CN115618723A - Hydrogen production network operation method considering quitting of coal-fired unit and gas station - Google Patents

Hydrogen production network operation method considering quitting of coal-fired unit and gas station Download PDF

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CN115618723A
CN115618723A CN202211200170.5A CN202211200170A CN115618723A CN 115618723 A CN115618723 A CN 115618723A CN 202211200170 A CN202211200170 A CN 202211200170A CN 115618723 A CN115618723 A CN 115618723A
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张弦
殷悦
高小岩
王怀智
王贵斌
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Shenzhen Graduate School Harbin Institute of Technology
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Abstract

The invention relates to a hydrogen production network operation method considering quitting of a coal-fired unit and a gas station. The method comprises the following steps of S1, establishing a low-carbon modification model of the power system by using the coal-fired unit exit cost, the electric power line construction cost, the new energy investment cost and the power generation operation cost which are the minimum and the maximum carbon emission reduction amount as the target; s2, optimizing and solving the model through a genetic algorithm; s3, determining a low-carbon emission node through a carbon emission model, establishing a hydrogen generation station on the node, and coupling a power network, a gas network and a hydrogen network into a comprehensive energy network; s4, establishing a comprehensive energy low-carbon modification model according to the minimum cost of withdrawing a gas station, the minimum cost of pipeline construction, the minimum cost of hydrogen station operation and the minimum cost of buying electricity and buying gas to prepare hydrogen and the maximum target of carbon emission reduction; and S5, optimizing and solving the model through a genetic algorithm. The method reduces the operation cost of the comprehensive energy network and the carbon emission of the hydrogen energy supply side of the electric automobile, and has important significance for replacing the traditional coal and petroleum resources and promoting the development of the hydrogen energy automobile.

Description

Hydrogen production network operation method considering quitting of coal-fired unit and gas station
Technical Field
The invention relates to the field of low-carbon optimization of a comprehensive energy system, in particular to a hydrogen production network operation method considering quitting of a coal-fired unit and a gas station.
Background
Energy crisis and climate warming become key problems restricting sustainable development of human society, and energy and traffic are two major sources of carbon dioxide emission. The hydrogen energy automobile takes hydrogen as fuel, has the characteristics of high energy conversion efficiency, zero emission and the like, and can effectively replace the traditional fuel oil automobile and reduce carbon emission. However, the low carbon capacity of the hydrogen energy vehicle is closely related to the hydrogen production mode of the hydrogen energy, and a large amount of carbon emission is still generated in the hydrogen production process. The hydrogen energy automobile does not realize real whole-course zero carbon emission, but transfers the carbon emission to the fossil energy power generation link.
In view of this, it is necessary to provide a low-carbon optimization method of a comprehensive energy system, which is helpful for low-carbon operation of a power system, reduces hydrogen and carbon emission, and promotes popularization and development of hydrogen energy vehicles.
Disclosure of Invention
The invention provides a hydrogen production network operation method considering quitting of a coal-fired unit and a gas station, aiming at effectively reducing the operation cost of a comprehensive energy network and the carbon emission of a hydrogen energy supply side of an electric automobile by considering quitting of the coal-fired unit and quitting of the gas station and considering the operation planning of the hydrogen production network, and having important significance for replacing the traditional coal and petroleum resources and promoting the development of hydrogen energy automobiles.
The invention provides a hydrogen production network operation method considering quitting of a coal-fired unit and a gas station, which comprises the following steps:
s1, a low-carbon transformation stage of the power system: establishing a dual-target low-carbon modification model of the power system with the coal-fired unit exit cost, the electric power line construction cost, the new energy investment cost and the power generation operation cost being the minimum and the carbon emission reduction being the maximum targets;
s2, performing optimization solution on the low-carbon transformation model of the power system through a genetic algorithm to determine an optimal output result;
s3, determining a low-carbon emission node through a carbon emission model, establishing a hydrogen generation station on the node, and coupling a power network, a gas network and a hydrogen network into a comprehensive energy network;
s4, comprehensive energy low-carbon transformation stage: establishing a dual-target comprehensive energy low-carbon modification model by using the lowest cost of withdrawing a gas station, the lowest cost of constructing pipelines, the lowest cost of operating a hydrogen station and the lowest cost of buying electricity and buying hydrogen and the highest target of reducing the carbon emission;
and S5, optimizing and solving the comprehensive energy low-carbon transformation model through a genetic algorithm, and determining an optimal output result.
The required relevant data comprises the load power of the power system, the capacity of the coal-fired unit, the actual output and capacity of the photovoltaic unit and the actual output and capacity of the wind power unit in the step S1; the unit maintenance cost, the quit cost and the disposal income of the coal-fired unit, the reference age of the coal-fired unit and the unit construction cost of the electric power pipeline. The calculation of the required related data comprises the load capacity of the hydrogen filling station, the position of the gas station, the energy required by hydrogen production of the electrolyzed water unit, the energy required by hydrogen production of the steam methane reforming unit, the market price of electric power and natural gas, the unit construction cost of a hydrogen pipeline and a natural gas pipeline, and the carbon emission factor of each energy source in the step S4.
The invention has the beneficial effects that: and the energy network is improved to realize the real low-carbon clean hydrogen production. The hydrogen energy automobile is an effective substitute of a fuel oil automobile, carbon emission can be greatly reduced, but the hydrogen production mode and the electric energy source can influence the carbon emission generated by hydrogen production. On the premise of meeting the load requirement of a power system, new energy sources such as wind power, photoelectricity and the like are reasonably utilized to replace thermal power for power generation. Meanwhile, the hydrogen production station is built on the node with low carbon emission intensity, so that the energy source for hydrogen production is cleaner. A reasonable hydrogen production mode is selected by optimizing a system for coupling electric power, natural gas and hydrogen. The coal-fired unit and the gas station are withdrawn while lines, pipelines and other equipment are built, and the emission of carbon dioxide is further reduced. The contradiction between the cost and the environmental requirement is solved, and a comprehensive energy planning scheme with the minimum average emission reduction cost is provided.
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FIG. 1 is a flow chart of a method of operating a hydrogen production network in accordance with the present invention that considers the exit of a coal-fired unit and a gas station;
FIG. 2 is a flow chart of solving low carbon modification of a power system by using a genetic algorithm in the present invention;
FIG. 3 is a flow chart of the present invention for solving the low carbon transformation of an integrated energy system using a genetic algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments.
According to the invention, the low-carbon transformation is carried out on the power system, a coal-fired unit with high carbon emission is withdrawn, and a renewable energy power station is introduced. And obtaining an optimal planning scheme of the coal-fired unit, the renewable energy source and the power transmission line by adopting a genetic algorithm. And (3) constructing a hydrogen generation station at a low-carbon emission intensity node by applying a carbon emission flow model, and coupling an electricity, gas and hydrogen network. By carrying out low-carbon transformation on the comprehensive energy system, withdrawing from a gas station with high carbon emission and building a hydrogenation station, dynamically selecting a proper hydrogen production mode, and further reducing the carbon emission. And obtaining the optimal planning scheme of the gas station, the gas network pipeline, the hydrogen station and the hydrogen production ratio by adopting a genetic algorithm. According to the invention, the power system and the comprehensive energy system are improved, high carbon emission equipment such as a coal-fired unit and a gas station is withdrawn, the permeability of renewable energy is increased, and the carbon emission on the energy supply side is reduced.
As shown in figure 1, the invention adopts a hydrogen production network optimization operation method considering coal-fired unit exit and gas station exit, which comprises the following steps:
s1, a low-carbon transformation stage of the power system: establishing a dual-target low-carbon power system transformation model with the goals of minimum coal-fired unit exit cost, electric power line construction cost, new energy investment cost and power generation operation cost and maximum carbon emission reduction; the method specifically comprises the following steps.
S11, in the low-carbon transformation stage of the power system, the minimum unit emission reduction cost is taken as an objective function, and the objective function is as follows:
Figure BDA0003872158030000031
in the formula, C CFPP For the total cost of exit of the coal-fired unit, C Line Is the construction cost of the power line, C Invest Investment cost of new energy, C Operation Is the operating cost of the power plant, Δ R 1 Is the carbon emission reduction amount. The calculation process of the respective costs includes the following steps.
S111, the total exit cost of the coal-fired unit: in this embodiment, the total exit cost of the coal-fired unit mainly includes two parts, one part is the exit cost of the coal-fired unit, the other part is the maintenance cost of the coal-fired unit, and the objective function is:
Figure BDA0003872158030000041
in the formula (I), the compound is shown in the specification,
Figure BDA0003872158030000042
the exit cost of the ith coal-fired unit in the y year;
Figure BDA0003872158030000043
the exit rate of the ith coal-fired unit in the y year is shown;
Figure BDA0003872158030000044
the unit dismantling cost of the coal-fired unit;
Figure BDA0003872158030000045
the unit residual value income of the coal-fired unit;
Figure BDA0003872158030000046
the unit maintenance cost of the coal-fired unit;
Figure BDA0003872158030000047
the initial capacity of the ith coal-fired unit; epsilon is the discount rate of net present value calculation; r is a radical of hydrogen m Increased maintenance rates for aging of coal-fired units;
Figure BDA0003872158030000048
the reference age of the ith coal-fired unit; and Y is the low-carbon transformation life of the power system.
S112, construction cost of the power line: in this embodiment, the capacity of the transmission line is limited, and the network needs to be expanded to meet the increased load demand with better power flow distribution, so that a power line needs to be constructed. The objective function is as follows:
Figure BDA0003872158030000049
in the formula (I), the compound is shown in the specification,
Figure BDA00038721580300000410
the number of transmission lines of the i node, the j node in the y year,
Figure BDA00038721580300000411
the number of the bits is i,the cost of building lines between j nodes.
S113, investment cost of new energy: in this embodiment, after the coal-fired unit exits, the power supply side in the power system is reduced, and therefore energy should be compensated. Renewable energy is an effective alternative scheme of a coal-fired unit, and clean energy such as photovoltaic energy and wind energy can effectively reduce carbon emission in a power system. Therefore, a new energy power station needs to be added to the power system modification, and the objective function is as follows:
Figure BDA0003872158030000051
in the formula, c PV The unit construction cost of the photovoltaic power station is reduced; c. C W The unit construction cost of the wind power station;
Figure BDA0003872158030000052
building the capacity of the photovoltaic power station at the i node for the y year;
Figure BDA0003872158030000053
and building the capacity of the wind power station at the i node for the y year.
S114, operating cost of a power station: the coal-fired unit needs to operate to consume fuel cost, and the objective function is as follows:
Figure BDA0003872158030000054
in the formula, a i ，b i ，c i The fuel cost coefficient of the ith coal-fired generator;
Figure BDA0003872158030000055
the output power of the ith coal-fired unit at the time t of the y year; d t,y The running time of the coal-fired generator at the time t of the y year.
S115, carbon emission reduction: the carbon emission amount of the power system before and after low-carbon transformation is deviated, the node carbon emission intensity of the load side before and after transformation is reduced in the embodiment, and the objective function is as follows:
Figure BDA0003872158030000056
in the formula, R 1 The carbon emission before low-carbon transformation of the power system is achieved; r is 1 ' the carbon emission after low-carbon transformation of the power system;
Figure BDA0003872158030000057
load power at the inode;
Figure BDA0003872158030000058
before modification, the carbon emission intensity at the i node is improved;
Figure BDA0003872158030000059
the carbon emission intensity at the i node is improved.
S12, the constraint conditions of the low-carbon transformation stage of the power system comprise: the power system flow constraint and renewable energy power station comprises the constraints of a photovoltaic unit and a wind generating set, the constraint of system rotation reserve capacity, the constraint of a power system transmission line, the power generation constraint of a coal-fired unit and the quit constraint. The specific constraint calculation is as follows.
S121, power flow constraint of the power system:
in order to ensure the normal operation of the power system, some variables in the power flow problem should satisfy certain power balance and phase angle balance conditions, and the expression is as follows:
Figure BDA00038721580300000510
in the formula (I), the compound is shown in the specification,
Figure BDA00038721580300000511
is the output power of the generator;
Figure BDA00038721580300000512
the output power of the photovoltaic unit;
Figure BDA00038721580300000513
the output power of the wind turbine generator is obtained;
Figure BDA0003872158030000061
is the power flow from node i to node j;
Figure BDA0003872158030000062
is the line load;
Figure BDA0003872158030000063
admittance of an i node to a j node; theta ij,t,y Is the phase angle difference from the i node to the j node; s ij,t,y Is the apparent power from inode to j node.
S122, constraint of a renewable energy power station (photovoltaic unit and wind power unit):
the power generated by renewable energy sources and the quantity of equipment thereof influence the balance of a system, in order to better adapt to the exit of a coal-fired unit and meet the shortage of thermal power, the addition of a renewable energy power station needs to restrict the power and the quantity thereof, and the expression is as follows:
Figure BDA0003872158030000064
Figure BDA0003872158030000065
Figure BDA0003872158030000066
in the formula, g PV And g W Output power equations of a photovoltaic power station and a wind power station are respectively;
Figure BDA0003872158030000067
and
Figure BDA0003872158030000068
the capacities of the photovoltaic power station and the wind power station at the i node in the y year respectively; the capacity of the renewable energy power station in the y year is equal to the capacity of the last year plus the capacity of the renewable energy power station in this year.
S123, system rotation reserve capacity constraint:
in order to balance the load fluctuation and the load prediction error of the power grid, the system should reserve the reserve capacity, and according to the technical guidance of the power system, the rotation reserve rate should be controlled to be 2% -5%, and the expression is as follows:
Figure BDA0003872158030000069
in the formula (I), the compound is shown in the specification,
Figure BDA00038721580300000610
reserve capacity for total system rotation; s i,t The rotational reserve capacity that the ith generator can provide at time t;
Figure BDA00038721580300000611
the initial capacity of the ith generator;
Figure BDA00038721580300000612
the output power of the ith generator;
Figure BDA00038721580300000613
the exit rate of the ith coal-fired unit in the y year; ζ% is the load spin up standby index; χ% is a requirement for the reserve index of upward spin due to reduced renewable energy production.
S124, power system power transmission line constraint:
the transmission capacity is restricted by physical constraints, reliability and other requirements, and the violation of the constraints can cause the system to block, especially in a power grid with weak connection. The constraint expression of the power system transmission line is as follows:
Figure BDA0003872158030000071
in the formula (I), the compound is shown in the specification,
Figure BDA0003872158030000072
is the power flow from node i to node j;
Figure BDA0003872158030000073
the maximum value of the power flow from the node i to the node j is obtained;
Figure BDA0003872158030000074
the number of transmission lines of the i, j node in the y year is determined by the construction of the previous year, and once the line is constructed, the line is not dismantled, and the constraint is as follows:
Figure BDA0003872158030000075
Figure BDA0003872158030000076
in the formula (I), the compound is shown in the specification,
Figure BDA0003872158030000077
the number of the transmission lines of the i and j nodes in the (y-1) th year.
S125, power generation constraint and quit constraint of the coal-fired unit:
the output power of the coal-fired unit has an upper limit and a lower limit, depending on its initial capacity and exit coefficient, with the constraints as follows:
Figure BDA0003872158030000078
in the formula (I), the compound is shown in the specification,
Figure BDA0003872158030000079
is the minimum value of the output power of the ith coal-fired unit, P i cap The initial capacity of the ith coal-fired unit.
The exit factor for a coal-fired unit is a natural number between 0 and 1, indicating the percentage of capacity still being used by the coal-fired unit, which should be smaller and smaller as the projected year increases. The capacity after the coal fired power plant withdrawal should therefore be lower than the last year capacity with the constraints as follows:
Figure BDA00038721580300000710
Figure BDA00038721580300000711
in the formula (I), the compound is shown in the specification,
Figure BDA00038721580300000712
the exit rate of the ith coal-fired unit in the y year is shown;
Figure BDA00038721580300000713
the withdrawal rate of the ith coal-fired unit in the y-1 year.
And S2, carrying out optimization solution on the low-carbon transformation optimization model of the power system through a genetic algorithm, and determining an optimal output result.
The solution model algorithm proposed in this example is an improved genetic algorithm, improves the search efficiency by preserving excellent individuals and increasing initialization constraints, and belongs to a global adaptive probability search algorithm. A random Halton sequence is used to generate an initial population consisting of a set of individuals (initial solutions), which are chromosomes consisting of binary gene codes. And (3) constructing a new population by the initial population through continuous selection, crossover and variation by using a bionic genetic mode, selecting a parent according to the adaptive capacity of the individuals, reserving the individuals with the highest fitness, and continuously iterating to enable the optimal individuals in the population to be continuously close to the optimal solution.
As shown in fig. 2, the genetic algorithm for low-carbon transformation of the power system mainly comprises the following steps:
s21, setting initial parameters:
encoding the collective number set, and setting initial parameters, wherein the set parameters mainly comprise population individual number, crossover and mutation probability, termination conditions, number of reserved elite individuals, number of new individual invasion and the like. In the low-carbon transformation stage of the power system, the coal-fired power generation system further comprises 6 candidate photovoltaic power stations, 6 candidate wind power stations and 7 candidate power lines besides 30 withdrawal rates of 6 coal-fired units in 5 years.
S22, population initialization:
each individual in the genetic algorithm is determined by a different chromosome, each individual is a solution of the optimization problem, and the population is a set of individuals. The invention adopts direct binary coding, and each individual is composed of a chromosome consisting of 11 binary genes. The initial population is generated by adopting a random Halton sequence, so that individuals in the generated population have low difference, the randomness is reduced, and the uniformity is improved. Generating an initial population in the power system transformation stage: 100 (number of population individuals) × 30 (amount to be solved for the withdrawal rate of the coal-fired unit), 100 (number of population individuals) × 6 (number of candidate photovoltaic power stations) × 6 (number of candidate wind power stations) and 100 (number of population individuals) × 7 (number of candidate power lines), and the withdrawal of the coal-fired unit is preliminarily restricted in an initial sequence, and initial individuals which do not meet requirements are excluded.
S23, calculating an individual fitness value:
when low-carbon transformation is carried out on an electric power system, a target function is set
Figure BDA0003872158030000081
As a function of fitness
Figure BDA0003872158030000082
Namely, it is
Figure BDA0003872158030000083
S24, elite reservation:
and (4) arranging according to the size of the individual fitness value, directly reserving the most elegant individual to the next generation population as an offspring, and skipping over a selection operator. This step speeds up the search efficiency of the genetic algorithm.
S25, selecting a parent:
and selecting excellent individuals to enter a cross mutation link through a selection operator. The invention adopts a roulette algorithm to eliminate the excellence and the disadvantage of the population, and the obtained moderate values of the objective function are accumulated and normalized. The individuals are selected in a random probability mode, the probability that the individuals in the population are selected as parents is in direct proportion to the fitness value of the individuals, and the higher the fitness value is, the higher the probability that the individuals are used as parents is. The roulette betting method comprises the following steps:
s251, calculating chromosomes in population
Figure BDA0003872158030000091
And
Figure BDA0003872158030000092
moderate value of (d):
Figure BDA0003872158030000093
s252, calculating the sum of the appropriate values of the chromosomes in the population:
Figure BDA0003872158030000094
s253, calculating the selection probability of chromosomes in the population:
Figure BDA0003872158030000095
s254, calculating the selection probability of chromosomes in the population:
Figure BDA0003872158030000096
s255. In [0,1 ]]Randomly generating a probability number r in the interval if
Figure BDA0003872158030000097
Then select the first chromosome
Figure BDA0003872158030000098
And
Figure BDA0003872158030000099
if it is used
Figure BDA00038721580300000910
Selecting the q chromosome so that
Figure BDA00038721580300000911
This is true.
S26, cross updating individuals:
crossover operation is the exchange of two individuals for respective partial genes in a certain designated way, and the operation simulates the selective crossover process of genes in the biological evolution process to form a new individual. The cross operation can greatly improve the optimizing capability of the genetic algorithm.
S27, mutation updating individuals:
the mutation operation refers to the change of certain genes in individuals, simulates the process of gene mutation in biological evolution, can form new individuals, and can maintain the diversity of populations to a certain extent. First, the mutation probability p of chromosome is set m . A random probability r is generated. When p is m When r is less than r, the mth gene of the chromosome is not mutated; when p is m When the gene is more than r, the m-th gene is subjected to mutation heredity, and the original gene code is changed. The specific process of mutation can be described as: binary code 0 is varied to 1 and code 1 is varied to 0. The global search capability of the algorithm is improved in a gene variation mode, the problem of falling into the local optimal solution can be prevented, and the solving time can be shortened.
S28, adding a new individual:
after cross variation, the invasion of the foreign individuals in the nature is simulated, and the randomly initialized individuals are added as filial generations for iteration, so that the problem of local optimum can be solved.
S29, calculating carbon emission intensity and carbon emission, judging whether the carbon emission intensity and the carbon emission meet a judgment standard, and if so, terminating and exporting an optimal coal-fired unit quit decision, a photovoltaic power station and wind power construction decision and an electric power system circuit construction decision; otherwise, the procedure returns to step S22.
S210, optimal solution decoding:
and after iteration is finished, converting the binary gene codes of the optimal solution into corresponding decimal values according to the exit decision of the optimal coal-fired unit, the photovoltaic power station and wind power construction decision and the line construction decision variables of the power system. The method comprises the following steps: the value range of the 11-bit binary system is 0-2047, the conversion is carried out according to the upper limit and the lower limit of the variable, the optimal chromosome can be converted into a decimal system, and the optimal solution is obtained, such as the gradual exit rate of 6 coal-fired units in 5 years, the photovoltaic power station and wind power construction positions and the electric power system line construction positions.
And S3, determining a low-carbon emission node through a carbon emission model, establishing a hydrogen generation station on the node, and coupling a power network, a gas network and a hydrogen network into a comprehensive energy network. The method specifically comprises the following steps.
And S31, a coal-fired unit, wind energy, solar energy, natural gas and the like in the comprehensive energy system provide primary energy sources for the system to serve as an input side.
And S32, the hydrogen production station realizes the conversion of electric energy and natural gas into hydrogen to be used as an energy conversion utilization side. The hydrogen production station is the core of mutual coupling of comprehensive energy sources, is upwards connected with an electric-gas network, and adopts two modes of electrolyzing seawater and reforming methane steam to produce hydrogen. And the hydrogen network is downwards connected and used as a hydrogen source of the hydrogen energy network.
And S33, taking the hydrogen station, the electric load and the natural gas load as the output side of the comprehensive energy system. The hydrogen is conveyed to each hydrogenation station through a conveying pipeline and then can be used by hydrogen energy automobiles in a traffic network.
And S34, outputting an optimal planning result of the power system after the power system is low-carbon in the step S2, and calculating the carbon emission intensity of each node. The node with the lower carbon emission intensity is selected as a candidate node of the coupled electro-gas-hydrogen network of the hydrogen generation station.
S4, comprehensive energy low-carbon transformation stage: the method comprises the steps of establishing a dual-target comprehensive energy low-carbon modification model by using the gas station withdrawal cost, the pipeline construction cost, the hydrogen station operation cost and the electricity buying and gas buying hydrogen production cost to be the minimum, and the maximum carbon emission reduction amount to be the target. The method comprises the following steps.
S41, in the comprehensive energy low-carbon transformation stage, the minimum unit emission reduction cost is taken as an objective function, and the objective function is as follows:
Figure BDA0003872158030000111
in the formula, C Oil Is the total cost of the gas station to exit, C In Cost of gas network and hydrogen network pipeline construction, C Opr Is the operating cost of the hydrogen station, C HP Is the operating cost of the hydrogen generation station. The calculation process of each cost includes the following steps.
S411, the gas station exit cost:
the gas station quit cost is divided into two parts, one part is the dismantling cost of the gas station, the other part is the disposal income of the gas station, and the objective function is as follows:
Figure BDA0003872158030000112
Figure BDA0003872158030000113
in the formula (I), the compound is shown in the specification,
Figure BDA0003872158030000114
the exit cost for the ith gasoline station in the y year;
Figure BDA0003872158030000115
the exit rate of the ith gasoline station in the y year;
Figure BDA0003872158030000116
unit demolition cost for gas stations;
Figure BDA0003872158030000117
a unit residual profit for the gasoline station; cap oil Is the initial capacity of the gasoline station; epsilon is the discount rate of the net present value calculation; y is the low-carbon transformation life of the comprehensive energy system. For safety reasons, the gasoline station should be retrofitted as a whole. If the gas station and the charging station are built together, the hidden danger of explosion can be buried.
S412, pipeline construction cost:
the hydrogen load is greatly increased by replacing a gas station with a hydrogenation station, more pipelines are constructed to prevent the capacity of the hydrogen system from being limited after the load information of the hydrogen system is transmitted to a power grid and a natural gas network, and the objective function is as follows:
Figure BDA0003872158030000118
in the formula (I), the compound is shown in the specification,
Figure BDA0003872158030000121
the number of the air network pipelines of the i, j nodes in the y year,
Figure BDA0003872158030000122
the cost of constructing gas network pipelines between the i, j nodes;
Figure BDA0003872158030000123
the number of hydrogen network pipelines of the node i and the node j in the y year,
Figure BDA0003872158030000124
the cost of building hydrogen network pipelines between the i, j nodes.
S413, the operation cost of the hydrogenation station is as follows:
the hydrogen station operating cost, i.e., the normal operating cost of the hydrogen network, is related to the hydrogen demand caused by the hydrogen-powered vehicle, and the objective function is as follows:
Figure BDA0003872158030000125
in the formula (I), the compound is shown in the specification,
Figure BDA0003872158030000126
the unit operation cost of the ith hydrogenation station is;
Figure BDA0003872158030000127
the hydrogen production quantity of the ith hydrogen station at the time t of the y year.
S414, the cost of hydrogen production by buying electricity and gas:
the hydrogen generation station is used as a hydrogen source of a hydrogen energy network, and two modes of seawater electrolysis and methane steam reforming are adopted for hydrogen production. The power network provides electric energy for the hydrogen production station for electrolyzing water to produce hydrogen, and the natural gas network provides natural gas for the hydrogen production station for reforming methane steam to produce hydrogen. Hydrogen production requires the purchase of electricity from the grid and natural gas from the gas grid with the objective function as follows:
Figure BDA0003872158030000128
in the formula, c Ele Is the purchase price of unit electricity quantity; c. C Gas Is the purchase price of unit natural gas;
Figure BDA0003872158030000129
the electric quantity needed to be purchased for hydrogen production of the h hydrogenation stations;
Figure BDA00038721580300001210
the natural gas purchased for the h hydrogenation stations to produce hydrogen.
S415, carbon emission reduction:
the carbon emission before the low carbon of integrated system is reformed transform and after the transformation has the deviation, and the filling station demand reduces around reforming transform in this embodiment, and the hydrogen energy demand increases, leads to total carbon emission to descend, and this objective function is as follows:
Figure BDA00038721580300001211
in the formula, R 2 The method is the carbon emission before low-carbon modification of the comprehensive energy system; r 2 The method is characterized in that the carbon emission is obtained after low-carbon transformation of the comprehensive energy system;
Figure BDA0003872158030000131
specific carbon emission intensity for gas stations;
Figure BDA0003872158030000132
the demand of a gas station before transformation;
Figure BDA0003872158030000133
the demand of the gas station after transformation;
Figure BDA0003872158030000134
the carbon emission intensity of the ith hydrogenation station in the hydrogen network in the y year;
Figure BDA0003872158030000135
the hydrogen demand of the ith hydrogen station in the hydrogen network in the y year.
S42, the constraint conditions of the comprehensive energy low-carbon transformation stage comprise: the system comprises a supply and demand balance constraint of a hydrogen generation station, a hydrogen network power flow balance constraint, a network flow constraint, a carbon emission constraint and a gas network and hydrogen network pipeline construction constraint. The specific constraint calculation is as follows.
S421, supply and demand balance constraint of a hydrogen generation station:
in order to ensure the supply and demand balance and stable operation of the hydrogen production station, the hydrogen production quantity and the load quantity need to meet the balance condition, namely, the electric power required by hydrogen production is equivalent to the natural gas and the hydrogen produced by hydrogen production, and the expression is as follows:
Figure BDA0003872158030000136
in the formula (I), the compound is shown in the specification,
Figure BDA0003872158030000137
hydrogen production capacity of 1kW electricity by adopting a seawater electrolysis hydrogen production mode;
Figure BDA0003872158030000138
is 1m 3 Hydrogen production amount corresponding to natural gas;
Figure BDA0003872158030000139
the electric power required by the hydrogen generation station h at the y-th year t;
Figure BDA00038721580300001310
the photovoltaic power and the wind power required by the hydrogen generation station h at the y-th year t moment are respectively, and the upper limit and the lower limit are restricted as follows:
Figure BDA00038721580300001311
Figure BDA00038721580300001312
Figure BDA00038721580300001313
Figure BDA00038721580300001314
in the formula (I), the compound is shown in the specification,
Figure BDA00038721580300001315
upper limits for photovoltaic power and wind power, respectively;
Figure BDA00038721580300001316
Figure BDA00038721580300001317
the upper and lower limits of the power of the coal-fired unit and the SMR equipment are respectively.
Figure BDA00038721580300001318
For hydrogen plant h in the y yearthe required airflow at time t;
Figure BDA00038721580300001319
the hydrogen capacity of the hydrogen station h at the time t is shown; the input amount of the natural gas of each hydrogen production station is used by the SMR equipment for producing hydrogen, the total amount of the natural gas of the SMR equipment in each hydrogen production station is equal to the sum of the consumption amount of each equipment, and the expression is as follows:
Figure BDA00038721580300001320
Figure BDA0003872158030000141
in the formula (I), the compound is shown in the specification,
Figure BDA0003872158030000142
supplying the gas flow of the hydrogen production station h to the SMR device i at the time t of the y year;
Figure BDA0003872158030000143
the maximum power of the hydrogen generation station h.
S422, hydrogen network power flow balance constraint:
in order to ensure the safe and stable operation of a hydrogen conveying system, a hydrogen network needs to satisfy a linearized node flow balance constraint equation, the tidal current in a hydrogen pipeline is limited within a certain range, and the expression is as follows:
Figure BDA0003872158030000144
in the formula, H n A hydrogen energy source node incidence matrix;
Figure BDA0003872158030000145
the output power of the hydrogenation station i; a. The n,l A hydrogen pipeline node incidence matrix;
Figure BDA0003872158030000146
hydrogen flow rate of the hydrogen pipeline;
Figure BDA0003872158030000147
is the hydrogen load at node n;
Figure BDA0003872158030000148
the maximum value of the output power of the hydrogenation station i.
S423, network flow constraint:
because the pipeline gas flow model is a nonlinear function of the gas pressure difference between the terminal nodes, the gas flow in the natural gas pipeline needs to be limited within a range so as to meet the safe operation of the natural gas network. In the same way, for ensuring the safe and stable operation of the hydrogen conveying system, the gas flow in the hydrogen network pipeline also needs to be restricted, and the expression is as follows:
Figure BDA0003872158030000149
Figure BDA00038721580300001410
in the formula (I), the compound is shown in the specification,
Figure BDA00038721580300001411
the upper limit and the lower limit of the natural gas pipeline flow are respectively set;
Figure BDA00038721580300001412
respectively the upper and lower limits of the hydrogen pipeline flow.
S424, carbon emission constraint:
the total carbon emission of hydrogen production needs to be less than the upper limit of the carbon emission constraint, and the expression is as follows:
Figure BDA00038721580300001413
in the formula (I), the compound is shown in the specification,
Figure BDA00038721580300001414
is the carbon emission intensity at grid i-node;
Figure BDA00038721580300001415
is the power at grid inode;
Figure BDA00038721580300001416
is the carbon emission intensity at the j node of the gas network;
Figure BDA0003872158030000151
is the power at the j node of the air grid;
Figure BDA0003872158030000152
the upper carbon emission limit assigned for year y.
S425, construction constraint of gas network and hydrogen network pipelines:
Figure BDA0003872158030000153
the number of gas network pipelines and hydrogen network pipelines of the i and j nodes in the y year is determined by the construction of the previous year, and once a line is constructed, the line is not dismantled, and the constraint is as follows:
Figure BDA0003872158030000154
in the formula (I), the compound is shown in the specification,
Figure BDA0003872158030000155
the number of the gas network pipelines and the number of the hydrogen network pipelines of the i node and the j node in the y-1 year are respectively.
And S5, optimizing and solving the comprehensive energy low-carbon modification model through a genetic algorithm, and determining an optimal output result. As shown in fig. 3, the genetic algorithm for low-carbon modification of integrated energy mainly comprises the following steps.
S51, setting initial parameters:
encoding the collective number set, and setting initial parameters, wherein the set parameters mainly comprise population individual number, crossover and mutation probability, termination conditions, number of reserved elite individuals, number of new individual invasion and the like. In the low-carbon modification stage of the comprehensive energy system, except for a hydrogen production ratio of a hydrogen production station of 24 hours in 5 years, the low-carbon modification stage further comprises 4 candidate hydrogen pipelines, 2 candidate gas network pipelines and 6 oil stations exit decisions.
S52, population initialization:
each individual in the genetic algorithm is determined by a different chromosome, each individual is a solution of the optimization problem, and the population is a set of individuals. The invention adopts direct binary coding, and each individual is composed of a chromosome consisting of 11 binary genes. The initial population is generated by adopting a random Halton sequence, so that individuals in the generated population have low difference, the randomness is reduced, and the uniformity is improved. Generating an initial population in a low-carbon transformation stage of the comprehensive energy system: 100 (number of individual population) × 5 (planning age limit) × 24 (proportion of hydrogen production by water electrolysis in 24 hours), 100 (number of individual population) × 4 (hydrogen pipeline candidate) × 2 (gas network pipeline candidate), 100 (number of individual population) × 6 (oil station exit decision), and initial constraint is carried out on the exit of the oil station in an initial sequence, and initial individuals which do not meet the requirements are excluded.
S53, calculating an individual fitness value:
when the comprehensive energy system is transformed at low carbon, a target function is set
Figure BDA0003872158030000161
As a fitness function
Figure BDA0003872158030000162
Namely that
Figure BDA0003872158030000163
S54, elite reservation:
and (4) arranging according to the size of the individual fitness value, directly reserving the most elegant individual to the next generation population as an offspring, and skipping over a selection operator. This step speeds up the search efficiency of the genetic algorithm.
S55, selecting a parent:
and selecting excellent individuals to enter a cross mutation link through a selection operator. The invention adopts a roulette algorithm to eliminate the excellence and the disadvantage of the population, and the obtained moderate values of the objective function are accumulated and normalized. The individuals are selected in a random probability mode, the probability that the individuals in the population are selected as the parents is in direct proportion to the fitness value of the individuals, and the higher the fitness value is, the higher the probability is that the individuals are used as the parents. The roulette betting method comprises the following steps:
s551, calculating chromosomes in the population
Figure BDA0003872158030000164
And
Figure BDA0003872158030000165
moderate value of (d):
Figure BDA0003872158030000166
s552, calculating the sum of the appropriate values of the chromosomes in the population:
Figure BDA0003872158030000167
s553, calculating the selection probability of chromosomes in the population:
Figure BDA0003872158030000168
s554, calculating the selection probability of chromosomes in the population:
Figure BDA0003872158030000169
s555. In [0,1 ]]Randomly generating a probability number r in the interval if
Figure BDA00038721580300001610
Then the first chromosome is selected
Figure BDA00038721580300001611
And
Figure BDA00038721580300001612
if it is used
Figure BDA00038721580300001613
Selecting the q chromosome so that
Figure BDA00038721580300001614
If true;
s56, cross updating individuals:
crossover operation is the exchange of two individuals for respective partial genes in a certain designated way, and the operation simulates the selective crossover process of genes in the biological evolution process to form a new individual. The cross operation can greatly improve the optimizing capability of the genetic algorithm.
S57, mutation updating individual:
the mutation operation refers to the change of certain genes in individuals, simulates the process of gene mutation in biological evolution, can form new individuals, and can maintain the diversity of populations to a certain extent. First, the mutation probability p of chromosome is set m . A random probability r is generated. When p is m When r is less than r, the m gene of the chromosome is not mutated; when p is m When the gene is more than r, the m gene carries out variant inheritance, and the original gene code is changed. The specific process of mutation can be described as: binary code 0 is varied to 1 and code 1 is varied to 0. The global search capability of the algorithm is improved in a gene variation mode, the problem of falling into the local optimal solution can be prevented, and the solving time can be shortened.
S58, adding a new individual:
after cross variation, the invasion of the foreign individuals in the nature is simulated, and randomly initialized individuals are added as offspring for iteration, so that the problem of local optimum can be solved.
S59, calculating carbon emission intensity and carbon emission, judging whether the carbon emission intensity and the carbon emission meet a judgment standard, and if so, terminating and deriving an optimal electrolyzed water and steam methane reforming hydrogen production proportion, a gas network pipeline and hydrogen network pipeline construction decision and a gas station exit decision; otherwise, the process returns to step S52.
S510, optimal solution decoding:
and after iteration is finished, converting the binary gene code of the optimal solution into a corresponding decimal numerical value according to the optimal hydrogen production ratio of the electrolyzed water and the steam methane reforming, the construction decision of the gas network pipeline and the hydrogen network pipeline and the exit decision variable of the gas station.
The foregoing is a further detailed description of the invention in connection with specific preferred embodiments and it is not intended to limit the invention to the specific embodiments described. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (10)

1. A hydrogen production network operation method considering quitting of a coal-fired unit and a gas station is characterized by comprising the following steps:
s1, a low-carbon transformation stage of the power system: establishing a dual-target low-carbon power system transformation model with the goals of minimum coal-fired unit exit cost, electric power line construction cost, new energy investment cost and power generation operation cost and maximum carbon emission reduction;
s2, performing optimization solution on the low-carbon transformation model of the power system through a genetic algorithm to determine an optimal output result;
s3, determining a low-carbon emission node through a carbon emission model, establishing a hydrogen generation station on the node, and coupling a power network, a gas network and a hydrogen network into a comprehensive energy network;
s4, comprehensive energy low-carbon transformation stage: establishing a dual-target comprehensive energy low-carbon modification model according to the target of the minimum cost of withdrawing a gas station, the minimum cost of pipeline construction, the minimum cost of operating a hydrogen station and the minimum cost of buying electricity and buying gas to prepare hydrogen and the maximum reduction of carbon emission;
and S5, optimizing and solving the comprehensive energy low-carbon modification model through a genetic algorithm, and determining an optimal output result.
2. The method for operating a hydrogen production network in consideration of the exit of a coal-fired unit and a gas station as claimed in claim 1, wherein the step S1 specifically comprises the steps of:
s11, in the low-carbon transformation stage of the power system, the minimum unit emission reduction cost is taken as an objective function
Figure FDA0003872158020000011
The objective function is specifically as follows:
Figure FDA0003872158020000012
in the formula, C CFPP For the total cost of exit of the coal-fired unit, C Line Is the construction cost of the power line, C Invest Investment cost of new energy, C Operation Is the operating cost of the power plant, Δ R 1 Is carbon emission reduction;
s12, the constraint conditions of the low-carbon transformation stage of the power system comprise: the power system flow constraint and renewable energy power station comprises the constraints of a photovoltaic unit and a wind generating set, the constraint of system rotation reserve capacity, the constraint of a power system transmission line, the power generation constraint of a coal-fired unit and the quit constraint.
3. The method for operating a hydrogen generation network in consideration of the exit of a coal-fired unit and a gas station according to claim 2, wherein the calculation process of each cost in the step S11 comprises the steps of:
s111, the total exit cost of the coal-fired unit: the system mainly comprises two parts, one part is the exit cost of the coal-fired unit, the other part is the maintenance cost of the coal-fired unit, and the objective function is as follows:
Figure FDA0003872158020000021
Figure FDA0003872158020000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003872158020000023
the exit cost of the ith coal-fired unit in the y year;
Figure FDA0003872158020000024
the exit rate of the ith coal-fired unit in the y year is shown;
Figure FDA0003872158020000025
the unit dismantling cost of the coal-fired unit is saved;
Figure FDA0003872158020000026
the unit residual value income of the coal-fired unit;
Figure FDA0003872158020000027
the unit maintenance cost of the coal-fired unit; p i cap The initial capacity of the ith coal-fired unit; epsilon is the discount rate of net present value calculation; r is m Increased maintenance rates for aging of coal-fired units;
Figure FDA0003872158020000028
the reference age of the ith coal-fired unit; y is the low-carbon transformation life of the power system;
s112, construction cost of the power line: the objective function is as follows:
Figure FDA0003872158020000029
in the formula (I), the compound is shown in the specification,
Figure FDA00038721580200000210
the number of transmission lines of the i node, the j node in the y year,
Figure FDA00038721580200000211
the cost of constructing lines between the i, j nodes;
s113, investment cost of new energy: adding a new energy power station in the electric power system transformation, wherein the objective function is as follows:
Figure FDA00038721580200000212
in the formula, c PV The unit construction cost of the photovoltaic power station is saved; c. C W The unit construction cost of the wind power station;
Figure FDA00038721580200000213
building the capacity of the photovoltaic power station at the i node for the y year;
Figure FDA00038721580200000214
building the capacity of the wind power station at the i node for the y year;
s114, operating cost of a power station: the coal-fired unit operates to consume fuel cost, and the objective function is as follows:
Figure FDA0003872158020000031
in the formula, a i ，b i ，c i The fuel cost coefficient of the ith coal-fired generator;
Figure FDA0003872158020000032
the output power of the ith coal-fired unit at the time t of the y year; d t,y The running time of the coal-fired generator at the time t of the y year;
s115, carbon emission reduction: the objective function is as follows:
Figure FDA0003872158020000033
in the formula, R 1 The carbon emission before low-carbon transformation of the power system is achieved; r 1 ' the carbon emission after low-carbon transformation of the power system;
Figure FDA0003872158020000034
load power at the inode;
Figure FDA0003872158020000035
before the improvement, the carbon emission intensity at the i node is improved;
Figure FDA0003872158020000036
the carbon emission intensity at the i node is improved.
4. The hydrogen production network operation method considering quitting of a coal-fired unit and a gas station according to claim 2, wherein in the step S12, the constraint condition of the low-carbon modification stage of the power system specifically comprises the following steps:
s121, power flow constraint of the power system: variables in the tidal current problem should satisfy power balance and phase angle balance conditions, and the expression is as follows:
Figure FDA0003872158020000037
Figure FDA0003872158020000038
in the formula (I), the compound is shown in the specification,
Figure FDA0003872158020000039
is the output power of the generator;
Figure FDA00038721580200000310
the output power of the photovoltaic unit;
Figure FDA00038721580200000311
the output power of the wind turbine generator is obtained;
Figure FDA00038721580200000312
is the power flow from node i to node j;
Figure FDA00038721580200000313
is the line load;
Figure FDA00038721580200000314
admittance of an i node to a j node; theta.theta. ij,t,y Is the phase angle difference from the i node to the j node; s. the ij,t,y Apparent power from inode to j node;
s122, the renewable energy power station comprises constraints of a photovoltaic unit and a wind generating unit: the power generated by the renewable energy source and the number of devices of the renewable energy source are constrained, and the expression is as follows:
Figure FDA00038721580200000315
Figure FDA00038721580200000316
Figure FDA0003872158020000041
in the formula, g PV And g W Output power equations of a photovoltaic power station and a wind power station are respectively;
Figure FDA0003872158020000042
and
Figure FDA0003872158020000043
the capacities of the photovoltaic power station and the wind power station at the i node in the y year respectively; the year y can be repeatedThe capacity of a renewable energy power station in the year is equal to the capacity of the last year plus the capacity of the renewable energy power station in the year;
s123, system rotation reserve capacity constraint: controlling the rotation standby rate, wherein the expression is as follows:
Figure FDA0003872158020000044
in the formula (I), the compound is shown in the specification,
Figure FDA0003872158020000045
reserve capacity for total system rotation; s i,t The rotational reserve capacity that the ith generator can provide at time t;
Figure FDA0003872158020000046
the initial capacity of the ith generator;
Figure FDA0003872158020000047
the output power of the ith generator;
Figure FDA0003872158020000048
the exit rate of the ith coal-fired unit in the y year;
Figure FDA0003872158020000049
rotating the standby index upward for the load; chi% is the requirement of upward rotating reserve index caused by the reduction of renewable energy yield;
s124, power system power transmission line constraint: the expression is as follows:
Figure FDA00038721580200000410
in the formula (I), the compound is shown in the specification,
Figure FDA00038721580200000411
power flow from i node to j node；
Figure FDA00038721580200000412
The maximum value of the power flow from the node i to the node j is obtained;
Figure FDA00038721580200000413
the number of transmission lines of the i, j node in the y year is defined as follows:
Figure FDA00038721580200000414
Figure FDA00038721580200000415
in the formula (I), the compound is shown in the specification,
Figure FDA00038721580200000416
the number of the transmission lines of the i, j nodes in the y-1 year;
s125, power generation constraint and quit constraint of a coal-fired unit: the output power of the coal-fired unit has an upper limit and a lower limit, and is limited as follows according to the initial capacity and the exit coefficient:
Figure FDA00038721580200000417
in the formula (I), the compound is shown in the specification,
Figure FDA00038721580200000418
is the minimum value of the output power of the ith coal-fired unit, P i cap The initial capacity of the ith coal-fired unit;
the capacity after the coal fired power plant withdrawal is lower than the capacity of the last year, with the constraints as follows:
Figure FDA0003872158020000051
Figure FDA0003872158020000052
in the formula (I), the compound is shown in the specification,
Figure FDA0003872158020000053
the exit rate of the ith coal-fired unit in the y year is shown;
Figure FDA0003872158020000054
the withdrawal rate of the ith coal-fired unit in the y-1 year.
5. The method for operating a hydrogen production network in consideration of the quit of a coal-fired unit and a gas station as claimed in claim 1, wherein the step S2 specifically comprises:
s21, setting initial parameters: in the low-carbon transformation stage of the power system, the initial parameters comprise the number of population individuals, the quantity to be solved of the withdrawal rate of the coal burner group, the number of candidate photovoltaic power stations, the number of candidate wind power stations and the number of candidate power lines;
s22, population initialization: direct binary coding is adopted, and each individual is composed of a chromosome consisting of 11 binary genes; generating an initial population by adopting a random Halton sequence, and generating the initial population at the power system transformation stage: the method comprises the following steps of counting the number of population individuals, the number of candidate photovoltaic power stations, the number of candidate power lines, and preliminarily constraining the exit of the coal-fired unit in an initial sequence to eliminate initial individuals which do not meet requirements;
s23, calculating an individual fitness value: when low-carbon transformation is carried out on the electric power system, an objective function is set
Figure FDA0003872158020000055
As a function of fitness
Figure FDA0003872158020000056
Namely, it is
Figure FDA0003872158020000057
S24, elite reservation: arranging according to the size of the individual fitness value, directly reserving the most elegant individual to the next generation population as an offspring, and skipping over a selection operator;
s25, selecting a parent: adopting a roulette algorithm to eliminate the excellence and the disadvantage of the population, accumulating the obtained moderate values of the objective function and carrying out normalization processing; the method specifically comprises the following steps:
s251, calculating chromosomes in population
Figure FDA0003872158020000058
And
Figure FDA0003872158020000059
moderate value of (d):
Figure FDA00038721580200000510
s252, calculating the sum of the appropriate values of the chromosomes in the population:
Figure FDA00038721580200000511
s253, calculating the selection probability of chromosomes in the population:
Figure FDA00038721580200000512
s254, calculating the selection probability of chromosomes in the population:
Figure FDA0003872158020000061
S255.in the [0,1 ]]Randomly generating a probability number r within the interval if
Figure FDA0003872158020000062
Then the first chromosome is selected
Figure FDA0003872158020000063
And
Figure FDA0003872158020000064
if it is not
Figure FDA0003872158020000065
Selecting the q chromosome so that
Figure FDA0003872158020000066
Establishing;
s26, cross updating individuals: exchanging the two individuals according to a certain designated mode for respective partial genes;
s27, variation updating of individuals: setting mutation probability p of chromosome m Generating a random probability r, when p m When r is less than r, the mth gene of the chromosome is not mutated; when p is m When the gene is more than r, the mth gene carries out variant inheritance, and the original gene code is changed;
s28, adding a new individual: after cross variation, simulating the invasion of a foreign individual in nature, and adding a randomly initialized individual as a filial generation for iteration;
s29, calculating carbon emission intensity and carbon emission, judging whether the carbon emission intensity and the carbon emission meet a judgment standard, and if so, terminating and leading out an exit decision of an optimal coal-fired unit, a photovoltaic power station and wind power construction decision and a power system line construction decision; otherwise, returning to the step S22;
s210, optimal solution decoding: and after iteration is finished, converting the binary gene codes of the optimal solution into corresponding decimal values according to the exit decision of the optimal coal-fired unit, the photovoltaic power station and wind power construction decision and the line construction decision variables of the power system.
6. The method for operating a hydrogen production network in consideration of quitting of coal-fired units and gas stations as claimed in claim 1, wherein the step S3 specifically comprises:
s31, a coal-fired unit, wind energy, solar energy and natural gas in the comprehensive energy system provide primary energy of energy sources for the system to serve as an input side;
s32, converting electric energy and natural gas into hydrogen by the hydrogen generation station to be used as an energy conversion utilization side;
s33, taking the hydrogen station, the electric load and the natural gas load as the output side of the comprehensive energy system;
and S34, outputting an optimal planning result of the power system after the power system is low-carbon in the step S2, calculating the carbon emission intensity of each node, and selecting the node with lower carbon emission intensity as a candidate node of the coupling electricity-gas-hydrogen network of the hydrogen generation station.
7. The method for operating a hydrogen production network in consideration of quitting of a coal-fired unit and a gas station according to claim 1, wherein the step S4 specifically comprises the steps of:
s41, in the comprehensive energy low-carbon transformation stage, the minimum unit emission reduction cost is taken as an objective function, and the objective function is as follows:
Figure FDA0003872158020000071
in the formula, C Oil Is the total cost of the gas station to exit, C In Is the construction cost of gas network and hydrogen network pipelines, C Opr Is the operating cost of the hydrogen station, C HP Is the operating cost of the hydrogen plant;
s42, the constraint conditions of the comprehensive energy low-carbon transformation stage comprise: supply and demand balance constraint, hydrogen network power flow balance constraint, network flow constraint, carbon emission constraint, and gas network and hydrogen network pipeline construction constraint of the hydrogen generation station.
8. The method for operating a hydrogen production network in consideration of the exit of a coal-fired unit and a gas station as set forth in claim 7, wherein the calculation process of each cost in S41 includes the steps of:
s411, the gas station exit cost: the method is divided into two parts, wherein one part is the dismantling cost of the gas station, the other part is the disposal income of the gas station, and the objective function is as follows:
Figure FDA0003872158020000072
Figure FDA0003872158020000073
in the formula (I), the compound is shown in the specification,
Figure FDA0003872158020000074
the exit cost for the ith gasoline station in the y year;
Figure FDA0003872158020000075
the exit rate of the ith gasoline station in the y year;
Figure FDA0003872158020000076
unit demolition cost for gas stations;
Figure FDA0003872158020000077
is the unit residual value income of the gas station; cap oil Is the initial capacity of the gasoline station; epsilon is the discount rate of net present value calculation; y is the low-carbon transformation life of the comprehensive energy system;
s412, pipeline construction cost:
Figure FDA0003872158020000078
in the formula (I), the compound is shown in the specification,
Figure FDA0003872158020000079
is node i, j atThe number of the air network pipelines in y years,
Figure FDA00038721580200000710
the cost of constructing gas network pipelines between the i, j nodes;
Figure FDA0003872158020000081
the number of hydrogen network pipelines of the i, j nodes in the y year,
Figure FDA0003872158020000082
the cost of building hydrogen network pipelines between the i, j nodes;
s413, the operation cost of the hydrogenation station is as follows:
Figure FDA0003872158020000083
in the formula (I), the compound is shown in the specification,
Figure FDA0003872158020000084
the unit operation cost of the ith hydrogenation station is;
Figure FDA0003872158020000085
hydrogen production quantity of the ith hydrogen station at the y year t moment;
s414, the cost of hydrogen production by buying electricity and gas:
Figure FDA0003872158020000086
in the formula, c Ele Is the purchase price of unit electricity quantity; c. C Gas Is the purchase price of unit natural gas;
Figure FDA0003872158020000087
the electric quantity needed to be purchased for hydrogen production of the h hydrogenation stations;
Figure FDA0003872158020000088
is hThe hydrogen production of the hydrogen station requires purchased natural gas;
s415, carbon emission reduction:
Figure FDA0003872158020000089
in the formula, R 2 The method is the carbon emission before low-carbon modification of the comprehensive energy system; r is 2 The emission of carbon is obtained after the low-carbon transformation of the comprehensive energy system;
Figure FDA00038721580200000810
specific carbon emission intensity for gas stations;
Figure FDA00038721580200000811
the demand of a gas station before transformation;
Figure FDA00038721580200000812
the demand of the gas station after transformation;
Figure FDA00038721580200000813
the carbon emission intensity of the ith hydrogenation station in the hydrogen network in the y year;
Figure FDA00038721580200000814
the hydrogen demand of the ith hydrogen station in the hydrogen network in the y year.
9. The method for operating the hydrogen production network in consideration of quitting of the coal-fired unit and the gas station as recited in claim 7, wherein the constraint conditions of the low-carbon modification stage of the comprehensive energy sources in the step S42 specifically include the following steps:
s421, supply and demand balance constraint of a hydrogen generation station:
the power required by hydrogen production is equivalent to the natural gas and the hydrogen produced, and the expression is as follows:
Figure FDA0003872158020000091
in the formula (I), the compound is shown in the specification,
Figure FDA0003872158020000092
the hydrogen production quantity of 1kW electricity by adopting a seawater electrolysis hydrogen production mode;
Figure FDA0003872158020000093
is 1m 3 Hydrogen production amount corresponding to natural gas;
Figure FDA0003872158020000094
the electric power required by the hydrogen generation station h at the y-th year t;
Figure FDA0003872158020000095
the photovoltaic power and the wind power required by the hydrogen generation station h at the y year t are respectively, and the upper limit and the lower limit are constrained as follows:
Figure FDA0003872158020000096
Figure FDA0003872158020000097
Figure FDA0003872158020000098
Figure FDA0003872158020000099
in the formula (I), the compound is shown in the specification,
Figure FDA00038721580200000910
upper limits for photovoltaic power and wind power, respectively;
Figure FDA00038721580200000911
Figure FDA00038721580200000912
the upper and lower limits of the power of the coal-fired unit and the SMR equipment are respectively;
Figure FDA00038721580200000913
the gas flow required by the hydrogen production station h at the y year t moment;
Figure FDA00038721580200000914
the hydrogen capacity of the hydrogen generation station h at the time t is shown; the total amount of natural gas of the SMR equipment in each hydrogen production station is equal to the sum of the consumption of the equipment, and the expression is as follows:
Figure FDA00038721580200000915
Figure FDA00038721580200000916
in the formula (I), the compound is shown in the specification,
Figure FDA00038721580200000917
supplying the gas flow of the hydrogen production station h for the SMR device i at the y year t;
Figure FDA00038721580200000918
the maximum power of the hydrogen generation station h;
s422, hydrogen network power flow balance constraint:
the hydrogen network needs to satisfy a linearized node flow balance constraint equation, and the expression is as follows:
Figure FDA00038721580200000919
Figure FDA00038721580200000920
in the formula, H n A hydrogen energy source node incidence matrix;
Figure FDA00038721580200000921
the output power of the hydrogenation station i; a. The n,l A hydrogen pipeline node incidence matrix;
Figure FDA0003872158020000101
hydrogen flow rate of the hydrogen pipeline;
Figure FDA0003872158020000102
is the hydrogen load at node n;
Figure FDA0003872158020000103
the maximum value of the output power of the hydrogenation station i is obtained;
s423, network flow constraint:
the gas flow in the hydrogen network pipeline is constrained, and the expression is as follows:
Figure FDA0003872158020000104
Figure FDA0003872158020000105
in the formula (I), the compound is shown in the specification,
Figure FDA0003872158020000106
the upper limit and the lower limit of the natural gas pipeline flow are respectively set;
Figure FDA0003872158020000107
are respectively a hydrogen pipeUpper and lower limits of the channel flow;
s424, carbon emission constraint:
the total carbon emission of hydrogen production needs to be less than the upper limit of the carbon emission constraint, and the expression is as follows:
Figure FDA0003872158020000108
in the formula (I), the compound is shown in the specification,
Figure FDA0003872158020000109
is the carbon emission intensity at grid i-node;
Figure FDA00038721580200001010
is the power at grid inode;
Figure FDA00038721580200001011
is the carbon emission intensity at the j node of the gas network;
Figure FDA00038721580200001012
is the power at the j node of the air grid;
Figure FDA00038721580200001013
upper carbon emissions limit assigned for year y;
s425, construction constraint of gas network and hydrogen network pipelines:
Figure FDA00038721580200001014
the number of gas network pipelines and hydrogen network pipelines of the i and j nodes in the y year is determined by the construction of the previous year, and the constraint is as follows:
Figure FDA00038721580200001015
Figure FDA00038721580200001016
in the formula (I), the compound is shown in the specification,
Figure FDA00038721580200001017
the number of the gas network pipelines and the number of the hydrogen network pipelines of the i and j nodes in the (y-1) th year are respectively.
10. The method for operating a hydrogen production network in consideration of the quit of a coal-fired unit and a gas station as claimed in claim 1, wherein the step S5 specifically comprises:
s51, setting initial parameters: in the low-carbon transformation stage of the comprehensive energy system, the initial parameters comprise population number, planning age, 24-hour water electrolysis hydrogen production proportion, candidate hydrogen pipeline number, candidate gas pipeline number and oil station exit decision number;
s52, population initialization: direct binary coding is adopted, and each individual is composed of a chromosome consisting of 11-bit binary genes; generating an initial population by adopting a random Halton sequence, and generating the initial population in a low-carbon transformation stage of the comprehensive energy system: the number of individuals of the population, the planning age limit, the hydrogen production proportion by water electrolysis in 24 hours, the number of individuals of the population, the number of candidate hydrogen pipelines, the number of individuals of the population, the number of oil station exit decision-making numbers, and the initial restraint is carried out on the exit of a gas station in an initial sequence, and the initial individuals which do not meet the requirements are excluded;
s53, calculating an individual fitness value: when the comprehensive energy system is subjected to low-carbon transformation, a target function is set
Figure FDA0003872158020000111
As a function of fitness
Figure FDA0003872158020000112
Namely, it is
Figure FDA0003872158020000113
S54, elite reservation: arranging according to the individual fitness value, directly reserving the best individual to the next generation population as a filial generation, and skipping over a selection operator;
s55, selecting a parent: adopting a wheel disc to eliminate the excellence and the disadvantage of the population by adopting a betting algorithm, accumulating the obtained moderate value of the target function and carrying out normalization processing; the method specifically comprises the following steps:
s551, calculating chromosomes in the population
Figure FDA0003872158020000114
And
Figure FDA0003872158020000115
moderate value of (d):
Figure FDA0003872158020000116
s552, calculating the sum of the appropriate values of chromosomes in the population:
Figure FDA0003872158020000117
s553, calculating the selection probability of chromosomes in the population:
Figure FDA0003872158020000118
s554, calculating the selection probability of chromosomes in the population:
Figure FDA0003872158020000119
s555. In [0,1 ]]Randomly generating a probability number r within the interval if
Figure FDA00038721580200001110
Then the first chromosome is selected
Figure FDA00038721580200001111
And
Figure FDA00038721580200001112
if it is used
Figure FDA00038721580200001113
Selecting the q chromosome so that
Figure FDA00038721580200001114
If true;
s56, cross updating individuals: exchanging the two individuals according to a certain designated mode for respective partial genes;
s57, mutation updating individual: setting mutation probability p of chromosome m Generating a random probability r when p m When r is less than r, the m gene of the chromosome is not mutated; when p is m When the gene is more than r, the mth gene carries out variant inheritance, and the original gene code is changed;
s58, adding a new individual: after cross variation, simulating the invasion of a foreign individual in nature, and adding a randomly initialized individual as a filial generation for iteration;
s59, calculating the carbon emission intensity and the carbon emission amount: judging whether the judgment standard is met or not, if so, terminating and deriving the optimal hydrogen production ratio of the electrolyzed water and the steam methane reforming, the construction decision of the gas network pipeline and the hydrogen network pipeline and the quitting decision of the gas station; otherwise, returning to the step S52;
s510, optimal solution decoding: and after iteration is finished, converting the binary gene code of the optimal solution into a corresponding decimal numerical value according to the optimal hydrogen production ratio of electrolyzed water and steam methane reforming, the construction decision of a gas network pipeline and a hydrogen network pipeline and the exit decision variable of a gas station.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116093994A (en) * 2023-02-14 2023-05-09 国网冀北电力有限公司电力科学研究院 Self-balancing target-oriented distribution area resource allocation method and device

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110178833A1 (en) * 2010-01-20 2011-07-21 International Business Machines Corporation Developing an optimal long term electricity generation capacity resource plan under a carbon dioxide regulatory regime
CN113315242A (en) * 2021-05-31 2021-08-27 天津大学 Virtual wind abandoning-hydrogen production combination for promoting wind abandoning consumption based on hydrogen energy economy
CN114996952A (en) * 2022-06-13 2022-09-02 上海交通大学 Comprehensive energy system optimization method considering seasonal hydrogen storage and hydrogen turbine utilization

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110178833A1 (en) * 2010-01-20 2011-07-21 International Business Machines Corporation Developing an optimal long term electricity generation capacity resource plan under a carbon dioxide regulatory regime
CN113315242A (en) * 2021-05-31 2021-08-27 天津大学 Virtual wind abandoning-hydrogen production combination for promoting wind abandoning consumption based on hydrogen energy economy
CN114996952A (en) * 2022-06-13 2022-09-02 上海交通大学 Comprehensive energy system optimization method considering seasonal hydrogen storage and hydrogen turbine utilization

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
白永亮;张浅;王越;黄少良;严岩;: "基于我国能源政策变迁视角的低碳政策取向研究", 前沿, no. 19 *
肖兵;: "安庆石化氢气资源和系统优化利用探讨", 炼油技术与工程, no. 04 *
袁铁江;李国军;张增强;张龙;蔡高雷;梅生伟;: "风电―氢储能与煤化工多能耦合系统设备投资规划优化建模", 电工技术学报, no. 14 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116093994A (en) * 2023-02-14 2023-05-09 国网冀北电力有限公司电力科学研究院 Self-balancing target-oriented distribution area resource allocation method and device

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