacity of a power distribution network.
Background
Because of inherent volatility and randomness characteristics of distributed photovoltaic, high-proportion access of the distributed photovoltaic is safe and stable to the operation of a power distribution network. The distributed energy storage can effectively stabilize the strong randomness of the distributed photovoltaic, and the collaborative development of the distributed photovoltaic becomes a necessary choice for promoting the digestion of the distributed photovoltaic. But the operation characteristics of the original power distribution network are changed by the distributed optical storage access power distribution network, and the difficulty of power distribution network energy storage planning is increased. Therefore, research on an energy storage planning method for improving the bearing capacity of the power distribution network is urgently needed.
Disclosure of Invention
The invention provides an energy storage planning method for improving the bearing capacity of a power distribution network, which is used for solving the technical problem that the operation characteristics of the original power distribution network are changed by a distributed optical storage access power distribution network, and the energy storage of the power distribution network cannot be planned.
The invention provides an energy storage planning method for improving the bearing capacity of a power distribution network, which comprises the following steps:
step 1, calculating the distribution network bearing capacity according to multidimensional indexes, wherein the expression for calculating the distribution network bearing capacity is as follows:
Figure SMS_1
，
in the method, in the process of the invention,
Figure SMS_2
for the distribution network bearing capacity, <' > the load>
Figure SMS_3
Is a voltage qualification rate index, < >>
Figure SMS_4
Is the voltage fluctuation index>
Figure SMS_5
Is a harmonic distortion index->
Figure SMS_6
Is a line loss rate index->
Figure SMS_7
The maximum load rate index of the line is used;
step 2, calculating energy storage planning cost according to investment cost, operation cost and energy storage degradation cost, wherein the expression for calculating the energy storage planning cost is as follows:
Figure SMS_8
，
in the method, in the process of the invention,
Figure SMS_9
planning costs for energy storage>
Figure SMS_10
For the investment cost of energy storage->
Figure SMS_11
For the energy storage operating costs>
Figure SMS_12
Is the energy storage degradation cost;
the expression for calculating the energy storage degradation cost is as follows:
Figure SMS_13
，
in the method, in the process of the invention,
Figure SMS_14
for the degenerated cost factor, < >>
Figure SMS_15
For maximum operating power of stored energy->
Figure SMS_16
To plan the total years->
Figure SMS_17
Planning a year for an nth;
and 3, constructing a multi-objective optimization model for energy storage planning of the distribution network by taking the maximum bearing capacity of the distribution network and the minimum energy storage planning cost as objective functions, and solving the multi-objective optimization model for energy storage planning of the distribution network to obtain an energy storage planning scheme.
Further, in step 1, the multidimensional index includes a voltage qualification rate index, a voltage fluctuation index, a harmonic distortion rate index, a line loss rate index, and a line maximum load rate index;
the expression for calculating the voltage qualification rate index is as follows:
Figure SMS_18
，
in the method, in the process of the invention,
Figure SMS_19
for the number of nodes in the distribution network meeting the voltage level requirement, < >>
Figure SMS_20
The total number of nodes meeting the voltage level requirement in the power distribution network is calculated;
the expression for calculating the voltage fluctuation index is as follows:
Figure SMS_21
，
in the method, in the process of the invention,
Figure SMS_22
for the voltage level before the energy storage is switched on, +.>
Figure SMS_23
For the voltage level after energy storage switch-on, +.>
Figure SMS_24
To optimize the time interval;
the expression for calculating the harmonic distortion index is as follows:
Figure SMS_25
，
in the method, in the process of the invention,
Figure SMS_26
is the highest order harmonic->
Figure SMS_27
For fundamental voltage, +.>
Figure SMS_28
Is +.>
Figure SMS_29
A second harmonic;
the expression for calculating the line loss rate index is as follows:
Figure SMS_30
，
in the method, in the process of the invention,
Figure SMS_31
is->
Figure SMS_32
Current amplitude of branch,/, and>
Figure SMS_33
is->
Figure SMS_34
Resistance of branch, ">
Figure SMS_35
Supply power value for line->
Figure SMS_36
Is a branch collection;
the expression for calculating the maximum load rate index of the line is as follows:
Figure SMS_37
，
in the method, in the process of the invention,
Figure SMS_38
is the average load.
Further, in step 2, the expression for calculating the degradation cost coefficient is:
Figure SMS_39
，
in the method, in the process of the invention,
Figure SMS_41
for the degradation cost coefficient of the i-th stage, +.>
Figure SMS_50
Investment cost for unit capacity of battery, +.>
Figure SMS_52
For battery capacity>
Figure SMS_42
To design battery life, +.>
Figure SMS_44
Is the multiplying factor of the battery, +.>
Figure SMS_47
For the discharge coefficient>
Figure SMS_49
For rated discharge power, < >>
Figure SMS_40
Maximum value of SOH of battery at i-th stage of life cycle, +.>
Figure SMS_48
Is the minimum value of SOH of the battery at the i-th stage of the battery life cycle, +.>
Figure SMS_51
For the rated depth of discharge of the stored energy +.>
Figure SMS_53
Fitting coefficients for the model +.>
Figure SMS_43
Fitting coefficients for the model +.>
Figure SMS_45
For energy storage health status->
Figure SMS_46
The nth year is planned.
Further, in step 2, the expression for calculating the energy storage investment cost is:
Figure SMS_54
，
in the method, in the process of the invention,
Figure SMS_55
for the rate of discount, add>
Figure SMS_56
To plan the total years->
Figure SMS_57
Investment cost for energy storage unit energy capacity, +.>
Figure SMS_58
Investment cost for energy storage unit power capacity, +.>
Figure SMS_59
For energy storage capacity, +.>
Figure SMS_60
Is the energy storage power capacity;
the expression for calculating the energy storage operation cost is as follows:
Figure SMS_61
，
in the method, in the process of the invention,
Figure SMS_62
for the energy storage operating cost factor>
Figure SMS_63
Is the stored energy maximum operating power.
Further, in step 3, the objective function of the multi-objective optimization model for energy storage planning of the distribution network is:
Figure SMS_64
，
in the method, in the process of the invention,
Figure SMS_65
for the objective function of maximum load capacity of the distribution network, +.>
Figure SMS_66
The method is an objective function with minimum economic cost of the power distribution network;
the constraint conditions of the distribution network energy storage planning multi-objective optimization model comprise site selection constraint and capacity constraint, and the expression of the site selection constraint is as follows:
Figure SMS_67
，
in the method, in the process of the invention,
Figure SMS_68
a variable 0-1, indicating that no energy storage is provided, ">
Figure SMS_69
Representing configuration energy storage;
the capacity constraint is expressed as:
Figure SMS_70
，
in the method, in the process of the invention,
Figure SMS_71
for storing energy, the%>
Figure SMS_72
For maximum discharge rate +.>
Figure SMS_73
Is the maximum capacity of stored energy.
Further, in step 3, the solving the multi-objective optimization model of the energy storage planning of the distribution network to obtain an energy storage planning scheme includes:
building a Stackelberg game form of a distribution network energy storage multi-target planning model:
Figure SMS_74
，
in the method, in the process of the invention,
Figure SMS_75
、
Figure SMS_76
the target function with the largest bearing capacity of the distribution network and the target function with the smallest economic cost of the distribution network are respectively +.>
Figure SMS_77
、
Figure SMS_78
Policy set for objective function with maximum load capacity of distribution network and policy set for objective function with minimum economic cost of distribution network are respectively +.>
Figure SMS_79
Are all S 1 Is->
Figure SMS_80
Are all S 2 Is a game strategy of (2);
the method comprises the following steps of calculating a target function game party strategy set:
objective function with maximum bearing capacity for distribution network
Figure SMS_82
And an objective function with minimum cost of power distribution network economy +.>
Figure SMS_84
Performing multi-objective optimization to obtain optimized solution of each objective>
Figure SMS_86
And->
Figure SMS_83
Wherein->
Figure SMS_85
，
Figure SMS_87
，
Figure SMS_88
Are all S 1 Optimal gaming strategy,/->
Figure SMS_81
Are all S 2 Is a game strategy;
objective function with maximum load bearing capacity for power distribution network
Figure SMS_89
Arbitrary design variable +.>
Figure SMS_90
Step length +.>
Figure SMS_91
Equally divided into T sections>
Figure SMS_92
Design variable->
Figure SMS_93
Objective function with minimum cost for power distribution network economy>
Figure SMS_94
Influence factor of->
Figure SMS_95
The method comprises the following steps:
Figure SMS_96
，
in the middle of
Figure SMS_97
、
Figure SMS_98
Is F 1 Is set for the optimum design variables of the model;
let the classified sample be
Figure SMS_99
，
Figure SMS_102
Is arbitrary +>
Figure SMS_106
Objective function of the individual design variables for maximum load capacity of the distribution network>
Figure SMS_100
And an objective function with minimum cost of power distribution network economy +.>
Figure SMS_104
Is to classify the whole sample into
Figure SMS_105
，
Figure SMS_107
For the nth classified sample, fuzzy clustering is carried out on the classified samples, and the design variable set X is classified into a strategy set of each game party +.>
Figure SMS_101
And policy set->
Figure SMS_103
；
Assuming an objective function with maximum load capacity of the distribution network
Figure SMS_108
Optimizing preferentially for game leader, objective function with minimum economic cost of distribution network ∈>
Figure SMS_109
For the follower of the game, according to the objective function of maximum load capacity of the distribution network +.>
Figure SMS_110
Policy set->
Figure SMS_111
And policy set->
Figure SMS_112
Through multi-round game optimization, a game equilibrium solution is finally obtained, and an objective function with the maximum bearing capacity of the power distribution network is calculated and obtained>
Figure SMS_113
And an objective function with minimum cost of power distribution network economy +.>
Figure SMS_114
And (3) obtaining the energy storage planning scheme.
According to the energy storage planning method for improving the bearing capacity of the power distribution network, the bearing capacity of the power distribution network is calculated according to the multidimensional index, the energy storage planning cost is calculated according to the investment cost, the operation cost and the energy storage degradation cost, a multi-objective optimization model for the energy storage planning of the power distribution network is built by taking the maximum bearing capacity of the power distribution network and the minimum energy storage planning cost as objective functions, the model is solved to obtain an energy storage planning scheme, the problem that the operation characteristics of the original power distribution network are changed and the energy storage of the power distribution network cannot be planned by distributed optical storage access power distribution network is solved, and the mathematical relationship between the energy storage health state and the energy storage degradation cost coefficient is established, so that the health state of the energy storage is considered when the energy storage degradation cost of a battery is calculated.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an energy storage planning method for improving a load bearing capacity of a power distribution network according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. 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.
Referring to fig. 1, a flowchart of an energy storage planning method for improving a load bearing capacity of a power distribution network is shown.
As shown in fig. 1, the energy storage planning method for improving the bearing capacity of the power distribution network specifically includes the following steps:
and step 1, calculating the distribution network bearing capacity according to the multidimensional index.
In this embodiment, the multidimensional index includes a voltage qualification rate index, a voltage fluctuation index, a harmonic distortion rate index, a line loss rate index, and a line maximum load rate index;
the voltage level qualification rate is a ratio of the number of nodes meeting the voltage level requirement in an assigned power grid to the total number of nodes in the power distribution network, and is used for evaluating whether the voltage level of the power distribution network reaches a technical reasonable level after the electric vehicle charging station is accessed, and the expression for calculating the voltage qualification rate index is as follows:
Figure SMS_115
，（1）
in the method, in the process of the invention,
Figure SMS_116
for the number of nodes in the distribution network meeting the voltage level requirement, < >>
Figure SMS_117
The total number of nodes meeting the voltage level requirement in the power distribution network is calculated;
the voltage fluctuation refers to the change of the voltage amplitude of the same node in two adjacent sampling periods, and the expression for calculating the voltage fluctuation index is as follows:
Figure SMS_118
，（2）
in the method, in the process of the invention,
Figure SMS_119
for the voltage level before the energy storage is switched on, +.>
Figure SMS_120
For the voltage level after energy storage switch-on, +.>
Figure SMS_121
To optimize the time interval;
the harmonic distortion rate reflects the proportion of higher harmonic waves of voltage relative to fundamental waves, and the expression for calculating the harmonic distortion rate index is as follows:
Figure SMS_122
，（3）
in the method, in the process of the invention,
Figure SMS_123
is the highest order harmonic->
Figure SMS_124
For fundamental voltage, +.>
Figure SMS_125
Is +.>
Figure SMS_126
A second harmonic;
the line loss rate refers to the percentage of active power loss of a feeder line to the input power of the initial end of the feeder line, and the expression for calculating the line loss rate index is as follows:
Figure SMS_127
，（4）
in the method, in the process of the invention,
Figure SMS_128
is->
Figure SMS_129
Current amplitude of branch,/, and>
Figure SMS_130
is->
Figure SMS_131
Resistance of branch, ">
Figure SMS_132
Supply power value for line->
Figure SMS_133
Is a branch collection;
the maximum load rate of the line is the ratio of the maximum load of the line to the maximum transmission active power of the line, and the expression of calculating the maximum load rate index of the line is as follows:
Figure SMS_134
，（5）
in the method, in the process of the invention,
Figure SMS_135
is the average load;
specifically, the expression for calculating the distribution network bearing capacity is:
Figure SMS_136
，（6）
in the method, in the process of the invention,
Figure SMS_137
for the distribution network bearing capacity, <' > the load>
Figure SMS_138
Is a voltage qualification rate index, < >>
Figure SMS_139
Is the voltage fluctuation index>
Figure SMS_140
Is a harmonic distortion index->
Figure SMS_141
Is a line loss rate index->
Figure SMS_142
Is the maximum load rate index of the line.
And 2, calculating energy storage planning cost according to the investment cost, the operation cost and the energy storage degradation cost.
In this embodiment, the expression for calculating the energy storage planning cost is:
Figure SMS_143
，（7）
in the method, in the process of the invention,
Figure SMS_144
planning costs for energy storage>
Figure SMS_145
For the investment cost of energy storage->
Figure SMS_146
For the energy storage operating costs>
Figure SMS_147
Is the energy storage degradation cost;
the expression for calculating the energy storage investment cost is as follows:
Figure SMS_148
，（8）
in the method, in the process of the invention,
Figure SMS_149
for the rate of discount, add>
Figure SMS_150
To plan the year->
Figure SMS_151
Investment cost for energy storage unit energy capacity, +.>
Figure SMS_152
Investment cost for energy storage unit power capacity, +.>
Figure SMS_153
For energy storage capacity, +.>
Figure SMS_154
Is the energy storage power capacity; />
The expression for calculating the energy storage operation cost is:
Figure SMS_155
，（9）
in the method, in the process of the invention,
Figure SMS_156
for the energy storage operating cost factor>
Figure SMS_157
Is the stored energy maximum operating power.
The expression for calculating the energy storage degradation cost is:
Figure SMS_158
，（10）
in the method, in the process of the invention,
Figure SMS_159
for the degenerated cost factor, < >>
Figure SMS_160
For maximum operating power of stored energy->
Figure SMS_161
To plan the total years->
Figure SMS_162
Planning a year for an nth;
it should be noted that the state of health of a battery is characterized by the ratio of the remaining cycle life of the battery to the cycle life of a new battery of the same type. The actual cycle life of the battery and the DoD satisfy the following relationship.
Figure SMS_163
，（11）
Figure SMS_164
，（12）
In the method, in the process of the invention,
Figure SMS_166
for the actual cycle life of the battery, < > for>
Figure SMS_169
And->
Figure SMS_173
DoD and discharge power under standard cycle conditions respectively,
Figure SMS_165
for cycle life under standard cycle conditions, +.>
Figure SMS_168
、
Figure SMS_171
Are model fitting coefficients +.>
Figure SMS_174
For the actual depth of discharge,
Figure SMS_167
for the battery discharge coefficient, < >>
Figure SMS_170
Is the average charge power or discharge power, +.>
Figure SMS_172
For battery capacity>
Figure SMS_175
Is a correction coefficient.
In the process of charging and discharging the battery, an approximate proportional relationship exists between the discharging power and the current, and a linear relationship exists between the discharging current and the cycle life of the battery:
Figure SMS_176
，（13）
in the method, in the process of the invention,
Figure SMS_177
is the battery multiplying power coefficient.
By taking equations (12) - (13) into equation (11), the mathematical expression of the actual cycle life of the battery can be derived as:
Figure SMS_178
，（14）
under normal conditions, the remaining capacity of the battery gradually decays. The degradation cost per charge/discharge power of the battery at different health stages can be represented by the formulas (15) - (16), the degradation cost per charge/discharge power at each stage being a function of the DoD at that stage.
Figure SMS_179
，（15）
Figure SMS_180
，（16）
In the method, in the process of the invention,
Figure SMS_181
for the degradation cost coefficient of the ith stage, < +.>
Figure SMS_182
Investment cost for unit capacity of battery, +.>
Figure SMS_183
For battery capacity>
Figure SMS_184
For depth of discharge +.>
Figure SMS_185
The actual cycle life of the battery for the i-th stage;
depending on battery characteristics, doD is severely limited by battery capacity, which is related to battery degradation. It can thus be concluded that the maximum DoD of the battery at different phases of health is a function of the degradation coefficient of the battery.
Figure SMS_186
，（17）
Figure SMS_187
，（18）
In the method, in the process of the invention,
Figure SMS_188
for maximum storage capacity of ES, +.>
Figure SMS_189
For the battery degradation coefficient, < >>
Figure SMS_190
Is the maximum depth of discharge.
In addition, SOH is the ratio of the maximum capacity of the battery to the rated capacity at each stage of health, according to the definition of SOH of the battery.
Figure SMS_191
，（19）
In the method, in the process of the invention,
Figure SMS_192
is the energy storage health state of the ith stage;
the relationship between the maximum DoD and SOH of the battery can be obtained by the formulas (17) - (19).
Figure SMS_193
，（20）
In the method, in the process of the invention,
Figure SMS_194
is the maximum depth of discharge of the i-th stage;
in the initial health stage, the battery is in the optimal health state, the capacity is large, the deep discharge potential is large, and the degradation cost coefficient of the battery in the initial health stage is higher than that in the later health stage. As the battery continues to run, the capacity of the battery begins to drop and the maximum DOD becomes smaller, reducing the degradation cost of the battery at the later stages of the life cycle. Thus, the maximum DoD can be used to clearly distinguish the degradation of the battery at different stages of the life cycle.
Figure SMS_195
，（21）
The mean value of SOH is expressed as the median of the maximum and minimum SOH for each health phase.
Figure SMS_196
，（22）
In the method, in the process of the invention,
Figure SMS_197
maximum value of SOH of battery at i-th stage of life cycle, +.>
Figure SMS_198
Is the minimum value of the SOH of the battery at the i-th stage of the battery life cycle.
Equation (21) -equation (22) is a joint modeling, and the degradation cost coefficient of each health stage ES can be obtained.
Figure SMS_199
，（23）
In the method, in the process of the invention,
Figure SMS_201
is the battery degradation cost coefficient of the ith stage, < +.>
Figure SMS_204
Is the investment cost per unit capacity of the battery,
Figure SMS_208
for battery capacity>
Figure SMS_203
To design battery life, +.>
Figure SMS_207
Is the multiplying factor of the battery, +.>
Figure SMS_210
For the discharge coefficient>
Figure SMS_212
For rated discharge power, < >>
Figure SMS_200
Maximum value of SOH of battery at i-th stage of life cycle, +.>
Figure SMS_206
Is the minimum value of SOH of the battery at the i-th stage of the battery life cycle, +.>
Figure SMS_209
For the rated depth of discharge of the stored energy +.>
Figure SMS_211
、
Figure SMS_202
Are model fitting coefficients +.>
Figure SMS_205
Is the energy storage health state of the ith stage.
And 3, constructing a multi-objective optimization model for energy storage planning of the distribution network by taking the maximum bearing capacity of the distribution network and the minimum energy storage planning cost as objective functions, and solving the multi-objective optimization model for energy storage planning of the distribution network to obtain an energy storage planning scheme.
In the embodiment, two factors of the distribution network bearing capacity after energy storage access and the economy of energy storage planning are comprehensively considered, and a distribution network energy storage planning multi-objective optimization model with the largest bearing capacity and the smallest economic cost is constructed. The objective function of the distribution network energy storage planning multi-objective optimization model is as follows:
Figure SMS_213
，（24）
in the method, in the process of the invention,
Figure SMS_214
for the objective function of maximum load capacity of the distribution network, +.>
Figure SMS_215
The method is an objective function with minimum economic cost of the power distribution network;
the constraint conditions of the distribution network energy storage planning multi-objective optimization model comprise site selection constraint and capacity constraint, wherein the expression of the site selection constraint is as follows:
Figure SMS_216
，（25）
in the method, in the process of the invention,
Figure SMS_217
is 0-1 variable, ">
Figure SMS_218
Indicating that no energy storage is configured, ">
Figure SMS_219
Representing configuration energy storage
The capacity constraint is expressed as:
Figure SMS_220
，（26）
in the method, in the process of the invention,
Figure SMS_221
for storing energy, the%>
Figure SMS_222
For maximum discharge rate +.>
Figure SMS_223
Is the maximum capacity of stored energy.
It should be noted that, for the constructed multi-objective optimization model of the energy storage planning of the distribution network, a multi-objective Stackelberg Game solving algorithm (Multiple Objectives-Stackelberg Game, MO-SG) is adopted for solving, including:
building a Stackelberg game form of a distribution network energy storage multi-target planning model:
Figure SMS_224
，
in the method, in the process of the invention,
Figure SMS_225
、
Figure SMS_226
the target function with the largest bearing capacity of the distribution network and the target function with the smallest economic cost of the distribution network are respectively +.>
Figure SMS_227
、
Figure SMS_228
Policy set for objective function with maximum load capacity of distribution network and policy set for objective function with minimum economic cost of distribution network are respectively +.>
Figure SMS_229
Are all S 1 Is->
Figure SMS_230
Are all S 2 Is a game strategy of (2);
the method comprises the following steps of calculating a target function game party strategy set:
objective function with maximum bearing capacity for distribution network
Figure SMS_232
And an objective function with minimum cost of power distribution network economy +.>
Figure SMS_235
Performing multi-objective optimization to obtain optimized solution of each objective>
Figure SMS_236
And->
Figure SMS_233
Wherein->
Figure SMS_234
，
Figure SMS_237
，
Figure SMS_238
Are all S 1 Optimal gaming strategy,/->
Figure SMS_231
Are all S 2 Is a game strategy;
objective function with maximum load bearing capacity for power distribution network
Figure SMS_239
Arbitrary design variable +.>
Figure SMS_240
Step length +.>
Figure SMS_241
Equally divided into T sections>
Figure SMS_242
Design variable->
Figure SMS_243
Objective function with minimum cost for power distribution network economy>
Figure SMS_244
Influence factor of->
Figure SMS_245
The method comprises the following steps:
Figure SMS_246
，
in the method, in the process of the invention,
Figure SMS_247
、
Figure SMS_248
are all F 1 Is set for the optimum design variables of the model;
let the classified sample be
Figure SMS_250
，
Figure SMS_252
Is arbitrary +>
Figure SMS_255
Objective function of the individual design variables for maximum load capacity of the distribution network>
Figure SMS_251
And an objective function with minimum cost of power distribution network economy +.>
Figure SMS_254
Is to classify the whole sample into
Figure SMS_256
，
Figure SMS_257
For the nth classified sample, fuzzy clustering is carried out on the classified samples, and the design variable set X is classified into a strategy set of each game party +.>
Figure SMS_249
And policy set->
Figure SMS_253
；
Assuming an objective function with maximum load capacity of the distribution network
Figure SMS_258
Optimizing preferentially for game leader, objective function with minimum economic cost of distribution network ∈>
Figure SMS_259
For the follower of the game, according to the objective function of maximum load capacity of the distribution network +.>
Figure SMS_260
Policy set->
Figure SMS_261
And policy set->
Figure SMS_262
Through multi-round game optimization, a game equilibrium solution is finally obtained, and an objective function with the maximum bearing capacity of the power distribution network is calculated and obtained>
Figure SMS_263
And an objective function with minimum cost of power distribution network economy +.>
Figure SMS_264
And (3) obtaining the energy storage planning scheme.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. An energy storage planning method for improving bearing capacity of a power distribution network is characterized by comprising the following steps:
step 1, calculating the distribution network bearing capacity according to multidimensional indexes, wherein the expression for calculating the distribution network bearing capacity is as follows:
Figure QLYQS_1
，
in the method, in the process of the invention,
Figure QLYQS_2
for the distribution network bearing capacity, <' > the load>
Figure QLYQS_3
Is a voltage qualification rate index, < >>
Figure QLYQS_4
Is the voltage fluctuation index>
Figure QLYQS_5
Is a harmonic distortion index->
Figure QLYQS_6
Is a line loss rate index->
Figure QLYQS_7
The maximum load rate index of the line is used;
step 2, calculating energy storage planning cost according to investment cost, operation cost and energy storage degradation cost, wherein the expression for calculating the energy storage planning cost is as follows:
Figure QLYQS_8
，
in the method, in the process of the invention,
Figure QLYQS_9
planning costs for energy storage>
Figure QLYQS_10
For the investment cost of energy storage->
Figure QLYQS_11
For the energy storage operating costs>
Figure QLYQS_12
Is the energy storage degradation cost;
the expression for calculating the energy storage degradation cost is as follows:
Figure QLYQS_13
，
in the method, in the process of the invention,
Figure QLYQS_14
for the degenerated cost factor, < >>
Figure QLYQS_15
For maximum operating power of stored energy->
Figure QLYQS_16
To plan the total years->
Figure QLYQS_17
Planning a year for an nth;
and 3, constructing a multi-objective optimization model for energy storage planning of the distribution network by taking the maximum bearing capacity of the distribution network and the minimum energy storage planning cost as objective functions, and solving the multi-objective optimization model for energy storage planning of the distribution network to obtain an energy storage planning scheme.
2. The energy storage planning method for improving bearing capacity of a power distribution network according to claim 1, wherein in step 1, the multidimensional index includes a voltage qualification rate index, a voltage fluctuation index, a harmonic distortion rate index, a line loss rate index and a line maximum load rate index;
the expression for calculating the voltage qualification rate index is as follows:
Figure QLYQS_18
，
in the method, in the process of the invention,
Figure QLYQS_19
for the number of nodes in the distribution network meeting the voltage level requirement, < >>
Figure QLYQS_20
The total number of nodes meeting the voltage level requirement in the power distribution network is calculated;
the expression for calculating the voltage fluctuation index is as follows:
Figure QLYQS_21
，
in the method, in the process of the invention,
Figure QLYQS_22
for the voltage level before the energy storage is switched on, +.>
Figure QLYQS_23
For the voltage level after energy storage switch-on, +.>
Figure QLYQS_24
To optimize the time interval;
the expression for calculating the harmonic distortion index is as follows:
Figure QLYQS_25
，
in the method, in the process of the invention,
Figure QLYQS_26
is the highest order harmonic->
Figure QLYQS_27
For fundamental voltage, +.>
Figure QLYQS_28
Is +.>
Figure QLYQS_29
A second harmonic;
the expression for calculating the line loss rate index is as follows:
Figure QLYQS_30
，
in the method, in the process of the invention,
Figure QLYQS_31
is->
Figure QLYQS_32
Current amplitude of branch,/, and>
Figure QLYQS_33
is->
Figure QLYQS_34
Resistance of branch, ">
Figure QLYQS_35
For the supply power value of the line,
Figure QLYQS_36
is a branch collection;
the expression for calculating the maximum load rate index of the line is as follows:
Figure QLYQS_37
，
in the method, in the process of the invention,
Figure QLYQS_38
is the average load.
3. The energy storage planning method for improving the bearing capacity of a power distribution network according to claim 1, wherein in the step 2, the expression for calculating the degradation cost coefficient is as follows,
Figure QLYQS_39
，
in the method, in the process of the invention,
Figure QLYQS_41
for the degradation cost coefficient of the i-th stage, +.>
Figure QLYQS_44
Investment cost for unit capacity of battery, +.>
Figure QLYQS_49
For battery capacity>
Figure QLYQS_43
To design battery life, +.>
Figure QLYQS_47
Is the multiplying factor of the battery, +.>
Figure QLYQS_50
For the discharge coefficient>
Figure QLYQS_52
For the rated discharge power to be the same,
Figure QLYQS_40
maximum value of SOH of battery at i-th stage of life cycle, +.>
Figure QLYQS_45
Is the minimum value of SOH of the battery at the i-th stage of the battery life cycle, +.>
Figure QLYQS_51
For the rated depth of discharge of the stored energy +.>
Figure QLYQS_53
Fitting coefficients for the model +.>
Figure QLYQS_42
The coefficients are fitted to the model and,
Figure QLYQS_46
for energy storage health status->
Figure QLYQS_48
The nth year is planned.
4. The energy storage planning method for improving bearing capacity of power distribution network according to claim 1, wherein in step 2, the expression for calculating the energy storage investment cost is:
Figure QLYQS_54
，
in the method, in the process of the invention,
Figure QLYQS_55
for the rate of discount, add>
Figure QLYQS_56
To plan the total years->
Figure QLYQS_57
Investment cost for energy storage unit energy capacity, +.>
Figure QLYQS_58
Investment cost for energy storage unit power capacity, +.>
Figure QLYQS_59
For energy storage capacity, +.>
Figure QLYQS_60
Is the energy storage power capacity;
the expression for calculating the energy storage operation cost is as follows:
Figure QLYQS_61
，
in the method, in the process of the invention,
Figure QLYQS_62
for the energy storage operating cost factor>
Figure QLYQS_63
Is the stored energy maximum operating power.
5. The energy storage planning method for improving bearing capacity of power distribution network according to claim 1, wherein in step 3, an objective function of the multi-objective optimization model for energy storage planning of the power distribution network is:
Figure QLYQS_64
，
in the method, in the process of the invention,
Figure QLYQS_65
for the objective function of maximum load capacity of the distribution network, +.>
Figure QLYQS_66
The method is an objective function with minimum economic cost of the power distribution network;
the constraint conditions of the distribution network energy storage planning multi-objective optimization model comprise site selection constraint and capacity constraint, and the expression of the site selection constraint is as follows:
Figure QLYQS_67
，
in the method, in the process of the invention,
Figure QLYQS_68
is 0-1 variable, ">
Figure QLYQS_69
Indicating that no energy storage is configured, ">
Figure QLYQS_70
Representing configuration energy storage;
the capacity constraint is expressed as:
Figure QLYQS_71
，
in the method, in the process of the invention,
Figure QLYQS_72
for storing energy, the%>
Figure QLYQS_73
For maximum discharge rate +.>
Figure QLYQS_74
Is the maximum capacity of stored energy.
6. The energy storage planning method for improving the bearing capacity of a power distribution network according to claim 1, wherein in step 3, the solving the multi-objective optimization model of the energy storage planning of the power distribution network to obtain an energy storage planning scheme includes:
building a Stackelberg game form of a distribution network energy storage multi-target planning model:
Figure QLYQS_75
，
in the method, in the process of the invention,
Figure QLYQS_76
、
Figure QLYQS_77
the target function with the largest bearing capacity of the distribution network and the target function with the smallest economic cost of the distribution network are respectively +.>
Figure QLYQS_78
、
Figure QLYQS_79
Policy set for objective function with maximum load capacity of distribution network and policy set for objective function with minimum economic cost of distribution network are respectively +.>
Figure QLYQS_80
Are all S 1 Is->
Figure QLYQS_81
Are all S 2 Is a game strategy of (2); />
The method comprises the following steps of calculating a target function game party strategy set:
target with maximum bearing capacity for distribution networkFunction of
Figure QLYQS_83
And an objective function with minimum cost of power distribution network economy +.>
Figure QLYQS_86
Performing multi-objective optimization to obtain optimized solution of each objective>
Figure QLYQS_88
And->
Figure QLYQS_84
Wherein->
Figure QLYQS_85
，
Figure QLYQS_87
，
Figure QLYQS_89
Are all S 1 Optimal gaming strategy,/->
Figure QLYQS_82
Are all S 2 Is a game strategy;
objective function with maximum load bearing capacity for power distribution network
Figure QLYQS_90
Arbitrary design variable +.>
Figure QLYQS_91
Step length +.>
Figure QLYQS_92
Equally divided into T sections>
Figure QLYQS_93
Design variable->
Figure QLYQS_94
Objective function with minimum cost for power distribution network economy>
Figure QLYQS_95
Influence factor of->
Figure QLYQS_96
The method comprises the following steps:
Figure QLYQS_97
，
in the method, in the process of the invention,
Figure QLYQS_98
、
Figure QLYQS_99
are all F 1 Is set for the optimum design variables of the model;
let the classified sample be
Figure QLYQS_101
，
Figure QLYQS_103
Is arbitrary +>
Figure QLYQS_106
Objective function with maximum load capacity of distribution network by using design variables
Figure QLYQS_102
And an objective function with minimum cost of power distribution network economy +.>
Figure QLYQS_104
Is classified into +.>
Figure QLYQS_107
，
Figure QLYQS_108
For the nth classified sample, fuzzy clustering is carried out on the classified samples, and the design variable set X is classified into a strategy set of each game party +.>
Figure QLYQS_100
And policy set->
Figure QLYQS_105
；
Assuming an objective function with maximum load capacity of the distribution network
Figure QLYQS_109
Optimizing preferentially for game leader, objective function with minimum economic cost of distribution network ∈>
Figure QLYQS_110
For the follower of the game, according to the objective function of maximum load capacity of the distribution network +.>
Figure QLYQS_111
Policy set->
Figure QLYQS_112
And policy set->
Figure QLYQS_113
Through multi-round game optimization, a game equilibrium solution is finally obtained, and an objective function with the maximum bearing capacity of the power distribution network is calculated and obtained>
Figure QLYQS_114
And an objective function with minimum cost of power distribution network economy +.>
Figure QLYQS_115
And (3) obtaining the energy storage planning scheme. />
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