CN114696362A - Power distribution network operation optimization method containing hydrogen production-storage-hydrogenation station - Google Patents

Power distribution network operation optimization method containing hydrogen production-storage-hydrogenation station Download PDF

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CN114696362A
CN114696362A CN202210431570.0A CN202210431570A CN114696362A CN 114696362 A CN114696362 A CN 114696362A CN 202210431570 A CN202210431570 A CN 202210431570A CN 114696362 A CN114696362 A CN 114696362A
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hydrogen
power
distribution network
power distribution
time
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季克勤
黄健
侯健生
王赢聪
郑航
金坚锋
蒋建勇
卢昊威
周子欣
池源
孟庆昊
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Chongqing University
Jinhua Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Chongqing University
Jinhua Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J15/00—Systems for storing electric energy
    • H02J15/008—Systems for storing electric energy using hydrogen as energy vector
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy

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  • Power Engineering (AREA)
  • Fuel Cell (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to the technical field of power system optimization, in particular to a method for optimizing the operation of a power distribution network containing hydrogen production-storage-hydrogenation stations, which comprises the following steps: establishing a mathematical model of a photovoltaic system and a mathematical model of a hydrogen system, and constructing a target function and constraint conditions for operation optimization of a power distribution network to obtain a multi-target optimization model containing a hydrogen production-storage-hydrogenation station; step two, carrying out linearization processing on the objective function and the constraint condition to obtain a multi-objective optimization model of mixed integer linear programming; solving the multi-objective optimization model of the mixed integer linear programming by using an epsilon constraint method to obtain an optimal scheduling scheme of the active power distribution network containing the hydrogen production-storage-hydrogenation station; and step four, scheduling the photovoltaic system, the hydrogen system and the power distribution network by using the optimized scheduling scheme obtained in the step three. The method can effectively solve the problem of voltage deviation when the hydrogenation station supplies power to the power grid, and improves the quality of the power supplied to the power grid.

Description

Power distribution network operation optimization method containing hydrogen production-storage-hydrogenation station
Technical Field
The invention relates to the technical field of power system optimization, in particular to a power distribution network operation optimization method comprising a hydrogen production-storage-hydrogenation station.
Background
Under the background of great advocation of green energy development at present, photovoltaic power generation becomes a current research hotspot as a main form of new energy power generation. Because new energy power generation can be influenced by environmental conditions, the output power fluctuates, and the output electric energy quality can be further influenced. The hydrogen energy receives wide attention with the advantages of cleanness, high efficiency and the like, unstable electric energy is converted into hydrogen for storage in a mode of electrolyzing water to prepare hydrogen through electric energy generated by photovoltaic, and the stored hydrogen is converted into stable electric energy through the fuel cell when the department electric energy is needed. Therefore, the hydrogen energy conversion mechanism becomes a hot-door conversion mechanism for photovoltaic power generation. However, in actual use, because the photovoltaic system and the electric hydrogen coincidence are affected by factors such as environmental conditions and user behaviors, and have certain uncertainty, in order to ensure the stability of the whole process and provide stable electric energy for a power grid, part scholars in the industry establish an optimized scheduling model of an electric hydrogen hybrid energy storage system comprising a photovoltaic system, a storage battery, an electrolytic cell, a hydrogen storage tank and a fuel cell, and solve the problem by using optimization methods such as a particle swarm algorithm, a genetic algorithm and a simulated annealing algorithm.
Through the mode, power can be stably supplied to a power grid through the hydrogen energy conversion mechanism. However, this method requires a special hydrogen energy transfer mechanism, and is costly. In order to maximize the resource utilization rate, the applicant provides a technical scheme for directly using a distributed hydrogen refueling station to perform energy conversion. By adopting the mode, the number of the energy conversion mechanisms can be reduced, the cost is saved, the economic benefit of the hydrogen station can be improved, and the popularization of the hydrogen station is facilitated.
However, in actual use, since the hydrogen station itself is not a mechanism dedicated to energy conversion, the hydrogen station plays a role in supplying hydrogen energy to the hydrogen energy source automobile. If the hydrogenation station is used for energy conversion to supply power to the power grid, the hydrogenation station also needs to provide hydrogen energy for the hydrogen energy source while supplying power to the power grid, so that the voltage deviation problem exists when the hydrogenation station supplies power to the power grid, and particularly the voltage deviation problem of the hydrogenation station for supplying power to the power grid becomes serious in the peak period of hydrogenation of a hydrogen energy vehicle. And thus may affect the safe and stable operation of the power system. The implementation of this solution is not ideal.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the operation optimization method of the power distribution network comprising the hydrogen production-hydrogen storage-hydrogenation station, which can effectively solve the problem of voltage deviation when the hydrogenation station supplies power to the power grid and improve the quality of the power supplied to the power grid.
In order to solve the technical problems, the invention adopts the following technical scheme:
a power distribution network operation optimization method containing hydrogen production-storage-hydrogenation stations comprises the following steps:
establishing a mathematical model of a photovoltaic system and a mathematical model of a hydrogen system, and constructing a target function and constraint conditions for operation optimization of a power distribution network to obtain a multi-target optimization model containing a hydrogen production-storage-hydrogenation station; the hydrogen system comprises an electrolytic cell, a hydrogen storage tank and a fuel cell; the objective function comprises an economic objective function and an electric energy quality objective function; the constraint conditions comprise photovoltaic system constraint conditions, hydrogen system constraint conditions and distribution network constraint conditions;
step two, carrying out linearization processing on the objective function and the constraint condition to obtain a multi-objective optimization model of mixed integer linear programming;
solving the multi-objective optimization model of the mixed integer linear programming by using an epsilon constraint method to obtain an optimal scheduling scheme of the active power distribution network containing the hydrogen production-storage-hydrogenation station;
and step four, scheduling the photovoltaic system, the hydrogen system and the power distribution network by using the optimized scheduling scheme obtained in the step three.
Preferably, in the step one, the mathematical model of the photovoltaic system is:
Figure BDA0003610858630000021
in the formula, PSTThe rated output power of the photovoltaic power generation under the standard test condition; gtIs the intensity of the illumination; epsilon is a power temperature coefficient; t isjIs ambient temperature;
Figure BDA0003610858630000022
the number of modules connected in series for the photovoltaic cells;
Figure BDA0003610858630000023
parallel connection of photovoltaic cellsThe number of blocks.
Preferably, in the first step, the mathematical model of the hydrogen system is:
Figure BDA0003610858630000024
Figure BDA0003610858630000025
Figure BDA0003610858630000026
in the formula (I), the compound is shown in the specification,
Figure BDA0003610858630000027
the hydrogen production of the electrolytic cell; etaELThe efficiency of the cell; pt EL,joElectrical power consumed for the electrolysis cell; P2HELA power conversion factor for hydrogen production for the electrolyzer;
Figure BDA0003610858630000028
is the hydrogen consumption of the fuel cell; p ist FCElectrical power output for the fuel cell; etaFCIs the efficiency of the fuel cell; H2PFCA conversion factor for the fuel cell to convert hydrogen to electrical energy;
Figure BDA0003610858630000029
the hydrogen storage state of the hydrogen storage tank at the time t;
Figure BDA00036108586300000210
the hydrogen storage state of the hydrogen storage tank at the time t-1 is shown; epsilonDSPIs the hydrogen dissipation ratio of the hydrogen storage tank; HDEtThe demand of hydrogen for hydrogen energy vehicles.
Preferably, the electric power P consumed by the electrolyzert EL,joIncluding electrical power output by an upstream distribution grid and electrical power output by photovoltaic generation.
Preferably, in the first step, the economic objective function is:
Figure BDA0003610858630000031
in the formula, Pt UPElectric energy purchased from an upstream power grid for the power distribution network at the moment t;
Figure BDA0003610858630000032
unit electricity price for the upstream power grid; pt DGOutputting power for distributed generation at time t;
Figure BDA0003610858630000033
operating cost for distributed generation units; pt PVOutputting power for photovoltaic power generation at the time t;
Figure BDA0003610858630000034
the operating cost of a photovoltaic power generation unit is calculated; pt ELThe electric power consumed by the electrolytic cell at the moment t; pt FCThe electric power output by the fuel cell at time t.
Preferably, in the first step, the power quality objective function is:
Figure BDA0003610858630000035
in the formula, omegaiIs a voltage mass coefficient; vt,iRepresenting the actual voltage of the ith node of the power distribution network;
Figure BDA0003610858630000036
representing the nominal voltage of node i.
Preferably, in the step one, the photovoltaic system constraint condition includes a capacity constraint of a photovoltaic system inverter:
Figure BDA0003610858630000037
the hydrogen system constraints include operating constraints of the electrolyzer:
Figure BDA0003610858630000038
Figure BDA0003610858630000039
operating constraints of the fuel cell:
Figure BDA00036108586300000310
Figure BDA00036108586300000311
the operation constraint conditions of the hydrogen storage tank are as follows:
Figure BDA00036108586300000312
Figure BDA00036108586300000313
and capacity constraints of the hydrogen system inverter:
Figure BDA00036108586300000314
the power distribution network constraint conditions comprise the following alternating current power flow constraint conditions:
Figure BDA00036108586300000315
Figure BDA00036108586300000316
Figure BDA0003610858630000041
Figure BDA0003610858630000042
wherein, Pt PVOutputting power for photovoltaic power generation at the time t;
Figure BDA0003610858630000043
the reactive power is the reactive power output by the photovoltaic inverter at the moment t;
Figure BDA0003610858630000044
is the capacity of the photovoltaic inverter;
Figure BDA0003610858630000045
is the minimum capacity of the cell; pt EL,joUsing power in a coordinated mode of photovoltaic export and upstream grid power for the electrolyzer; ph EL,MaxIs the maximum capacity of the cell;
Figure BDA0003610858630000046
is a binary variable in a hydrogen system and is defined as deltat HSThe fuel cell output is 0 when 1, when
Figure BDA0003610858630000047
The hydrogen yield of the electrolytic cell is 0;
Figure BDA0003610858630000048
the amount of hydrogen produced by the electrolyzer;
Figure BDA0003610858630000049
the maximum amount of hydrogen produced by the electrolyzer;
Ph FC,Minminimum output of the fuel cell; pt FCThe output at the moment t of the fuel cell; ph FC,MaxThe maximum output of the fuel cell;
Figure BDA00036108586300000410
the amount of hydrogen consumed for the fuel cell at time t;
Figure BDA00036108586300000411
a maximum amount of hydrogen consumed for the fuel cell;
Figure BDA00036108586300000412
is the minimum value of the mass of the hydrogen in the hydrogen storage tank;
Figure BDA00036108586300000413
the mass of hydrogen in the hydrogen storage tank at the time t;
Figure BDA00036108586300000414
the maximum value of the hydrogen mass in the hydrogen storage tank;
Figure BDA00036108586300000415
the initial value of the quality of the hydrogen in the hydrogen storage tank is obtained;
Figure BDA00036108586300000416
is the initial hydrogen production in the hydrogen storage tank;
Pt ELthe electric power consumed by the electrolytic cell at the moment t; pt FCThe electric power output by the fuel cell at the time t; qt HSThe reactive power output by the hydrogen energy storage inverter at the moment t;
Figure BDA00036108586300000417
is the capacity of the hydrogen system;
Figure BDA00036108586300000418
injecting active power for the net of the node; pt UGRepresenting active power from an upstream power grid at time t;
Figure BDA00036108586300000419
representing the reactive power from the upstream grid at time t;
Figure BDA00036108586300000420
injecting reactive power for the net of the node; pt DGThe active power output by the distributed generator at the moment t is obtained; qt DGThe reactive power output by the distributed generator at the moment t; gi,jThe conductance parameter of the line between the nodes i and j is shown; b isi,jThe susceptance parameter of the line between the nodes i and j is obtained; vt,iActual voltage of the ith node of the power distribution network at the moment t; vt,jThe actual voltage of the jth node of the power distribution network at the moment t; thetat,iThe phase angle of the node voltage at the time t; thetat,jThe phase angle of the j node voltage at time t.
Preferably, in the second step, the electric energy quality objective function is changed into:
Figure BDA0003610858630000051
Figure BDA0003610858630000052
Figure BDA0003610858630000053
wherein Min.PQI is the minimum value of the voltage deviation ratio; omegaiIs a voltage mass coefficient; delta Vt,iVoltage of node i at time tA deviation amount;
Figure BDA0003610858630000054
the positive deviation of the voltage of the node i at the moment t;
Figure BDA0003610858630000055
the negative deviation of the voltage of the node i is t; vt,iRepresenting the actual voltage of the ith node of the power distribution network at the moment t;
Figure BDA0003610858630000056
represents the rated voltage of the node i at the moment t;
Figure BDA0003610858630000057
the positive voltage deviation amount of the node i at the time t;
Figure BDA0003610858630000058
the amount of negative voltage deviation at node i at time t.
Preferably, in the second step, after the ac power flow constraint condition linearization process, the following steps are performed:
Figure BDA0003610858630000059
Figure BDA00036108586300000510
PLt,(i,j)＝-Gi,j(Vt,i+Vt,j)+Bi,j(θt,i-θt,j)
QLt,(i,j)＝Bi,j(Vt,i+Vt,j)+Gi,j(θt,i-θt,j)
in the formula, PLt,(i,j)、QLt,(i,j)Respectively the active power and the reactive power between nodes i and j.
Preferably, step three comprises:
step 3.1, taking the economic objective function as a main objective function, taking the electric energy quality objective function after linear processing as a constraint condition, and changing the multi-objective optimization model of the mixed integer linear programming into:
an objective function: minimize OCt
Constraint conditions are as follows:
Figure BDA00036108586300000511
solving the optimal target value;
step 3.2, taking the electric energy quality target function after linear processing as a main target function, simultaneously taking the optimal target value obtained in the step 3.1 as a constraint condition, and changing the multi-target optimization model of the mixed integer linear programming into:
an objective function: minimize PQIt
Constraint conditions are as follows:
Figure BDA00036108586300000512
in the formula (I), the compound is shown in the specification,
Figure BDA00036108586300000513
represents the minimum cost found in step 3.1; lambdaOCRepresents a parameter that achieves a compromise between the two objective functions, representing an economic objective that can be sacrificed in order to minimize voltage deviations during the optimization of step 3.2;
and by a parameter lambdaOCThe compromise of two objective functions is realized, and the optimal scheduling scheme of the active power distribution network containing the hydrogen production-storage-hydrogenation station is obtained.
Compared with the prior art, the invention has the following beneficial effects:
1. the method fully considers the characteristics of the photovoltaic system, the hydrogen system and the power distribution network, and the method is used for scheduling the photovoltaic system, the hydrogen system and the power distribution network, so that the economical efficiency of the operation of the power distribution network can be realized, the problem of large node voltage drop of the hydrogen system caused by large demand of the fuel automobile for hydrogen can be solved, and the quality of electric energy of the operation of the power distribution network containing the hydrogen system is improved. In conclusion, the method solves the problem of voltage deviation when the hydrogenation station supplies power to the power grid, and can improve the quality of the power supplied to the power grid.
2. Tests show that the model established by the method can be solved within the second level, online real-time scheduling of a photovoltaic system, a hydrogen system and a power distribution network can be realized, the online programmed calculation advantage is achieved, and the method has important significance for realizing economic, safe and stable operation of the future green urban power distribution network.
3. The invention also fully considers the cooperative operation of the photovoltaic system and the hydrogen system, the electric energy of the electrolytic cell can be purchased from a power grid, and can also be supplied by the photovoltaic system, and the production cost of the hydrogen can be reduced, thereby reducing the economic threshold of the hydrogenation station applied to the power supply system and being beneficial to the popularization and the application of the technology.
Drawings
For a better understanding of the objects, solutions and advantages of the present invention, reference will now be made in detail to the present invention, which is illustrated in the accompanying drawings, in which:
FIG. 1 is a flow chart in an embodiment;
FIG. 2 is a schematic diagram of a hydrogen system in an example.
Detailed Description
The following is further detailed by the specific embodiments:
examples
As shown in fig. 1 and fig. 2, this embodiment discloses a method for optimizing the operation of a power distribution network including a hydrogen production-storage-hydrogenation station, which includes the following steps:
establishing a mathematical model of a photovoltaic system and a mathematical model of a hydrogen system, and constructing a target function and constraint conditions for operation optimization of a power distribution network to obtain a multi-target optimization model containing a hydrogen production-storage-hydrogenation station. The hydrogen system comprises an electrolytic cell, a hydrogen storage tank and a fuel cell; the objective function comprises an economic objective function and an electric energy quality objective function; the constraint conditions comprise photovoltaic system constraint conditions, hydrogen system constraint conditions and distribution network constraint conditions.
Specifically, the mathematical model of the photovoltaic system is:
Figure BDA0003610858630000061
in the formula, PSTThe rated output power of the photovoltaic power generation under the standard test condition; gtIs the intensity of the illumination; epsilon is a power temperature coefficient; t isjIs ambient temperature;
Figure BDA0003610858630000071
the number of modules connected in series for the photovoltaic cells;
Figure BDA0003610858630000072
number of modules connected in parallel for the photovoltaic cells.
The mathematical model of the hydrogen system is:
Figure BDA0003610858630000073
in the formula (I), the compound is shown in the specification,
Figure BDA0003610858630000074
the hydrogen production of the electrolytic cell; etaELThe efficiency of the cell; pt EL,joElectrical power consumed for the electrolysis cell; P2HELPower conversion factor for hydrogen production from the electrolyzer;
Figure BDA0003610858630000075
is the hydrogen consumption of the fuel cell; pt FCElectrical power output for the fuel cell; etaFCIs the efficiency of the fuel cell; H2PFCA conversion factor for the fuel cell to convert hydrogen to electrical energy;
Figure BDA0003610858630000076
the hydrogen storage state of the hydrogen storage tank at the time t;
Figure BDA0003610858630000077
for hydrogen storage tankA hydrogen storage state at time t-1; epsilonDSPIs the hydrogen dissipation ratio of the hydrogen storage tank; HDEtThe demand of hydrogen for hydrogen energy vehicles.
Wherein the electric power P consumed by the electrolytic cellt EL,joIncluding electrical power output by an upstream distribution grid and electrical power output by photovoltaic generation.
The economic objective function is expressed by the total operation cost of the power distribution network, and specifically comprises the following steps:
Figure BDA0003610858630000078
in the formula, Pt UPActive power from an upstream power grid at time t;
Figure BDA0003610858630000079
unit electricity price for the upstream power grid; pt DGOutputting power for distributed generation at time t;
Figure BDA00036108586300000710
operating cost for distributed generation units; pt PVOutputting power for photovoltaic power generation at the time t;
Figure BDA00036108586300000711
the unit operating cost of photovoltaic power generation; pt ELThe electric power consumed by the electrolytic cell at the moment t; pt FCThe electric power output by the fuel cell at time t.
The power quality objective function is measured by the voltage deviation of the node of the power distribution network, and is expressed as follows:
Figure BDA00036108586300000712
in the formula, ωiIs a voltage mass coefficient; vt,iRepresenting the actual voltage of the ith node of the power distribution network;
Figure BDA00036108586300000713
representing the nominal voltage of node i. A better voltage distribution can be obtained by the increase.
The constraint conditions specifically include:
Figure BDA00036108586300000714
Figure BDA0003610858630000081
Figure BDA0003610858630000082
Figure BDA0003610858630000083
Figure BDA0003610858630000084
Figure BDA0003610858630000085
Figure BDA0003610858630000086
Figure BDA0003610858630000087
Figure BDA0003610858630000088
Figure BDA0003610858630000089
Figure BDA00036108586300000810
Figure BDA00036108586300000811
equation (5) represents the capacity constraint of the photovoltaic system inverter; expressions (6) to (12) represent the operation constraints of the hydrogen system, including the operation constraints of the electrolyzer (expressions (6) and (7)), the operation constraints of the fuel cell (expressions (8) and (9)), the operation constraints of the hydrogen storage tank (expressions (10) and (11)), and the capacity constraints of the inverter of the hydrogen system (expression (12)); equations (13) to (16) represent the ac power flow constraints of the distribution network.
Wherein, Pt PVOutputting power for photovoltaic power generation at the time t;
Figure BDA00036108586300000812
the reactive power is the reactive power output by the photovoltaic inverter at the moment t;
Figure BDA00036108586300000813
is the capacity of the photovoltaic inverter;
Ph EL,minis the minimum capacity of the cell; p ist EL,joUsing power in a coordinated mode of photovoltaic export and upstream grid power for the electrolyzer;
Figure BDA00036108586300000814
is the maximum capacity of the cell;
Figure BDA00036108586300000815
is a binary variable in a hydrogen system and is defined as
Figure BDA00036108586300000816
The fuel cell output is 0 when
Figure BDA00036108586300000817
The hydrogen yield of the electrolytic cell is 0;
Figure BDA00036108586300000818
the amount of hydrogen produced by the electrolyzer;
Figure BDA00036108586300000819
the maximum amount of hydrogen produced by the electrolyzer;
Ph FC,Minminimum output of the fuel cell; p ist FCThe output at the moment t of the fuel cell; ph FC,MaxThe maximum output of the fuel cell;
Figure BDA00036108586300000820
the amount of hydrogen consumed for the fuel cell at time t;
Figure BDA00036108586300000821
a maximum amount of hydrogen consumed for the fuel cell;
Figure BDA0003610858630000091
is the minimum value of the mass of the hydrogen in the hydrogen storage tank;
Figure BDA0003610858630000092
the mass of hydrogen in the hydrogen storage tank at time t;
Figure BDA0003610858630000093
the maximum value of the hydrogen mass in the hydrogen storage tank;
Figure BDA0003610858630000094
the initial value of the quality of the hydrogen in the hydrogen storage tank is obtained;
Figure BDA0003610858630000095
is the initial hydrogen production in the hydrogen storage tank;
Pt ELthe electric power consumed by the electrolytic cell at the moment t; p ist FCThe electric power output by the fuel cell at the time t;
Figure BDA0003610858630000096
the reactive power output by the hydrogen energy storage inverter at the moment t;
Figure BDA0003610858630000097
is the capacity of the hydrogen system;
Figure BDA0003610858630000098
injecting active power for the net of the node; pt UGRepresenting the active power from the upstream power grid at time t;
Figure BDA0003610858630000099
representing the reactive power from the upstream grid at time t;
Figure BDA00036108586300000910
injecting reactive power for the net of the node; pt DGThe active power output by the distributed generator at the moment t;
Figure BDA00036108586300000911
the reactive power output by the distributed generator at the moment t; gi,jThe conductance parameter of the line between the nodes i and j is shown; bi,jThe susceptance parameter of the line between the nodes i and j is obtained; vt,iThe actual voltage of the ith node of the power distribution network at the moment t; vt,jThe actual voltage of the jth node of the power distribution network at the moment t; theta.theta.t,iThe phase angle of the voltage of the i node at the time t; thetat,jThe phase angle of the j node voltage at time t.
By arranging the inverters for the photovoltaic system and the hydrogen system, more reactive power can be provided to improve the quality of electric energy of automobile nodes containing fuel cells in the power distribution network.
And step two, carrying out linearization treatment on the objective function and the constraint condition to obtain a multi-objective optimization model of the mixed integer linear programming.
Specifically, the electric energy quality objective function becomes, after being linearized:
Figure BDA00036108586300000912
Figure BDA00036108586300000913
Figure BDA00036108586300000914
wherein Min.PQI is the minimum value of the voltage deviation ratio; omegaiIs a voltage mass coefficient; delta Vt,iIs the voltage deviation of the node i at the time t;
Figure BDA00036108586300000915
the positive deviation of the voltage of the node i at the time t;
Figure BDA00036108586300000916
the negative deviation of the voltage of the node i is t; vt,iRepresenting the actual voltage of the ith node of the power distribution network at the moment t;
Figure BDA00036108586300000917
represents the rated voltage of the node i at the time t;
Figure BDA00036108586300000918
the positive voltage deviation amount of the node i at the time t;
Figure BDA00036108586300000919
the amount of negative voltage deviation at node i at time t.
After the alternating current power flow constraint condition is linearized, the method is changed into the following steps:
Figure BDA0003610858630000101
Figure BDA0003610858630000102
PLt,(i,j)＝-Gi,j(Vt,i+Vt,j)+Bi,j(θt,i-θt,j)
QLt,(i,j)＝Bi,j(Vt,i+Vt,j)+Gi,j(θt,i-θt,j) (18)
in the formula, PLt,(i,j)、QLt,(i,j)Respectively the active power and the reactive power between nodes i and j.
And forming a multi-objective optimization model of the mixed integer linear programming by using objective functions (3) and (17) and system operation constraints (5) to (12) and (18).
And thirdly, solving the multi-objective optimization model of the mixed integer linear programming by using an epsilon constraint method to obtain an optimal scheduling scheme of the active power distribution network containing the hydrogen production-storage-hydrogenation station.
Specifically, the third step comprises:
step 3.1, taking the economic objective function as a main objective function, taking the electric energy quality objective function after linear processing as a constraint condition, and changing the multi-objective optimization model of the mixed integer linear programming into:
Figure BDA0003610858630000103
solving the optimal target value;
step 3.2, taking the electric energy quality target function after linear processing as a main target function, simultaneously taking the optimal target value obtained in the step 3.1 as a constraint condition, and changing the multi-target optimization model of the mixed integer linear programming into:
an objective function: minimize PQIt
Constraint conditions are as follows:
Figure BDA0003610858630000104
wherein the content of the first and second substances,
Figure BDA0003610858630000105
represents the minimum cost found in step 3.1; lambda [ alpha ]OCRepresents a parameter that achieves a compromise between the two objective functions, representing an economic objective that can be sacrificed in order to minimize voltage deviations during the optimization of step 3.2; by a parameter lambdaOCAnd the compromise of the two objective functions is realized, and the optimal scheduling scheme of the active power distribution network containing the hydrogen production-storage-hydrogenation station is obtained. For example, selecting λOCThe final optimization result can be considered as the optimal result of the power quality found when the economic indicator deviates from the optimal value by 10%. In specific implementation, the solution can be performed by using a mixed integer programming toolkit CPLEX.
And step four, scheduling the photovoltaic system, the hydrogen system and the power distribution network by using the optimized scheduling scheme obtained in the step three.
The method fully considers the characteristics of the photovoltaic system, the hydrogen system and the power distribution network, and the method is used for scheduling the photovoltaic system, the hydrogen system and the power distribution network, so that the economical efficiency of the operation of the power distribution network can be realized, the problem of large node voltage drop of the hydrogen system caused by large demand of the fuel automobile for hydrogen can be solved, and the quality of electric energy of the operation of the power distribution network containing the hydrogen system is improved. The method solves the problem of voltage deviation when the hydrogenation station supplies power to the power grid, and can improve the quality of the power supplied to the power grid. Besides, the invention fully considers the cooperative operation of the photovoltaic system and the hydrogen system, the electric energy of the electrolytic cell can be purchased from a power grid, and can also be supplied by the photovoltaic system, and the production cost of the hydrogen can be reduced, thereby reducing the economic threshold of the hydrogen station applied to the power supply system and being beneficial to the popularization and the application of the technology.
Tests show that the model established by the method can be solved within the second level, online real-time scheduling of a photovoltaic system, a hydrogen system and a power distribution network can be realized, the online programmed computation advantage is achieved, and the method has important significance for realizing economic, safe and stable operation of a future green urban power distribution network.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the technical solutions, and those skilled in the art should understand that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all that should be covered by the claims of the present invention.

Claims (10)

1. A power distribution network operation optimization method containing hydrogen production-storage-hydrogenation stations is characterized by comprising the following steps:
establishing a mathematical model of a photovoltaic system and a mathematical model of a hydrogen system, and constructing a target function and constraint conditions for operation optimization of a power distribution network to obtain a multi-target optimization model containing a hydrogen production-storage-hydrogenation station; the hydrogen system comprises an electrolytic cell, a hydrogen storage tank and a fuel cell; the objective function comprises an economic objective function and an electric energy quality objective function; the constraint conditions comprise photovoltaic system constraint conditions, hydrogen system constraint conditions and distribution network constraint conditions;
step two, carrying out linearization processing on the objective function and the constraint condition to obtain a multi-objective optimization model of mixed integer linear programming;
solving the multi-objective optimization model of the mixed integer linear programming by using an epsilon constraint method to obtain an optimal scheduling scheme of the active power distribution network containing the hydrogen production-storage-hydrogenation station;
and step four, scheduling the photovoltaic system, the hydrogen system and the power distribution network by using the optimized scheduling scheme obtained in the step three.
2. The method for optimizing the operation of a power distribution network including a hydrogen-producing-storing-hydrogenating station as set forth in claim 1, wherein: in the first step, the mathematical model of the photovoltaic system is:
Figure FDA0003610858620000011
in the formula, PSTThe rated output power of the photovoltaic power generation under the standard test condition; gtIs the intensity of the illumination; epsilon is a power temperature coefficient; t isjIs ambient temperature;
Figure FDA0003610858620000012
the number of modules connected in series for the photovoltaic cells;
Figure FDA0003610858620000013
number of modules connected in parallel for the photovoltaic cells.
3. The method for optimizing the operation of a power distribution network including a hydrogen-producing-storing-hydrogenating station as set forth in claim 2, wherein: in the first step, the mathematical model of the hydrogen system is as follows:
Figure FDA0003610858620000014
Figure FDA0003610858620000015
Figure FDA0003610858620000016
in the formula (I), the compound is shown in the specification,
Figure FDA0003610858620000017
the hydrogen production of the electrolytic cell; etaELThe efficiency of the electrolytic cell; pt EL,joElectrical power consumed for the electrolysis cell; P2HELPower conversion factor for hydrogen production from the electrolyzer;
Figure FDA0003610858620000018
is the hydrogen consumption of the fuel cell; pt FCElectrical power output for the fuel cell; etaFCIs the efficiency of the fuel cell; H2PFCA conversion factor for the fuel cell to convert hydrogen to electrical energy;
Figure FDA0003610858620000019
the hydrogen storage state of the hydrogen storage tank at the time t;
Figure FDA00036108586200000110
the hydrogen storage state of the hydrogen storage tank at the time t-1 is shown; epsilonDSPIs the hydrogen dissipation ratio of the hydrogen storage tank; HDEtThe demand of hydrogen for hydrogen energy vehicles.
4. The method for optimizing the operation of a power distribution network including a hydrogen-producing-storing-hydrogenating station as set forth in claim 3, wherein: electric power P consumed by the electrolytic cellt EL,joIncluding electrical power output by an upstream distribution grid and electrical power output by photovoltaic generation.
5. The method for optimizing the operation of a power distribution network including a hydrogen-producing-storing-hydrogenating station as set forth in claim 4, wherein: in the first step, the economic objective function is:
Figure FDA0003610858620000021
in the formula, Pt UPElectric energy purchased from an upstream power grid for the distribution network at time t;
Figure FDA0003610858620000022
unit electricity price for the upstream power grid; pt DGOutputting power for distributed generation at time t;
Figure FDA0003610858620000023
operating cost for distributed generation units; pt PVOutputting power for photovoltaic power generation at the time t;
Figure FDA0003610858620000024
the operating cost of a photovoltaic power generation unit is calculated; pt ELThe electric power consumed by the electrolytic cell at the moment t; pt FCThe electric power output by the fuel cell at time t.
6. The method for optimizing the operation of a power distribution network including a hydrogen-producing-storing-hydrogenating station as set forth in claim 5, wherein: in the first step, the power quality objective function is:
Figure FDA0003610858620000025
in the formula, ωiIs a voltage mass coefficient; vt,iRepresenting the actual voltage of the ith node of the power distribution network;
Figure FDA0003610858620000026
representing the nominal voltage of node i.
7. The method for optimizing the operation of a power distribution network including a hydrogen-producing-storing-hydrogenating station as set forth in claim 6, wherein: in the first step, the photovoltaic system constraint condition includes a capacity constraint of a photovoltaic system inverter:
Figure FDA0003610858620000027
the hydrogen system constraints include operating constraints of the electrolyzer:
Figure FDA0003610858620000028
Figure FDA0003610858620000029
operating constraints of the fuel cell:
Figure FDA00036108586200000210
Figure FDA00036108586200000211
the operation constraint conditions of the hydrogen storage tank are as follows:
Figure FDA00036108586200000212
Figure FDA00036108586200000213
and capacity constraints of the hydrogen system inverter:
Figure FDA00036108586200000214
the power distribution network constraint conditions comprise the following alternating current power flow constraint conditions:
Figure FDA0003610858620000031
Figure FDA0003610858620000032
Figure FDA0003610858620000033
Figure FDA0003610858620000034
wherein, Pt PVOutputting power for photovoltaic power generation at the time t; qt PVThe reactive power is the reactive power output by the photovoltaic inverter at the moment t;
Figure FDA0003610858620000035
is the capacity of the photovoltaic inverter;
Ph EL,minis the minimum capacity of the cell; (ii) a Pt EL,joUsing power in a coordinated mode of photovoltaic export and upstream grid power for the electrolyzer;
Figure FDA0003610858620000036
is the maximum capacity of the cell;
Figure FDA0003610858620000037
is a binary variable in a hydrogen system and is defined as
Figure FDA0003610858620000038
The fuel cell output is 0 when
Figure FDA0003610858620000039
The hydrogen yield of the electrolytic cell is 0;
Figure FDA00036108586200000310
the amount of hydrogen produced by the electrolyzer;
Figure FDA00036108586200000311
the maximum amount of hydrogen produced by the electrolyzer;
Ph FC,Minminimum output of the fuel cell; pt FCAt time t of the fuel cellForce is exerted; ph FC,MaxThe maximum output of the fuel cell;
Figure FDA00036108586200000312
the amount of hydrogen consumed for the fuel cell at time t;
Figure FDA00036108586200000313
a maximum amount of hydrogen consumed for the fuel cell;
Figure FDA00036108586200000314
the mass of the hydrogen in the hydrogen storage tank is the minimum value;
Figure FDA00036108586200000315
the mass of hydrogen in the hydrogen storage tank at time t;
Figure FDA00036108586200000316
the maximum value of the hydrogen mass in the hydrogen storage tank;
Figure FDA00036108586200000317
the initial value of the quality of the hydrogen in the hydrogen storage tank is obtained;
Figure FDA00036108586200000318
is the initial hydrogen production in the hydrogen storage tank;
Pt ELthe electric power consumed by the electrolytic cell at the moment t; pt FCThe electric power output by the fuel cell at the time t; qt HSThe reactive power output by the hydrogen energy storage inverter at the moment t;
Figure FDA00036108586200000319
the capacity of the hydrogen system;
Figure FDA00036108586200000320
injecting active power for the net of the node; p ist UGRepresenting the active power from an upstream power grid at the moment t; qt UGRepresenting the reactive power from the upstream grid at time t;
Figure FDA00036108586200000321
injecting reactive power for the net of the node; p ist DGThe active power output by the distributed generator at the moment t;
Figure FDA00036108586200000322
the reactive power output by the distributed generator at the moment t; gi,jThe conductance parameter of the line between the nodes i and j is shown; b isi,jThe susceptance parameter of the line between the nodes i and j is obtained; vt,iThe actual voltage of the ith node of the power distribution network at the moment t; vt,jThe actual voltage of the jth node of the power distribution network at the moment t; thetat,iThe phase angle of the voltage of the i node at the time t; thetat,jThe phase angle of the j node voltage at time t.
8. The method for optimizing the operation of a power distribution network including a hydrogen-producing-storing-and-hydrogenating station as set forth in claim 7, wherein: in the second step, the electric energy quality objective function becomes:
Figure FDA0003610858620000041
Figure FDA0003610858620000042
Figure FDA0003610858620000043
wherein Min.PQI is the minimum voltage deviation ratioA value; omegaiIs a voltage mass coefficient; delta Vt,iIs the voltage deviation of the node i at the time t;
Figure FDA0003610858620000044
the positive deviation of the voltage of the node i at the time t;
Figure FDA0003610858620000045
the negative deviation of the voltage of the node i is t time; vt,iRepresenting the actual voltage of the ith node of the power distribution network at the moment t;
Figure FDA0003610858620000046
represents the rated voltage of the node i at the time t;
Figure FDA0003610858620000047
the positive voltage deviation amount of the node i at the time t;
Figure FDA0003610858620000048
the amount of negative voltage deviation at node i at time t.
9. The method for optimizing the operation of a power distribution network including a hydrogen-producing-storing-and-hydrogenating station as set forth in claim 8, wherein: in the second step, the alternating current power flow constraint condition is changed into:
Figure FDA0003610858620000049
Figure FDA00036108586200000410
PLt,(i,j)＝-Gi,j(Vt,i+Vt,j)+Bi,j(θt,i-θt,j)
QLt,(i,j)＝Bi,j(Vt,i+Vt,j)+Gi,j(θt,i-θt,j)
in the formula, PLt,(i,j)、QLt,(i,j)Respectively the active power and the reactive power between nodes i and j.
10. The method for optimizing the operation of a power distribution network including a hydrogen-producing-storing-hydrogenating station as set forth in claim 9, wherein the third step comprises:
step 3.1, taking the economic objective function as a main objective function, taking the electric energy quality objective function after linearization processing as a constraint condition, and changing the multi-objective optimization model of the mixed integer linear programming into:
an objective function: minimize OCt
Constraint conditions are as follows:
Figure FDA00036108586200000411
solving the optimal target value;
step 3.2, taking the electric energy quality target function after linear processing as a main target function, simultaneously taking the optimal target value obtained in the step 3.1 as a constraint condition, and changing the multi-target optimization model of the mixed integer linear programming into:
an objective function: minimize PQIt
Constraint conditions are as follows:
Figure FDA0003610858620000051
in the formula (I), the compound is shown in the specification,
Figure FDA0003610858620000052
represents the minimum cost found in step 3.1; lambdaOCRepresents a parameter that achieves a compromise between the two objective functions, representing an economic objective that can be sacrificed in order to minimize voltage deviations during the optimization of step 3.2;
and by a parameter lambdaOCRealizing the compromise of two objective functions to obtain the active power distribution network containing hydrogen production-storage-hydrogenation stationsAnd (5) optimizing a scheduling scheme.
CN202210431570.0A 2022-04-22 2022-04-22 Power distribution network operation optimization method containing hydrogen production-storage-hydrogenation station Pending CN114696362A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115307054A (en) * 2022-08-22 2022-11-08 西南交通大学 Hydrogenation station equipment capacity optimal configuration method based on microgrid residual electricity hydrogen production
CN116307021A (en) * 2022-10-08 2023-06-23 中国大唐集团科学技术研究总院有限公司 Multi-target energy management method of new energy hydrogen production system

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115307054A (en) * 2022-08-22 2022-11-08 西南交通大学 Hydrogenation station equipment capacity optimal configuration method based on microgrid residual electricity hydrogen production
CN115307054B (en) * 2022-08-22 2024-05-03 西南交通大学 Hydrogen station equipment capacity optimization configuration method based on micro-grid surplus electricity hydrogen production
CN116307021A (en) * 2022-10-08 2023-06-23 中国大唐集团科学技术研究总院有限公司 Multi-target energy management method of new energy hydrogen production system
CN116307021B (en) * 2022-10-08 2024-03-22 中国大唐集团科学技术研究总院有限公司 Multi-target energy management method of new energy hydrogen production system

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