CN115936265B - Robust planning method for electric hydrogen energy system by considering electric hydrogen coupling - Google Patents

Robust planning method for electric hydrogen energy system by considering electric hydrogen coupling Download PDF

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CN115936265B
CN115936265B CN202310160616.4A CN202310160616A CN115936265B CN 115936265 B CN115936265 B CN 115936265B CN 202310160616 A CN202310160616 A CN 202310160616A CN 115936265 B CN115936265 B CN 115936265B
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CN115936265A (en
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彭春华
范国柱
阙炜新
孙惠娟
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East China Jiaotong University
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Abstract

The invention relates to an electro-hydrogen energy system robust planning method considering electro-hydrogen coupling, which is characterized in that an electro-hydrogen coupling model is established by considering electro-hydrogen energy interconversion, a carbon transaction mechanism is introduced, a step carbon transaction cost calculation model is established, a double-layer electro-hydrogen energy system planning model is established under the condition of considering electro-hydrogen coupling and step carbon transaction, a multi-scene confidence interval decision theory is combined with an upper layer model, finally, an electro-hydrogen energy system confidence interval robust planning model is formed, and an improved firework algorithm based on a elimination tournament mechanism is used for solving the electro-hydrogen energy system confidence interval robust planning model. The method can be used for solving the robust planning design and solving of the electro-hydrogen energy system under the uncertainty condition, and overcomes the defects of low convergence speed and easiness in premature of the traditional method.

Description

Robust planning method for electric hydrogen energy system by considering electric hydrogen coupling
Technical Field
The invention relates to a robust planning method of an electro-hydrogen energy system considering electro-hydrogen coupling, and belongs to the technical field of energy planning configuration.
Background
An electro-hydrogen energy system (EHS) is a novel clean energy system taking electricity and hydrogen as cores, and is an energy carrier capable of realizing high-proportion renewable energy supply. How to optimize and coordinate various devices in the electro-hydrogen energy system and improve the utilization rate of renewable energy are important points of attention and research, and scientific and effective system optimization planning is important.
The reasonable configuration of the capacity of each device in the electro-hydrogen energy system is an important content of the planning and the design of the electro-hydrogen energy system, and has important guiding significance in the aspects of stable operation of the system, optimal investment cost and the like. There are a number of current scholars who have conducted extensive research on capacity planning of electrical hydrogen energy systems. Most of the current research is aimed at system economics or reliability. The carbon transaction mechanism is also researched and introduced into the planning of the electric hydrogen energy system, but the electric hydrogen is mostly only used as a mode for storing electric energy, the coupling relation of the electric hydrogen in the electric hydrogen energy system in a bidirectional conversion mode is not considered, the influence of the mutual conversion of the electric hydrogen on the carbon transaction of the system is not considered, and the superiority of the electric hydrogen energy system cannot be fully embodied.
In addition, in view of the fact that the electro-hydrogen energy system generally comprises a large amount of renewable energy sources such as wind power, photovoltaic and the like for generating electricity, the problem of typical uncertainty planning is solved. At present, scholars at home and abroad develop a small amount of researches on electric hydrogen energy system planning considering wind-light uncertainty. However, in the existing report, a plurality of scenes are obtained through scene reduction to conduct deterministic planning, and the traversal of the interval is easy to lose. The robust optimization modeling method of the multi-scenario confidence interval decision theory (MCGDT) solves the problems that the multi-scenario planning does not have interval traversal and the uncertain variable description is too rough in the information interval decision theory (IGDT). Therefore, the invention aims to solve a robust planning scheme under an uncertainty condition by adopting a multi-scene confidence interval decision theory.
Disclosure of Invention
In order to improve the absorption of new energy and promote the development of a clean energy network, the invention provides a robust planning method of an electro-hydrogen energy system taking electro-hydrogen coupling into consideration, and the control of the system on the carbon emission is guided by introducing a ladder carbon transaction mechanism, so that the carbon emission in the electro-hydrogen energy system is reduced; and load transfer is carried out through the hybrid energy storage of electric hydrogen coupling so as to stabilize the output fluctuation of renewable energy sources, reduce the electricity purchasing cost and reduce the waste wind and waste light. Aiming at the intermittence and uncertainty of wind and light in an electric hydrogen energy system, a robust planning scheme under uncertainty parameters is solved by adopting a multi-scene confidence interval decision theory.
An electro-hydrogen energy system robust planning method considering electro-hydrogen coupling comprises the following steps:
step one: establishing an electric hydrogen coupling model by considering the mutual conversion of electric hydrogen energy sources;
step two: introducing a carbon transaction mechanism, and constructing a ladder carbon transaction cost calculation model;
step three: establishing a double-layer planning model of the electro-hydrogen energy system under the condition of considering electro-hydrogen coupling and a ladder carbon transaction mechanism;
step four: combining the multi-scene confidence interval decision theory with the upper model and forming a confidence interval robust planning model of the electro-hydrogen energy system together with the lower model;
step five: and solving the confidence interval robust planning model of the electro-hydrogen energy system by using an improved firework algorithm based on a elimination tournament mechanism.
Further preferably, the electro-hydrogen energy system which is constructed in the first step and takes electro-hydrogen coupling into consideration consists of a wind turbine generator, a photovoltaic unit, a storage battery, a hydrogen fuel cell, an alkaline electrolytic tank and a hydrogen storage tank; wherein, the electric hydrogen coupling unit model composed of an alkaline electrolytic tank, a hydrogen storage tank and a hydrogen fuel cell is expressed as follows:
the output power of the alkaline electrolytic cell is:
Figure SMS_1
in the formula ：
Figure SMS_2
the output power of the alkaline electrolytic cell; />
Figure SMS_3
The input power of the alkaline electrolytic cell; />
Figure SMS_4
Is the efficiency of the alkaline electrolytic cell;
the output power of the hydrogen fuel cell is as follows:
Figure SMS_5
in the formula ：
Figure SMS_6
is the output power of the hydrogen fuel cell; />
Figure SMS_7
For the input power of the hydrogen fuel cell, +.>
Figure SMS_8
Is hydrogen fuel cell efficiency;
the mathematical model of the hydrogen storage tank is expressed as:
Figure SMS_9
in the formula ：
Figure SMS_10
the energy stored by the hydrogen storage tank at the time t; />
Figure SMS_11
The energy stored in the hydrogen storage tank at the time t-1;
Figure SMS_12
the efficiency of the input to the hydrogen storage tank for the alkaline electrolyzer; />
Figure SMS_13
Efficiency of outputting to the hydrogen fuel cell for the hydrogen storage tank; />
Figure SMS_14
For hydrogen storage tanksWorking efficiency;
the maximum input power of the alkaline electrolyzer and the maximum output power of the hydrogen fuel cell are expressed as:
Figure SMS_15
Figure SMS_16
in the formula ：
Figure SMS_18
the maximum input power of the alkaline electrolytic cell; />
Figure SMS_20
Maximum output power for hydrogen fuel cell, < >>
Figure SMS_22
Is the capacity of the alkaline electrolyzer; />
Figure SMS_19
Is the capacity of the hydrogen fuel cell; />
Figure SMS_21
An upper limit of energy storage capacity of the hydrogen storage tank; />
Figure SMS_23
A lower limit of the energy storage capacity of the hydrogen storage tank; />
Figure SMS_24
For the capacity of the hydrogen storage tank>
Figure SMS_17
In time steps.
Further preferably, in the second embodiment, the step carbon transaction cost calculation model is:
Figure SMS_25
in the formula ：
Figure SMS_26
cost for carbon trade; />
Figure SMS_27
A trade price for carbon; />
Figure SMS_28
Is a carbon emission price increase coefficient; />
Figure SMS_29
Is the total carbon emission; />
Figure SMS_30
Is an excess region of carbon emissions; />
Figure SMS_31
Is carbon emission quota.
Further preferably, the electro-hydrogen energy system double-layer planning model comprises an upper layer model and a lower layer model;
the decision variables of the upper model comprise the capacity of the wind turbine, the capacity of the photovoltaic unit and the capacity of the storage battery; the objective function of the upper model is:
Figure SMS_32
in the formula ：
Figure SMS_39
is the total number of scenes; />
Figure SMS_40
For scene->
Figure SMS_46
Weight coefficient of (2); />
Figure SMS_37
The total cost for multi-scenario planning; />
Figure SMS_45
Is a scene
Figure SMS_41
The total cost of the system is reduced; />
Figure SMS_47
For scene->
Figure SMS_36
Equipment investment cost in the power-down hydrogen energy system; />
Figure SMS_44
For scene->
Figure SMS_33
Carbon transaction cost in the power-down hydrogen energy system; />
Figure SMS_42
For scene->
Figure SMS_35
Equipment maintenance cost in the electricity-down hydrogen energy system; />
Figure SMS_43
For scene->
Figure SMS_38
Electricity purchasing cost in the electricity hydrogen energy system; />
Figure SMS_48
For scene->
Figure SMS_34
The wind and light discarding punishment cost is reduced in the power-down hydrogen energy system;
decision variables of the lower model include the capacity of the alkaline electrolyzer, the capacity of the hydrogen fuel cell, the capacity of the hydrogen storage tank; the objective function of the underlying model is:
Figure SMS_49
in the formula ：
Figure SMS_50
for the objective function value of the lower model, +.>
Figure SMS_51
The actual consumption of the wind turbine generator in the electric hydrogen energy system at the time t is obtained; />
Figure SMS_52
The actual consumption of the photovoltaic unit in the electro-hydrogen energy system at the time t is obtained; />
Figure SMS_53
Generating energy of the wind turbine at the time t; />
Figure SMS_54
And the generated energy of the photovoltaic unit at the moment T is the total time T.
The fourth step of the invention adopts a multi-scene confidence interval decision theory (MCGDT) and an upper model to combine to form a confidence interval robust planning model, and the confidence interval robust planning model is as follows:
Figure SMS_55
wherein ,
Figure SMS_59
for confidence robustness, ++>
Figure SMS_65
For uncertain variable confidence level, +.>
Figure SMS_71
For scene->
Figure SMS_57
Uncertainty variable confidence level below, +.>
Figure SMS_64
For scene->
Figure SMS_70
Uncertainty of the uncertainty variable, +.>
Figure SMS_76
For scene->
Figure SMS_60
Target significance level, omega s For scene->
Figure SMS_67
Weight coefficient of>
Figure SMS_72
For decision variable matrix->
Figure SMS_77
For scene->
Figure SMS_58
Wind power generation in next t period->
Figure SMS_66
For scene->
Figure SMS_73
Photovoltaic output at t time interval->
Figure SMS_78
For scene->
Figure SMS_61
The optimal solution of the deterministic model is determined; />
Figure SMS_69
Representing a wind power confidence uncertainty interval; />
Figure SMS_75
To represent a photovoltaic confidence uncertainty interval, +.>
Figure SMS_79
For scene->
Figure SMS_56
Wind power predicted value of next t period，/>
Figure SMS_63
For scene->
Figure SMS_68
Next t period photovoltaic predictive value, +.>
Figure SMS_74
For the inverse cumulative distribution function of wind power, +.>
Figure SMS_62
Is the inverse cumulative distribution function of the photovoltaic.
Further preferably, the constraint conditions in the upper and lower layer models include:
and (3) the access capacity constraint of the wind turbine generator and the photovoltaic turbine generator is as follows:
Figure SMS_80
Figure SMS_81
in the formula ：
Figure SMS_82
for the capacity of the wind turbine, the capacity is%>
Figure SMS_83
Maximum capacity allowed to be accessed for the wind turbine generator; />
Figure SMS_84
Accessing capacity for photovoltaic units, < >>
Figure SMS_85
The maximum capacity allowed to be accessed for the photovoltaic unit;
battery constraints, including battery power rating constraints, battery capacity constraints, and battery state of charge constraints:
Figure SMS_86
Figure SMS_87
Figure SMS_88
in the formula ,
Figure SMS_91
rated power of accumulator, < >>
Figure SMS_94
Is the lower limit of the rated power of the storage battery; />
Figure SMS_96
Is the lower limit of the rated power of the storage battery; />
Figure SMS_90
Is the capacity of the accumulator; />
Figure SMS_93
Is the upper limit of the capacity of the storage battery; />
Figure SMS_95
Is the lower limit of the capacity of the storage battery; />
Figure SMS_97
The state of charge of the storage battery at the moment t; />
Figure SMS_89
Is the upper limit of the state of charge of the battery; />
Figure SMS_92
Is the lower limit of the state of charge of the battery;
alkaline electrolyzer and hydrogen fuel cell capacity constraints:
Figure SMS_98
in the formula ,
Figure SMS_99
is the maximum capacity of the alkaline electrolyzer; />
Figure SMS_100
Is the maximum capacity of the hydrogen fuel cell.
Further preferably, the improved firework algorithm based on the elimination tournament mechanism adopts a self-adaptive dynamic radius adjustment strategy to consider heuristic information in the optimization process, and dynamically adjusts the explosion radius of the firework according to the evolution results of different iteration stages of the algorithm, namely the information of the current optimal firework position from other firework positions; the number of explosion sparks of each firework depends on the ranking of the fitness value of the firework, and the number of sparks of each firework is determined by adopting the self-adaptive dynamic explosion sparks;
Figure SMS_101
in the formula ：
Figure SMS_103
is->
Figure SMS_108
Fireworks +.>
Figure SMS_112
Number of explosion sparks of generation; />
Figure SMS_104
Is->
Figure SMS_109
Fireworks +.>
Figure SMS_113
Number of explosion sparks of generation; />
Figure SMS_115
Is->
Figure SMS_102
Fireworks +.>
Figure SMS_106
A fitness value of the generation; />
Figure SMS_110
Is->
Figure SMS_114
Fireworks +.>
Figure SMS_105
A fitness value of the generation; />
Figure SMS_107
A first adjustment factor for the number of sparks; />
Figure SMS_111
And the second adjustment coefficient is the spark number.
Further preferably, the specific solving process in the fifth step is as follows:
s1: inputting power grid parameters, wind power, photovoltaic annual historical data and load data; setting population scale and iteration times; carrying out multidimensional scene clustering on wind power and photovoltaic annual historical data to obtain the weight occupied by each scene;
s2: initializing an upper firework population, randomly generating planning individuals of the upper firework population, wherein the dimensions of the individuals of the upper firework population are three-dimensional, and the capacities of a wind turbine generator set, a photovoltaic unit and a storage battery are included;
s3: clustering wind power and photovoltaic annual historical data, and optimally solving an upper deterministic model to obtain an optimal solution f 0 The method comprises the steps of carrying out a first treatment on the surface of the Solving the confidence level and the upper layer objective function value of all fireworks in each scene;
s4: sequencing all fireworks according to confidence level;
s5: determining the explosion radius and the explosion spark number of each firework according to the self-adaptive dynamic radius and the self-adaptive dynamic spark number;
s6: generating explosion sparks and variation sparks according to algorithm parameters, and carrying out boundary mapping on sparks beyond boundaries;
s7: predicting the final objective function value of each firework based on a prediction mechanism, and eliminating and re-initializing the firework which does not meet the conditions;
s8: updating the firework population and repeating the steps S4 to S7, and continuously iterating until the maximum iteration number of the upper model is reached to obtain a current optimization scheme;
s9: initializing to generate a lower firework population, randomly generating planned individuals, wherein the dimensions of the individuals of the lower firework population are three-dimensional, and the lower firework population comprises the capacities of an electrolytic tank, a hydrogen fuel cell and a hydrogen storage tank; substituting the optimization scheme obtained by the upper layer into calculation to obtain a lower layer objective function value;
s10: updating the firework population and repeating the steps S5 to S7, and continuously iterating until the maximum iteration times of the lower model are reached to obtain a current optimization scheme;
s11: updating the firework population, repeating the steps S4 to S10, and continuously iterating until the maximum iteration number of the confidence interval robust planning model of the whole electric hydrogen energy system is reached, and obtaining an optimal planning scheme.
On the basis of the existing research, the invention establishes an electro-hydrogen energy system planning model taking electro-hydrogen coupling and ladder carbon transaction into consideration by taking the minimum annual total cost of the planning system as an optimization target, takes mixed energy storage of the electro-hydrogen coupling into consideration, forms an electro-hydrogen energy interconversion system through an alkaline electrolytic tank and a hydrogen fuel cell, has peak clipping and valley filling effects, and can realize space-time transfer of system load. And secondly, introducing a carbon trading mechanism, constructing a ladder-based carbon trading cost calculation model, and analyzing the influence of different carbon trading mechanisms and carbon trading parameters on a system planning scheme. Aiming at the intermittence and uncertainty of wind and light, a robust planning scheme under the uncertainty condition of wind and light output is solved by adopting a multi-scene confidence interval decision theory, so as to form a confidence interval robust planning model of the electric hydrogen energy system. Because the established confidence interval robust planning model of the electro-hydrogen energy system has the characteristic of high-dimensional nonlinearity, the optimization solving difficulty is high, and the problems of low later convergence speed, easy premature and the like of the conventional optimization algorithm generally exist.
Drawings
Fig. 1 is a schematic diagram of an electro-hydrogen energy system.
Detailed Description
An electro-hydrogen energy system robust planning method considering electro-hydrogen coupling comprises the following steps:
step one: establishing an electric hydrogen coupling model by considering the mutual conversion of electric hydrogen energy sources;
step two: introducing a carbon transaction mechanism, and constructing a ladder carbon transaction cost calculation model;
step three: establishing a double-layer planning model of the electro-hydrogen energy system under the condition of considering electro-hydrogen coupling and a ladder carbon transaction mechanism;
step four: combining the multi-scene confidence interval decision theory with the upper model and forming a confidence interval robust planning model of the electro-hydrogen energy system together with the lower model;
step five: and solving the confidence interval robust planning model of the electro-hydrogen energy system by using an improved firework algorithm based on a elimination tournament mechanism.
The electric hydrogen energy system which is constructed by the embodiment and takes the electric hydrogen coupling into consideration mainly comprises a wind turbine generator, a photovoltaic turbine generator, a storage battery, a hydrogen fuel cell, an alkaline electrolytic tank, a hydrogen storage tank and other devices. The energy storage system is an electric-hydrogen hybrid energy storage system and comprises an electric energy storage system mainly comprising a storage battery and a hydrogen energy storage system mainly comprising a hydrogen fuel cell, an alkaline electrolytic tank and a hydrogen storage tank, wherein the hydrogen fuel cell and the alkaline electrolytic tank are electric-hydrogen coupling equipment, and can realize the mutual conversion of electric hydrogen and the load space-time transfer so as to achieve the effects of peak clipping and valley filling.
The mathematical models of wind turbine generator and photovoltaic power generation in the electro-hydrogen energy system are the prior art and are not described in detail herein.
The storage battery can effectively stabilize supply and demand fluctuation caused by unstable wind power and photovoltaic output in the electric hydrogen energy system. When the new energy output power is larger than the load demand power, the storage battery is in a charging state; otherwise, the storage battery is in a discharging state. The mathematical model of the battery in this embodiment is expressed as follows:
when the storage battery is charged:
Figure SMS_116
when the storage battery is discharged:
Figure SMS_117
in the formula ：
Figure SMS_118
the electricity quantity stored in the storage battery at the time t; />
Figure SMS_119
Charging power for the battery; />
Figure SMS_120
Discharging power for the storage battery; />
Figure SMS_121
The charging efficiency of the storage battery is improved; />
Figure SMS_122
For the discharge efficiency of the battery, the present embodiment is set to 90%; />
Figure SMS_123
For the time step, this example takes 1h.
In the running process, the charging and discharging processes of the storage battery are constrained by the charge states of the storage battery, and the maximum charging power and the discharging power of the storage battery can be respectively expressed as:
Figure SMS_124
Figure SMS_125
in the formula ：
Figure SMS_126
the maximum charging power of the storage battery at the moment t; />
Figure SMS_127
The maximum discharge power of the storage battery at the moment t;
Figure SMS_128
is the lower limit of the state of charge of the battery; />
Figure SMS_129
Is the upper limit of the state of charge of the battery; />
Figure SMS_130
Is the capacity of the battery.
The alkaline electrolyzer-hydrogen storage tank-hydrogen fuel cell established by the invention is key equipment for realizing the electric hydrogen coupling of an electric hydrogen energy system, and the alkaline electrolyzer has the characteristic of stable operation under high voltage, high current density and low voltage, and the unit hydrogen production cost of the alkaline electrolyzer is lower than that of the proton exchange membrane electrolyzer, so the alkaline electrolyzer is selected in the embodiment. The electric hydrogen coupling unit has the same energy storage function as the storage battery, namely under the condition that wind power and photovoltaic resources are sufficient, when the storage battery does not have redundant capacity to store redundant new energy output power, the alkaline electrolytic tank can convert redundant electric energy of the electric hydrogen energy system into hydrogen and store the hydrogen in the hydrogen storage tank; when the output power of wind power and photovoltaic power of the electric hydrogen energy system is insufficient, the hydrogen fuel cell can utilize the hydrogen stored in the hydrogen storage tank to generate power, so that the system load requirement is met. The model of the electric hydrogen coupling unit consisting of the alkaline electrolyzer, the hydrogen storage tank and the hydrogen fuel cell is expressed as follows:
the output power of the alkaline electrolytic cell is:
Figure SMS_131
in the formula ：
Figure SMS_132
the output power of the alkaline electrolytic cell; />
Figure SMS_133
The input power of the alkaline electrolytic cell; />
Figure SMS_134
For alkaline cell efficiency, this example takes 60%.
The hydrogen fuel cell adopted in the embodiment is a proton exchange membrane fuel cell, when the output power of renewable energy sources of the system is insufficient to meet the load demand, the hydrogen fuel cell uses hydrogen in a hydrogen storage tank as fuel to convert chemical energy into electric energy to provide the load demand, and the output power is as follows:
Figure SMS_135
in the formula ：
Figure SMS_136
is the output power of the hydrogen fuel cell; />
Figure SMS_137
For the input power of the hydrogen fuel cell, +.>
Figure SMS_138
Is hydrogen fuel cell efficiency.
The hydrogen storage tank is used for storing hydrogen generated by water electrolysis of the alkaline electrolytic tank, and can also provide hydrogen for the hydrogen fuel cell, so that the flexibility of the electro-hydrogen energy system is improved, and the mathematical model can be expressed as follows:
Figure SMS_139
in the formula ：
Figure SMS_140
the energy stored by the hydrogen storage tank at the time t; />
Figure SMS_141
The energy stored in the hydrogen storage tank at the time t-1;
Figure SMS_142
the efficiency of the input to the hydrogen storage tank for the alkaline electrolyzer; />
Figure SMS_143
Efficiency of outputting to the hydrogen fuel cell for the hydrogen storage tank; />
Figure SMS_144
Is the working efficiency of the hydrogen storage tank. The maximum input power of the alkaline electrolytic cell and the maximum output power of the hydrogen fuel cell are limited by the capacity of the alkaline electrolytic cell and the residual energy storage capacity of the hydrogen storage tank, and can be respectively expressed as follows:
Figure SMS_145
Figure SMS_146
in the formula ：
Figure SMS_148
the maximum input power of the alkaline electrolytic cell; />
Figure SMS_152
Maximum output power for hydrogen fuel cell, < >>
Figure SMS_155
Is the capacity of the alkaline electrolyzer; />
Figure SMS_149
Is the capacity of the hydrogen fuel cell; />
Figure SMS_154
An upper limit of energy storage capacity of the hydrogen storage tank; />
Figure SMS_157
To store energy for hydrogen storage tankA lower limit of capacity; />
Figure SMS_158
For the capacity of the hydrogen storage tank>
Figure SMS_147
Is the time step; in this embodiment->
Figure SMS_151
=0.8/>
Figure SMS_153
，/>
Figure SMS_156
=0.2/>
Figure SMS_150
。
The operation control strategy of the electric hydrogen energy system determines the output sequence of each device in the system, and the working conditions of the storage battery and the hydrogen storage system are directly influenced, so that the advantages and disadvantages of the whole system planning scheme are influenced. Therefore, a reasonable system operation control strategy needs to be formulated. The operation strategy of the electro-hydrogen energy system applied in the embodiment is as follows: when the output power of the new energy is larger than the load power born by the system, the redundant electric energy charges the storage battery of the system preferentially, and under the condition that the residual capacity of the hydrogen storage tank and the existing pressure of the hydrogen storage tank are met, the residual energy can be converted into hydrogen through the alkaline electrolytic tank and stored in the hydrogen storage tank. When the power provided by the new energy is smaller than the load power, the storage battery releases electric energy preferentially to supplement the shortage power in the system, and then the hydrogen in the hydrogen storage tank is used for generating electricity through the hydrogen fuel cell to make up the shortage of the residual power.
In step two of this embodiment, according to the electro-hydrogen energy system shown in fig. 1, the carbon emission mainly originates from thermal power purchased by the external power grid, so the gratuitous carbon emission quota of the carbon transaction is as follows:
Figure SMS_159
in the formula ：
Figure SMS_160
is carbon emission quota; />
Figure SMS_161
Carbon emission quota for outsourcing unit electric quantity; />
Figure SMS_162
Power purchased for the t period of time to the external grid; t is the settlement period of the carbon transaction fee.
The step carbon transaction is to divide the carbon emission of the system into a plurality of intervals, and the more the total carbon emission of the system exceeds the gratuitous quota distributed by the supervision department, the higher the price of the carbon transaction, the more penalties need to be paid by the system at the same time, and the greater the carbon transaction cost. The step carbon transaction cost calculation model is as follows:
Figure SMS_163
in the formula ：
Figure SMS_164
cost for carbon trade; />
Figure SMS_165
A trade price for carbon; />
Figure SMS_166
Is a carbon emission price increase coefficient; />
Figure SMS_167
Is the total carbon emission; />
Figure SMS_168
Is an excess region of carbon emissions; />
Figure SMS_169
Is carbon emission quota.
Under the economic operation target, the capacity optimization of the alkaline electrolytic tank and the hydrogen fuel cell is influenced by the investment cost of the electrolytic tank and the hydrogen fuel cell, the capacity of the hydrogen storage tank and other factors, so that the capacity optimization configuration of the electric hydrogen energy system provides an effective and feasible solution for stabilizing wind-solar power fluctuation of a wind turbine generator and light Fu Jizu, and provides a double-layer planning model of the electric hydrogen energy system, comprising an upper-layer model and a lower-layer model, so that the double-layer optimization configuration of economic and wind-solar energy absorption is realized.
In order to embody the economy of the electric hydrogen energy system, the annual cost minimization in the system is used as an optimization target in the upper model. Under the economic operation goal of the system, the capacity of each device in the system is optimized and is influenced by factors such as the investment cost of an alkaline electrolytic tank and a hydrogen fuel cell, the hydrogen storage capacity and the like, and decision variables of an upper model comprise the capacity of a wind turbine generator, the capacity of a photovoltaic turbine generator and the capacity of a storage battery; the objective function of the upper model is:
Figure SMS_170
in the formula ：
Figure SMS_174
is the total number of scenes; />
Figure SMS_173
For scene->
Figure SMS_183
Weight coefficient of (2); />
Figure SMS_176
The total cost for multi-scenario planning; />
Figure SMS_181
Is a scene
Figure SMS_175
The total cost of the system is reduced; />
Figure SMS_186
For scene->
Figure SMS_177
Equipment investment cost in the power-down hydrogen energy system; />
Figure SMS_185
For scene->
Figure SMS_171
Carbon transaction cost in the power-down hydrogen energy system; />
Figure SMS_180
For scene->
Figure SMS_178
Equipment maintenance cost in the electricity-down hydrogen energy system; />
Figure SMS_184
For scene->
Figure SMS_179
Electricity purchasing cost in the electricity hydrogen energy system; />
Figure SMS_182
For scene->
Figure SMS_172
And the wind and light discarding punishment cost is realized in the power-down hydrogen energy system.
Investment cost of equipment in electric hydrogen energy system under scene s
Figure SMS_187
Mainly comprises the equipment acquisition cost during investment construction:
Figure SMS_188
in the formula ,
Figure SMS_191
the unit investment cost of the wind turbine generator system is->
Figure SMS_196
Unit investment costs for photovoltaic units, < >>
Figure SMS_200
Unit investment costs for accumulator->
Figure SMS_192
Unit investment cost for alkaline cell,/-for alkaline cell>
Figure SMS_195
Is the unit investment cost of the hydrogen fuel cell,
Figure SMS_198
The unit investment cost of the hydrogen storage tank; />
Figure SMS_201
Is the capacity of the wind turbine generator system->
Figure SMS_189
For the capacity of the photovoltaic unit,/->
Figure SMS_193
For the capacity of the accumulator->
Figure SMS_197
Is the capacity of the alkaline electrolyzer>
Figure SMS_199
Is the capacity of a hydrogen fuel cell, +.>
Figure SMS_190
For the capacity of the hydrogen storage tank>
Figure SMS_194
Is the discount rate; n is the service life of the device.
Equipment maintenance cost in electric hydrogen energy system under scene s
Figure SMS_202
Refers to the costs expended in equipment wear and maintenance.
Figure SMS_203
in the formula ,
Figure SMS_204
the maintenance cost is the unit of the wind turbine generator; />
Figure SMS_205
The maintenance cost is the unit of the photovoltaic unit; />
Figure SMS_206
Maintenance costs for units of the battery; />
Figure SMS_207
The unit maintenance cost for the alkaline electrolyzer; />
Figure SMS_208
Unit maintenance cost for the hydrogen fuel cell; />
Figure SMS_209
Is the unit maintenance cost of the hydrogen storage tank.
Wind and light discarding punishment cost in electric hydrogen energy system under scene s
Figure SMS_210
Refers to the penalty cost added to the electric energy which should be utilized, because the load is low and there is not enough energy storage capacity to cause the waste of the electric energy, the expression is:
Figure SMS_211
in the formula ：
Figure SMS_212
the actual consumption of the wind turbine generator set at the moment t under the scene s is obtained; />
Figure SMS_213
The actual consumption of the photovoltaic unit at the moment t under the scene s is as follows; />
Figure SMS_214
Generating power of the wind turbine generator set at the moment t under the scene s; />
Figure SMS_215
Generating power of the photovoltaic unit at the moment t under the scene s; />
Figure SMS_216
And punishing cost for the wind and light discarding unit.
Electricity purchasing cost of electric hydrogen energy system under scene s
Figure SMS_217
The method is characterized in that energy supply equipment in the system cannot meet the current system load, which is the cost required by purchasing electricity to an upper power grid, and the expression is as follows:
Figure SMS_218
in the formula ：
Figure SMS_219
for the electricity purchasing quantity of the electro-hydrogen energy system at the moment t, < >>
Figure SMS_220
And the electricity purchase price at the time t is obtained.
In the lower model, the optimization target is the maximum wind-solar absorption rate, and the decision variables are the capacities of an alkaline electrolytic tank, a hydrogen fuel cell and a hydrogen storage tank in the electric hydrogen energy system. The objective function of the underlying model is:
Figure SMS_221
in the formula ：
Figure SMS_222
for the objective function value of the lower model, +.>
Figure SMS_223
The actual consumption of the wind turbine generator in the electric hydrogen energy system at the time t is obtained; />
Figure SMS_224
The actual consumption of the photovoltaic unit in the electro-hydrogen energy system at the time t is obtained; />
Figure SMS_225
Generating energy of the wind turbine at the time t; />
Figure SMS_226
And the generated energy of the photovoltaic unit at the moment T is the total time T.
The confidence interval robust planning model formed by combining the multi-scene confidence interval decision theory (MCGDT) and the upper model is as follows:
Figure SMS_227
wherein ,
Figure SMS_233
for confidence robustness, ++>
Figure SMS_242
For uncertain variable confidence level, +.>
Figure SMS_247
For scene->
Figure SMS_231
Uncertainty variable confidence level below, +.>
Figure SMS_239
For scene->
Figure SMS_245
Uncertainty of the uncertainty variable, +.>
Figure SMS_251
For scene->
Figure SMS_232
Target significance level, omega s For scene->
Figure SMS_237
Weight coefficient of>
Figure SMS_244
For decision variable matrix->
Figure SMS_250
For scene->
Figure SMS_234
Wind power generation in next t period->
Figure SMS_241
For scene->
Figure SMS_248
Photovoltaic output at t time interval->
Figure SMS_252
For scene->
Figure SMS_229
The optimal solution of the deterministic model is determined; />
Figure SMS_235
Representing a wind power confidence uncertainty interval; />
Figure SMS_240
To represent a photovoltaic confidence uncertainty interval, +.>
Figure SMS_246
For scene->
Figure SMS_228
Wind power predictive value of next t period of time>
Figure SMS_236
For scene->
Figure SMS_243
Next t period photovoltaic predictive value, +.>
Figure SMS_249
For the inverse cumulative distribution function of wind power, +.>
Figure SMS_230
For the inverse cumulative distribution function of the photovoltaic, in +.>
Figure SMS_238
As an objective function value in scene s.
The model of the multi-scenario confidence interval decision theory (MCGDT) contains uncertainty opportunity constraints, which the present invention has deterministic conversion according to.
The constraint conditions in the upper model and the lower model comprise:
and (3) the access capacity constraint of the wind turbine generator and the photovoltaic turbine generator is as follows:
Figure SMS_253
Figure SMS_254
in the formula ：
Figure SMS_255
for the capacity of the wind turbine, the capacity is%>
Figure SMS_256
Maximum capacity allowed to be accessed for the wind turbine generator; />
Figure SMS_257
Accessing capacity for photovoltaic units, < >>
Figure SMS_258
The maximum capacity allowed for the photovoltaic unit.
Battery constraints, including battery power rating constraints, battery capacity constraints, and battery state of charge constraints:
Figure SMS_259
Figure SMS_260
Figure SMS_261
in the formula ,
Figure SMS_263
rated power of accumulator, < >>
Figure SMS_267
Is the lower limit of the rated power of the storage battery; />
Figure SMS_269
Is the lower limit of the rated power of the storage battery; />
Figure SMS_264
Is the capacity of the accumulator; />
Figure SMS_266
Is the upper limit of the capacity of the storage battery; />
Figure SMS_268
Is the lower limit of the capacity of the storage battery; />
Figure SMS_270
The state of charge of the storage battery at the moment t; />
Figure SMS_262
Is the upper limit of the state of charge of the battery; />
Figure SMS_265
Is the lower limit of the state of charge of the battery;
alkaline electrolyzer and hydrogen fuel cell capacity constraints:
Figure SMS_271
in the formula ,
Figure SMS_272
is the maximum capacity of the alkaline electrolyzer; />
Figure SMS_273
Is the maximum capacity of the hydrogen fuel cell.
The confidence interval robust planning model of the electro-hydrogen energy system has the characteristic of high-dimensional nonlinearity, the difficulty of optimizing and solving is great, and if the conventional optimizing algorithm is adopted for solving, the problem that the electro-hydrogen energy system is easy to mature and falls into local optimum exists. Therefore, the invention provides an improved firework algorithm based on a elimination tournament mechanism to solve the planning model.
The firework algorithm (LoTFWA) based on the elimination tournament mechanism firstly predicts the fitness value of the last iteration of each firework, and then compares the fitness value with the optimal fitness value of the current generation respectively; if the predicted value is inferior to the current optimal fitness value, the firework is considered to have no evolutionary potential, and is generated by reinitialization after being eliminated, namely, a new position is regenerated in a feasible region space, so that the local searching capability of a firework algorithm can be enhanced. LoTFWA uses a power law profile to determine the number of sparks per firework:
Figure SMS_274
in the formula ,
Figure SMS_275
is the fitness rank of fireworks, < >>
Figure SMS_276
Is the total number of fireworks>
Figure SMS_277
For the explosion spark coefficient>
Figure SMS_278
Is a parameter controlling the shape of the distribution, +.>
Figure SMS_279
The larger the good fireworks produce more explosion sparks.
However, loTFWA may have a problem of generating a large number of explosion sparks when the fitness values of two fireworks are relatively close,
Figure SMS_280
the value of (2) is a fixed value set subjectively, which can lead to consistent explosion spark number generated by each generation of evolution, influence the optimizing performance of the later stage of the algorithm and easily sink into local optimization. For this purpose, the invention further proposes an improved LoTFWA, which operates as follows:
firstly, adopting a self-adaptive dynamic radius adjustment strategy to consider heuristic information in an optimization process, instead of relying on the current iteration times and the total iteration times, dynamically adjusting the explosion radius of the fireworks according to the evolution results of different iteration stages of the algorithm, namely the information of the current optimal fireworks position from other fireworks positions, thereby balancing the global and local searching capability of the algorithm. Second, the number of explosion sparks per firework depends on the ranking of its fitness value, rather than the fitness value itself, so an adaptive dynamic number of explosion sparks is used to determine the number of sparks per firework. The improvement formula is as follows:
Figure SMS_281
in the formula ：
Figure SMS_283
is->
Figure SMS_287
Fireworks +.>
Figure SMS_291
Number of explosion sparks of generation; />
Figure SMS_284
Is->
Figure SMS_290
Fireworks +.>
Figure SMS_294
Number of explosion sparks of generation; />
Figure SMS_295
Is->
Figure SMS_282
Fireworks +.>
Figure SMS_286
A fitness value of the generation; />
Figure SMS_288
Is->
Figure SMS_292
Fireworks +.>
Figure SMS_285
A fitness value of the generation; />
Figure SMS_289
A first adjustment factor for the number of sparks; />
Figure SMS_293
And the second adjustment coefficient is the spark number.
The method for solving the confidence interval robust planning model of the electro-hydrogen energy system comprises the following specific steps:
s1: inputting power grid parameters, wind power, photovoltaic annual historical data and load data; setting population scale and iteration times; carrying out multidimensional scene clustering on wind power and photovoltaic annual historical data to obtain the weight occupied by each scene;
s2: initializing an upper firework population, randomly generating planning individuals of the upper firework population, wherein the dimensions of the individuals of the upper firework population are three-dimensional, and the capacities of a wind turbine generator set, a photovoltaic unit and a storage battery are included;
s3: clustering wind power and photovoltaic annual historical data, and optimally solving an upper deterministic model to obtain an optimal solution f 0 The method comprises the steps of carrying out a first treatment on the surface of the Solving the confidence level and the upper layer objective function value of all fireworks in each scene;
s4: sequencing all fireworks according to confidence level;
s5: determining the explosion radius and the explosion spark number of each firework according to the self-adaptive dynamic radius and the self-adaptive dynamic spark number;
s6: generating explosion sparks and variation sparks according to algorithm parameters, and carrying out boundary mapping on sparks beyond boundaries;
s7: predicting the final objective function value of each firework based on a prediction mechanism, and eliminating and re-initializing the firework which does not meet the conditions;
s8: updating the firework population and repeating the steps S4 to S7, and continuously iterating until the maximum iteration number of the upper model is reached to obtain a current optimization scheme;
s9: initializing to generate a lower firework population, randomly generating planned individuals, wherein the dimensions of the individuals of the lower firework population are three-dimensional, and the lower firework population comprises the capacities of an electrolytic tank, a hydrogen fuel cell and a hydrogen storage tank; substituting the optimization scheme obtained by the upper layer into calculation to obtain a lower layer objective function value;
s10: updating the firework population and repeating the steps S5 to S7, and continuously iterating until the maximum iteration number of the lower model is reached, so as to obtain the current optimization scheme.
S11: updating the firework population, repeating the steps S4 to S10, and continuously iterating until the maximum iteration number of the confidence interval robust planning model of the whole electric hydrogen energy system is reached, and obtaining an optimal planning scheme.
The invention considers the electric hydrogen coupling and forms an 'electric-hydrogen-electric' energy closed-loop structure through the hydrogen fuel cell, has peak clipping and valley filling effects, realizes the space-time transfer of load, and improves the wind-solar energy absorption capacity and the overall benefit of the system; meanwhile, a carbon transaction mechanism is introduced, a ladder carbon transaction cost calculation model is built, and the carbon emission of the system can be effectively reduced.
The MCGDT realizes the fine quantization of mass scenes and the collaborative optimization of confidence robustness, and more accurately and reasonably measures the nonlinear robustness, so that the system planning scheme has better adaptability to the uncertainty changes of wind, light and the like, and accurate and reasonable uncertainty planning is realized.
The improved LoTFWA algorithm can keep population diversity in the later period of evolution due to the introduction of the self-adaptive dynamic explosion spark number, balances the global and local searching capability of the algorithm, avoids the algorithm from sinking into local optimum, improves the searching efficiency of the algorithm, and realizes the deep optimization and efficient solution of the model.
By considering an electric hydrogen coupling and hybrid energy storage electric hydrogen energy system planning model, redundant electric energy is converted into hydrogen for storage when the load is low, and then the hydrogen fuel cell is used for generating electricity to make up for load vacancy in a load shortage period, so that the system electricity purchasing cost and the waste wind and light discarding electric quantity can be effectively reduced, and the effective utilization of energy is realized.
The robust planning method of the electro-hydrogen energy system considering electro-hydrogen coupling and ladder carbon transaction mechanism can improve the configuration capacity of renewable energy sources, effectively reduce the carbon emission of the system and can powerfully promote the development of a low-carbon clean energy network.

Claims (5)

1. The robust planning method of the electro-hydrogen energy system considering electro-hydrogen coupling is characterized by comprising the following steps of:
step one: establishing an electric hydrogen coupling model by considering the mutual conversion of electric hydrogen energy sources;
step two: introducing a carbon transaction mechanism, and constructing a ladder carbon transaction cost calculation model;
step three: establishing a double-layer planning model of the electro-hydrogen energy system under the condition of considering electro-hydrogen coupling and a ladder carbon transaction mechanism;
the electric hydrogen energy system double-layer planning model comprises an upper layer model and a lower layer model;
the decision variables of the upper model comprise the capacity of the wind turbine, the capacity of the photovoltaic unit and the capacity of the storage battery; the objective function of the upper model is:
Figure QLYQS_1
；
in the formula ：
Figure QLYQS_14
is the total number of scenes; />
Figure QLYQS_9
For scene->
Figure QLYQS_16
Weight coefficient of (2); />
Figure QLYQS_7
The total cost for multi-scenario planning; />
Figure QLYQS_17
For scene->
Figure QLYQS_5
The total cost of the system is reduced; />
Figure QLYQS_11
For scene->
Figure QLYQS_3
Equipment investment cost in the power-down hydrogen energy system; />
Figure QLYQS_13
For scene->
Figure QLYQS_2
Carbon transaction cost in the power-down hydrogen energy system; />
Figure QLYQS_10
For scene->
Figure QLYQS_4
Equipment maintenance cost in the electricity-down hydrogen energy system; />
Figure QLYQS_12
For scene->
Figure QLYQS_8
Electricity purchasing cost in the electricity hydrogen energy system; />
Figure QLYQS_15
For scene->
Figure QLYQS_6
The wind and light discarding punishment cost is reduced in the power-down hydrogen energy system;
decision variables of the lower model include the capacity of the alkaline electrolyzer, the capacity of the hydrogen fuel cell, the capacity of the hydrogen storage tank; the objective function of the underlying model is:
Figure QLYQS_18
；
in the formula ：
Figure QLYQS_19
for the objective function value of the lower model, +.>
Figure QLYQS_20
In an electro-hydrogen energy system, a wind turbine generator is arranged in the electro-hydrogen energy systemtThe actual amount of consumption at the moment; />
Figure QLYQS_21
In an electro-hydrogen energy system, a photovoltaic unit is arranged in the electro-hydrogen energy systemtThe actual amount of consumption at the moment; />
Figure QLYQS_22
For wind turbine generator systemtGenerating capacity at moment; />
Figure QLYQS_23
For the photovoltaic unittGenerating capacity at moment, T is total time;
constraints in the upper model and the lower model include:
and (3) the access capacity constraint of the wind turbine generator and the photovoltaic turbine generator is as follows:
Figure QLYQS_24
；
Figure QLYQS_25
；
in the formula ：
Figure QLYQS_26
for the capacity of the wind turbine, the capacity is%>
Figure QLYQS_27
Maximum capacity allowed to be accessed for the wind turbine generator; />
Figure QLYQS_28
Accessing capacity for photovoltaic units, < >>
Figure QLYQS_29
The maximum capacity allowed to be accessed for the photovoltaic unit;
battery constraints, including battery power rating constraints, battery capacity constraints, and battery state of charge constraints:
Figure QLYQS_30
；
Figure QLYQS_31
；
Figure QLYQS_32
；
in the formula ,
Figure QLYQS_34
for the rated power of the storage battery,/>
Figure QLYQS_38
is the lower limit of the rated power of the storage battery; />
Figure QLYQS_40
Is the lower limit of the rated power of the storage battery; />
Figure QLYQS_35
Is the capacity of the accumulator; />
Figure QLYQS_37
Is the upper limit of the capacity of the storage battery; />
Figure QLYQS_39
Is the lower limit of the capacity of the storage battery; />
Figure QLYQS_41
The state of charge of the storage battery at the moment t; />
Figure QLYQS_33
Is the upper limit of the state of charge of the battery; />
Figure QLYQS_36
Is the lower limit of the state of charge of the battery;
alkaline electrolyzer and hydrogen fuel cell capacity constraints:
Figure QLYQS_42
；
in the formula ,
Figure QLYQS_43
is the capacity of the alkaline electrolyzer; />
Figure QLYQS_44
Is the capacity of the hydrogen fuel cell; />
Figure QLYQS_45
Is the maximum capacity of the alkaline electrolyzer; />
Figure QLYQS_46
Is the maximum capacity of the hydrogen fuel cell;
step four: combining the multi-scene confidence interval decision theory with an upper model, and forming a confidence interval robust planning model of the electric hydrogen energy system together with a lower model;
the confidence interval robust planning model formed by combining the multi-scene confidence interval decision theory and the upper model is as follows:
Figure QLYQS_47
；
wherein ,
Figure QLYQS_50
for confidence robustness, ++>
Figure QLYQS_60
For uncertain variable confidence level, +.>
Figure QLYQS_66
For scene->
Figure QLYQS_49
Uncertainty variable confidence level below, +.>
Figure QLYQS_58
For scene->
Figure QLYQS_64
Uncertainty of the uncertainty variable, +.>
Figure QLYQS_70
For scene->
Figure QLYQS_51
The level of significance of the object is determined,ω s for scene->
Figure QLYQS_56
Weight coefficient of>
Figure QLYQS_61
For decision variable matrix->
Figure QLYQS_67
For scene->
Figure QLYQS_52
Lower part(s)tWind power output in time period->
Figure QLYQS_57
For scene->
Figure QLYQS_63
Lower part(s)tThe photovoltaic output is generated in a period of time,
Figure QLYQS_69
for scene->
Figure QLYQS_54
The optimal solution of the deterministic model is determined; />
Figure QLYQS_59
Representing a wind power confidence uncertainty interval; />
Figure QLYQS_65
To represent a photovoltaic confidence uncertainty interval, +.>
Figure QLYQS_71
For scene->
Figure QLYQS_48
Lower part(s)tTime period wind power predictive value->
Figure QLYQS_55
For scene->
Figure QLYQS_62
Lower part(s)tPeriod photovoltaic prediction value->
Figure QLYQS_68
For the inverse cumulative distribution function of wind power, +.>
Figure QLYQS_53
Is the inverse cumulative distribution function of the photovoltaic;
step five: and solving the confidence interval robust planning model of the electro-hydrogen energy system by using an improved firework algorithm based on a elimination tournament mechanism.
2. The robust planning method of hydrogen power system according to claim 1, wherein in the hydrogen coupling model, the hydrogen coupling unit is composed of a hydrogen fuel cell, an alkaline electrolyzer and a hydrogen storage tank, and the following steps are shown:
the output power of the alkaline electrolytic cell is:
Figure QLYQS_72
；
in the formula ：
Figure QLYQS_73
the output power of the alkaline electrolytic cell; />
Figure QLYQS_74
The input power of the alkaline electrolytic cell; />
Figure QLYQS_75
Is the efficiency of the alkaline electrolytic cell;
the output power of the hydrogen fuel cell is as follows:
Figure QLYQS_76
；
in the formula ：
Figure QLYQS_77
is the output power of the hydrogen fuel cell; />
Figure QLYQS_78
For the input power of the hydrogen fuel cell, +.>
Figure QLYQS_79
Is hydrogen fuel cell efficiency;
the mathematical model of the hydrogen storage tank is expressed as:
Figure QLYQS_80
；
in the formula ：
Figure QLYQS_81
is thattEnergy stored in the hydrogen storage tank at any time; />
Figure QLYQS_82
Is thatt-1 time the energy stored by the hydrogen storage tank; />
Figure QLYQS_83
The efficiency of the input to the hydrogen storage tank for the alkaline electrolyzer; />
Figure QLYQS_84
Efficiency of outputting to the hydrogen fuel cell for the hydrogen storage tank; />
Figure QLYQS_85
The working efficiency of the hydrogen storage tank is;
the maximum input power of the alkaline electrolyzer and the maximum output power of the hydrogen fuel cell are expressed as:
Figure QLYQS_86
；
Figure QLYQS_87
；
in the formula ：
Figure QLYQS_88
the maximum input power of the alkaline electrolytic cell; />
Figure QLYQS_89
Maximum output power for hydrogen fuel cell, < >>
Figure QLYQS_90
An upper limit of energy storage capacity of the hydrogen storage tank; />
Figure QLYQS_91
A lower limit of the energy storage capacity of the hydrogen storage tank; />
Figure QLYQS_92
For the capacity of the hydrogen storage tank>
Figure QLYQS_93
In time steps.
3. The robust planning method of hydrogen-electricity energy system according to claim 2, wherein the step two model for calculating the step carbon transaction cost is:
Figure QLYQS_94
；
in the formula ：
Figure QLYQS_95
cost for carbon trade; />
Figure QLYQS_96
A trade price for carbon; />
Figure QLYQS_97
Is a carbon emission price increase coefficient; />
Figure QLYQS_98
Is the total carbon emission; />
Figure QLYQS_99
Is an excess region of carbon emissions; />
Figure QLYQS_100
Is carbon emission quota.
4. The robust planning method of the electric hydrogen energy system considering electric hydrogen coupling according to claim 3, wherein the improved firework algorithm based on the elimination tournament mechanism adopts a self-adaptive dynamic radius adjustment strategy to consider heuristic information in the optimization process, and the firework explosion radius is dynamically adjusted according to the evolution results of different iteration stages of the algorithm, namely the information of the current optimal firework position from other firework positions; the number of explosion sparks of each firework depends on the ranking of the fitness value of the firework, and the number of sparks of each firework is determined by adopting the self-adaptive dynamic explosion sparks;
Figure QLYQS_101
；
in the formula ：
Figure QLYQS_103
is->
Figure QLYQS_107
Fireworks +.>
Figure QLYQS_113
Number of explosion sparks of generation; />
Figure QLYQS_104
Is->
Figure QLYQS_108
Fireworks +.>
Figure QLYQS_112
Number of explosion sparks of generation;
Figure QLYQS_115
is->
Figure QLYQS_102
Fireworks +.>
Figure QLYQS_106
A fitness value of the generation; />
Figure QLYQS_110
Is->
Figure QLYQS_114
Fireworks +.>
Figure QLYQS_105
A fitness value of the generation; />
Figure QLYQS_109
A first adjustment factor for the number of sparks; />
Figure QLYQS_111
And the second adjustment coefficient is the spark number.
5. The robust planning method of an electro-hydrogen energy system with electro-hydrogen coupling in mind of claim 4, wherein the specific solution process of step five is as follows:
s1: inputting power grid parameters, wind power, photovoltaic annual historical data and load data; setting population scale and iteration times; carrying out multidimensional scene clustering on wind power and photovoltaic annual historical data to obtain the weight occupied by each scene;
s2: initializing an upper firework population, and randomly generating planning individuals of the upper firework population, wherein the dimensions of the individuals of the upper firework population are three-dimensional, and the dimensions comprise the capacity of a wind turbine generator, the capacity of a photovoltaic unit and the capacity of a storage battery;
s3: clustering wind power and photovoltaic annual historical data, and optimally solving an upper deterministic model to obtain an optimal solutionf 0 The method comprises the steps of carrying out a first treatment on the surface of the Solving the confidence level and the upper layer objective function value of all fireworks in each scene;
s4: sequencing all fireworks according to confidence level;
s5: determining the explosion radius and the explosion spark number of each firework according to the self-adaptive dynamic radius and the self-adaptive dynamic spark number;
s6: generating explosion sparks and variation sparks according to algorithm parameters, and carrying out boundary mapping on sparks beyond boundaries;
s7: predicting the final objective function value of each firework based on a prediction mechanism, and eliminating and re-initializing the firework which does not meet the conditions;
s8: updating the firework population and repeating the steps S4 to S7, and continuously iterating until the maximum iteration number of the upper model is reached to obtain a current optimization scheme;
s9: initializing to generate a lower firework population, randomly generating planned individuals, wherein the dimensions of the individuals of the lower firework population are three-dimensional, and the lower firework population comprises the capacities of an electrolytic tank, a hydrogen fuel cell and a hydrogen storage tank; substituting the optimization scheme obtained by the upper layer into calculation to obtain a lower layer objective function value;
s10: updating the firework population and repeating the steps S5 to S7, and continuously iterating until the maximum iteration times of the lower model are reached to obtain a current optimization scheme;
s11: updating the firework population, repeating the steps S4 to S10, and continuously iterating until the maximum iteration number of the confidence interval robust planning model of the whole electric hydrogen energy system is reached, and obtaining an optimal planning scheme.
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