AU2021107028A4 - Method and system for optimal energy allocation of a hydrogen production station - Google Patents
Method and system for optimal energy allocation of a hydrogen production station Download PDFInfo
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- 239000001257 hydrogen Substances 0.000 title claims abstract description 117
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 117
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 111
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000005338 heat storage Methods 0.000 claims abstract description 29
- 230000035772 mutation Effects 0.000 claims description 19
- 238000010521 absorption reaction Methods 0.000 claims description 11
- 230000005611 electricity Effects 0.000 abstract description 5
- 238000013178 mathematical model Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 3
- 238000004146 energy storage Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 150000002431 hydrogen Chemical class 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- YMBCJWGVCUEGHA-UHFFFAOYSA-M tetraethylammonium chloride Chemical compound [Cl-].CC[N+](CC)(CC)CC YMBCJWGVCUEGHA-UHFFFAOYSA-M 0.000 description 2
- 102100026891 Cystatin-B Human genes 0.000 description 1
- 101000912191 Homo sapiens Cystatin-B Proteins 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000005868 electrolysis reaction Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000010287 polarization Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 230000002269 spontaneous effect Effects 0.000 description 1
Classifications
-
- 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
- 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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
-
- 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
-
- 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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
-
- 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
-
- 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
- H02J2300/28—The renewable source being wind energy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/30—Hydrogen technology
- Y02E60/36—Hydrogen production from non-carbon containing sources, e.g. by water electrolysis
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Electrolytic Production Of Non-Metals, Compounds, Apparatuses Therefor (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a method and system for optimal energy allocation of a
hydrogen production station, comprising the setting model of multiple devices of the
hydrogen production station at various moments; setting objective function
according to the device model and capacity; setting conditions of constraint of the
objective function; the conditions of constraint comprise device constraint, electrical
power balance constraint, thermal power balance constraint, and cold power
balance constraint; solving the objective function based on a preset algorithm to
obtain the capacity allocation of each device when the conditions of constraint are
met. For one thing, the hydrogen production, electric power demand and the heat
and cold energy demand required during the operation of the hydrogen production
station are all considered when allocating the hydrogen station, so that the optimal
allocation is more reasonable; for another, the optimal allocation of main devices of
the hydrogen production station is considered, and the optimal state of main devices
at each operation time is planned, so that the optimal allocation is more
comprehensive.
1
1/2
Windpower PV power
Electric
generation generation energy
---- Hydrogen
DC bus
nCe Electrolytic
Hydrogenand cell
Storage electricity
DoDC 4-Wload of
battery pack hydrogen
production
station 4------- - ---------- Hydrogen
storage tank
Figure 1
Thermal cycle - - Electric energy
Thermacysem generation generation 4.. - - - Thermal energy
. - Cold energy
DC bus----Hdoe
Absorptiongu4 - ---
chiller Hydrogen, |
heat, cold and
----- electricity _--.. -Hydrog
load of
hydrogen
Soaeproduction 4------+ Heat storage
battery pack saintn
Figure 2
Description
1/2
Windpower PV power Electric generation generation energy ---- Hydrogen
DC bus
nCe Electrolytic
Hydrogenand cell
Storage electricity DoDC 4-Wload of battery pack hydrogen production station 4------- - ---------- Hydrogen storage tank
Figure 1
Thermal cycle - - Electric energy Thermacysem generation generation 4.. - - - Thermal energy
. - Cold energy
DC bus----Hdoe
Absorptiongu4 - ---
chiller Hydrogen, | heat, cold and ----- electricity _--.. -Hydrog load of hydrogen Soaeproduction 4------+ Heat storage
battery pack saintn
Figure 2
Method and system for optimal energy allocation of a hydrogen production
station
Field of the Invention
The present invention relates to the technical field of hydrogen production, in
particular to a method and system for optimal energy allocation of a hydrogen
production station.
Background of the Invention
The purpose of the off-grid wind and solar energy storage hydrogen production
station is to convert the electric energy converted from wind and solar energy into
hydrogen through electrolytic cell and provide it to users. Its core equipment is an
electrolytic cell, and the method of producing hydrogen is as follows: energize the
electrolytic cell to produce hydrogen. The structures and optimal allocation methods
of prior off-grid wind and solar energy storage hydrogen production stations mainly
have the following defects: 1. The hydrogen production station has its own heat and
cold energy consumption, and prior art only considers hydrogen production and
electricity demand, but do not comprehensively consider the heat and cold energy
demand during the operation of the hydrogen production station. 2. Prior art mainly
considers the optimal capacity allocation of main devices of the hydrogen production
station, but fail to plan the optimal state of the main devices at various moments.
Summary of the Invention
In view of the defects in the prior art, the present invention provides a method
and system for optimal energy allocation of a hydrogen production station, which
aims to solve the two problems.
Firstly
The present invention provides a method for optimal energy allocation of a
hydrogen production station, comprising:
Setting the device model of multiple devices of the hydrogen production station
at various moments;
Setting objective function according to the device model and capacity;
Setting conditions of constraint of the objective function; the conditions of constraint comprise device constraint, electrical power balance constraint, thermal power balance constraint, and cold power balance constraint; the device constraint is determined according to the device model and the maximum capacity; the electrical power balance constraint is determined according to the device model and the electric power generated by the device; the thermal power balance constraint is determined according to the device model and the thermal power generated by the device; the cold power balance constraint is determined according to the device model and the cold power generated by the device; Solving the objective function based on a preset algorithm to obtain the capacity allocation of each device when the conditions of constraint are met; Allocating every device of the hydrogen production station according to the capacity allocation results. Preferably, the multiple devices of the hydrogen production station comprise a wind turbine, a photovoltaic module, an electrolytic cell, a heat storage tank, a hydrogen storage tank, a storage battery and an absorption chiller. Preferably, the objective function is determined aiming at minimizing daily costs. Preferably, the objective function is solved based on a preset algorithm under the premise that the conditions of constraint are met, and the capacity allocation results of each device are obtained as follows: Setting preset parameters, which comprise population size, crossover rate, mutation rate, and parameters of each device model; creating an initial population according to the device model, population size, and device constraints, and using the initial population as the parent population; Setting the negative value of the objective function as the fitness function; calculating the fitness function based on the parent population, and selecting the individual with the largest fitness value as the optimal individual of the parent; According to the fitness function, the crossover rate and the mutation rate, performing crossover and mutation operations on the parent population to obtain the offspring population, calculating the fitness function based on the offspring population, and selecting the individual with the largest fitness value as the optimal individual of the offspring;
Determining whether the absolute value of the result of the fitness function value
of the offspring's optimal individual minus the fitness function value of the parent's
optimal individual is less than the preset threshold; if yes, compare the fitness
function value of the offspring's optimal individual with the fitness function value of
the parent's optimal individual, take the individual corresponding to the larger fitness
function value as the optimal individual, and decode the optimal individual to obtain
the capacity allocation result of each device.
Secondly
The present invention provides an optimal energy allocation system for a
hydrogen production station, comprising:
A model building unit, which is used to set the device model of multiple devices
of the hydrogen production station at various moments;
An objective function building unit, which is used to set objective function
according to device model and capacity;
A constraint condition building unit, which is used to set constraint conditions of
the objective function; the constraint conditions comprise device constraints, electric
power balance constraints, thermal power balance constraints and cold power balance
constraints; the device constraint is determined according to the device model and the
maximum capacity; the electrical power balance constraint is determined according to
the device model and the electrical power generated by the device; the thermal power
balance constraint is determined according to the device model and the thermal power
generated by the device; the cold power balance constraint is determined according to
the device model and the cold power generated by the device;
A solving unit, which is used to solve the objective function based on a preset
algorithm to obtain the capacity allocation result of each device under the premise that
the conditions of constraint are met;
An allocation unit, which is used to allocate each device of the hydrogen
production station according to the capacity allocation result.
Preferably, the multiple devices of hydrogen production station comprise a wind
turbine, a photovoltaic module, an electrolytic cell, a heat storage tank, a hydrogen
storage tank, a storage battery and an absorption chiller.
Preferably, the objective function is determined aiming at minimizing daily costs.
Preferably, the solving unit is specifically used for:
Setting preset parameters, which comprise population size, crossover rate,
mutation rate, and parameters of each device model; creating an initial population
according to the device model, population size, and device constraints, and using the
initial population as the parent population;
Setting the negative value of the objective function as the fitness function;
calculating the fitness function based on the parent population, and selecting the
individual with the largest fitness value as the optimal individual of the parent;
According to the fitness function, the crossover rate and the mutation rate,
performing crossover and mutation operations on the parent population to obtain the
offspring population, calculating the fitness function based on the offspring
population, and selecting the individual with the largest fitness value as the optimal
individual of the offspring;
Determining whether the absolute value of the result of the fitness function value
of the offspring's optimal individual minus the fitness function value of the parent's
optimal individual is less than the preset threshold; if yes, compare the fitness
function value of the offspring's optimal individual with the fitness function value of
the parent's optimal individual, take the individual corresponding to the larger fitness
function value as the optimal individual, and decode the optimal individual to obtain
the capacity allocation result of each device.
A method and system for optimal energy allocation of a hydrogen production
station provided by the present invention, for one thing, the hydrogen production,
electric power demand and the heat and cold energy demand required during the
operation of the hydrogen production station are all considered when allocating the
hydrogen station, so that the optimal allocation is more reasonable; for another, the
optimal allocation of main devices of the hydrogen production station is considered, and the optimal state of main devices at each operation time is planned, so that the optimal allocation is more comprehensive.
Brief Description of the Drawings In order to explain the detailed description of the preferred embodiment of the present invention or the technical proposals in the prior art more clearly, the drawings that need to be used in the detailed description of the preferred embodiment or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to actual scale. Figure 1 is a schematic diagram of the structure and energy flow of a traditional hydrogen production station; Figure 2 is a schematic diagram of the structure and energy flow of a hydrogen production station according to the embodiment of the present invention; Figure 3 is a schematic flowchart of step S4 in the embodiment of the present invention.
Detailed Description of the Preferred Embodiment
The embodiment of the technical proposal of the present invention will be described in detail below in conjunction with the drawings. The embodiment below is only used to illustrate the technical proposal of the present invention more clearly. Therefore, it is only used as an example, and cannot be used to limit the protection scope of the present invention. It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present application shall have the usual meanings understood by those skilled in the art to which the present invention belongs. As shown in Figure 1, in the allocation method of a traditional off-grid new energy hydrogen production station, the main devices comprise a wind turbine, a photovoltaic module, a storage battery, an electrolytic cell and a hydrogen storage tank. The wind turbine and the photovoltaic module are connected to the DC bus through a converter to provide electrical energy to the system. The electrolytic cell is connected to the DC bus through a converter to receive electric energy and electrolyze water to produce hydrogen, and the hydrogen outlet of the electrolytic cell is connected to the inlet of the hydrogen storage tank. The hydrogen storage tank stores hydrogen for use. The storage battery is connected to the bus through a converter. When the power provided by the wind turbine and the photovoltaic module is excessive, the storage battery is charged; when the power provided by the wind turbine and the photovoltaic module is insufficient to meet the demand for hydrogen production of the electrolytic cell, the storage battery discharges to the bus to maintain hydrogen production. The embodiment of the present invention provides a method for optimal energy allocation of the hydrogen production station, which comprise the following steps: Si. Setting device model of multiple devices of the hydrogen production station at various moments; S2. Setting objective function according to the device model and capacity; S3. Setting conditions of constraint of the objective function; the conditions of constraint comprise device constraints, electrical power balance constraints, thermal power balance constraints, and cold power balance constraints; the device constraint is determined according to the device model and the maximum capacity; the electrical power balance constraint is determined according to the device model and the electric power generated by the device; the thermal power balance constraint is determined according to the device model and the thermal power generated by the device; the cold power balance constraint is determined according to the device model and the cold power generated by the device; S4. Solving the objective function based on a preset algorithm to obtain the capacity allocation of each device when the conditions of constraint are met; S5. Allocating every device of the hydrogen production station according to the capacity allocation results.
Figure 2 is a schematic diagram of the structure and energy flow of a hydrogen
production station according to the embodiment of the present invention. The wind
turbine and the photovoltaic module are connected to the DC bus through a converter
to provide electrical energy to the system. The electrolytic cell is connected to the DC
bus through a converter to receive electric energy and electrolyze water to produce
hydrogen, and the hydrogen outlet of the electrolytic cell is connected to the inlet of
the hydrogen storage tank. The hydrogen storage tank stores hydrogen for use. The
storage battery is connected to the bus through a converter. When the power provided
by the wind turbine and the photovoltaic module is excessive, the storage battery is
charged; when the power provided by the wind turbine and the photovoltaic module is
insufficient to meet the demand for hydrogen production of the electrolytic cell, the
storage battery discharges to the bus to maintain hydrogen production. The
electrolytic cell also generates a large amount of heat energy during the hydrogen
production process, and the heat energy is discharged to the thermal cycle system
through the cooling system of the electrolytic cell. The heat storage tank is connected
to the thermal cycle system. When the heat energy in the thermal cycle system is
excessive, the heat storage tank stores the heat energy; when the heat energy in the
thermal cycle system cannot meet the thermal load and the heat energy demand of the
absorption chiller, the heat storage tank releases the heat energy to the thermal cycle
system. The absorption chiller is connected to the thermal cycle system and converts
heat energy into cold energy, and the cold energy is supplied to meet the cold energy
demand of the hydrogen production station.
Wherein, multiple devices of the hydrogen production station comprise a wind
turbine, a photovoltaic module, an electrolytic cell, a heat storage tank, a hydrogen
storage tank, a storage battery and an absorption chiller. The objective function is
determined aiming at minimizing daily costs.
The objective function and constraints for the optimal energy allocation of the
hydrogen production station are determined in the following process: For ease of
description, English abbreviations are used in the following statements to express
some nouns, as follows: EL stands for electrolytic cell; PV stands for photovoltaic module; WT stands for wind turbine; HST stands for heat storage tank; STB stands for storage battery; ACM stands for absorption chiller; AC/DC stands for AC/DC converter; DC/DC stands for DC/DC converter.
Device model:
The theoretical electric output model of a single wind turbine is:
0, v(t)< v v, (t) -v < PwmN X ' 3',vi.;vt)<v PWT (t) vN Vi.
PwTN, VN V(u< t
0, V.<n v(t)
Wherein, PWT(t) is the output electric power of the wind turbine at time t, in kW,
an intermediate variable; PwTN is the rated output power of a single wind turbine, in
kW, a parameter; v(t) is the wind speed at time t, in m/s, a given value; vi. is the cut-in
wind speed, in m/s, a parameter; vout is the cut-out wind speed, in m/s, a parameter; vn
is the rated wind speed, in m/s, a parameter.
PV: The theoretical electric output Ppv(t) model of a single photovoltaic module
during period t is:
G(t) PPV PVN fPV (Gt 1+a(P ~
Wherein, PPVN is the rated output power of a single PV, in kW, a parameter; fpv is
the PV operating efficiency, a parameter; G(t) is the solar radiation intensity during
period t, in W/m 2 , a given value; Gref is the reference radiation intensity, in W/m 2 , a
parameter; a is the temperature coefficient, a parameter; Tpv is the operating
temperature of photovoltaic cell, in °C, a parameter; Ts is the operating reference
temperature of photovoltaic cell, in °C, a parameter.
The electrolytic cell consists of multiple sub-cells working in series, and the
mathematical model of its operating voltage during period t is as follows:
UEL() =[Uv+UaCt(t)+Uh (t)]-NEL
Wherein, NEL is the number of sub-cells in series. The reaction of electrolyzed
water in a sub-cell is not spontaneous. The reversible voltage Urev is the minimum
voltage required to complete the electrolysis process in a sub-cell, in V, an
intermediate variable. Uact is the activation polarization overvoltage of a sub-cell, in
V, an intermediate variable. The ohmic overvoltage Uohm is the overpotential caused
by the ohmic loss of the components in a sub-cell, in V, an intermediate variable. The
three voltages can be described as a function of electrolytic cell current:
-.(AH -T, - AS) 2F
Umt(t) =(S 1 +S 2 TEL +S3 7 ln( 1 U+ )n1+t2 7TL) /TEA L +t 3 /TEL ELEQ)+l1) AEL
U rr2 T
[E(t) AEL
Wherein, F is the Faraday constant, 96485 C/mol, a constant; TEL is the
operating temperature of the electrolytic cell, in K, a parameter; AH is the enthalpy of
formation, in kJ/mol, a parameter; AS is the entropy, in kJ/mol •K, a parameter; AEL
is the area of a sub-cell, in m2, a parameter; rl, r2, s, s2, s3, tl, t2, and t3 are all
characteristic parameters of electrolytic cell, and IEL(t) is the current of the
electrolytic cell at time t, an undetermined variable.
The model of the electric power PEL(t) of the electrolytic cell is as follows:
PEL (t -EL (0EL(t
The model of hydrogen production rate nEL(t) of the electrolytic cell is as
follows:
EL nL Mt) =rF X NEL )
2F
F f (EL AEL 2 2
Wherein, iF is the Faraday efficiency, an intermediate variable; fl, f2 are
Faraday efficiency parameters; PEL (t), in kW, is an intermediate variable.
The model of heat production QEL(t) of the electrolytic cell is as follows:
(TEL - )(EL cwo QEL(t)=(hd+h-,,h IEL()X In[(r.; - TI)/ (rn -r )].
Wherein, hcond is heat exchanger conduction index, in W/°C, a parameter;
hconv is heat exchanger convection index, in W/°C-A, a parameter; Tcwi is the inlet
temperature of the heat exchanger, in °C, a parameter; Tcwo is the outlet temperature
of the heat exchanger, in °C, a parameter.
The mathematical model of heat storage in the heat storage tank at time t is:
WQHST (t) = WQHST (t- 1)+(TEHsT_QHST i (t)QHST -out))A TEST-out
The mathematical model of heat storage state STHST(t) of the heat storage tank
during period t is:
STHST(t)= WQUST() WQHSTN
Wherein, WQHST(t) is the heat energy stored in the heat storage tank during the
period t, in kJ, an intermediate variable. TEHST-in and TEHSTout are the input and
output efficiency of the heat storage tank respectively, parameters; QHST-in(t) and
QHSTout(t) are the input and output thermal power of the heat storage tank during
period t, in W, intermediate variables. At is the duration of the time period, which is 1
hour; WQHSTN is the rated heat storage capacity of the heat storage tank, in kJ, an
undetermined variable; STHST(t) is the heat storage state of the heat storage tank
during the period t, an intermediate variable.
The mathematical model of the hydrogen storage capacity FHT(t) of the
hydrogen storage tank during period t is:
FHT(t) = FHT (t-1 EL(t) x At -F,,dg (t
The mathematical model of gas pressure SHT(t) in the hydrogen storage tank
during period t is:
S(t FHT (t)RTH2 VHT
The mathematical model of the hydrogen storage tank state STHT(t) during
period t is:
STur (t) = H(t S HTN Wherein, Fload(t) is the hydrogen demand during period t, in mol, an
intermediate variable. TH2 is the hydrogen temperature, in K, a parameter; VHT is the
volume of hydrogen storage tank, in m3, a parameter; R is the general gas constant,
8.314J/(mol-K), a constant; SHTN is the rated pressure of hydrogen storage tank, in
Pa, a parameter; STHT(t) is the pressure state of the hydrogen storage tank during
period t, an intermediate variable.
The power WSTB(t) in the storage battery at time t is:
Storage battery charging:
WSTB()= (-sd )WSTB (-1)+ STB_in(t)At
Storage battery discharging:
WTB(t)= (I-osdr)WSTB (t-)-STBou1 Q)At
Storage state STSTB(t) of the storage battery at time t:
WSTB (t) STSTB() WSTBN
Wherein, asdr is the self-discharge rate of storage battery, a parameter;
PSTB-in(t) and PSTBout(t) are the input and output electric power of the storage
battery during period t, in W, intermediate variables; WSTBN is the rated storage
capacity of the storage battery, in kJ, a variable; STSTB(t) is the power storage state of STB during period t, an intermediate variable.
The mathematical model of the produced cold power QACM-cool(t) of the
absorption chiller during period t is:
QACM coo (t)=COPAcM X QACM _hot(t)
Wherein, COPACM is the energy efficiency coefficient of the absorption chiller,
a parameter.
The method for optimal energy allocation of a wind and solar energy storage
hydrogen production station proposed in the embodiment of the present invention
aims to minimize the daily cost of hydrogen production stations, and the
corresponding mathematical objective function is:
min f =(CEL ELNKL ACM ACMK °C,,MNPPK°"+CN.PNK° +CHSTWQHSNK ,",+CFHTK,°+CSTBWTBNK"°") + (EE EL(t)+ECMQACMcoo,(t)+EnN.P,(t)+E,,N,,P,())
CEL is the investment cost per unit power of EL, in RMB/kW, a parameter; CACM
is the investment cost per unit power of ACM, in RMB/kW, a parameter; Cpv is the
investment cost per unit power of PV, in RMB/kW, a parameter; CWT is the
investment cost per unit power of WT, in RMB/kW, a parameter; CHST is the
investment cost per unit heat of HST, in RMB/kW, a parameter; CHT is the investment
cost per unit hydrogen storage of HT, in RMB/ kW, a parameter; CSTB is the
investment cost per unit power storage of STB, in RMB/kW, a parameter; PELN is the
rated power of the electrolytic cell, an undetermined variable; QACMN is the rated
power of the ACM, in kW, an undetermined variable; PWTN is the rated output power
of a single wind turbine, in kW, a parameter; PPVN is the rated output power of a single
PV, in kW, a parameter; NWT is the number of WT units, an undetermined variable;
NPv is the number of PV sets, an undetermined variable; WQHSTN is the rated heat
storage capacity of the heat storage tank, an undetermined variable; FHTN is the rated
hydrogen storage capacity of HTN, in mol, an undetermined variable; WSTBN is the
rated power storage capacity of STB, inkJ, an undetermined variable; Kofx, Xe {EL,
ACM, PV, WT, HST, HT, STB} is the conversion factor for calculating the daily
purchase cost of device X. The calculation formula is as follows:
TN, YearHPS x 365
Year TN = ceil ( H DYx
) YearHps is the designed service life of the hydrogen production station, TNx is
the number of replacements of device X within the YearmPs years, DYx is the service
life of device X, and ceil() is a rounding up operation.
PEL(t) is the power of EL during period t, in kW, an undetermined variable;
QACM ool(t) is the power of ACM during period t, in kW, an undetermined variable;
PWT(t) is the output electric power of a single wind turbine at time t, kW, an
intermediate variable; Pv(t) is the output electric power of a single photovoltaic
module at time t, an undetermined variable; EEL, EACM, EPv, EWT are the operation and
maintenance cost of EL, ACM, PV and WT in unit time at unit power respectively, in
RMB/ kW.
Conditions of constraint:
Electric power balance constraint:
P111eJo (t)=TEAC/DC XWT ( +TEDCDCXPP(0- PEL( TEDC/DC
STBout- STB in + TESTBout x TEDCIDC TEDC/DC X TESTBin
Wherein, TESTBji and TEsTBout are the battery charging and discharging
efficiency respectively, parameters; TEAC/DC and TEDC/DC are the conversion
efficiency of AC/DC and DC/DC respectively, parameters.
Thermal power balance constraint:
Q_ (t hot _load QEL =EL QHS n'+E HST _in _x (t) + TEHST _out HST_out ACM_hot TEHSTin
Cold power balance constraint:
Qcooi_load(t) QACM _cool(t
Device constraint:
1<N<N Wherein, due to site restrictions, NWT mx is the maximum number of WTs that
can be positioned, in unit, a parameter.
-:! NPV: - Pmax p
Wherein, due to site restrictions, Npv max is the maximum number of PVs that
can be positioned, in block, a parameter.
0 () (PEL 4ELmax
max(PELt)) ELN EL_max
Wherein, PEL_maxis the maximum electric power of EL, kW.
ST i HST SHST_max
WQHST min WQHSTN WQHsT _
Wherein, WQHST min and WQHSTmax are the maximum and minimum heat
storage capacity of the heat storage tank, in KJ, parameters; STHSTmax and STHST_min
are the upper and lower limits of the heat storage state value of the heat storage tank,
parameters.
STHT mSTHT (0 SHTmax
FTr:! FHTN <FTr_
Wherein, FHT and mn FHT max are the maximum and minimum hydrogen storage capacity of the hydrogen storage tank, in mol, parameters; STHTmax and STHT_mm are the upper and lower limits of the hydrogen storage state value of the hydrogen storage tank, parameters.
STSTB mm TS(B -) STB
WQSTB-mm WQSTBN WQSTBma Wherein, WQSTB min and WQsTBmax are the maximum and minimum storage capacity of the storage battery, in KJ, parameters; STSTB-max and STSTBmin are the
upper and lower limits of the storage state value of the storage battery, parameters.
max(QACM ()) ,(o QACMN QACM max
0 QcM coo (t) QAC max
Wherein, QACM max is the maximum cold power that ACM can provide, in kW, a
parameter.
Figure 3 is a schematic flowchart of step S4 in the embodiment of the present
invention,
Step 1. Set the preset parameters. The preset parameters comprise population
size, crossover rate, mutation rate and the parameters of each device model; set the
heat storage state STHST(t) and other intermediate variables of the heat storage tank at
during period t and the upper and lower limits of the current EL(t) and other
undetermined variables the electrolytic cell at time t.
Step 2. Encode the current lEL(t) and other undetermined variables of the
electrolytic cell at time t, and transform the objective function f into a fitness function.
Step 3. Create an initial population according to the device model, population
size and device constraints, and use the initial population as the parent population.
Step 4. Calculate the fitness function, and select the individual with the largest
fitness value as the optimal individual of the parent.
Step 5. Perform selection operation and use the optimal selection.
Step 6. Perform crossover operation (crossover probability 80%), and use single
point crossover.
Step 7. Perform mutation operation (mutation probability 50%) and use simple
mutation to obtain the offspring population.
Step 8. Calculate the fitness function based on the offspring population, and
select the individual with the largest fitness value as the optimal individual of
offspring.
Step 9. Determine whether the absolute value of the result of the fitness function
value of the offspring's optimal individual minus the fitness function value of the
parent's optimal individual is less than the set value (0.001). If yes, go to Step 10; if
no, go to Step 5.
Step 10. Compare the fitness function value of the offspring's optimal individual
with the fitness function value of the parent's optimal individual, take the individual
corresponding to the larger fitness function value as the optimal individual, output the
optimal individual and corresponding fitness function value of the optimal individual,
and decode the optimal individual to obtain the capacity allocation result of each
device.
The embodiment of the present invention also provides an optimal energy
allocation system for a hydrogen production station, comprising:
A model building unit, which is used to set the device model of multiple devices
of the hydrogen production station at various moments;
An objective function building unit, which is used to set objective function
according to device model and capacity;
A constraint condition building unit, which is used to set constraint conditions of
the objective function; the constraint conditions comprise device constraints, electric
power balance constraints, thermal power balance constraints and cold power balance
constraints; the device constraint is determined according to the device model and the
maximum capacity; the electrical power balance constraint is determined according to
the device model and the electrical power generated by the device; the thermal power
balance constraint is determined according to the device model and the thermal power generated by the device; the cold power balance constraint is determined according to the device model and the cold power generated by the device; A solving unit, which is used to solve the objective function based on a preset algorithm to obtain the capacity allocation result of each device under the premise that the conditions of constraint are met; An allocation unit, which is used to allocate each device of the hydrogen production station according to the capacity allocation result. Wherein, the multiple devices of hydrogen production station comprise a wind turbine, a photovoltaic module, an electrolytic cell, a heat storage tank, a hydrogen storage tank, a storage battery and an absorption chiller. The objective function is determined aiming at minimizing daily costs. The solving unit is specifically used for: Setting preset parameters, which comprise population size, crossover rate, mutation rate, and parameters of each device model; creating an initial population according to the device model, population size, and device constraints, and using the initial population as the parent population; Setting the negative value of the objective function as the fitness function; calculating the fitness function based on the parent population, and selecting the individual with the largest fitness value as the optimal individual of the parent; According to the fitness function, the crossover rate and the mutation rate, performing crossover and mutation operations on the parent population to obtain the offspring population, calculating the fitness function based on the offspring population, and selecting the individual with the largest fitness value as the optimal individual of the offspring;
Determining whether the absolute value of the result of the fitness function value of the offspring's optimal individual minus the fitness function value of the parent's optimal individual is less than the preset threshold; if yes, compare the fitness function value of the offspring's optimal individual with the fitness function value of the parent's optimal individual, take the individual corresponding to the larger fitness function value as the optimal individual, and decode the optimal individual to obtain the capacity allocation result of each device.
A method and system for optimal energy allocation of a hydrogen production station provided by the embodiment of the present invention, for one thing, the hydrogen production, electric power demand and the heat and cold energy demand required during the operation of the hydrogen production station are all considered when allocating the hydrogen station, so that the optimal allocation is more reasonable; for another, the optimal allocation of main devices of the hydrogen production station is considered, and the optimal state of main devices at each operation time is planned, so that the optimal allocation is more comprehensive.
Finally, it should be noted that the embodiment is only used to illustrate the technical proposal of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiment, those of ordinary skill in the art should understand that the technical proposals recorded in the foregoing embodiment can still be modified, or part or all of the technical features can be equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical proposal deviate from the scope of the technical proposal of the embodiment of the present invention, and shall be included in the scope of the claims and specification of the present invention.
Claims (8)
1. An optimal energy allocation method for a hydrogen production station, which
is characterized in that it comprises:
Setting the device model of multiple devices of the hydrogen production station
at various moments;
Setting objective function according to the device model and capacity;
Setting conditions of constraint of the objective function; the conditions of
constraint comprise device constraints, electrical power balance constraints, thermal
power balance constraints, and cold power balance constraints; the device constraint
is determined according to the device model and the maximum capacity; the electrical
power balance constraint is determined according to the device model and the electric
power generated by the device; the thermal power balance constraint is determined
according to the device model and the thermal power generated by the device; the
cold power balance constraint is determined according to the device model and the
cold power generated by the device;
Solving the objective function based on a preset algorithm to obtain the capacity
allocation of each device when the conditions of constraint are met;
Allocating every device of the hydrogen production station according to the
capacity allocation results.
2. A method for optimal energy allocation of a hydrogen production station
according to Claim 1, characterized in that the multiple devices of hydrogen
production station comprise a wind turbine, a photovoltaic module, an electrolytic cell,
a heat storage tank, a hydrogen storage tank, a storage battery and an absorption
chiller.
3. A method for optimal energy allocation of a hydrogen production station
according to Claim 1, characterized in that the objective function is determined
aiming at minimizing daily costs.
4. A method for optimal energy allocation of a hydrogen production station
according to Claim 1, characterized in that the objective function is solved based on a
preset algorithm under the premise that the conditions of constraint are met, and the capacity allocation results of each device is obtained as follows: Setting preset parameters, which comprise population size, crossover rate, mutation rate, and parameters of each device model; creating an initial population according to the device model, population size, and device constraints, and using the initial population as the parent population; Setting the negative value of the objective function as thefitness function; calculating the fitness function based on the parent population, and selecting the individual with the largest fitness value as the optimal individual of the parent; According to the fitness function, the crossover rate and the mutation rate, performing crossover and mutation operations on the parent population to obtain the offspring population, calculating the fitness function based on the offspring population, and selecting the individual with the largest fitness value as the optimal individual of the offspring; Determining whether the absolute value of the result of the fitness function value of the offspring's optimal individual minus the fitness function value of the parent's optimal individual is less than the preset threshold; if yes, compare the fitness function value of the offspring's optimal individual with the fitness function value of the parent's optimal individual, take the individual corresponding to the larger fitness function value as the optimal individual, and decode the optimal individual to obtain the capacity allocation result of each device.
5. An optimal energy allocation system for a hydrogen production station, which is characterized in that it comprises: A model building unit, which is used to set the device model of multiple devices of the hydrogen production station at various moments; An objective function building unit, which is used to set objective function according to device model and capacity; A constraint condition building unit, which is used to set constraint conditions of the objective function; the constraint conditions comprise device constraints, electric power balance constraints, thermal power balance constraints and cold power balance constraints; the device constraint is determined according to the device model and the maximum capacity; the electrical power balance constraint is determined according to the device model and the electrical power generated by the device; the thermal power balance constraint is determined according to the device model and the thermal power generated by the device; the cold power balance constraint is determined according to the device model and the cold power generated by the device;
A solving unit, which is used to solve the objective function based on a preset
algorithm to obtain the capacity allocation result of each device under the premise that
the conditions of constraint are met;
An allocation unit, which is used to allocate each device of the hydrogen
production station according to the capacity allocation result.
6. An optimal energy allocation system for a hydrogen production station
according to Claim 5, characterized in that the multiple devices of hydrogen
production station comprise a wind turbine, a photovoltaic module, an electrolytic cell,
a heat storage tank, a hydrogen storage tank, a storage battery and an absorption
chiller.
7. An optimal energy allocation system for a hydrogen production station
according to Claim 5, characterized in that the objective function is determined
aiming at minimizing daily costs.
8. An optimal energy allocation system for a hydrogen production station
according to Claim 5, characterized in that the solving unit is specifically used for:
Setting preset parameters, which comprise population size, crossover rate,
mutation rate, and parameters of each device model; creating an initial population
according to the device model, population size, and device constraints, and using the
initial population as the parent population;
Setting the negative value of the objective function as the fitness function;
calculating the fitness function based on the parent population, and selecting the
individual with the largest fitness value as the optimal individual of the parent;
According to the fitness function, the crossover rate and the mutation rate,
performing crossover and mutation operations on the parent population to obtain the
offspring population, calculating the fitness function based on the offspring population, and selecting the individual with the largest fitness value as the optimal individual of the offspring;
Determining whether the absolute value of the result of the fitness function value
of the offspring's optimal individual minus the fitness function value of the parent's
optimal individual is less than the preset threshold; if yes, compare the fitness
function value of the offspring's optimal individual with the fitness function value of
the parent's optimal individual, take the individual corresponding to the larger fitness
function value as the optimal individual, and decode the optimal individual to obtain
the capacity allocation result of each device.
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| CN111144620B (en) * | 2019-12-06 | 2023-06-16 | 东南大学溧阳研究院 | Electric hydrogen comprehensive energy system considering seasonal hydrogen storage and robust planning method thereof |
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