CN118054466A - Long-time hydrogen energy storage operation optimization method, equipment, medium and product - Google Patents
Long-time hydrogen energy storage operation optimization method, equipment, medium and product Download PDFInfo
- Publication number
- CN118054466A CN118054466A CN202410220526.4A CN202410220526A CN118054466A CN 118054466 A CN118054466 A CN 118054466A CN 202410220526 A CN202410220526 A CN 202410220526A CN 118054466 A CN118054466 A CN 118054466A
- Authority
- CN
- China
- Prior art keywords
- hydrogen
- power
- energy storage
- expenditure
- unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 198
- 239000001257 hydrogen Substances 0.000 title claims abstract description 198
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 185
- 238000004146 energy storage Methods 0.000 title claims abstract description 109
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000005457 optimization Methods 0.000 title claims abstract description 29
- 238000010248 power generation Methods 0.000 claims abstract description 47
- 230000005540 biological transmission Effects 0.000 claims abstract description 31
- 230000007774 longterm Effects 0.000 claims abstract description 28
- 150000002431 hydrogen Chemical class 0.000 claims abstract description 18
- 238000003860 storage Methods 0.000 claims description 90
- 238000010276 construction Methods 0.000 claims description 29
- 238000012983 electrochemical energy storage Methods 0.000 claims description 25
- 238000004590 computer program Methods 0.000 claims description 21
- 239000000446 fuel Substances 0.000 claims description 21
- 230000005611 electricity Effects 0.000 claims description 19
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 15
- 229910052799 carbon Inorganic materials 0.000 claims description 14
- 238000002485 combustion reaction Methods 0.000 claims description 14
- 239000003245 coal Substances 0.000 claims description 10
- 239000002699 waste material Substances 0.000 claims description 10
- 238000009826 distribution Methods 0.000 claims description 9
- 238000005286 illumination Methods 0.000 claims description 8
- 238000010977 unit operation Methods 0.000 claims description 8
- 230000009194 climbing Effects 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 claims description 4
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 4
- 239000002028 Biomass Substances 0.000 claims description 3
- VBIZUNYMJSPHBH-OQLLNIDSSA-N salinazid Chemical compound OC1=CC=CC=C1\C=N\NC(=O)C1=CC=NC=C1 VBIZUNYMJSPHBH-OQLLNIDSSA-N 0.000 claims description 3
- 239000000428 dust Substances 0.000 claims description 2
- 125000004435 hydrogen atom Chemical group [H]* 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 claims description 2
- 239000003345 natural gas Substances 0.000 claims description 2
- 239000008000 CHES buffer Substances 0.000 claims 1
- MKWKNSIESPFAQN-UHFFFAOYSA-N N-cyclohexyl-2-aminoethanesulfonic acid Chemical compound OS(=O)(=O)CCNC1CCCCC1 MKWKNSIESPFAQN-UHFFFAOYSA-N 0.000 claims 1
- 239000007789 gas Substances 0.000 description 13
- 238000007599 discharging Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005868 electrolysis reaction Methods 0.000 description 1
- 229910021389 graphene Inorganic materials 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
Classifications
-
- 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
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a long-time hydrogen energy storage operation optimization method, equipment, medium and product, belonging to the field of power grid optimization, wherein the method comprises the following steps: determining the wind-solar unit-hour power generation power according to wind-solar resource data of a high-proportion renewable energy source area in a historical setting period; according to the wind-solar unit-hour power generation power, the installed capacity of a hydrogen energy storage system, the installed capacity of various power supplies, the installed capacity of conventional energy storage, the electric load demand and the renewable hydrogen demand, a long-term operation optimization model of the hydrogen energy storage is built with the minimum total investment operation cost as a target, and the new installed capacities of the hydrogen energy storage system, various power supplies, conventional energy storage and transmission lines in a high-proportion renewable energy source area, the total hydrogen transmission capacity of the hydrogen transmission line and the output of the hydrogen energy storage system, various power supplies and conventional energy storage in a future set period are solved. The invention can realize the energy balance of supply and demand in the power system.
Description
Technical Field
The invention relates to the field of power grid optimization, in particular to a long-time hydrogen energy storage operation optimization method, equipment, medium and product for a high-proportion renewable energy power system.
Background
Under the large background of the construction of a novel power system taking renewable energy as a main body, the grid-connected proportion of the renewable energy is gradually improved, but due to the problems of intermittence, volatility and randomness, the access of the renewable energy with high proportion provides new challenges for the safe and stable operation of a power grid. With the renewable energy source becoming a main power source, how to realize power and energy balance on different time scales is critical to overall development of energy storage with different functional positioning. Electrochemical energy storage mainly solves the problem of power balance in the short-term scale of the system, and in order to cope with the energy imbalance problem in the long-term scale of the week, month and season, the introduction of an advanced long-term energy storage technology is needed.
Disclosure of Invention
The invention aims to provide a long-time hydrogen energy storage operation optimization method, equipment, medium and product, which can realize energy supply and demand balance in an electric power system.
In order to achieve the above object, the present invention provides a long-term hydrogen storage operation optimization method, comprising:
Acquiring wind-solar resource data of a high-proportion renewable energy source area, installed capacity of a hydrogen energy storage system, installed capacity of various power supplies, installed capacity of conventional energy storage, electric load demand and renewable hydrogen demand in a history setting period; the hydrogen energy storage system comprises an electrolytic tank, a hydrogen fuel cell, a hydrogen combustion engine and a hydrogen storage tank;
determining the photoelectric unit-hour power generation power and the wind power unit-hour power generation power of the high-proportion renewable energy source region in the history setting period according to the wind-light resource data of the high-proportion renewable energy source region in the history setting period;
Clustering the photoelectric unit-hour power generation power and the wind power unit-hour power generation power of the high-proportion renewable energy source region in the historical setting period to obtain the photoelectric unit-hour power generation power and the wind power unit-hour power generation power of the typical week of four seasons;
According to the photoelectric unit hour power generation power, the wind power unit hour power generation power of four seasons and the high proportion renewable energy source area hydrogen energy storage system installed capacity, the installed capacity of various power sources, the installed capacity of conventional energy storage, the electric load demand and the renewable hydrogen demand in a history setting period, a hydrogen energy storage long-term operation optimization model is established with the minimum total investment operation cost as a target; the total investment operation cost comprises the total construction and operation expenditure of various power supplies, the operation expenditure of power transmission and distribution lines, the construction and operation expenditure of a hydrogen energy storage system, the construction expenditure of conventional energy storage, the hydrogen transportation expenditure, the electricity discarding punishment expenditure and the carbon emission expenditure; constraint conditions of the long-term operation optimization model of hydrogen energy storage comprise electric power balance constraint, hydrogen power balance constraint, power capacity constraint, maximum annual utilization hour constraint, generator set climbing constraint, electrolyzer operation constraint, hydrogen storage tank operation constraint, conventional energy storage operation constraint, capacity expansion constraint and carbon emission constraint;
Solving the long-term operation optimization model of the hydrogen energy storage to obtain the newly-increased installed capacity of the hydrogen energy storage system, the newly-increased installed capacity of various power supplies, the newly-increased installed capacity of conventional energy storage, the output of the hydrogen energy storage system, the output of various power supplies, the output of conventional energy storage, the installed capacity of a power transmission line and the total hydrogen transmission capacity of the hydrogen transmission line in the high-proportion renewable energy source area in a set period in the future.
To achieve the above object, the present invention also provides a computer apparatus comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer program to perform the steps of the method described above.
To achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above method.
To achieve the above object, the present invention also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the above method.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the method, the power generated in a photovoltaic unit hour and the power generated in a wind power unit hour are determined according to wind-solar resource data of a high-proportion renewable energy source region, the total construction and operation expenditure of various power supplies, the operation expenditure of a power transmission and distribution line, the construction and operation expenditure of a hydrogen energy storage system, the construction expenditure of conventional energy storage, the hydrogen transportation expenditure, the electricity discarding punishment expenditure and the minimum sum of carbon emission expenditure are taken as targets, and the power balance constraint, the hydrogen power balance constraint, the power capacity constraint, the maximum annual utilization hour constraint, the generator set climbing constraint, the electrolyzer operation constraint, the hydrogen storage tank operation constraint, the conventional energy storage operation constraint, the capacity expansion constraint and the carbon emission constraint are considered, so that a long-term operation optimization model of hydrogen energy storage can be established, the configuration condition of the hydrogen energy storage system in the future high-proportion renewable energy source region can be predicted more accurately, the operation of the hydrogen energy storage system is optimized, the capacity of the hydrogen energy storage for promoting the dissipation and the utilization of the renewable energy source is fully excavated, the long-time energy storage benefit of the hydrogen energy storage spanning the season long-time period is embodied, and decision reference is provided for the hydrogen energy storage operation optimization, and the energy supply and demand balance in the power system is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for optimizing long-term hydrogen storage operation provided by the invention;
FIG. 2 is a graph of wind and solar unit output for a typical week of 4 seasons after clustering;
FIG. 3 is a schematic diagram of the installed state of various power and energy storage devices in the high-ratio renewable energy region 2025 and 2030;
FIG. 4 is a graph comparing the installed capacities of the devices of the hydrogen storage systems of 2025 and 2030;
FIG. 5 is a graph of typical hourly cycle operation in the hydrogen storage spring of 2030;
FIG. 6 is a graph of typical hourly cycle operation in the 2030 hydrogen storage summer;
FIG. 7 is a graph of typical cycle-hour operation in the autumn with hydrogen storage of 2030
FIG. 8 is a graph of typical hourly cycle operation in the winter for 2030 hydrogen storage;
Fig. 9 is a graph of hydrogen tank hour scale operation.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The hydrogen energy storage is taken as a novel energy storage, has the functions of long period, cross-season, large scale and cross-space storage, and based on the long-time energy storage function of hydrogen in the power system, the cross-season energy storage capacity of the hydrogen energy storage can promote the absorption and utilization of renewable energy sources, and plays an important role in the construction of the novel power system. At the source end, the hydrogen energy storage can play a role of long-time energy storage, and smooth surfing of a fluctuation power supply is promoted; at the network end, the hydrogen energy storage can participate in peak shaving, redundant electric quantity is stored in seasons with smaller energy demands throughout the year, and the electric quantity is supplemented in seasons with larger energy demands. The invention aims to provide a long-time hydrogen energy storage operation optimization method, equipment, medium and product, which better realize the energy supply and demand balance in an electric power system.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the method for optimizing long-term hydrogen energy storage operation provided by the invention comprises the following steps:
S1: wind-solar resource data of a high-proportion renewable energy source area, installed capacity of a hydrogen energy storage system, installed capacity of various power supplies, installed capacity of conventional energy storage, electric load demand and renewable hydrogen demand in a historical setting period are obtained.
The wind-solar resource data comprises: the number of photovoltaic panels, short-circuit current, real-time illumination intensity, rated illumination intensity, peak current, peak voltage, actual temperature of the photovoltaic panels, rated temperature of the photovoltaic panels, ambient temperature, rated power of the fan, actual wind speed, cut-in wind speed, cut-out wind speed and rated wind speed.
The hydrogen energy storage system comprises an electrolytic tank, a hydrogen fuel cell, a hydrogen combustion engine and a hydrogen storage tank.
The power sources in the high-proportion renewable energy source region comprise conventional hydropower, coal power, natural gas power generation, nuclear power, wind power generation, photovoltaic power generation and biomass power generation.
Conventional energy storage includes pumped storage and electrochemical storage.
S2: and determining the photoelectric unit-hour power generation power and the wind power unit-hour power generation power of the high-proportion renewable energy source region in the history setting period according to the wind-light resource data of the high-proportion renewable energy source region in the history setting period.
Specifically, wind-light resource data such as the temperature of 2 meters, the wind speed of 50 meters and the irradiance of the clear sky surface shortwave downwards are obtained by selecting points on the NASA website, and a wind-light unit power model is constructed to obtain the wind-light unit hour power generation power of the high-proportion renewable energy region 8760 h.
The actual generated power of the photovoltaic panel is mainly influenced by the intensity of solar radiation and the ambient temperature, and the calculation model of the generated power of the photovoltaic panel in each 1kW corresponding to the hour is as follows:
Ppv＝NpvIpvVpvfTfs；
Ipv＝Isc(Rad/Rad,rated-1)+Ipm；
Vpv＝Vpm(1+0.0593lg(Rad/Rad,rated))；
fT＝1-(Tpv,real-Trated)/200；
Tpv,real＝Tae+30Rad/800；
Wherein P pv is the power generated by the photovoltaic panel per 1kW for the corresponding hours, that is, the power generated by the photovoltaic unit hours, N pv is the number of photovoltaic panels, I pv is the real-time current, in unit a, V pv is the real-time voltage, in unit V, f T is the temperature correction coefficient, f s is the dust correction coefficient, I sc is the short-circuit current, in unit a, R ad is the real-time illumination intensity, in unit lx, R ad,rated is the rated illumination intensity, in unit lx, I pm is the peak current, in unit a, V pm is the peak voltage, in unit V, T pv,real is the actual temperature of the photovoltaic panel, in unit in the temperature of the photovoltaic panel, T rated is the rated temperature of the photovoltaic panel, in unit in the temperature of the environment, and T ae is the temperature.
The actual power of the fan is mainly determined by the cut-in wind speed and the cut-out wind speed, and the calculation model of the generated power of the fan in each 1kW corresponding to the hour is as follows:
wherein, P wt is the power generated by the fan every 1kW corresponding to the hour, namely the power generated by the wind power unit is the power generated by the fan in the hour, P wt,rated is the rated power of the single fan, the unit is kW, v is the actual wind speed, the unit is kW, v in is the cut-in wind speed, the unit is kW, v out is the cut-out wind speed, the unit is kW, and v rated is the rated wind speed, and the unit is kW.
S3: and clustering the photoelectric unit-hour power generation power and the wind power unit-hour power generation power of the high-proportion renewable energy source region in the historical setting period to obtain the photoelectric unit-hour power generation power and the wind power unit-hour power generation power of the typical week of four seasons.
Specifically, a K-means clustering algorithm is adopted to cluster the 8760h wind-solar unit hour power generation data obtained in the step S2. Namely, randomly selecting 4 different points as initial clustering centers, and then calculating the distance from each data to each clustering center, wherein the Euclidean distance is adopted in the method, and the formula is as follows:
d (x, y) is the distance of data point x from data point y, (x 1,x2,...,xA) is the coordinate of data point x in the A-dimensional space, (y 1,y2,...,yA) is the coordinate of data point y in the A-dimensional space.
Dividing the data points into classes where the cluster centers closest to the data points are located to form 4 data sets (namely 4 clusters), then recalculating the average value of the data points of each cluster, taking the average value as a new cluster center, finally calculating the distance from each data point to the new 4 cluster centers, repartitioning, repeating iteration until all the data points cannot be updated to other data sets, and finally outputting a wind-solar unit output curve of 4 seasons and typical weeks, as shown in figure 2.
S4: and (3) establishing a long-term operation optimization model of hydrogen energy storage according to the photoelectric unit hour power generation, the wind power unit hour power generation, the installed capacity of the hydrogen energy storage system of the high-proportion renewable energy source area in the historical setting period, the installed capacity of various power sources, the installed capacity of conventional energy storage, the electric load demand and the renewable hydrogen demand in the four seasons with minimum total investment operation cost as a target.
The invention is oriented to a high-proportion renewable energy source access area, and by taking 2020 as a reference year, an optimization model is established by inputting the installed capacity of a 2020 hydrogen energy storage system, various power supplies and other energy storage, the electric load demand and renewable hydrogen demand of the area and taking the total investment running cost as a target, and the new installed capacity and hour-level running output conditions of the hydrogen energy storage, various power supplies and other energy storage in the area in 2025-2030 are scientifically and quantitatively predicted.
The total investment operation cost comprises construction and operation expenditure of various power supplies, operation expenditure of power transmission and distribution lines, construction and operation expenditure of a hydrogen energy storage system, construction expenditure of conventional energy storage, hydrogen transportation expenditure, electric discarding punishment and carbon emission expenditure. Namely, the target function of the long-time operation optimization model of hydrogen energy storage is as follows:
minf＝CGen+CGri+CGes+CPs+CEs+CHt+CWaste+CEmi；
Wherein f is total investment operation cost, C Gen is construction and operation expenditure of various power supplies, C Gri is operation expenditure of power transmission and distribution lines, C Hes is construction and operation expenditure of a hydrogen energy storage system, C Ps is construction expenditure of pumped storage, C Es is construction expenditure of electrochemical energy storage, C Ht is hydrogen transportation expenditure, C waste is electricity discarding punishment, and C Emi is carbon emission expenditure. The coefficient data involved in the model calculation are all post-labeling data.
(1) The total construction and operation expenditure of various power supplies are as follows:
CGen＝Cfix+Crun；
Wherein, C Gen is the total construction and operation expenditure of various power supplies, C fix is the total fixed construction expenditure of various power supplies, C run is the total operation expenditure, Fixed construction expenditure for the unit of the ith power supply,/>For the total newly installed capacity of the ith power supply, T i is the service life of the ith power supply, alpha is the depreciation rate,/>For the unit operation expenditure of coal electricity,/>For the unit operation expenditure of gas and electricity,/>The unit is ten thousands kW,/>, the total installed capacity of coal power is increasedThe total newly-increased installed capacity of the gas-electricity is ten thousands kW. h cp is the number of annual operating hours of coal electricity in hours, and h gas is the number of annual operating hours of gas electricity in hours.
(2) The operation expenditure of the power transmission and distribution line is as follows:
wherein, C Gri is the operation expenditure of the power transmission and distribution line, The total new installed capacity of the power transmission line is increased by ten thousand kW, the unit of C gri is the unit expenditure of the power transmission line, the unit is ten thousand yuan/ten thousand kW, and the T gri is the planned service life of the power transmission and distribution line.
(3) The construction and operation expenditure of the hydrogen energy storage system are as follows:
CHES＝Cel+Cfc+Chyt+Csh；
Wherein, C el is an electrolyzer, C fc is a hydrogen fuel cell, C hyt is a hydrogen gas engine, C sh is a hydrogen storage tank, For newly increasing the installed capacity of the electrolytic tank, C el is the unit investment expenditure of the electrolytic tank,/>For the unit operation expenditure of the electrolyzer, P el is the output (operating power) of the electrolyzer, T el is the service life of the electrolyzer,/>For the newly increased capacity of the hydrogen fuel cell, C fc is the unit investment expenditure of the hydrogen fuel cell,/>For the unit operation expenditure of the hydrogen fuel cell, P fc is the output (power generation) of the hydrogen fuel cell, T fc is the service life of the hydrogen fuel cell,/>Is the newly increased installed capacity of the hydrogen combustion engine, C hyt is the unit investment expenditure of the hydrogen combustion engine,/>For the unit operation expenditure of the hydrogen combustion engine, P hyt is the output (power generation) of the hydrogen combustion engine, T hyt is the service life of the hydrogen combustion engine,/>For the newly increased installed capacity of the hydrogen storage tank, C sh is the unit investment expenditure of the hydrogen storage tank, and T sh is the service life of the hydrogen storage tank.
(4) The construction expenditure of conventional energy storage comprises pumped storage expenditure and electrochemical energy storage expenditure.
The expenditure of pumped storage is as follows:
wherein, C Ps is the expenditure of pumped storage, The total newly-increased installed capacity for pumped storage is given in the unit of ten thousand kWh, C Ps is given in the unit of expenditure for pumped storage, T Ps is the service life of the pumped storage equipment.
The expenditure of electrochemical energy storage is as follows:
Wherein, C Es is the expenditure of electrochemical energy storage, The total new installed capacity for electrochemical energy storage is increased in the unit of ten thousand kWh, C Es is the unit expenditure of electrochemical energy storage, the unit of ten thousand yuan/ten thousand kWh and T Es is the service life of the electrochemical energy storage equipment.
(5) The hydrogen transportation expenditure is as follows:
wherein, C Ht is the hydrogen transportation expenditure, The unit of the total hydrogen transportation amount is ten thousand kg in the j-th transportation mode, and the transportation mode comprises long tube trailer and pipeline transportation, and is that/>The unit transportation expenditure of the j-th hydrogen transportation mode is ten thousand yuan/ten thousand kg.
(6) The electric punishment expenditure is as follows:
CWaste＝EWastecWaste；
Wherein, C Waste is the electricity discarding punishment expenditure, E Waste is the total electricity discarding quantity, the unit is ten thousand Wh, C Waste is the electricity discarding expenditure, and the unit is ten thousand yuan/ten thousand Wh.
(7) The carbon emission expenditure is as follows:
Wherein, C Emi is carbon emission expenditure, E cp is the generated energy of coal electricity, the unit is ten thousand kWh, E gas is the generated energy of gas electricity, the unit is ten thousand kWh, I cp is the unit emission intensity of coal electricity, the unit is ton/ten thousand kW, I gas is the unit emission intensity of gas electricity, the unit is ton/ten thousand kW, And is a unit carbon emission expenditure, ten thousand yuan/ton.
Constraint conditions of the long-term operation optimization model of hydrogen energy storage comprise electric power balance constraint, hydrogen power balance constraint, power capacity constraint, maximum annual utilization hour constraint, generator set climbing constraint, electrolyzer operation constraint, hydrogen storage tank operation constraint, conventional energy storage operation constraint, capacity expansion constraint and carbon emission constraint;
(1) The electric power balance constraint is:
∑iPi+Pfc+Phyt+PPs_d+PEs_d+PinGri＝Dt+Pel+PPs_c+PEs_c+PoutGri;
Wherein, P i is the output of the ith power supply, P fc is the output of a hydrogen fuel cell, P hyt is the output of a hydrogen combustion engine, P Ps_d is the discharge power of pumped storage, P Es_d is the discharge power of electrochemical energy storage, P inGri is the input power of a power transmission line, D t is the electric load demand (namely electric load power), P el is the power consumption of an electrolytic tank, P Ps_c is the charge power of pumped storage, P Es_c is the charge power of electrochemical energy storage, and P outGri is the output power of the power transmission line, and the unit is ten thousands kW.
The calculation formulas of the output P WT of wind power generation and the output of the photovoltaic power generation P PV are as follows:
wherein, For the total newly increased installed capacity of wind power generation,/>The total installed capacity of the photovoltaic power generation is newly increased.
(2) The hydrogen power balance constraint is:
Wel+WinHt+WinSh＝hload+WoutHt+Wfc+Whyt+WoutSh；
Wherein, W el is hydrogen production power of an electrolytic tank, W inHt is external input hydrogen power, W outHt is outward transmission hydrogen power, h load is renewable hydrogen demand, E fc is hydrogen consumption power of a hydrogen fuel cell, W hyt is hydrogen consumption power of a hydrogen combustion engine, W inSh is hydrogen charging power of a hydrogen storage tank, and W outSh is hydrogen consumption power of the hydrogen storage tank, and the units are ten thousand kg/h.
(3) The power capacity constraint is:
wherein, Is a collection of charge and discharge power, comprising the charge and discharge power (output) of each device, g represents the collection of various power generation technologies, electrochemical energy storage, pumped storage, power transmission and transformation, electrolytic tanks, hydrogen combustion engines and hydrogen fuel cells,/>The units are all ten thousands kW for the total loader capacity of the corresponding equipment.
(4) The maximum annual hours of utilization constraint is:
wherein, The average hour-level output of m kinds of equipment, m includes thermal power, gas power, nuclear power, biomass power generation, conventional water power and electrolysis tank,/>The unit is ten thousands kW for the total capacity of m kinds of equipment, and H m is the actual annual maximum full hair hour number of m kinds of equipment, and the unit is hours.
(5) The climbing constraint of the generator set is as follows:
wherein, P n,t is the output of the nth equipment at the time t, n comprises coal electricity, gas electricity and water electricity, the unit is ten thousands kW, For the total loader capacity of the nth device,/>For the maximum ramp down rate of the nth device,/>Is the maximum ramp up rate of the nth device.
(6) The cell operating constraints are:
wherein, For maximum output of the electrolytic cell at time t,/>For the minimum output of the electrolytic tank at the time t, τ el,t is the maximum value of M, onoff el,t is the integral variable of the start and stop of the electrolytic tank, which represents the start and stop state of the electrolytic tank at the time t, and P el,t is the output of the electrolytic tank at the time t,/>For maximum downward slope rate of the electrolytic cell,/>Is the maximum upward slope rate of the electrolytic tank.
(7) The hydrogen storage tank operation constraint is:
Wherein SOC sh,t is the residual charge state of the hydrogen storage tank at the time t, omega sh is the self-loss rate of the hydrogen storage tank, W inSh is the hydrogen charging power of the hydrogen storage tank, W outSh is the hydrogen consumption power of the hydrogen storage tank, The hydrogen charging and releasing efficiency of the hydrogen storage tank is/>Is the minimum charge state of the hydrogen storage tank,/>Is the maximum state of charge of the hydrogen storage tank.
(8) Conventional energy storage operating constraints include pumped storage operating constraints and electrochemical energy storage operating constraints.
The pumped storage operation constraint is as follows:
0≤PPs_c≤M*onoffps,t；
0≤PPs_d≤M*(1-onoffps,t)；
Pps,t＝PPs_d,t-PPs_c,t；
Wherein, P Ps_c is the charge power of the pumped storage, P Ps_d is the discharge power of the pumped storage, onoff ps,t is the integer variable of the charge and discharge state of the pumped storage, 0 represents discharge, 1 represents charge, SOC ps,t is the residual charge state of the pumped storage at the time t, Charge and discharge efficiency for pumped storage,/>For minimum state of charge of pumped storage,/>For the maximum charge state of pumped storage, P ps,t is the output of pumped storage at the time t, P ps_c,t is the charging power of pumped storage at the time t, and P Ps_,td is the discharging power of pumped storage at the time t,/>Maximum downward ramp rate for pumped storage,/>Maximum upward ramp rate for pumped storage,/>Minimum charge duration for pumped storage,/>For the minimum discharge duration of the pumped-hydro energy storage.
The electrochemical energy storage operation constraint is as follows:
0≤PEs_c≤M*onoffes,t；
0≤PEs_d≤M*(1-onoffes,t)；
Wherein, P Es_c is the charging power of the electrochemical energy storage, P Es_d is the discharging power of the electrochemical energy storage, onoff es,t is the integer variable of the charging and discharging states of the electrochemical energy storage, 0 represents discharging, 1 represents charging, SOC es,t is the residual charge state of the electrochemical energy storage at the time t, omega es is the self-loss rate of the electrochemical energy storage, Charge-discharge efficiency of electrochemical energy storage,/>For the minimum state of charge of electrochemical energy storage,/>For maximum state of charge of electrochemical energy storage,/>Duration of minimum charge for electrochemical energy storage,/>For the minimum duration of discharge of the electrochemical energy storage.
(9) The capacity expansion constraint is as follows:
wherein, For minimum newly added installed capacity of the g-th device,/>The maximum newly increased installed capacity of the g-th equipment is g represents the collection of various power generation technologies, electrochemical energy storage, pumped storage, power transmission and transformation, electrolytic tank, hydrogen gas engine and hydrogen fuel cell,/>The unit is ten thousands kW,/>, which is the installed capacity of the reference yearAnnual maximum rate of increase in installed capacity for each device,/>For the annual minimum rate of increase in installed capacity of each device, r represents the predicted year r.
(10) The carbon emission constraints are:
Wherein E cp is the generated energy of coal power, the unit is ten thousand kWh, E gas is the generated energy of gas power, the unit is ten thousand kWh, I cp is the unit emission intensity of coal power, the unit is ton/ten thousand kW, I gas is the unit emission intensity of gas power, the unit is ton/ten thousand kW, For limiting the maximum emission of CO 2, the unit is ton.
S5: solving the long-term operation optimization model of the hydrogen energy storage to obtain the newly-increased installed capacity of the hydrogen energy storage system, the newly-increased installed capacity of various power supplies, the newly-increased installed capacity of conventional energy storage, the output of the hydrogen energy storage system, the output of various power supplies, the output of conventional energy storage, the installed capacity of a power transmission line and the total hydrogen transmission capacity of the hydrogen transmission line in the high-proportion renewable energy source area in a set period in the future.
Specifically, a cplex solver is adopted to solve the long-term operation optimization model of hydrogen energy storage, so that the installation conditions of various power supplies and energy storage devices in the high-proportion renewable energy source areas 2025 and 2030 are obtained, and the installation conditions are shown in fig. 3. The installed capacity pairs of each device of the 2025 and 2030 hydrogen storage systems are shown in fig. 4; typical cycle-hour operating curves of 2030 hydrogen storage spring, summer, autumn and winter are shown in fig. 5 to 8; the hydrogen storage tank hour scale operating curve is shown in fig. 9.
According to the method, the actual wind-solar resource condition of the high-proportion renewable energy source region is fully considered, and the hydrogen energy storage system, various power generation devices and energy storage devices are comprehensively considered, so that the long-term operation optimization model of the hydrogen energy storage constructed by the method can more accurately and carefully predict the configuration condition of the hydrogen energy storage system in the high-proportion renewable energy source region in the future and optimize the operation of the hydrogen energy storage system, and meanwhile, the operation characteristics of long-term and short-term energy storage are compared, and the coordination of energy storage is promoted. In summary, the invention fully digs the capability of hydrogen energy storage to promote the digestion and utilization of renewable energy sources, reflects the long-term energy storage benefit of the hydrogen energy storage in a long period of time across seasons, provides decision reference for optimizing the operation of the hydrogen energy storage, realizes high-efficiency investment, and promotes the realization of emission reduction targets.
In one embodiment, a computer device is provided, which may be a database. The computer device includes a processor, a memory, an Input/Output interface (I/O), and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store the pending transactions. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by the processor to implement a long-term hydrogen storage operation optimization method.
In one embodiment, there is also provided a computer device comprising a memory and a processor to store a computer program on the memory and executable on the processor, the processor implementing the steps in the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the object information (including, but not limited to, object device information, object personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) related to the present application are both information and data authorized by the object or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related countries and regions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as Static Random access memory (Static Random access memory AccessMemory, SRAM) or dynamic Random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. The long-term hydrogen energy storage operation optimization method is characterized by comprising the following steps of:
Acquiring wind-solar resource data of a high-proportion renewable energy source area, installed capacity of a hydrogen energy storage system, installed capacity of various power supplies, installed capacity of conventional energy storage, electric load demand and renewable hydrogen demand in a history setting period; the hydrogen energy storage system comprises an electrolytic tank, a hydrogen fuel cell, a hydrogen combustion engine and a hydrogen storage tank;
determining the photoelectric unit-hour power generation power and the wind power unit-hour power generation power of the high-proportion renewable energy source region in the history setting period according to the wind-light resource data of the high-proportion renewable energy source region in the history setting period;
Clustering the photoelectric unit-hour power generation power and the wind power unit-hour power generation power of the high-proportion renewable energy source region in the historical setting period to obtain the photoelectric unit-hour power generation power and the wind power unit-hour power generation power of the typical week of four seasons;
According to the photoelectric unit hour power generation power, the wind power unit hour power generation power of four seasons and the high proportion renewable energy source area hydrogen energy storage system installed capacity, the installed capacity of various power sources, the installed capacity of conventional energy storage, the electric load demand and the renewable hydrogen demand in a history setting period, a hydrogen energy storage long-term operation optimization model is established with the minimum total investment operation cost as a target; the total investment operation cost comprises the total construction and operation expenditure of various power supplies, the operation expenditure of power transmission and distribution lines, the construction and operation expenditure of a hydrogen energy storage system, the construction expenditure of conventional energy storage, the hydrogen transportation expenditure, the electricity discarding punishment expenditure and the carbon emission expenditure; constraint conditions of the long-term operation optimization model of hydrogen energy storage comprise electric power balance constraint, hydrogen power balance constraint, power capacity constraint, maximum annual utilization hour constraint, generator set climbing constraint, electrolyzer operation constraint, hydrogen storage tank operation constraint, conventional energy storage operation constraint, capacity expansion constraint and carbon emission constraint;
Solving the long-term operation optimization model of the hydrogen energy storage to obtain the newly-increased installed capacity of the hydrogen energy storage system, the newly-increased installed capacity of various power supplies, the newly-increased installed capacity of conventional energy storage, the output of the hydrogen energy storage system, the output of various power supplies, the output of conventional energy storage, the installed capacity of a power transmission line and the total hydrogen transmission capacity of the hydrogen transmission line in the high-proportion renewable energy source area in a set period in the future.
2. The method for optimizing long-term hydrogen storage operation of claim 1, wherein the wind-solar resource data comprises: the number of the photovoltaic panels, the short-circuit current, the real-time illumination intensity, the rated illumination intensity, the peak current, the peak voltage, the actual temperature of the photovoltaic panels, the rated temperature of the photovoltaic panels and the ambient temperature;
The photoelectric unit hour generating power of the high-proportion renewable energy source area is determined by adopting the following formula:
Ppv＝NpvIpvVpvfTfs；
Ipv＝Isc(Rad/Rad,rated-1)+Ipm；
Vpv＝Vpm(1+0.0593lg(Rad/Rad,rated))；
fT＝1-(Tpv,real-Trated)/200；
Tpv,real＝Tae+30Rad/800；
Wherein, P pv is the power generated per hour of the photovoltaic unit, N pv is the number of photovoltaic panels, I pv is the real-time current, V pv is the real-time voltage, f T is the temperature correction coefficient, f s is the dust correction coefficient, I sc is the short-circuit current, R ad is the real-time illumination intensity, R ad,rated is the rated illumination intensity, I pm is the peak current, V pm is the peak voltage, T pv,real is the actual temperature of the photovoltaic panels, T rated is the rated temperature of the photovoltaic panels, and T ae is the ambient temperature.
3. The method for optimizing long-term hydrogen storage operation according to claim 1, wherein the wind-solar resource data comprises rated power, actual wind speed, cut-in wind speed, cut-out wind speed and rated wind speed of a fan;
the wind power generation power per hour of the high-proportion renewable energy source area is determined by adopting the following formula:
Wherein, P wt is the power generated in wind power unit hour, P wt,rated is the rated power of a single fan, v is the actual wind speed, v in is the cut-in wind speed, v out is the cut-out wind speed, and v rated is the rated wind speed.
4. The method of optimizing long term hydrogen storage operation of claim 1 wherein said conventional energy storage comprises pumped storage and electrochemical storage; the objective function of the long-time operation optimization model of hydrogen energy storage is as follows:
minf＝CGen+CGri+CHes+CPs+CEs+CHt+CWaste+CEmi；
Wherein f is total investment operation cost, C Gen is construction and operation expenditure of various power supplies, C Gri is operation expenditure of power transmission and distribution lines, C Hes is construction and operation expenditure of a hydrogen energy storage system, C Ps is construction expenditure of pumped storage, C Es is construction expenditure of electrochemical energy storage, C Ht is hydrogen transportation expenditure, C waste is electricity discarding punishment, and C Emi is carbon emission expenditure.
5. The method for optimizing long term hydrogen storage operation of claim 4 wherein the construction and operational expenditures of the hydrogen storage system are:
CHES＝Cel+Cfc+Chyt+Csh；
Wherein, C el is an electrolyzer, C fc is a hydrogen fuel cell, C hyt is a hydrogen gas engine, C sh is a hydrogen storage tank, For newly increasing the installed capacity of the electrolytic tank, C el is the unit investment expenditure of the electrolytic tank,/>For the unit operation expenditure of the electrolytic cell, P el is the output of the electrolytic cell, T el is the service life of the electrolytic cell,/>For the newly increased capacity of the hydrogen fuel cell, C fc is the unit investment expenditure of the hydrogen fuel cell,/>For the unit operation expenditure of the hydrogen fuel cell, P fc is the output of the hydrogen fuel cell, T fc is the service life of the hydrogen fuel cell,/>Is the newly increased installed capacity of the hydrogen combustion engine, C hyt is the unit investment expenditure of the hydrogen combustion engine,/>For the unit operation expenditure of the hydrogen gas engine, P hyt is the output of the hydrogen gas engine, T hyt is the service life of the hydrogen gas engine,For the newly increased installed capacity of the hydrogen storage tank, C sh is the unit investment expenditure of the hydrogen storage tank, T sh is the service life of the hydrogen storage tank, and alpha is the depreciation rate.
6. The method of optimizing long term hydrogen storage operation of claim 1 wherein said conventional energy storage comprises pumped storage and electrochemical storage; the electric power balance constraint is:
∑iPi+Pfc+Phyt+PPs_d+PEs_d+PinGri＝Dt+Pel+PPs_c+PEs_c+PoutGri;
Wherein, P i is the output of the ith power source, the power sources in the high-proportion renewable energy source region comprise hydroelectric power, coal power, natural gas power generation, nuclear power, wind power generation, photovoltaic power generation and biomass power generation, P fc is the output of a hydrogen fuel cell, P hyt is the output of a hydrogen combustion engine, P Ps_d is the discharge power of pumped storage, P Es_d is the discharge power of electrochemical storage, P inGri is the input power of a power transmission line, D t is the electrical load demand, P el is the power consumption of an electrolytic tank, P Ps_c is the charge power of pumped storage, P Es_c is the charge power of electrochemical storage, and P outGri is the output power of the power transmission line.
7. The long term hydrogen storage operation optimization method of claim 1 wherein the hydrogen power balance constraint is:
Wel+WinHt+Winsh＝hload+WoutHt+Wfc+Whyt+WoutSh；
wherein, W el is hydrogen production power of the electrolyzer, W inHt is external input hydrogen power, W outHt is outward transmission hydrogen power, h load is renewable hydrogen demand, W fc is hydrogen consumption power of the hydrogen fuel cell, W hyt is hydrogen consumption power of the hydrogen gas engine, W inSh is hydrogen charging power of the hydrogen storage tank, and W outSh is hydrogen consumption power of the hydrogen storage tank.
8. A computer device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the method of any one of claims 1-7.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
10. A computer program product comprising a computer program, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the method of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410220526.4A CN118054466A (en) | 2024-02-28 | 2024-02-28 | Long-time hydrogen energy storage operation optimization method, equipment, medium and product |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410220526.4A CN118054466A (en) | 2024-02-28 | 2024-02-28 | Long-time hydrogen energy storage operation optimization method, equipment, medium and product |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118054466A true CN118054466A (en) | 2024-05-17 |
Family
ID=91051753
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410220526.4A Pending CN118054466A (en) | 2024-02-28 | 2024-02-28 | Long-time hydrogen energy storage operation optimization method, equipment, medium and product |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118054466A (en) |
-
2024
- 2024-02-28 CN CN202410220526.4A patent/CN118054466A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109325608B (en) | Distributed power supply optimal configuration method considering energy storage and considering photovoltaic randomness | |
| Ma et al. | Optimal design of an autonomous solar–wind-pumped storage power supply system | |
| Darras et al. | Sizing of photovoltaic system coupled with hydrogen/oxygen storage based on the ORIENTE model | |
| CN106485358A (en) | Binding sequence computing and the independent micro-capacitance sensor Optimal Configuration Method of particle cluster algorithm | |
| CN113285490B (en) | Power system scheduling method, device, computer equipment and storage medium | |
| Xu et al. | Implementation of repowering optimization for an existing photovoltaic‐pumped hydro storage hybrid system: A case study in Sichuan, China | |
| CN111313480A (en) | Multi-objective optimization-based design method of multi-energy complementary system | |
| CN107359611B (en) | Power distribution network equivalence method considering various random factors | |
| Liu et al. | Short-and long-duration cooperative energy storage system: Optimizing sizing and comparing rule-based strategies | |
| CN111126675A (en) | Multi-energy complementary microgrid system optimization method | |
| CN113410854B (en) | Optimized operation method of multi-type energy storage system | |
| RaguRaman et al. | MFLRS‐RDF technique for optimal sizing and performance analysis of HRES | |
| CN117114281A (en) | Determination method of flexible resource multi-stage planning scheme | |
| CN116845983A (en) | Wind-solar hydrogen production power pre-distribution method based on continuous electrolytic cell power | |
| Wang et al. | Optimal capacity allocation of wind-light-water multi-energy complementary capacity based on improved multi-objective optimization algorithm | |
| CN116054250A (en) | Scheduling method of wind-solar-energy-storage-coupled hydrogen production system based on energy storage grouping | |
| Alnejaili et al. | An advanced energy management system with an economical optimization for a multi-sources stand-alone Home | |
| CN110334856A (en) | A kind of wind-light storage method for planning capacity based on carbon transaction mechanism | |
| CN118054466A (en) | Long-time hydrogen energy storage operation optimization method, equipment, medium and product | |
| CN114548536A (en) | Renewable energy planning method considering energy storage influence in electric power market environment | |
| Chen | Optimize configuration of multi-energy storage system in a standalone microgrid | |
| Matsuura et al. | Optimal control of a hybrid system of photovoltaic and fuel cell in residential use | |
| Liu et al. | Capacity optimization of energy storage based on intelligent optimization algorithm and photovoltaic power prediction error data | |
| CN113178896A (en) | Fixed-output light storage combined power station installed capacity configuration method and system | |
| Wang et al. | Capacity Optimization of Island Integrated Energy System Considering Hydrogen Energy Access |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |