CN116436099A - Micro-grid robust optimal scheduling method and system - Google Patents

Micro-grid robust optimal scheduling method and system Download PDF

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CN116436099A
CN116436099A CN202310687315.7A CN202310687315A CN116436099A CN 116436099 A CN116436099 A CN 116436099A CN 202310687315 A CN202310687315 A CN 202310687315A CN 116436099 A CN116436099 A CN 116436099A
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CN116436099B (en
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于浩
林岩
刘雨佳
王亚超
郭霄宇
左秀江
王皓
王庆彬
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Inner Mongolia Hmhj Aluminum Electricity Co ltd
Beijing Herui Energy Storage Technology Co ltd
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Beijing Herui Energy Storage Technology Co ltd
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Abstract

The invention discloses a robust optimal scheduling method and a robust optimal scheduling system for a micro-grid, wherein the method comprises the steps of modeling each device in the micro-grid system to obtain a mathematical model or a constraint model of each device in the micro-grid system; obtaining a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system; acquiring an objective function of optimal scheduling of the micro-grid system according to a carbon transaction cost model of the micro-grid system; constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device of the micro-grid system; and constructing an uncertain set according to uncertain variables in the micro-grid system, and solving the two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result. The carbon capture equipment is considered in the running of the micro-grid, and a carbon transaction mechanism is introduced, so that carbon emission generated by the running of the micro-grid is effectively reduced.

Description

Micro-grid robust optimal scheduling method and system
Technical Field
The invention relates to the technical field of energy optimization scheduling, in particular to a micro-grid robust optimization scheduling method and system.
Background
In order to overcome the defects of high construction cost, high operation and maintenance difficulty and the like of a large-scale power grid, a micro-grid is generated, a distributed power supply close to a load is adopted for supplying power, the power generation cost is reduced, the energy utilization rate is improved, however, renewable energy sources (such as wind power, photovoltaic and the like) generally have stronger uncertainty, and a considerable challenge is brought to safe and stable operation of the micro-grid; in order to achieve the goals of carbon peak reaching and carbon neutralization, a carbon trapping technology is adopted to realize large-scale emission reduction; however, the use of the carbon capture apparatus can also lead to an increase in the cost of power generation while reducing carbon dioxide emissions.
Therefore, researches are necessary for robust optimization scheduling of the multi-energy complementary micro-grid considering the carbon capture equipment, so that the micro-grid can realize low-carbon economic operation.
Disclosure of Invention
The invention aims to provide a micro-grid robust optimal scheduling method and system, which consider carbon capture equipment into the running of a micro-grid and introduce a carbon transaction mechanism at the same time, so that the carbon emission generated by the running of the micro-grid is effectively reduced.
In order to achieve the above object, the present invention provides a robust optimal scheduling method for a micro-grid, including:
modeling each device in the micro-grid system to obtain a mathematical model or a constraint model of each device in the micro-grid system;
Obtaining a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system;
acquiring an objective function of optimal scheduling of the micro-grid system according to a carbon transaction cost model of the micro-grid system;
constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device of the micro-grid system;
and constructing an uncertain set according to uncertain variables in the micro-grid system, and solving the two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result.
Further, modeling each device in the micro-grid system to obtain a mathematical model or constraint model of each device in the micro-grid system, including,
analyzing the operation characteristics and operation constraints of all the devices in the micro-grid system, and modeling all the devices in the micro-grid system to obtain a mathematical model or a constraint model of all the devices in the micro-grid system;
the micro-grid system comprises a wind turbine, an electric energy storage device, a large power grid, an electric load, a gas boiler, a micro gas turbine, carbon capture equipment, a heat storage device and a heat load;
the electric energy storage equipment meets the maximum and minimum charge and discharge power limit, the capacity constraint limit and the charge and discharge energy balance constraint related to the service life of the electric energy storage in the dispatching process; the mathematical model of the electric energy storage device is as follows:
Figure SMS_1
(1)
Figure SMS_2
(2)
Figure SMS_3
(3)
Figure SMS_4
(4)
Figure SMS_5
(5)
Wherein,,
Figure SMS_7
and->
Figure SMS_11
Respectively indicate->
Figure SMS_15
Charging power and discharging power of the time period electric energy storage device; />
Figure SMS_8
Representation->
Figure SMS_13
The charge and discharge states of the electric energy storage equipment in the period of time are represented when the value is 1, and the electric energy storage equipment is in a charge state when the value is 0; />
Figure SMS_17
Representing the maximum charging power of the electrical energy storage device, < >>
Figure SMS_20
Representing a maximum discharge power of the electrical energy storage device; />
Figure SMS_6
Scheduling a total period; />
Figure SMS_10
Representation->
Figure SMS_14
The capacity of the electrical energy storage device during the period; />
Figure SMS_18
And (3) with
Figure SMS_9
Respectively representing the maximum capacity and the minimum capacity allowed by the electric energy storage equipment in the scheduling process; />
Figure SMS_12
Initial scheduling capacity for the electrical energy storage device; />
Figure SMS_16
Representing the capacity of the electrical energy storage device at the last scheduling instant; />
Figure SMS_19
Representing the charge-discharge coefficient of the electrical energy storage device;
when an internal power supply of the micro-grid system cannot meet load requirements, the micro-grid system purchases electricity to a large power grid to meet power balance; conversely, when the internal electric energy of the micro-grid system is excessive, the micro-grid system sells electric energy to a large power grid to obtain benefits; in the electricity purchasing and selling process, the power interaction constraint model between the micro-grid system and the large grid should satisfy the formulas (6) to (7):
Figure SMS_21
(6)
Figure SMS_22
(7)
wherein,,
Figure SMS_23
and->
Figure SMS_24
Respectively represent micro-grid system- >
Figure SMS_25
The electricity purchasing power and the electricity selling power in the time period;
Figure SMS_26
the method comprises the steps of representing the electricity purchasing and selling state of a micro-grid system in a t period, representing that the micro-grid system purchases electricity to a large grid when the value is 1, and representing that the micro-grid system sells electricity to the large grid when the value is 0; />
Figure SMS_27
Representing the maximum purchase and selling electric power allowed when the micro-grid system interacts with the large grid power;
when the output heat power of the micro gas turbine is insufficient to meet the heat load, the gas boiler is used as an auxiliary heat supply device to compensate the heat power shortage; the mathematical model of the gas boiler is as follows:
Figure SMS_28
(8)
wherein,,
Figure SMS_29
indicating gas boiler->
Figure SMS_30
Thermal power output in a time period; />
Figure SMS_31
Is natural gas with low calorific value; />
Figure SMS_32
Indicating the thermal efficiency of the gas boiler; />
Figure SMS_33
Representation->
Figure SMS_34
Air inflow of the gas boiler in time period;
the miniature gas turbine can simultaneously supply heat to the outside in the process of gas power generation; the mathematical model of the miniature gas turbine is as follows:
Figure SMS_35
(9)
Figure SMS_36
(10)
Figure SMS_37
(11)
wherein,,
Figure SMS_39
for miniature gas turbines->
Figure SMS_43
The intake air amount of the period; />
Figure SMS_46
And->
Figure SMS_41
Respectively micro gas turbines at->
Figure SMS_44
Electric power and thermal power output in the period; />
Figure SMS_47
And->
Figure SMS_49
The efficiency of gas-to-electricity conversion and gas-to-heat conversion of the micro gas turbine are respectively; />
Figure SMS_38
For miniature gas turbines->
Figure SMS_42
Total carbon emissions during the time period; />
Figure SMS_45
Carbon emission intensity indicating unit output electric power of micro gas turbine,/- >
Figure SMS_48
Carbon emission intensity representing unit output thermal power of the micro gas turbine; />
Figure SMS_40
Is the duration of a unit time period;
the heat storage device meets the upper limit of charging and discharging power, the limit of capacity constraint and the balance constraint of charging and discharging capacity in the dispatching process; the mathematical model of the heat storage device is as follows:
Figure SMS_50
(12)
Figure SMS_51
(13)
Figure SMS_52
(14)
Figure SMS_53
(15)
Figure SMS_54
(16)
wherein,,
Figure SMS_57
and->
Figure SMS_62
Respectively indicate->
Figure SMS_66
The heat storage device is used for storing heat in a period of time;
Figure SMS_58
representation->
Figure SMS_60
The heat storage device is in a heat charging state when the value is 1, and is in a heat discharging state when the value is 0; />
Figure SMS_65
And->
Figure SMS_69
Respectively representing the upper limit of the heat storage device on the heat charging power and the heat discharging power; />
Figure SMS_55
Scheduling a total period; />
Figure SMS_59
Representation->
Figure SMS_63
The heat storage amount of the time period heat storage device; />
Figure SMS_67
And->
Figure SMS_56
Representing the maximum heat storage amount and the minimum heat storage amount allowed by the heat storage device in the dispatching process; />
Figure SMS_61
Indicating the initial heat storage capacity of the heat storage device;
Figure SMS_64
representing the heat storage amount of the heat storage device at the last scheduling moment; />
Figure SMS_68
To charge and discharge heat efficiency.
Further, the carbon capture apparatus includes a fixed mode of operation and a flexible mode of operation;
when the carbon capture equipment is in a fixed operation mode, the carbon capture level of the carbon capture equipment is kept unchanged, and the carbon capture energy consumption comprises fixed energy consumption and operation energy consumption; the mathematical model of the carbon capture device in the fixed operation mode is as follows:
Figure SMS_70
(17)
Figure SMS_71
(18)
Figure SMS_72
(19)
Figure SMS_74
Representing the total energy consumption of the carbon capture plant; />
Figure SMS_80
The operation energy consumption caused by carbon capture of the carbon capture equipment; />
Figure SMS_83
The fixed energy consumption of the carbon capture equipment; />
Figure SMS_76
Energy consumption required for capturing carbon dioxide units, < >>
Figure SMS_79
For carbon capture level, +.>
Figure SMS_84
The split ratio of the flue gas is; />
Figure SMS_87
And->
Figure SMS_73
Carbon emission intensity of electric power and thermal power output by the micro gas turbine unit respectively; />
Figure SMS_77
And->
Figure SMS_81
Respectively micro gas turbines at->
Figure SMS_85
Electric power and thermal power output in the period;
Figure SMS_75
indicating that the micro gas turbine is->
Figure SMS_78
Total carbon emissions for the period of time; />
Figure SMS_82
Representing that the carbon capture device is +.>
Figure SMS_86
Carbon dioxide emission of the micro gas turbine captured in a time period;
when the carbon capture equipment is in a flexible operation mode, the carbon capture level is changed according to the current grid electricity price; assuming that the product of the carbon capture level and the time-of-use electricity price is a constant, the product of the carbon capture level and the time-of-use electricity price is:
Figure SMS_88
(20)
wherein,,
Figure SMS_89
setting a constant of the product of the carbon capture level and the time-of-use electricity price for people; />
Figure SMS_90
Is the carbon capture level; />
Figure SMS_91
Is->
Figure SMS_92
Electricity purchase price in time period; as electricity purchase rates rise, the carbon capture level correspondingly decreases.
Further, the mathematical model or constraint model of each device in the micro-grid system obtains a carbon transaction cost model of the micro-grid system, including:
Assuming that carbon emission in the micro-grid system comes from a micro gas turbine, a gas boiler and outsourcing power, establishing and acquiring a carbon emission quota model of the micro-grid system according to a mathematical model of the gas boiler, a mathematical model of the micro gas turbine and a power interaction constraint model between the micro-grid system and a large power grid;
calculating and acquiring an actual carbon emission model of the micro power grid system according to a mathematical model of the carbon capture equipment in a fixed operation mode, a mathematical model of the micro gas turbine, a power interaction constraint model between the micro power grid system and a large power grid and a mathematical model of a gas boiler;
and calculating and acquiring a carbon transaction cost model of the micro-grid system according to the carbon emission allowance model and the actual carbon emission quantity model.
Further, it is assumed that carbon emissions inside the micro grid system are from micro gas turbines, gas boilers and outsourced power, and that outsourced power is from coal-fired units; the carbon emission allowance model of the micro-grid system is calculated as follows:
Figure SMS_93
(21)
wherein,,
Figure SMS_96
carbon emission allowance for the micro-grid system; />
Figure SMS_100
、/>
Figure SMS_103
And->
Figure SMS_97
Carbon emission quota coefficients of the micro gas turbine, the coal-fired unit and the gas boiler are respectively represented; / >
Figure SMS_99
And->Respectively micro gas turbines at->
Figure SMS_105
Electric power and thermal power output in the period; />
Figure SMS_94
To schedule the total period>
Figure SMS_101
Representation system->
Figure SMS_104
Time period power purchase, < >>
Figure SMS_106
Indicating gas boiler->
Figure SMS_95
Thermal power output in time period->
Figure SMS_98
Is the duration of a unit time period;
the actual carbon emission model of the micro-grid system is as follows:
Figure SMS_107
(22)
wherein,,
Figure SMS_108
the actual carbon emission is the actual carbon emission of the micro-grid system; />
Figure SMS_109
For miniature gas turbines->
Figure SMS_110
Total carbon emission in time period->
Figure SMS_111
And->
Figure SMS_112
The carbon emission intensity coefficients of the coal-fired unit and the gas boiler are respectively; />
Figure SMS_113
Representing that the carbon capture device is +.>
Figure SMS_114
Carbon dioxide emission of the micro gas turbine captured in a time period;
the traditional carbon transaction pricing mechanism is adopted, and the carbon transaction cost model is obtained by the following steps:
Figure SMS_115
(23)
wherein,,
Figure SMS_116
representing a carbon trade cost for the microgrid system; />
Figure SMS_117
Trade price for unit carbon emissions rights.
Further, according to the carbon transaction cost model of the micro-grid system, obtaining an objective function of optimal scheduling of the micro-grid system comprises:
acquiring the total running cost of the micro-grid system as an objective function of optimal scheduling of the micro-grid system according to the carbon transaction cost, the energy purchasing cost, the running maintenance cost and the energy consumption cost of the carbon capturing equipment of the micro-grid system;
the objective function of the optimal scheduling of the micro-grid system is as follows:
Figure SMS_118
(24)
In the method, in the process of the invention,
Figure SMS_119
the total cost of running the micro-grid system; />
Figure SMS_120
The energy purchasing cost of the micro-grid system; />
Figure SMS_121
The operation and maintenance cost of the micro-grid system is realized; />
Figure SMS_122
The energy consumption cost of the carbon capture equipment is reduced; />
Figure SMS_123
Cost for carbon trade;
the energy purchasing cost of the micro-grid system is as follows:
Figure SMS_124
(25)
wherein:
Figure SMS_128
is->
Figure SMS_131
The gas price of the time period; />
Figure SMS_136
Is->
Figure SMS_126
Electricity selling price in time period; />
Figure SMS_130
For miniature gas turbines>
Figure SMS_134
Electric power output in time period->
Figure SMS_138
The efficiency of gas-to-electricity conversion of the micro gas turbine is improved; />
Figure SMS_125
Indicating gas boiler
Figure SMS_129
Thermal power output in time period->
Figure SMS_133
Indicating the thermal efficiency of the gas boiler; />
Figure SMS_137
And->
Figure SMS_127
Respectively represent micro-grid system->
Figure SMS_132
The electricity purchasing power and the electricity selling power in the time period; />
Figure SMS_135
Scheduling a total period;
the operation and maintenance cost of the micro-grid system is as follows:
Figure SMS_139
(26)
in the method, in the process of the invention,
Figure SMS_142
、/>
Figure SMS_143
、/>
Figure SMS_146
and->
Figure SMS_141
Respectively representing maintenance cost coefficients of the micro gas turbine, the gas boiler, the electric energy storage equipment and the heat storage device; />
Figure SMS_144
And->
Figure SMS_147
Respectively indicate->
Figure SMS_149
Charging power and discharging power of the energy storage in a period;
Figure SMS_140
and->
Figure SMS_145
Respectively indicate->
Figure SMS_148
The heat storage device is used for storing heat in a period of time;
the energy consumption cost of the carbon capture equipment is as follows:
Figure SMS_150
(27)
in the method, in the process of the invention,
Figure SMS_151
the operation energy consumption caused by carbon capture of the carbon capture equipment; />
Figure SMS_152
The fixed energy consumption of the carbon capture equipment;
Figure SMS_153
is->
Figure SMS_154
Electricity purchase price in time period.
Further, according to the objective function and the operation constraint of each device in the micro-grid system, a micro-grid two-stage robust optimization scheduling model is constructed, which comprises,
based on the objective function, limiting by taking the operation constraint of each device in the micro-grid system as a constraint condition, and constructing a micro-grid two-stage robust optimization scheduling model by taking the specific output and interaction power of each device as optimization variables;
in order to cope with uncertainty of wind power output, the two-stage robust optimization scheduling model of the micro-grid is set to be of a three-layer structure of min-max-min, and an uncertain variable is obtained by taking the minimum value of an objective function as a target
Figure SMS_155
In uncertainty set->
Figure SMS_156
A scheduling scheme with optimal economy when the worst scene changes inwards;
the two-stage robust optimal scheduling model structure of the micro-grid is as follows:
Figure SMS_157
(28)
wherein the minimization problem of the outermost layer is the first stage problem, and the optimization variable is
Figure SMS_159
，
Figure SMS_161
The method comprises the steps of carrying out a first treatment on the surface of the Brackets are the second phase problem, the optimization variable is +.>
Figure SMS_163
And->
Figure SMS_160
，/>
Figure SMS_162
Wherein the minimization problem is equivalent to the objective function of equation (24), representing minimizing the total cost of operation; />
Figure SMS_164
For giving a group->
Figure SMS_165
Decision variable +.>
Figure SMS_158
Is a feasible region of (2).
Further, the constraint conditions comprise electric heating power balance constraint, micro gas turbine operation constraint, gas boiler constraint, carbon capture equipment constraint, micro power grid system and large power grid power interaction constraint, energy storage device constraint and heat storage device constraint;
The electrothermal power balance constraint is as follows:
Figure SMS_166
(29)
Figure SMS_167
(30)
wherein,,
Figure SMS_175
and->
Figure SMS_169
Respectively represent system->
Figure SMS_179
Time period purchase/sell electric power; />
Figure SMS_174
For miniature gas turbines>
Figure SMS_183
Electric power output in time period->
Figure SMS_173
An uncertainty variable of the wind power output introduced after the uncertainty is considered;
Figure SMS_185
representation->
Figure SMS_170
Charging power stored in a time period; />
Figure SMS_181
Is the total energy consumption of the carbon capture equipment; />
Figure SMS_168
And (3) with
Figure SMS_178
Respectively indicate->
Figure SMS_171
Charging/discharging power of the time period heat storage device; />
Figure SMS_180
For miniature gas turbines>
Figure SMS_177
Thermal power output in a time period; />
Figure SMS_184
Indicating gas boiler->
Figure SMS_172
Thermal power output in a time period; />
Figure SMS_182
And->
Figure SMS_176
The uncertainty of the electric load power and the thermal load power introduced after the uncertainty is considered are respectively;
the micro gas turbine operating constraints are:
Figure SMS_186
(31)
wherein,,
Figure SMS_187
representing the maximum output electric power of the micro gas turbine; />
Figure SMS_188
Representing the upper limit of the electric power climbing of the micro gas turbine; because of the existence of thermoelectric coupling, the maximum output thermal power and the upper limit of the thermal power climbing of the micro gas turbine are respectively determined by the maximum output electrical power and the upper limit of the electrical power climbing of the micro gas turbine;
the gas boiler is constrained as follows:
Figure SMS_189
(32)
Figure SMS_190
(33)
wherein,,
Figure SMS_191
the maximum output thermal power of the gas boiler is shown; />
Figure SMS_192
The upper limit of the climbing of the thermal power of the gas boiler is indicated.
Further, the uncertainty set is constructed from uncertainty variables within the microgrid system, including,
Assuming that wind power output, electric load and thermal load demand predicted values and maximum predicted deviation are known, constructing an uncertain set
Figure SMS_193
The method comprises the steps of carrying out a first treatment on the surface of the The uncertainty set is:
Figure SMS_194
(34)
wherein U represents an uncertainty set;
Figure SMS_200
、/>
Figure SMS_203
and->
Figure SMS_212
Respectively->
Figure SMS_202
The predicted power of the wind turbine generator, the electric load and the thermal load in the period; />
Figure SMS_214
、/>
Figure SMS_204
And->
Figure SMS_213
Respectively->
Figure SMS_197
Maximum fluctuation errors of wind power output, electric load power and thermal load power in a period of time; />
Figure SMS_209
、/>
Figure SMS_195
And->
Figure SMS_208
The uncertainty is the uncertainty variables of wind power output, electric load power and thermal load power which are introduced after the uncertainty is considered; />
Figure SMS_201
、/>
Figure SMS_211
And->
Figure SMS_205
The binary variables respectively representing the degree of deviating the uncertain variables of wind power output, electric load power and thermal load power from the predicted value are the same as the predicted value in the uncertain variable value of the corresponding time period when 0 is taken, and the uncertain variables of the corresponding time period when 1 is taken are the boundary of the interval; />
Figure SMS_210
、/>
Figure SMS_198
And->
Figure SMS_207
Uncertainty adjustment parameters of the induced wind power output, the electric load power and the thermal load power are respectively; />
Figure SMS_206
Vectors formed for the three uncertainty variables, i.e. uncertaintyVector; />
Figure SMS_216
、
Figure SMS_196
、/>
Figure SMS_215
The matrix constructed for facilitating the subsequent matrix operation has no practical significance; />
Figure SMS_199
To schedule the total period.
Further, solving the two-stage robust optimization scheduling model of the micro-grid by using the uncertain set to obtain scheduling results of the controllable unit, the electrothermal energy storage and the interactive power, wherein the method comprises the following steps:
The two-stage robust optimization model is as follows:
Figure SMS_217
(35)
wherein,,
Figure SMS_218
a coefficient matrix corresponding to the variable under constraint; />
Figure SMS_219
And->
Figure SMS_220
All of which represent the optimization variables,
Figure SMS_221
；/>
Figure SMS_222
the vector is composed of three uncertain variables, namely an uncertain vector, namely wind power output, electric load power and thermal load power; u represents an uncertainty set;
Figure SMS_223
；/>
Figure SMS_224
is a constant column vector;
solving a main problem and a sub-problem shown in a formula (36) and a formula (37) by adopting a column constraint generation algorithm (C & CG);
Figure SMS_225
(36)
Figure SMS_226
(37)
wherein,,
Figure SMS_227
the auxiliary variables introduced for decomposing the original problem into the main problem and the sub-problem have no practical significance; />
Figure SMS_228
To->
Figure SMS_229
A matrix of related cost coefficients; t represents the total scheduling period;
at a given point
Figure SMS_230
The minimization problem of the inner layer of the following formula (37) is a linear problem, and according to the strong pair theory and the corresponding relation of the constraint condition of the formula (37), the minimization problem can be converted into a maximization problem and combined with the maximization problem of the outer layer, so as to obtain the pair problem shown as the formula (38):
Figure SMS_231
(38)
when the uncertain variable quantity reaches the boundary value, the maximum or minimum value is correspondingly obtained for the dual problem, the bilinear term in the formula (38) can be subjected to linearization treatment by adopting a Big-M method, and the final expression form of the sub-problem is shown as the formula (39):
Figure SMS_232
(39)
Wherein,,
Figure SMS_233
is an uncertainty adjustment parameter of the induced wind power output; />
Figure SMS_234
The binary variable representing the degree of deviating the uncertain variable of the wind power output from the predicted value is the same as the predicted value in the uncertain variable of the corresponding period when taking 0, and the uncertain variable of the corresponding period when taking 1 is the boundary of the interval; />
Figure SMS_235
As an introduced dual variable; />
Figure SMS_236
Is an introduced continuous auxiliary variable; />
Figure SMS_237
Is a sufficiently large positive real number.
Based on the same inventive concept, the invention also provides a micro-grid robust optimization scheduling system, which is characterized by comprising a modeling unit, an acquisition unit, a construction unit and a solving unit,
the modeling unit is used for modeling each device in the micro-grid system to obtain a mathematical model or a constraint model of each device in the micro-grid system;
the acquisition unit is used for acquiring a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system; the method is also used for acquiring an objective function of optimal scheduling of the micro-grid system according to the carbon transaction cost of the micro-grid system;
the construction unit is used for constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device of the micro-grid system;
The solving unit is used for constructing an uncertain set according to uncertain variables in the micro-grid system, and solving the two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result.
The invention has the technical effects and advantages that: because wind power has volatility and intermittence, strong randomness is brought to the running of a micro-grid system after grid connection, and the traditional deterministic optimal scheduling method is difficult to adapt to the random balance requirement of the micro-grid; in order to cope with the uncertainty of renewable energy sources and ensure the stable operation of a micro-grid, a micro-gas turbine is often needed to purchase electricity to a large power grid, and a large amount of carbon dioxide is generated at the same time; aiming at the problems of wind power uncertainty and carbon emission, the carbon capture equipment is considered in the running of the micro-grid, and a carbon transaction mechanism is introduced, so that the carbon emission generated by the running of the micro-grid is effectively reduced; a two-stage robust optimization scheduling model is established, uncertainty of wind power output is described by adopting a box type uncertainty set, scheduling results obtained by solving the model can adapt to any scene in the uncertainty set, low-carbon economic operation of a micro-grid system is ensured, and renewable energy source capacity of the micro-grid is enhanced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
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 apparent that the drawings in the following description are only 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 a micro-grid robust optimization scheduling method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a micro-grid system according to an embodiment of the present invention;
FIG. 3 is a schematic representation of the pre-day prediction of wind power, electrical load and thermal load in an embodiment of the present invention;
FIG. 4 is a schematic diagram of electricity purchase price, electricity selling price and gas purchase price according to an embodiment of the present invention;
FIG. 5 is a graph of electrical power profiles for various devices of a micro-grid system in accordance with an embodiment of the present invention;
FIG. 6 is a graph of thermal power profiles for various devices of a micro-grid system in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram of energy consumption costs of a carbon capture device in two modes according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of carbon trade costs in two modes according to an embodiment of the present invention;
FIG. 9 is a graph showing carbon dioxide capture amounts in two modes according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a robust optimal scheduling system for a micro-grid according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
According to the invention, a robust optimization model is constructed by calling a YALMIP tool kit through MATLAB software, a CPLEX solver is adopted for solving, and the micro-grid system shown in FIG. 2 is taken as an embodiment to verify the effectiveness of the method provided by the invention. The predicted conditions of wind power, electric load and thermal load day ahead are shown in fig. 3, the electricity purchase price, the electricity selling price and the gas purchase price are shown in fig. 4, and the uncertainty adjustment parameters of wind power, electric load power and thermal load power are set to be 6, 12 and 12 respectively. The microgrid operating parameters are shown in table 1. The simulation analysis comprises three aspects of day-ahead dispatching results, comparison of different operation modes of the carbon capture equipment and comparison of dispatching cost under different uncertainties.
In order to solve the defects in the prior art, the invention discloses a micro-grid robust optimal scheduling method, which comprises the following steps as shown in fig. 1:
step 1, modeling each device in a micro-grid system to obtain a mathematical model or a constraint model of each device in the micro-grid system; comprising the following steps:
analyzing the operation characteristics and operation constraints of all the devices in the micro-grid system, and modeling all the devices in the micro-grid system to obtain a mathematical model or a constraint model of all the devices in the micro-grid system;
the micro-grid system comprises a wind turbine, an electric energy storage device, a large power grid, an electric load, a gas boiler, a micro gas turbine, carbon capture equipment, a heat storage device and a heat load as shown in fig. 2.
(1) The electric energy storage equipment can carry out bidirectional adjustment on energy, and is beneficial to flexibly maintaining the power balance of the micro-grid system; the electric energy storage equipment meets the maximum and minimum charge and discharge power limit, the capacity constraint limit and the charge and discharge energy balance constraint related to the service life of the electric energy storage in the dispatching process;
the mathematical model of the electric energy storage device is as follows:
Figure SMS_238
(1)
Figure SMS_239
(2)
Figure SMS_240
(3)
Figure SMS_241
(4)
Figure SMS_242
(5)
wherein,,
Figure SMS_246
and->
Figure SMS_250
Respectively indicate->
Figure SMS_254
Charging power and discharging power of the time period electric energy storage device;
Figure SMS_244
Representation->
Figure SMS_248
The charge and discharge states of the electric energy storage equipment in the period of time are represented when the value is 1, and the electric energy storage equipment is in a charge state when the value is 0; />
Figure SMS_252
And->
Figure SMS_256
Respectively representing the maximum charging power and the maximum discharging power of the electric energy storage equipment, and taking 500kW; />
Figure SMS_243
Taking 24h as a scheduling total period; />
Figure SMS_247
Representation->
Figure SMS_251
The capacity of the electrical energy storage device during the period; />
Figure SMS_255
And->
Figure SMS_245
Respectively representing the maximum capacity and the minimum capacity allowed by the electric energy storage equipment in the dispatching process, wherein the maximum capacity and the minimum capacity are respectively 1800kWh and 400kWh; />
Figure SMS_249
The initial scheduling capacity of the electric energy storage equipment is taken as 1000kWh; />
Figure SMS_253
Representing the capacity of the electrical energy storage device at the last scheduling instant; />
Figure SMS_257
The charge and discharge coefficient of the electrical energy storage device is represented and taken as 0.95.
(2) When an internal power supply of the micro-grid system cannot meet load requirements, the micro-grid system purchases electricity to a large power grid to meet power balance; conversely, when the internal electric energy of the micro-grid system is excessive, the micro-grid system sells electric energy to a large power grid to obtain benefits; in the electricity purchasing and selling process, the power interaction constraint model between the micro-grid system and the large grid should satisfy the formulas (6) to (7):
Figure SMS_258
(6)
Figure SMS_259
(7)
wherein,,
Figure SMS_260
and->
Figure SMS_261
Respectively represent micro-grid system- >
Figure SMS_262
The electricity purchasing power and the electricity selling power in the time period;
Figure SMS_263
the method comprises the steps of representing the electricity purchasing and selling state of a micro-grid system in a t period, representing that the micro-grid system purchases electricity to a large grid when the value is 1, and representing that the micro-grid system sells electricity to the large grid when the value is 0; />
Figure SMS_264
The maximum purchase and selling power allowed when the micro-grid system interacts with the large grid power is represented as 1000kW.
(3) When the output heat power of the micro gas turbine is insufficient to meet the heat load, the gas boiler is used as an auxiliary heat supply device to compensate the heat power shortage; the mathematical model of the gas boiler is as follows:
Figure SMS_265
(8)
wherein,,
Figure SMS_266
indicating gas boiler->
Figure SMS_267
Thermal power output in a time period; />
Figure SMS_268
The natural gas has a low calorific value of 9.7 kWh/m 3; />
Figure SMS_269
The thermal efficiency of the gas boiler was 0.9; />
Figure SMS_270
Representation->
Figure SMS_271
The air inflow of the gas boiler in the period.
(4) The miniature gas turbine can simultaneously supply heat to the outside in the process of gas power generation, so that the economic performance and efficiency of the whole system are improved; the mathematical model of the miniature gas turbine is as follows:
Figure SMS_272
(9)
Figure SMS_273
(10)
Figure SMS_274
(11)
wherein,,
Figure SMS_276
for miniature gas turbines->
Figure SMS_281
The intake air amount of the period; />
Figure SMS_284
Is natural gas with low calorific value; />
Figure SMS_277
And (3) with
Figure SMS_282
Respectively micro gas turbines at->
Figure SMS_285
Electric power and thermal power output in the period; />
Figure SMS_287
And->
Figure SMS_275
The gas-to-electricity and gas-to-heat efficiencies of the micro gas turbine are respectively 0.35 and 0.4; / >
Figure SMS_279
For miniature gas turbines->
Figure SMS_283
Total carbon emissions during the time period; />
Figure SMS_286
The carbon emission intensity representing the unit output electric power of the micro gas turbine is taken as 0.7g/kWh; />
Figure SMS_278
The carbon emission intensity representing the unit output heat power of the micro gas turbine is taken as 0.4g/kWh; />
Figure SMS_280
The time length of the unit time period is 1h;
(5) The heat storage device is similar to the electric energy storage device, and the heat storage device is required to meet the upper limit of charging and discharging power, the limit of capacity constraint and the balance constraint of charging and discharging quantity in the dispatching process; the mathematical model of the heat storage device is as follows:
Figure SMS_288
(12)
Figure SMS_289
(13)
Figure SMS_290
(14)
Figure SMS_291
(15)
Figure SMS_292
(16)
wherein,,
Figure SMS_295
and->
Figure SMS_298
Respectively indicate->
Figure SMS_302
The heat storage device is used for storing heat in a period of time;
Figure SMS_296
representation->
Figure SMS_299
The heat storage device is in a heat charging state when the value is 1, and is in a heat discharging state when the value is 0; />
Figure SMS_303
And->
Figure SMS_306
Respectively are provided withThe upper limit of the heat storage device heat filling power and heat release power is represented and is taken as 500kW; />
Figure SMS_293
Taking 24h as a scheduling total period; />
Figure SMS_297
Representation->
Figure SMS_301
The heat storage amount of the time period heat storage device; />
Figure SMS_305
And (3) with
Figure SMS_294
Representing the maximum heat storage capacity and the minimum heat storage capacity allowed by the heat storage device in the dispatching process, and respectively taking 1800kW and 400kW; />
Figure SMS_300
Indicating the initial heat storage capacity of the heat storage device; />
Figure SMS_304
Representing the heat storage amount of the heat storage device at the last scheduling moment; / >
Figure SMS_307
The heat charging and discharging efficiency was taken to be 0.9.
(6) The carbon capture device includes a fixed mode of operation and a flexible mode of operation;
(1) when the carbon capture equipment is in a fixed operation mode, the carbon capture level of the carbon capture equipment is kept unchanged, and the carbon emission reduction effect is obvious in the mode, but the cost is higher; and the carbon capture energy consumption comprises fixed energy consumption and operation energy consumption;
wherein, the fixed energy consumption is irrelevant to the running state of the carbon capture equipment, and the value of the fixed energy consumption is not changed; the operation energy consumption is related to the carbon capturing amount; the mathematical model of the carbon capture device in the fixed operation mode is as follows:
Figure SMS_308
(17)
Figure SMS_309
(18)
Figure SMS_310
(19)
Figure SMS_313
is the total energy consumption of the carbon capture equipment; />
Figure SMS_316
The operation energy consumption caused by carbon capture of the carbon capture equipment; />
Figure SMS_320
The fixed energy consumption of the carbon capture equipment is 50kW; />
Figure SMS_314
The energy consumption required for capturing the carbon dioxide unit is taken as 0.269kWh/g; />
Figure SMS_317
At carbon capture level, the fixed pattern was taken to be 0.85; />
Figure SMS_321
The split ratio of the flue gas is 0.7;
Figure SMS_325
and->
Figure SMS_311
Carbon emission intensity of electric power and thermal power output by the micro gas turbine unit respectively; />
Figure SMS_315
And (3) with
Figure SMS_319
Respectively micro gas turbines at->
Figure SMS_323
Electric power and thermal power output in the period; />
Figure SMS_312
Representing micro gas turbine->
Figure SMS_318
Total carbon emissions during the time period; />
Figure SMS_322
Representing that the carbon capture device is +. >
Figure SMS_324
Carbon dioxide emissions from the micro gas turbine captured over time.
(2) When the carbon capture equipment is in a flexible operation mode, the carbon capture level is changed according to the current power grid electricity price, so that the reduction of carbon capture energy consumption is realized, and finally, the carbon capture cost is reduced; for simplicity of discussion, the present invention assumes that the product of the carbon capture level and the time-of-use electricity price is a constant, and the product of the carbon capture level and the time-of-use electricity price is:
Figure SMS_326
(20)
wherein,,
Figure SMS_327
setting a constant of the product of the carbon capture level and the time-of-use electricity price for people; />
Figure SMS_328
Is the carbon capture level; />
Figure SMS_329
Is->
Figure SMS_330
Electricity purchase price of time period, units (yuan/kWh); when electricity purchase price is increased, carbon capture level is correspondingly reduced, so that the aim of reducing energy consumption cost is fulfilled.
Step 2, obtaining a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system, wherein the step comprises the following steps:
(1) Assuming that carbon emissions inside the micro-grid system are from micro gas turbines, gas boilers and outsourcing power, and assuming that outsourcing power is from coal-fired units; establishing and acquiring a carbon emission allowance model of the micro power grid system according to a mathematical model of the gas boiler, a mathematical model of the micro gas turbine and a power interaction constraint model between the micro power grid system and the large power grid;
The carbon emission allowance model of the micro-grid system is calculated as follows:
Figure SMS_331
(21)
wherein,,
Figure SMS_334
carbon emission allowance for the micro-grid system; />
Figure SMS_339
、/>
Figure SMS_342
And->
Figure SMS_335
The carbon emission quota coefficients of the micro gas turbine, the coal-fired unit and the gas boiler are respectively 0.224g/kWh, 0.302g/kWh and 0.152g/kWh; />
Figure SMS_338
And->
Figure SMS_341
Respectively micro gas turbines at->
Figure SMS_344
Electric power and thermal power output in the period; />
Figure SMS_332
Taking 24h as a scheduling total period; />
Figure SMS_336
Representation system->
Figure SMS_340
Time period power purchase, < >>
Figure SMS_343
Indicating gas boiler->
Figure SMS_333
Thermal power output in a time period; />
Figure SMS_337
Is a unit time period of 1h.
(2) Calculating and acquiring an actual carbon emission model of the micro power grid system according to a mathematical model of the carbon capture equipment in a fixed operation mode, a mathematical model of the micro gas turbine, a power interaction constraint model between the micro power grid system and a large power grid and a mathematical model of a gas boiler;
the actual carbon emission model of the micro-grid system is as follows:
Figure SMS_345
(22)
wherein,,
Figure SMS_348
the actual carbon emission is the actual carbon emission of the micro-grid system; />
Figure SMS_351
For miniature gas turbines->
Figure SMS_354
Total carbon emission in time period->
Figure SMS_347
And->
Figure SMS_350
Carbon emission intensity of coal-fired unit and gas boiler respectivelyCoefficients were taken as 0.8g/kWh and 0.2g/kWh, respectively; />
Figure SMS_353
Representation system- >
Figure SMS_356
Time period power purchase, < >>
Figure SMS_346
Indicating gas boiler->
Figure SMS_352
Thermal power output in a time period; />
Figure SMS_355
Taking 24h as a scheduling total period; />
Figure SMS_357
Representing that the carbon capture device is +.>
Figure SMS_349
Carbon dioxide emissions of the micro gas turbine captured in time period are in g.
(3) According to the carbon emission allowance model and the actual carbon emission quantity model, calculating and acquiring a carbon transaction cost model of the micro-grid system;
the traditional carbon transaction pricing mechanism is adopted, and the carbon transaction cost model is obtained by the following steps:
Figure SMS_358
(23)
wherein,,
Figure SMS_359
representing a carbon trade cost for the microgrid system; />
Figure SMS_360
Trade price for unit carbon emissions rights.
Step 3, obtaining an objective function of optimal scheduling of the micro-grid system according to a carbon transaction cost model of the micro-grid system; the method comprises the following steps:
acquiring the total running cost of the micro-grid system according to the carbon transaction cost, the energy purchasing cost, the running maintenance cost and the energy consumption cost of the carbon capturing equipment of the micro-grid system; taking the total running cost of the micro-grid system as an objective function of the optimal dispatching of the micro-grid system;
the objective function of the optimal scheduling of the micro-grid system is as follows:
Figure SMS_361
(24)
in the method, in the process of the invention,
Figure SMS_362
the total cost of running the micro-grid system; />
Figure SMS_363
The energy purchasing cost of the micro-grid system; />
Figure SMS_364
The operation and maintenance cost of the micro-grid system is realized; / >
Figure SMS_365
The energy consumption cost of the carbon capture equipment is reduced; />
Figure SMS_366
Cost for carbon trade;
the energy purchasing cost of the micro-grid system is as follows:
Figure SMS_367
(25)
wherein:
Figure SMS_369
is->
Figure SMS_375
Air price of period, units (yuan/kWh); />
Figure SMS_378
Is->
Figure SMS_370
Electricity selling price of time period, unit (yuan/kWh); />
Figure SMS_372
For miniature gas turbines>
Figure SMS_376
Electric power output in time period->
Figure SMS_380
The gas-to-electricity efficiency of the micro gas turbine is taken to be 0.35; />
Figure SMS_368
Indicating gas boiler->
Figure SMS_373
Thermal power output in time period->
Figure SMS_377
The thermal efficiency of the gas boiler was 0.9; />
Figure SMS_381
And->
Figure SMS_371
Respectively represent micro-grid system->
Figure SMS_374
The electricity purchasing power and the electricity selling power in the time period; />
Figure SMS_379
Taking 24h as a scheduling total period;
the operation and maintenance cost of the micro-grid system is as follows:
Figure SMS_382
(26)
in the method, in the process of the invention,
Figure SMS_386
、/>
Figure SMS_390
、/>
Figure SMS_393
and->
Figure SMS_384
The maintenance cost coefficients of the micro gas turbine, the gas boiler, the electric energy storage equipment and the heat storage device are respectively represented and respectively taken as 0.25 yuan/kW, 0.3 yuan/kW and 0.3 yuan/kW; />
Figure SMS_388
Indicating gas boiler->
Figure SMS_391
Thermal power output in time period->
Figure SMS_394
The thermal efficiency of the gas boiler was 0.9; />
Figure SMS_383
And->
Figure SMS_389
Respectively indicate->
Figure SMS_392
Charging power and discharging power of the energy storage in a period; />
Figure SMS_395
And->
Figure SMS_385
Respectively indicate->
Figure SMS_387
The heat storage device is used for storing heat in a period of time;
the energy consumption cost of the carbon capture equipment is as follows:
Figure SMS_396
(27)
Wherein:
Figure SMS_397
the operation energy consumption caused by carbon capture of the carbon capture equipment; />
Figure SMS_398
The fixed energy consumption of the carbon capture equipment; />
Figure SMS_399
Is->
Figure SMS_400
Electricity purchase price in time of day, units (yuan/kWh).
Step 4, constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device in the micro-grid system; comprising the following steps:
(1) Based on the objective function, limiting by taking the operation constraint of each device in the micro-grid system as a constraint condition, taking the specific output and interaction power of each device as optimization variables, and calling a YALMIP tool box through MATLAB software to construct a multi-energy complementary micro-grid two-stage robust optimization scheduling model considering carbon capture devices;
the constraint conditions comprise electrothermal power balance constraint, micro gas turbine operation constraint, gas boiler constraint, carbon capture equipment constraint, micro power grid system and large power grid power interaction constraint, energy storage device constraint and heat storage device constraint;
namely, the micro-grid system should meet the operation constraint of the micro-gas turbine, the gas boiler constraint, the carbon capture equipment constraint, the power interaction constraint of the micro-grid system and the large power grid, the energy storage device constraint and the heat storage device constraint in the dispatching process.
(1) The electrothermal power balance constraint is as follows:
Figure SMS_401
(29)
Figure SMS_402
(30)
wherein,,
Figure SMS_409
and->
Figure SMS_404
Respectively represent system->
Figure SMS_415
Time period purchase/sell electric power; />
Figure SMS_406
For miniature gas turbines>
Figure SMS_420
Electric power output in time period->
Figure SMS_412
An uncertainty variable of the wind power output introduced after the uncertainty is considered; />
Figure SMS_419
Representation->
Figure SMS_411
Charging power stored in a time period; />
Figure SMS_416
Is the total energy consumption of the carbon capture equipment; />
Figure SMS_403
And (3) with
Figure SMS_413
Respectively indicate->
Figure SMS_405
Charging/discharging power of the time period heat storage device; />
Figure SMS_414
For miniature gas turbines>
Figure SMS_410
Thermal power output in a time period; />
Figure SMS_417
Indicating gas boiler->
Figure SMS_407
Thermal power output in a time period; />
Figure SMS_418
And->
Figure SMS_408
The electrical load power and the thermal load power uncertainty variables introduced after the uncertainty is considered, respectively.
(2) The micro gas turbine operating constraints are:
Figure SMS_421
(31)
wherein,,
Figure SMS_422
for miniature gas turbines>
Figure SMS_423
Electric power output in a period; />
Figure SMS_424
Representing the maximum output electric power of the micro gas turbine, and taking 1000kW; />
Figure SMS_425
Representing the upper limit of the electromechanical power climbing of the miniature gas turbine, and taking the upper limit as 500kW/h; due to the existence of thermoelectric coupling, the maximum output thermal power and the upper limit of the thermal power climbing of the micro gas turbine are respectively determined by the maximum output electrical power and the upper limit of the electrical power climbing of the micro gas turbine.
(3) The gas boiler is constrained as follows:
Figure SMS_426
(32)
Figure SMS_427
(33)
wherein,,
Figure SMS_428
the maximum output thermal power of the gas boiler is shown; / >
Figure SMS_429
The upper limit of the climbing of the thermal power of the gas boiler is expressed as 300kW/h.
(4) Carbon capture equipment constraints, including fixed modes of operation and flexible modes of operation;
the mathematical model of the carbon capture device in the fixed operation mode is as follows:
Figure SMS_430
(17)
Figure SMS_431
(18)
Figure SMS_432
(19)
Figure SMS_436
is the total energy consumption of the carbon capture equipment; />
Figure SMS_438
The operation energy consumption caused by carbon capture of the carbon capture equipment; />
Figure SMS_442
The fixed energy consumption of the carbon capture equipment; />
Figure SMS_435
Energy consumption required for capturing carbon dioxide units, < >>
Figure SMS_440
For carbon capture level, +.>
Figure SMS_443
The split ratio of the flue gas is; />
Figure SMS_446
And->
Figure SMS_433
Carbon emission intensity of electric power and thermal power output by the micro gas turbine unit respectively; />
Figure SMS_437
And->
Figure SMS_441
Respectively micro gas turbines at->
Figure SMS_445
Electric power and thermal power output in the period;
Figure SMS_434
representing micro gas turbine->
Figure SMS_439
Total carbon emissions during the time period; />
Figure SMS_444
Representing that the carbon capture device is +.>
Figure SMS_447
Carbon dioxide emissions from the micro gas turbine captured over time.
When the carbon capture equipment is in a flexible operation mode, the carbon capture level is changed according to the current grid electricity price; assuming that the product of the carbon capture level and the time-of-use electricity price is a constant, the product of the carbon capture level and the time-of-use electricity price is:
Figure SMS_448
(20)
wherein,,
Figure SMS_449
setting a constant of the product of the carbon capture level and the time-of-use electricity price for people; />
Figure SMS_450
Is the carbon capture level; / >
Figure SMS_451
Is->
Figure SMS_452
Electricity purchase price of time period, units (yuan/kWh); as electricity purchase rates rise, the carbon capture level correspondingly decreases.
(5) Micro-grid power interaction constraint:
Figure SMS_453
(6)
Figure SMS_454
(7)
wherein,,
Figure SMS_455
and->
Figure SMS_456
Respectively represent micro-grid system->
Figure SMS_457
The electricity purchasing power and the electricity selling power in the time period;
Figure SMS_458
the method comprises the steps of representing the electricity purchasing and selling state of a micro-grid system in a t period, representing that the micro-grid system purchases electricity to a large grid when the value is 1, and representing that the micro-grid system sells electricity to the large grid when the value is 0; />
Figure SMS_459
The maximum purchase and selling power allowed when the micro-grid system interacts with the large grid power is represented as 1000kW.
(6) Electric energy storage device constraints:
Figure SMS_460
(1)
Figure SMS_461
(2)
Figure SMS_462
(3)
Figure SMS_463
(4)
Figure SMS_464
(5)
wherein,,
Figure SMS_466
and->
Figure SMS_470
Respectively indicate->
Figure SMS_474
Charging power and discharging power of the time period electric energy storage device;
Figure SMS_468
representation->
Figure SMS_472
The charge and discharge states of the electric energy storage equipment in the period of time are represented when the value is 1, and the electric energy storage equipment is in a charge state when the value is 0; />
Figure SMS_476
And->Respectively representing the maximum charging power and the maximum discharging power of the electric energy storage equipment, and taking 500kW;/>
Figure SMS_465
taking 24h as a scheduling total period; />
Figure SMS_469
Representation->
Figure SMS_473
The capacity of the electrical energy storage device during the period; />
Figure SMS_477
And->
Figure SMS_467
Respectively representing the maximum capacity and the minimum capacity allowed by the electric energy storage equipment in the dispatching process, wherein the maximum capacity and the minimum capacity are respectively 1800kWh and 400kWh; / >
Figure SMS_471
The initial scheduling capacity of the electric energy storage equipment is taken as 1000kWh; />
Figure SMS_475
Representing the capacity of the electrical energy storage device at the last scheduling instant; />
Figure SMS_478
The charge and discharge coefficient of the electrical energy storage device is represented and taken as 0.95.
(7) And (3) restraining a heat storage device:
Figure SMS_480
(12)
Figure SMS_481
(13)
Figure SMS_482
(14)
Figure SMS_483
(15)
Figure SMS_484
(16)
wherein:
Figure SMS_486
and->
Figure SMS_492
Respectively indicate->
Figure SMS_496
The heat storage device is used for storing heat in a period of time;
Figure SMS_488
representation->
Figure SMS_491
The heat storage device is in a heat charging state when the value is 1, and is in a heat discharging state when the value is 0; />
Figure SMS_495
And->
Figure SMS_499
Respectively representing the upper limit of the heat storage device heat charging power and the upper limit of the heat releasing power, and taking 500kW; />
Figure SMS_485
Taking 24h as a scheduling total period; />
Figure SMS_489
Representation->
Figure SMS_493
The heat storage amount of the time period heat storage device; />
Figure SMS_497
And->
Figure SMS_487
Respectively represent heat storage devicesThe maximum heat storage amount and the minimum heat storage amount allowed in the dispatching process are respectively 1800kW and 400kW; />
Figure SMS_490
Indicating the initial heat storage capacity of the heat storage device; />
Figure SMS_494
Representing the heat storage amount of the heat storage device at the last scheduling moment; />
Figure SMS_498
The heat charging and discharging efficiency was taken to be 0.9.
(2) In order to cope with uncertainty of wind power output, the two-stage robust optimization scheduling model of the micro-grid is set to be of a three-layer structure of min-max-min, and an uncertain variable is obtained by taking the minimum value of an objective function as a target
Figure SMS_500
In uncertainty set->
Figure SMS_501
A scheduling scheme with optimal economy when the worst scene changes inwards;
the two-stage robust optimization model of the micro-grid is as follows:
Figure SMS_502
(28)
wherein the minimization problem of the outermost layer is the first stage problem, and the optimization variable is
Figure SMS_505
，
Figure SMS_506
The method comprises the steps of carrying out a first treatment on the surface of the Brackets are the second phase problem, the optimization variable is +.>
Figure SMS_508
And->
Figure SMS_504
，/>
Figure SMS_507
Wherein the minimization problem is equivalent to the objective function of equation (24), representing minimizing the total cost of operation; />
Figure SMS_509
For giving a group->
Figure SMS_510
Decision variable +.>
Figure SMS_503
Is a feasible region of (2).
Step 5, constructing an uncertainty set according to uncertainty variables in the micro-grid system; solving the two-stage robust optimization scheduling model of the micro-grid by using a CPLEX solver and an uncertain set to obtain scheduling results of a controllable unit, electrothermal energy storage and interactive power, wherein the method comprises the following steps:
(1) Assuming that wind power output, electric load and thermal load demand predicted values and maximum predicted deviation are known, constructing a box type uncertain set
Figure SMS_511
；
The uncertainty set is:
Figure SMS_512
(34)
wherein U represents an uncertainty set;
Figure SMS_520
、/>
Figure SMS_518
and->
Figure SMS_529
Respectively->
Figure SMS_523
The predicted power of the wind turbine generator, the electric load and the thermal load in the period; />
Figure SMS_532
、/>
Figure SMS_517
And->
Figure SMS_528
Respectively->
Figure SMS_522
Maximum fluctuation errors of wind power output, electric load power and thermal load power in a period are respectively taken as 15%, 10% and 10% of predicted power;
Figure SMS_530
、/>
Figure SMS_513
And->
Figure SMS_525
The uncertainty is the uncertainty variables of wind power output, electric load power and thermal load power which are introduced after the uncertainty is considered; />
Figure SMS_515
、/>
Figure SMS_531
And->
Figure SMS_519
The binary variables respectively representing the degree of deviating the uncertain variables of wind power output, electric load power and thermal load power from the predicted value are the same as the predicted value in the uncertain variable value of the corresponding time period when 0 is taken, and the uncertain variables of the corresponding time period when 1 is taken are the boundary of the interval; />
Figure SMS_527
、/>
Figure SMS_521
And->
Figure SMS_533
Uncertainty adjustment parameters of the induced wind power output, the electric load power and the thermal load power are respectively; />
Figure SMS_524
A vector formed by the three uncertain variables, namely an uncertain vector; />
Figure SMS_534
、/>
Figure SMS_514
、/>
Figure SMS_526
The matrix constructed for facilitating the subsequent matrix operation has no practical significance; />
Figure SMS_516
Scheduling a total period;
the predicted conditions of wind power, electric load and thermal load are shown in figure 3.
(2) The two-stage robust optimization model is expressed as:
Figure SMS_535
(35)
in the method, in the process of the invention,
Figure SMS_536
a coefficient matrix corresponding to the variable under constraint; />
Figure SMS_537
And->
Figure SMS_538
All of which represent the optimization variables,
Figure SMS_539
；/>
Figure SMS_540
vectors formed by three uncertainty variables, namely uncertainty vectors, of wind power output, electric load power and thermal load power；
Figure SMS_541
The method comprises the steps of carrying out a first treatment on the surface of the U represents an uncertainty set; />
Figure SMS_542
Is a constant column vector;
solving a main problem and a sub-problem shown in a formula (36) and a formula (37) by adopting a column constraint generation algorithm (C & CG);
Figure SMS_543
(36)
Figure SMS_544
(37)
Wherein,,
Figure SMS_545
the auxiliary variables introduced for decomposing the original problem into the main problem and the sub-problem have no practical significance; />
Figure SMS_546
To->
Figure SMS_547
A matrix of related cost coefficients; t represents the total scheduling period; />
At a given point
Figure SMS_548
The minimization problem of the inner layer of the following formula (37) is a linear problem, and according to the strong pair theory and the corresponding relation of the constraint condition of the formula (37), the minimization problem can be converted into a maximization problem and combined with the maximization problem of the outer layer, so as to obtain the pair problem shown as the formula (38):
Figure SMS_549
(38)
when the uncertain variable quantity reaches the boundary value, the maximum or minimum value is correspondingly obtained for the dual problem, the bilinear term in the formula (54) can be subjected to linearization processing by adopting a Big-M method, and the final expression form of the sub-problem is shown as the formula (39):
Figure SMS_550
(39)
in the method, in the process of the invention,
Figure SMS_551
is an uncertainty adjustment parameter of the induced wind power output; />
Figure SMS_552
The binary variable representing the degree of deviating the uncertain variable of the wind power output from the predicted value is the same as the predicted value in the uncertain variable of the corresponding period when taking 0, and the uncertain variable of the corresponding period when taking 1 is the boundary of the interval; />
Figure SMS_553
As an introduced dual variable; />
Figure SMS_554
Is an introduced continuous auxiliary variable; />
Figure SMS_555
Is a sufficiently large positive real number.
The two-stage robust optimization model adopts a column constraint generation algorithm (C & CG) solving flow as follows:
(1) Given a set of uncertainty variables
Figure SMS_556
Setting the upper limit of the total cost of the scheduling scheme as the initial worst scene>
Figure SMS_557
Model lower bound->
Figure SMS_558
The method comprises the steps of carrying out a first treatment on the surface of the Iteration count->
Figure SMS_559
；
(2) According to the worst scene
Figure SMS_560
Solving the main problem to get (+)>
Figure SMS_561
,/>
Figure SMS_562
,/>
Figure SMS_563
……/>
Figure SMS_564
) The objective function value of the main problem is taken as a new lower bound;
(3) results of solving the Main problem
Figure SMS_565
Substituting the sub-problem and solving to obtain worst scene +.>
Figure SMS_566
Objective function value of sub-problem->
Figure SMS_567
Updating the upper bound of the model->
Figure SMS_568
；
(4) If it is
Figure SMS_569
Then output the solving result +.>
Figure SMS_570
And->
Figure SMS_571
Stopping iteration; otherwise, bringing the new worst scene into step (2) and generating a new variable +.>
Figure SMS_572
Adding the following constraint, and adding 1 to the iteration times; />
Figure SMS_573
(40)
As can be seen from fig. 5 and 6, at 1-7 h, although the wind power output can basically meet the electrical load demand, the micro gas turbine still outputs the maximum power, because if the micro gas turbine output is smaller, the thermal power correspondingly output under the condition of thermoelectric coupling is also smaller, and the heat storage device and the gas boiler are not enough to meet the thermal load demand. At 8-18 h, electricity is purchased through a large power grid to meet the electric load demand, and the electric load demand is also smaller at 8-18 hours because the output thermal power is also larger if the micro-combustion engine is excessively high in output, and the redundant thermal power cannot be completely consumed through the heat storage device. Finally, at 19-24 hours, because the heat load demand is larger, the micro-combustion engine is required to be mainly used for supplying heat, and the corresponding output electric power is too much, so that the excessive electric energy generated by electricity selling and electricity energy storage is consumed.
Considering the case where the electricity price is 0.4 as a standard,
Figure SMS_574
2.125 so that when the electricity price is changed, the carbon capture level of each period is also changed with the electricity price, in practice +.>
Figure SMS_575
Can be flexibly set according to the situation. As shown in fig. 7 to 9, when the electricity prices are the same, the energy consumption cost, the carbon transaction cost, and the carbon dioxide capturing amount of the carbon capturing device in the two modes are the same. When the electricity price is increased, the energy consumption cost of the carbon capture equipment in the flexible mode is generally lower than that in the fixed mode; carbon trade costs are generally higher than in fixed mode; the carbon dioxide capture amount is generally lower than the fixed mode. This is because as electricity prices rise, the carbon capture level in the flexible mode decreases with the resulting decrease in the energy consumption of the carbon capture plant operation, the amount of captured carbon dioxide decreases, and the carbon trade cost increases. At the same time, due to the complex relationship between the carbon capture level and the output power of the micro-combustion engine, the situation opposite to the general trend can occur in a very small part of time. In general, when carbon capture is providedWhen the device is in a flexible operation mode, the operation energy consumption is reduced, the carbon capturing amount is reduced, and the economical efficiency is ensured by sacrificing the low carbon property.
In order to verify that the optimization method provided by the invention can flexibly adjust the conservation of the scheduling scheme, three groups of uncertainty adjustment parameters are selected for comparison, and the parameter setting and the corresponding total running cost are shown in table 2. It can be seen that as the uncertainty adjustment parameters increase, the overall cost of operation of the micro-grid increases accordingly, as the uncertainty is more accounted for, resulting in a more conservative solution, resulting in an increase in overall cost.
Table 1 microgrid system operating parameters
Figure SMS_576
TABLE 2 Total cost of microgrid operation with different uncertainty adjustment parameters
Figure SMS_577
Based on the same inventive concept, the invention also provides a micro-grid robust optimization scheduling system, which is characterized by comprising a modeling unit, an acquisition unit, a construction unit and a solving unit,
the modeling unit is used for modeling each device in the micro-grid system and obtaining a mathematical model or a constraint model of each device in the micro-grid system;
the acquisition unit is used for acquiring a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system; the method is also used for acquiring an objective function of optimal scheduling of the micro-grid system according to the carbon transaction cost of the micro-grid system;
the construction unit is used for constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device of the micro-grid system;
the solving unit is used for constructing an uncertain set according to uncertain variables in the micro-grid system, and solving the two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result.
The specific manner in which the respective unit modules perform the operations in the above-described embodiments has been described in detail in relation to the embodiments of the method, and will not be described in detail herein.
The beneficial effects of the invention include: because wind power has volatility and intermittence, strong randomness is brought to the running of a micro-grid system after grid connection, and the traditional deterministic optimal scheduling method is difficult to adapt to the random balance requirement of the micro-grid; in order to cope with the uncertainty of renewable energy sources and ensure the stable operation of the micro-grid, a micro-gas turbine is often needed to purchase electricity to a large power grid, and a large amount of carbon dioxide is generated at the same time. Aiming at the problems of wind power uncertainty and carbon emission, the invention provides a robust optimization scheduling method of a multi-energy complementary micro-grid considering carbon capture equipment, which comprises the following steps: considering the carbon capture equipment into the running of the micro-grid, and introducing a carbon transaction mechanism at the same time, so that the carbon emission generated by the running of the micro-grid is effectively reduced; a two-stage robust optimization scheduling model is established, uncertainty of wind power output is described by adopting a box type uncertainty set, scheduling results obtained by solving the model can adapt to any scene in the uncertainty set, low-carbon economic operation of a micro-grid system is ensured, and renewable energy source capacity of the micro-grid is enhanced.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.

Claims (11)

1. The utility model provides a micro-grid robust optimal scheduling method, which is characterized by comprising the following steps:
modeling each device in the micro-grid system to obtain a mathematical model or a constraint model of each device in the micro-grid system;
obtaining a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system;
acquiring an objective function of optimal scheduling of the micro-grid system according to a carbon transaction cost model of the micro-grid system;
constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device of the micro-grid system;
and constructing an uncertain set according to uncertain variables in the micro-grid system, and solving the two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result.
2. The method for robust optimal scheduling of a micro-grid according to claim 1, wherein modeling each device in the micro-grid system to obtain a mathematical model or constraint model of each device in the micro-grid system comprises,
analyzing the operation characteristics and operation constraints of all the devices in the micro-grid system, and modeling all the devices in the micro-grid system to obtain a mathematical model or a constraint model of all the devices in the micro-grid system;
The micro-grid system comprises a wind turbine, an electric energy storage device, a large power grid, an electric load, a gas boiler, a micro gas turbine, carbon capture equipment, a heat storage device and a heat load;
the electric energy storage equipment meets the maximum and minimum charge and discharge power limit, the capacity constraint limit and the charge and discharge energy balance constraint related to the service life of the electric energy storage in the dispatching process; the mathematical model of the electric energy storage device is as follows:
Figure QLYQS_1
(1)
Figure QLYQS_2
(2)
Figure QLYQS_3
(3)
Figure QLYQS_4
(4)
Figure QLYQS_5
(5)
wherein,,
Figure QLYQS_9
and->
Figure QLYQS_13
Respectively indicate->
Figure QLYQS_17
Charging power and discharging power of the time period electric energy storage device; />
Figure QLYQS_8
Representation of
Figure QLYQS_12
The charge and discharge states of the electric energy storage equipment in the period of time are represented when the value is 1, and the electric energy storage equipment is in a charge state when the value is 0; />
Figure QLYQS_16
Representing the maximum charging power of the electrical energy storage device, < >>
Figure QLYQS_20
Representing a maximum discharge power of the electrical energy storage device; />
Figure QLYQS_6
Scheduling a total period; />
Figure QLYQS_10
Representation->
Figure QLYQS_14
The capacity of the electrical energy storage device during the period; />
Figure QLYQS_18
And->
Figure QLYQS_7
Respectively representing the maximum capacity and the minimum capacity allowed by the electric energy storage equipment in the scheduling process; />
Figure QLYQS_11
Initial scheduling capacity for the electrical energy storage device; />
Figure QLYQS_15
Representing the capacity of the electrical energy storage device at the last scheduling instant; />
Figure QLYQS_19
Representing the charge-discharge coefficient of the electrical energy storage device;
when an internal power supply of the micro-grid system cannot meet load requirements, the micro-grid system purchases electricity to a large power grid to meet power balance; conversely, when the internal electric energy of the micro-grid system is excessive, the micro-grid system sells electric energy to a large power grid to obtain benefits; in the electricity purchasing and selling process, the power interaction constraint model between the micro-grid system and the large grid should satisfy the formulas (6) to (7):
Figure QLYQS_21
(6)
Figure QLYQS_22
(7)
Wherein,,
Figure QLYQS_23
and->
Figure QLYQS_24
Respectively represent micro-grid system->
Figure QLYQS_25
The electricity purchasing power and the electricity selling power in the time period; />
Figure QLYQS_26
The method comprises the steps of representing the electricity purchasing and selling state of a micro-grid system in a t period, representing that the micro-grid system purchases electricity to a large grid when the value is 1, and representing that the micro-grid system sells electricity to the large grid when the value is 0; />
Figure QLYQS_27
Representing the maximum purchase and selling electric power allowed when the micro-grid system interacts with the large grid power;
when the output heat power of the micro gas turbine is insufficient to meet the heat load, the gas boiler is used as an auxiliary heat supply device to compensate the heat power shortage; the mathematical model of the gas boiler is as follows:
Figure QLYQS_28
(8)
wherein,,
Figure QLYQS_29
indicating gas boiler->
Figure QLYQS_30
Thermal power output in a time period; />
Figure QLYQS_31
Is natural gas with low calorific value; />
Figure QLYQS_32
Indicating the thermal efficiency of the gas boiler; />
Figure QLYQS_33
Representation->
Figure QLYQS_34
Air inflow of the gas boiler in time period;
the miniature gas turbine can simultaneously supply heat to the outside in the process of gas power generation; the mathematical model of the miniature gas turbine is as follows:
Figure QLYQS_35
(9)
Figure QLYQS_36
(10)
Figure QLYQS_37
(11)
wherein,,
Figure QLYQS_39
for miniature gas turbines->
Figure QLYQS_42
The intake air amount of the period; />
Figure QLYQS_46
And->
Figure QLYQS_41
Respectively micro gas turbines at->
Figure QLYQS_44
Electric power and thermal power output in the period; />
Figure QLYQS_47
And->
Figure QLYQS_49
Respectively miniature gas turbine gas rotationThe efficiency of electricity and gas transfer; />
Figure QLYQS_38
For miniature gas turbines->
Figure QLYQS_43
Total carbon emissions during the time period; / >
Figure QLYQS_45
Carbon emission intensity indicating unit output electric power of micro gas turbine,/->
Figure QLYQS_48
Carbon emission intensity representing unit output thermal power of the micro gas turbine; />
Figure QLYQS_40
Is the duration of a unit time period;
the heat storage device meets the upper limit of charging and discharging power, the limit of capacity constraint and the balance constraint of charging and discharging capacity in the dispatching process; the mathematical model of the heat storage device is as follows:
Figure QLYQS_50
(12)
Figure QLYQS_51
(13)
Figure QLYQS_52
(14)
Figure QLYQS_53
(15)
Figure QLYQS_54
(16)
wherein,,
Figure QLYQS_57
and->
Figure QLYQS_61
Respectively indicate->
Figure QLYQS_66
The heat storage device is used for storing heat in a period of time; />
Figure QLYQS_58
Representation->
Figure QLYQS_62
The heat storage device is in a heat charging state when the value is 1, and is in a heat discharging state when the value is 0; />
Figure QLYQS_65
And->
Figure QLYQS_69
Respectively representing the upper limit of the heat storage device on the heat charging power and the heat discharging power; />
Figure QLYQS_55
Scheduling a total period; />
Figure QLYQS_59
Representation->
Figure QLYQS_63
The heat storage amount of the time period heat storage device; />
Figure QLYQS_67
And->
Figure QLYQS_56
Representing the maximum heat storage amount and the minimum heat storage amount allowed by the heat storage device in the dispatching process; />
Figure QLYQS_60
Indicating the initial heat storage capacity of the heat storage device; />
Figure QLYQS_64
Representing the heat storage amount of the heat storage device at the last scheduling moment; />
Figure QLYQS_68
To charge and discharge heat efficiency.
3. A micro-grid robust optimal scheduling method according to claim 2, wherein,
the carbon capture device includes a fixed mode of operation and a flexible mode of operation;
When the carbon capture equipment is in a fixed operation mode, the carbon capture level of the carbon capture equipment is kept unchanged, and the carbon capture energy consumption comprises fixed energy consumption and operation energy consumption; the mathematical model of the carbon capture device in the fixed operation mode is as follows:
Figure QLYQS_70
(17)
Figure QLYQS_71
(18)
Figure QLYQS_72
(19)
Figure QLYQS_74
representing the total energy consumption of the carbon capture plant; />
Figure QLYQS_77
The operation energy consumption caused by carbon capture of the carbon capture equipment;
Figure QLYQS_81
the fixed energy consumption of the carbon capture equipment; />
Figure QLYQS_75
Energy consumption required for capturing carbon dioxide units, < >>
Figure QLYQS_79
For carbon capture level, +.>
Figure QLYQS_83
The split ratio of the flue gas is; />
Figure QLYQS_85
And->
Figure QLYQS_73
Carbon emission intensity of electric power and thermal power output by the micro gas turbine unit respectively; />
Figure QLYQS_78
And->
Figure QLYQS_82
Respectively micro gas turbines at->
Figure QLYQS_86
Electric power and thermal power output in the period; />
Figure QLYQS_76
Indicating that the micro gas turbine is->
Figure QLYQS_80
Total carbon emissions for the period of time; />
Figure QLYQS_84
Representing that the carbon capture device is +.>
Figure QLYQS_87
Carbon dioxide emission of the micro gas turbine captured in a time period;
when the carbon capture equipment is in a flexible operation mode, the carbon capture level is changed according to the current grid electricity price; assuming that the product of the carbon capture level and the time-of-use electricity price is a constant, the product of the carbon capture level and the time-of-use electricity price is:
Figure QLYQS_88
(20)
wherein,,
Figure QLYQS_89
setting a constant of the product of the carbon capture level and the time-of-use electricity price for people; />
Figure QLYQS_90
Is the carbon capture level;
Figure QLYQS_91
Is->
Figure QLYQS_92
Electricity purchase price in time period; as electricity purchase rates rise, the carbon capture level correspondingly decreases.
4. The method for robust optimization scheduling of a micro-grid according to claim 1, wherein the obtaining a carbon trade cost model of the micro-grid system by using a mathematical model or a constraint model of each device in the micro-grid system comprises:
assuming that carbon emission in the micro-grid system comes from a micro gas turbine, a gas boiler and outsourcing power, establishing and acquiring a carbon emission quota model of the micro-grid system according to a mathematical model of the gas boiler, a mathematical model of the micro gas turbine and a power interaction constraint model between the micro-grid system and a large power grid;
calculating and acquiring an actual carbon emission model of the micro power grid system according to a mathematical model of the carbon capture equipment in a fixed operation mode, a mathematical model of the micro gas turbine, a power interaction constraint model between the micro power grid system and a large power grid and a mathematical model of a gas boiler;
and calculating and acquiring a carbon transaction cost model of the micro-grid system according to the carbon emission allowance model and the actual carbon emission quantity model.
5. A micro-grid robust optimized scheduling method according to claim 4, wherein,
Assuming that carbon emissions inside the micro-grid system are from micro gas turbines, gas boilers and outsourcing power, and assuming that outsourcing power is from coal-fired units; the carbon emission allowance model of the micro-grid system is calculated as follows:
Figure QLYQS_93
(21)
wherein,,
Figure QLYQS_97
carbon emission allowance for the micro-grid system; />
Figure QLYQS_99
、/>
Figure QLYQS_103
And->
Figure QLYQS_95
Carbon emission quota coefficients of the micro gas turbine, the coal-fired unit and the gas boiler are respectively represented; />
Figure QLYQS_101
And->
Figure QLYQS_104
Respectively micro gas turbines at->
Figure QLYQS_106
Electric power and thermal power output in a time period；/>
Figure QLYQS_94
To schedule the total period>
Figure QLYQS_98
Representation system->
Figure QLYQS_102
Time period power purchase, < >>
Figure QLYQS_105
Indicating gas boiler->
Figure QLYQS_96
Thermal power output in time period->
Figure QLYQS_100
Is the duration of a unit time period;
the actual carbon emission model of the micro-grid system is as follows:
Figure QLYQS_107
(22)
wherein,,
Figure QLYQS_108
the actual carbon emission is the actual carbon emission of the micro-grid system; />
Figure QLYQS_109
For miniature gas turbines->
Figure QLYQS_110
Total carbon emission in time period->
Figure QLYQS_111
And->
Figure QLYQS_112
Respectively coal-fired unitCarbon emission intensity coefficient of the gas boiler; />
Figure QLYQS_113
Representing that the carbon capture device is +.>
Figure QLYQS_114
Carbon dioxide emission of the micro gas turbine captured in a time period;
the traditional carbon transaction pricing mechanism is adopted, and the carbon transaction cost model is obtained by the following steps:
Figure QLYQS_115
(23)
wherein,,
Figure QLYQS_116
representing a carbon trade cost for the microgrid system; />
Figure QLYQS_117
Trade price for unit carbon emissions rights.
6. The method for robust optimal scheduling of a micro-grid according to claim 1, wherein obtaining an objective function of optimal scheduling of the micro-grid system according to a carbon trade cost model of the micro-grid system comprises:
acquiring the total running cost of the micro-grid system as an objective function of optimal scheduling of the micro-grid system according to the carbon transaction cost, the energy purchasing cost, the running maintenance cost and the energy consumption cost of the carbon capturing equipment of the micro-grid system;
the objective function of the optimal scheduling of the micro-grid system is as follows:
Figure QLYQS_118
(24)
in the method, in the process of the invention,
Figure QLYQS_119
is a micro-gridThe total cost of system operation; />
Figure QLYQS_120
The energy purchasing cost of the micro-grid system; />
Figure QLYQS_121
The operation and maintenance cost of the micro-grid system is realized; />
Figure QLYQS_122
The energy consumption cost of the carbon capture equipment is reduced; />
Figure QLYQS_123
Cost for carbon trade;
the energy purchasing cost of the micro-grid system is as follows:
Figure QLYQS_124
(25)
wherein:
Figure QLYQS_127
is->
Figure QLYQS_130
The gas price of the time period; />
Figure QLYQS_134
Is->
Figure QLYQS_128
Electricity selling price in time period; />
Figure QLYQS_132
For miniature gas turbines>
Figure QLYQS_135
Electric power output in time period->
Figure QLYQS_138
The efficiency of gas-to-electricity conversion of the micro gas turbine is improved; />
Figure QLYQS_125
Indicating gas boiler->
Figure QLYQS_129
Thermal power output in time period->
Figure QLYQS_133
Indicating the thermal efficiency of the gas boiler; />
Figure QLYQS_137
And->
Figure QLYQS_126
Respectively represent micro-grid system->
Figure QLYQS_131
The electricity purchasing power and the electricity selling power in the time period; />
Figure QLYQS_136
Scheduling a total period;
The operation and maintenance cost of the micro-grid system is as follows:
Figure QLYQS_139
(26)
in the method, in the process of the invention,
Figure QLYQS_141
、/>
Figure QLYQS_145
、/>
Figure QLYQS_148
and->
Figure QLYQS_142
Respectively represent miniatureMaintenance cost coefficients for gas turbines, gas boilers, electrical energy storage devices, and heat storage devices; />
Figure QLYQS_144
And->
Figure QLYQS_147
Respectively indicate->
Figure QLYQS_149
Charging power and discharging power of the energy storage in a period; />
Figure QLYQS_140
And->
Figure QLYQS_143
Respectively indicate->
Figure QLYQS_146
The heat storage device is used for storing heat in a period of time;
the energy consumption cost of the carbon capture equipment is as follows:
Figure QLYQS_150
(27)
in the method, in the process of the invention,
Figure QLYQS_151
the operation energy consumption caused by carbon capture of the carbon capture equipment; />
Figure QLYQS_152
The fixed energy consumption of the carbon capture equipment;
Figure QLYQS_153
is->
Figure QLYQS_154
Electricity purchase price in time period.
7. The method for robust optimal scheduling of a micro-grid according to claim 1, wherein the method for robust optimal scheduling of a micro-grid comprises constructing a two-stage robust optimal scheduling model of a micro-grid according to the objective function and the operation constraint of each device in the micro-grid system,
based on the objective function, limiting by taking the operation constraint of each device in the micro-grid system as a constraint condition, and constructing a micro-grid two-stage robust optimization scheduling model by taking the specific output and interaction power of each device as optimization variables;
in order to cope with uncertainty of wind power output, the two-stage robust optimization scheduling model of the micro-grid is set to be of a three-layer structure of min-max-min, and an uncertain variable is obtained by taking the minimum value of an objective function as a target
Figure QLYQS_155
In uncertainty set->
Figure QLYQS_156
A scheduling scheme with optimal economy when the worst scene changes inwards;
the two-stage robust optimal scheduling model structure of the micro-grid is as follows:
Figure QLYQS_157
(28)
wherein the minimization problem of the outermost layer is the first stage problem, and the optimization variable is
Figure QLYQS_160
，
Figure QLYQS_162
The method comprises the steps of carrying out a first treatment on the surface of the Brackets are the second phase problem, the optimization variable is +.>
Figure QLYQS_164
And->
Figure QLYQS_159
，/>
Figure QLYQS_161
Wherein the minimization problem is equivalent to the objective function of equation (24), representing minimizing the total cost of operation; />
Figure QLYQS_163
For giving a group->
Figure QLYQS_165
Decision variable +.>
Figure QLYQS_158
Is a feasible region of (2).
8. The method for robust optimized dispatch of a micro-grid as claimed in claim 7, wherein,
the constraint conditions comprise electrothermal power balance constraint, micro gas turbine operation constraint, gas boiler constraint, carbon capture equipment constraint, micro power grid system and large power grid power interaction constraint, energy storage device constraint and heat storage device constraint;
the electrothermal power balance constraint is as follows:
Figure QLYQS_166
(29)
Figure QLYQS_167
(30)
wherein,,
Figure QLYQS_177
and->
Figure QLYQS_176
Respectively represent system->
Figure QLYQS_185
Time period purchase/sell electric power; />
Figure QLYQS_170
For miniature gas turbines>
Figure QLYQS_180
Electric power output in time period->
Figure QLYQS_172
An uncertainty variable of the wind power output introduced after the uncertainty is considered;
Figure QLYQS_182
representation->
Figure QLYQS_171
Charging power stored in a time period; />
Figure QLYQS_179
Is the total energy consumption of the carbon capture equipment; / >
Figure QLYQS_168
And (3) with
Figure QLYQS_178
Respectively indicate->
Figure QLYQS_174
Charging/discharging power of the time period heat storage device; />
Figure QLYQS_181
For miniature gas turbines>
Figure QLYQS_175
Thermal power output in a time period; />
Figure QLYQS_184
Indicating gas boiler->
Figure QLYQS_173
Thermal power output in a time period; />
Figure QLYQS_183
And->
Figure QLYQS_169
The uncertainty of the electric load power and the thermal load power introduced after the uncertainty is considered are respectively;
the micro gas turbine operating constraints are:
Figure QLYQS_186
(31)
wherein,,
Figure QLYQS_187
representing the maximum output electric power of the micro gas turbine; />
Figure QLYQS_188
Representing the upper limit of the electric power climbing of the micro gas turbine; because of the existence of thermoelectric coupling, the maximum output thermal power and the upper limit of the thermal power climbing of the micro gas turbine are respectively determined by the maximum output electrical power and the upper limit of the electrical power climbing of the micro gas turbine;
the gas boiler is constrained as follows:
Figure QLYQS_189
(32)
Figure QLYQS_190
(33)
wherein,,
Figure QLYQS_191
the maximum output thermal power of the gas boiler is shown; />
Figure QLYQS_192
Indicating gas cookerThe furnace thermal power climbs the upper limit.
9. A method of robust optimized micro-grid scheduling according to claim 1, wherein constructing the uncertainty set based on uncertainty variables within the micro-grid system comprises,
assuming that wind power output, electric load and thermal load demand predicted values and maximum predicted deviation are known, constructing an uncertain set
Figure QLYQS_193
The method comprises the steps of carrying out a first treatment on the surface of the The uncertainty set is:
Figure QLYQS_194
(34)
wherein U represents an uncertainty set;
Figure QLYQS_196
、/>
Figure QLYQS_201
And->
Figure QLYQS_214
Respectively->
Figure QLYQS_202
The predicted power of the wind turbine generator, the electric load and the thermal load in the period; />
Figure QLYQS_213
、/>
Figure QLYQS_199
And->
Figure QLYQS_212
Respectively->
Figure QLYQS_205
Time period wind power output, electric load power andmaximum fluctuation error of the thermal load power; />
Figure QLYQS_210
、/>
Figure QLYQS_195
And->
Figure QLYQS_208
The uncertainty is the uncertainty variables of wind power output, electric load power and thermal load power which are introduced after the uncertainty is considered; />
Figure QLYQS_203
、/>
Figure QLYQS_209
And
Figure QLYQS_206
the binary variables respectively representing the degree of deviating the uncertain variables of wind power output, electric load power and thermal load power from the predicted value are the same as the predicted value in the uncertain variable value of the corresponding time period when 0 is taken, and the uncertain variables of the corresponding time period when 1 is taken are the boundary of the interval; />
Figure QLYQS_211
、/>
Figure QLYQS_204
And->
Figure QLYQS_216
Uncertainty adjustment parameters of the induced wind power output, the electric load power and the thermal load power are respectively; />
Figure QLYQS_200
A vector formed by the three uncertain variables, namely an uncertain vector; />
Figure QLYQS_207
、/>
Figure QLYQS_197
、/>
Figure QLYQS_215
The matrix constructed for facilitating the subsequent matrix operation has no practical significance; />
Figure QLYQS_198
To schedule the total period.
10. The micro-grid robust optimization scheduling method according to claim 8, wherein solving the micro-grid two-stage robust optimization scheduling model by using the uncertain set to obtain scheduling results of controllable units, electrothermal energy storage and interactive power comprises:
The two-stage robust optimization model is as follows:
Figure QLYQS_217
(35)
wherein,,
Figure QLYQS_218
a coefficient matrix corresponding to the variable under constraint; />
Figure QLYQS_219
And->
Figure QLYQS_220
All of which represent the optimization variables,
Figure QLYQS_221
；/>
Figure QLYQS_222
vectors formed by three uncertainty variables, namely uncertainty vectors, of wind power output, electric load power and thermal load powerThe method comprises the steps of carrying out a first treatment on the surface of the U represents an uncertainty set;
Figure QLYQS_223
；/>
Figure QLYQS_224
is a constant column vector;
solving a main problem and a sub-problem shown in a formula (36) and a formula (37) by adopting a column constraint generation algorithm (C & CG);
Figure QLYQS_225
(36)
Figure QLYQS_226
(37)
wherein,,
Figure QLYQS_227
the auxiliary variables introduced for decomposing the original problem into the main problem and the sub-problem have no practical significance; />
Figure QLYQS_228
To->
Figure QLYQS_229
A matrix of related cost coefficients; t represents the total scheduling period;
at a given point
Figure QLYQS_230
The minimization problem of the inner layer of the following formula (37) is a linear problem, and according to the strong pair theory and the corresponding relation of the constraint condition of the formula (37), the minimization problem can be converted into a maximization problem and combined with the maximization problem of the outer layer, so as to obtain the pair problem shown as the formula (38):
Figure QLYQS_231
(38)
when the uncertain variable quantity reaches the boundary value, the maximum or minimum value is correspondingly obtained for the dual problem, the bilinear term in the formula (38) can be subjected to linearization treatment by adopting a Big-M method, and the final expression form of the sub-problem is shown as the formula (39):
Figure QLYQS_232
(39)
Wherein,,
Figure QLYQS_233
is an uncertainty adjustment parameter of the induced wind power output; />
Figure QLYQS_234
The binary variable representing the degree of deviating the uncertain variable of the wind power output from the predicted value is the same as the predicted value in the uncertain variable of the corresponding period when taking 0, and the uncertain variable of the corresponding period when taking 1 is the boundary of the interval; />
Figure QLYQS_235
As an introduced dual variable; />
Figure QLYQS_236
Is an introduced continuous auxiliary variable; />
Figure QLYQS_237
Is a sufficiently large positive real number.
11. A micro-grid robust optimization scheduling system is characterized by comprising a modeling unit, an acquisition unit, a construction unit and a solving unit,
the modeling unit is used for modeling each device in the micro-grid system to obtain a mathematical model or a constraint model of each device in the micro-grid system;
the acquisition unit is used for acquiring a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system; the method is also used for acquiring an objective function of optimal scheduling of the micro-grid system according to the carbon transaction cost of the micro-grid system;
the construction unit is used for constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device of the micro-grid system;
The solving unit is used for constructing an uncertain set according to uncertain variables in the micro-grid system, and solving the two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result.
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