text:
Power-to-Gas: Storing Surplus Electrical Energy. A Design Study. Academic research paper on "Chemical engineering"
Energy Procedia
{Power-to-Gas / "Sabatier reaction" / "CO2 hydrogenation" / "Energy storage concepts" / "Peak-load shaving"}
Abstract of research paper on Chemical engineering, author of scientific article — O.S. Buchholz, A.G.J. van der Ham, R. Veneman, D.W.F. Brilman, S.R.A. Kersten
Abstract In this work a conceptual design of a Power-to-Gas (PtG) process for storing electrical energy in form of synthetic natural gas (SNG) of gas grid quality H is presented. The combination with a conventional lignite fired power plant (LPP) was investigated for possible improvement of its economic performance in times of fluctuating renewable energy supply. In this study for a PtG facility using 80 MWel (10% of LPP nominal capacity) a capital expenditure (CAPEX) of M$ 126 and operational (OPEX) of 31-33 M$/a were estimated. PtG provides a good alternative for storing surplus electrical energy and guaranteeing a viable LPP operation if the remuneration for the flexible operation is above 45 k$/operating hour respectively 56 $/operating hour/MWel which is at least 50% additional operational&maintenance (O&M) costs for a LPP. With decreasing alkaline electrolysis costs and an increasing share of renewable energy supply this concept would represent an energy strategic as well as economic advantage for energy suppliers in future.
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Academic research paper on topic "Power-to-Gas: Storing Surplus Electrical Energy. A Design Study."
Procedía
GHGT-12
Power-to-Gas: Storing surplus electrical energy. A design study.
O.S. Buchholza*, A.G.J, van der Ham, R. Veneman, D.W.F. Brilman, S.R.A. Kersten
aUniversity of Twente, Department of Science and Technology, PO Box 217, 7500 AE Enschede, The Netherlands
In this work a conceptual design of a Power-to-Gas (PtG) process for storing electrical energy in form of synthetic natural gas (SNG) of gas grid quality H is presented. The combination with a conventional lignite fired power plant (LPP) was investigated for possible improvement of its economic performance in times of fluctuating renewable energy supply. In this study for a PtG facility using 80 MWel (10% of LPP nominal capacity) a capital expenditure (CAPEX) of M$ 126 and operational (OPEX) of 31-33 M$/a were estimated. PtG provides a good alternative for storing surplus electrical energy and guaranteeing a viable LPP operation if the remuneration for the flexible operation is above 45 k$/operating hour respectively 56 $/operating hour/MWel which is at least 50% additional operational&maintenance (O&M) costs for a LPP. With decreasing alkaline electrolysis costs and an increasing share of renewable energy supply this concept would represent an energy strategic as well as economic advantage for energy suppliers in future.
© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.Org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the Organizing Committee of GHGT-12
Keywords: Power-to-Gas; Sabatier reaction; C02 hydrogenation; Energy storage concepts; Peak-load shaving
From 2011 to 2040 the annual electricity generation is expected to grow from 4.1 to 5.3 trillion kWh. Renewable energy sources account for ca. 32 % of this increase [1]. Having a higher share of renewable energies the chance on a temporal surplus of electrical energy increases tremendously. On a sunny and windy day more electricity can be generated than is actually consumed. In case there is a high load of renewable energies into the grid (and when its input is prioritized by regulations, e.g. in Germany according to the Renewable Sources Act (EEG) [2]) the Lignite fired Power Plant (LPP) electricity production has to be reduced to, finally, its minimum operational load, which is about 30 to 40 %, depending on the type and age of LPP [3]. This reduction of full-load operational hours leads to higher cost per unit of electricity produced. As a result there is an increasing need for industrial sized storage of electric energy to store the temporary surplus.
The concept "Power-to-Gas" (PtG), proposed by the German Federal Network Agency in 2011 [4], is a chemical energy storage concept and utilizes the highly exothermic Sabatier reaction (C02 + 4H2 ^ CH4 + 2 H2O; AH°R= -165 kJ/mol) which already dates back to the beginning of the 20th century [5]. The main advantages of PtG compared to alternative storage technologies,
Energy Procedia 63 (2014) 7993 - 8009
* Corresponding author. E-mail address: o.s.buchholz@alumnus.utwente.nl
1876-6102 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
doi:10.1016/j.egypro.2014.11.836
such as mechanical or electrochemical concepts (pumped hydroelectric storage, redox flow batteries, compressed air energy storage etc.) is the high energy density of CH4 and the high storage capacities and already existing infrastructure for distribution (i.e. for Germany (2008): 217 TWh in the natural gas grid [6]).
In order to help improve the viability of LPPs in a growing renewable energy market, the PtG process is used to store surplus electrical energy from the electrical grid into the natural gas grid. Therefore, in a typical case used here, 10% of the electrical block capacity of a LPP (80 MWei of 800 MWei total) is used for hydrogen generation. C02 is scrubbed from the LPP flue gas via a conventional monoethanolamine (MEA) process and the produced heat of the Sabatier reaction is utilized for running the reboiler of the amine solvent regenerator. Excess heat from the Sabatier reaction is re-integrated to the LPP to increase its process efficiency.
This work shows the results of a conceptual design study and techno-economic evaluation of integrating a PtG process into a LPP. A technical representation of a PtG process integrated with the LPP is given in Figure 1.
Figure 1: Technical interaction of the LPP with the PtG process. 1 and I indicate high or low in concentrations. QTH = Thermal energy. QEL = Electrical energy.
2. Research Approach
The methodology used was based on the integral approach discussed in Seider et al. [7]. The design methodology consists of three major iterative parts. Part I: Problem definition, Process creation and Base case creation, deals with the basics for the process creation and collecting already available information. Simple simulation models are involved and economic checks (e.g. gross profit analysis) guarantee the viability of the process. Part II: Detailed process synthesis. Depending on whether it is a continuous or a batch process this step involves creating chemical reaction and separation networks and its optimal sequence. At the end of this stage at least one promising option is selected for Part III. This includes the equipment sizing, a CAPEX, operational expenditure (OPEX) estimation and its analysis.
Transferred to this project the optimal process design is influenced by a variety of input parameters. These can be seen in Figure 2.
Process set-up
^Type of reactor
of reactors "\Heat integration
Wobbe Index SNG quality—Hydrogene contentHigher alkanes
(Quality
,Inlet conditions (p, T)
Educt supply
CFP block capacity
^^Fixed/flexible design ^Utilities ^Viability
\N# of operating hours —>-
Heat integration to CFP
Process window catalyst /
Process conditions
Economics v
Heat integration
Method of estimation
Base case /
Figure 2: Fishbone diagram ofinfluencing parameters for the optimal design of a PtG process.
Technical data was obtained from a process model built in the process flowsheeting software tool Aspen Plus®. Required process conditions from a representative lignite fired power plant (LPP) were made accessible by a cooperating energy supplier in Germany for the evaluation of integrating the PtG. The capacity of synthetic natural gas (SNG) production was set at 2.53 t/h of quality H (according to German gas specifications for the Wobbe Index, see Appendix A), which is equivalent with the production of 80 MWei. Detailed reactor/particle simulations were performed using Matlab® and compared with data from experimental catalyst studies. All basic economic data were either obtained from literature correlations or given by the energy supplier.
3. Conceptual design
All conceptual design options, starting from CO2 and H2, were worked out based on the parameters given in Table 1. Electrical energy is supplied from the LPP and a conservative specific energy consumption of 4.9 kWh/Nm3 of H2 for production via electrolysis is assumed. This makes it possible to compute the amount of hydrogen produced and being available per hour. In Figure 3 the overall black box model is presented. Respective gas densities of hydrogen (first selection is at p = 1 bar; T = 20°C) were taken from the Aspen Property Analyzer which is required for determining the Wobbe Index.
For the Sabatier reaction to produce methane by stoichiometry the molar ratio of H2/CO2 is four. Hydrogen is supplied via an alkaline electrolysis and carbon dioxide from the regenerator of a conventional ME A C02 scrubber at respective conditions [8]. A high pressure alkaline electrolysis was selected.
Table 1: Usedprocess parameters for conceptual design of the Sabatier process.
Parameter Unit Reference
Electric energy available 80 MW 10 % of LPP block capacity
Specific energy consumption 4.9 kWh/Nm3H2 Electrolysis [9]
Molar H2/CO2 ratio 4 -
Inlet conditions H2 30/80 bar/°C [9]
Inlet conditions CO2 1.5/60 bar/°C [8]
CO2 ~7.5 t/h"
H2 ~1.4 t/h
CH4 ~2.7 t/h"
H2O ~6.2 t/h"
Figure 3: Blackbox model for the PtG process at 100% conversion.
Three different conceptual reactor design options (isothermal, adiabatic and mixed configuration) were evaluated with for this approach. Temperature conditions were chosen according to known lower and upper operating boundaries as presented in Table 2. It shows that temperatures cannot be too low (<200°C) because of the too low catalyst activity and for possible nickel carbonyl formation (when using Nickel based catalyst systems) and they cannot be too high (>400°C) due to the unfavourable chemical equilibrium and other aspects such as possible methane decomposition. Based on the "Le Chatelier's Principle" the reaction equilibrium position shifts to the product side at high pressures. However, due to the high cost of hydrogen compression and possible multiphase formation (water condensation) in the reaction system this factor was limited to < 60 bars. This number was found by the simulation of the Sabatier reaction in a single RGibbs reactor for all combinations of pressures between 20-100 bars and temperatures of 200-600°C.
Table 2: Temperature influences outside desired temperature range [10-11].
Low temperature (<200°C) High temperature (>400°C)_
Nickel carbonyl formation Methane decomposition (>90 vol%) Catalyst is not active Expensive construction materials (» 400oC) Methane formation less favourable (K,, (>400°C) « K,, (<200°C)) _Catalyst sintering, reduction of active sites (>>400°C)
For evaluation of different reactor configurations and -conditions, Aspen Plus® was used as simulator and the property package GERG2008 (Groupe Européen de Recherches Gazières) was applied which is an international standard (ISO-20765) for natural gas applications, including processing, transportation and storage [12]. Included compounds are: carbon dioxide (CO2), carbon monoxide (CO), hydrogen (H2), water (H20) and methane (CH4). As the catalyst and respective kinetic data was not selected at this stage, the RGibbs and RStoic reactor were used in the first stages of the process synthesis. RGibbs iteratively calculates chemical and phase equilibrium by minimizing the Gibbs free energy of the system [13], whereas RStoic calculates the outlet
concentrations based on a given conversion and reaction stoichiometry. It was ensured that the final product of the process was always SNG of quality H.
In Lalancette et al. [10] it was reported that solid carbon formation can take place during the methanation process which deactivates the active centers of the catalyst and leads to undesired by-products, such as carbonyls. In this study possible solid carbon formation was evaluated with the Aspen Plus® RGibbs model and found to be absent at the used process conditions.
When only looking at the equilibrium position for methane formation, high pressures and low temperatures are favourable. As the Wobbe Index is a function of heating values as well as gas densities (at p = 1 atm; T = 0°C), it is necessary to investigate the full range of operating conditions since unreacted hydrogen (max. 5 vol%) could give a huge contribution to the Wobbe index due to its extremely low density. Table 3 shows the main parameters for an isothermal as well as adiabatic reactor configuration.
From the simulations it is shown that either two isothermal or seven adiabatic reactors in series (including intermediate water separation) were needed to reach the required lower and higher Wobbe Index of natural gas quality H. The difference between the isothermal and adiabatic concepts arises from the high heat of reaction and the temperature restriction to 500°C. Therefore, the reaction has to be "stopped" at this temperature, cooled down/water split off before the reaction can be restarted in the next unit. A mixed configuration also requires two reactors in series. Finally, the isothermal option was selected as optimal especially due to the low number of equipment.
Table 3: Main parameters for both reactor configurations, isothermal as well as adiabatic.
Isothermal Adiabatic
Reactor model Aspen RGibbs RStoic
Reactions/Products Products: C02j CO; H20; CH4j H2 Sabatier reaction
Temperature / °C 250-400 250-500
Pressure / bar 10-60 30
In Figure 3 a simplified scheme of the Aspen model used to analyze the adiabatic/isothermal and mixed reactor-sequence is given. One and two RGIBBS reactors are analyzed at a set of operating conditions for isothermal conditions. Heating, cooling or pressure changing equipment is not considered at this stage. In reality the product gas streams need to be cooled to the condensation temperature. For all options, water separation was always assumed to have a separation efficiency of 99%. After each water separation the gas stream is analyzed according to its lower and higher Wobbe Index.
Reactor 1 Reactor 2
Figure 4: PFD of used Aspen Plus® model to obtain the number of reactors and the optimal operating conditions for isothermal reactors. Process indicator "1" and "2" give the location where respective Wobbe indices have been calculated.
Since the Sabatier reaction is extremely exothermic (AHr°= -165 kJ/mol, ATad=1260 K), heat integration is essential and already examined at this stage of process design. In total —12.6 MW of excess heat needs to be removed from the reaction. For the adiabatic configuration therefore a cascade of seven reactor-separators was needed to fulfil all criteria, whereas two were sufficient at isothermal operation.
There are several aspects playing a role in this analysis:
• Complexity: Is it necessary to form a complex heat integration network in order to save heating and cooling utilities?
• Controllability: Especially relevant for dynamic plants having process-to-process heat exchanger networks (HEN) [14]
• Number of units: Effect on operation and maintenance (O&M) and investment costs (CAPEX)?
Heat integration was studied by applying the PINCH method, using the software tool HINT® V2.2 [15], which was first introduced by Linnhoff and Hindmarsh [16]. The minimum number of heat exchangers, minimum required heating and cooling utilities can be obtained for all different process options which helps to choose the optimal one. By having all the mass and energy balances of the three process options, economic implications can be obtained with the use of the Overall factor methodby Lang [17].
All of the three process options (isothermal, adiabatic and mixed configuration) werejudged by using a multiple criteria analysis.
Aspects in this analysis were CAPEX, O&M, number of equipment, operability, controllability, maturity of technology, safety and flexibility. Each aspect was given a weighting factor according to its importance to the process.
The Wobbe Index profiles for the isothermal option at varying process parameters are given in Figure 5. It can be concluded from the left hand side that one single reactor is not sufficient to provide gas quality H (Wobbe Index between 12.8-15.7 kWh/m3). In this reactor the sensitivity towards an increase in pressure is high (compare red with black lines).
Figure 5: Wobbe Index calculated at point 1 and 2 (see Figure 3) as a function of reactor temperature.. Green lines indicate the Wobbe Index boundaries for gas quality H. Left: Lower and upper Wobbe Index at pReactori=10 bar (black) and pRe3ctori=30 bar (red) as a function of the temperature of Reactor 1. H2/C02=4. Right: Lower and upper Wobbe Indices at preactori=30 bar, preactor2=30 bar treactori=250oC (red) and treactori=400°C (black) as a function of the temperature of Reactor 2.
Dashed lines: H2/C02=4.05; solid lines: H2/C02=4.
Gas quality H is reached with a second reactor after water separation. In this reactor, no sensitivity for pressures up to 60 bars was found, whereas temperature (compare line colours) and H2/CO2 feed ratio (compare dashed with solid lines) had a clear influence on the Wobbe Index. When Reactor 1 is operated at 10 bars and 400°C, this specification could not be met for temperatures of > 360°C in Reactor 2 (not shown here). When changing the molar ratio of H2/CO2 at inlet conditions, a change in Wobbe Index was observed but not significantly enough to select an increased molar ratio. In order to fulfill gas quality H specifications, two reactors in series need to be applied. The selected operating range for Reactor 1 and 2 include p = 30 bar and 250°C <T< 400°C and the molar ratio H2/C02 is four.
4. Methanation reactor
The core of the process, the methanation reactor, was designed as a cooled tubular fixed bed reactor and modelled at different levels of detail (1-D pseudo homogenous, 1-D heterogeneous and 2-D pseudo-homogeneous). A kinetic rate expression by Lunde et al. [18] who used a Ru(0.5 wt%)/Al203 heterogeneous catalyst was selected. This formulation/representative catalyst was chosen due to its high activity at low temperatures (250-450°C). It is important because the aim is to utilize the heat of reaction for high pressure steam production which can either be used in the LPP or to run the MEA regenerator. Pressures and temperatures of utilities were selected based on given conditions in the LPP. The reactor was simulated at inlet conditions of p = 30 bar and T = 250°C. Reactor cooling was performed atT = 260°C by producing 47 bar steam.
Different levels of detailed reactor models were built for separate purposes. The 1-D pseudo homogenous reactor model was created for first reactor design aspects and did not include transport and reaction phenomena at a catalyst particle level. This was then added in the 1-D heterogeneous model. Heat and mass diffusion effects were included and the numerical effectiveness factor (^nnm) evaluated at each numerical element in the reactor. This model more accurately computed the axial temperature profiles inside the reactor, which was crucial for its final design. The 2-D pseudo homogeneous reactor was developed for the estimation of radial temperature profiles at different reactor conditions and dimensions. The analysis of it concluded that the 1-D heterogeneous reactor model suffices to calculate dimensions of and temperature profiles inside the reactor with high accuracy at the first design stage.
Operating conditions were adjusted in order to fulfil the requirements for a maximum temperature inside the reactor tube and the desired Wobbe Index for the product stream. Results for concentration, temperature and Wobbe Index profiles (excluding water with a removal efficiency of 99%) in Reactor 1 and Reactor 2 are shown in Figure 5.
Figure 6 a, b, c: Results of the 1-D heterogeneous reactor model for Reactor 1. Figure 5 d, e, f: Results for the 1-D heterogeneous reactor model for Reactor 2.Used process parameters are given in Table 4. Top: Axial concentration profiles. Middle: Axial temperature profile. Bottom: Axial Wobbe Index profile.
Process parameters are shown in Table 4 and outlet composition for both reactors in Table 5. It can be seen that there is a temperature excursion to ~ 690 K in the first reactor. At this location either a less active catalyst has to be used or the catalyst bed has to be diluted with quartz. The decrease in overall molar concentration at this location is due to the temperature effect. Here, the Wobbe Index and the methane concentration show its steepest increase. The hydrogen conversion in Reactor 1 is with 80% lower than that in Reactor 2, but the temperature effect in Reactor 1 is much higher due to the amount of heat released. Even though the Wobbe Index specification is already reached after ~ 2.5 m in Reactor 2 a conservative length of 4 m was selected, identical to the length of Reactor 1.
Table 4: Process parameters for reactor design based on the 1-D heterogeneous Table 5: Outlet molar % from Reactor 1 and 2. Wh,l = reactor model. Ds = Shell diameter reactor. Higher, Lower Wobbe Index.
Process/Design parameter Reactor 1 Reactor 2
dt [inch/mm] 0.75/17.8 1/25.5
us [m/s] 0.77 0.10
L[m] 4 4
Tm[K] 523 523
Tcool [K] 533 533
H2 conversion [-] 0.80 0.94
Total mass flow inlet [kg/s] 2.47 1.12
Mass flow per tube [kg/s] 1.37 10"3 0.47 10"3
Number oftubes [-] 1795 2365
Pressure drop [bar] 1.52 0.21
DB [mm] 1215 1962
Outlet molar Reactor 1 Reactor 2 fraction [-]_
C02 4.41 0.59
H2 17.62 2.35
CH4 25.99 75.02
H20 51.98 22.04
After condensation
W" 10.43 14.52
WL 9.51 13.10
When comparing the data obtained from the 1 -D heterogeneous model to the 1 -D pseudo-homogenous case it was seen that the inclusion of heat and mass transfer limitations has an influence on process conditions and consequently the reactor dimensions. Both reactors have to be longer and need to be run at a lower superficial gas velocity in order to reach the Wobbe Index specifications. This leads to a higher number of reactor tubes and thus a larger shell diameter Ds and higher CAPEX.
5. Process integration with a LPP
The integration of the PtG process to a LPP was one of the main objectives of this work. As already discussed in the introduction the disadvantage of high regenerative energy capacity is its fluctuating input to the electricity grid which forces conventional suppliers to reduce the load of their power units (often referred to as peak-load shaving, load following, cycling operation). If the input of regenerative energy is high, part of the electrical energy produced in the LPP is used in the PtG process, thereby reducing the output to the grid. This combination is a good way to cover the fluctuations from renewable energy supply and keep the load of the LPP constant. According to the process, the educt gases (C02 and H2) are supplied via C02 scrubbing and water electrolysis. The C02 source is flue gas from the LPP. Electricity for the water electrolysis is supplied from the power plant. As the Sabatier reaction is an exothermic process heat generated can be re-integrated to the LPP (e.g. into the pre-boiler feed water cycle) and the C02 absorption unit (reboiler).
After each reaction step there are only simple condensation separations needed to remove all produced water. During this process there is also heat released which can be re-integrated as well.
In Figure 7 a simplified scheme of steam and water cycles in a LPP are shown. Process conditions are only indicated within a certain operational range. The connecting point to the PtG is indicated behind the heat exchanger of the external cooling cycle. This point was found to be optimal because there the utilities have the lowest enthalpy in the process of the LPP. A possible point of re-integration is between "Pre-heater 2" and "Pre-boiler feed tank".
Turbine pi Turbine p2 Turbine p3
Figure 7: Simplified presentation of steam (thick dashed) and water (solid) cycles in a LPP. Thick solid line: Lignite supply.
Thin dashed line: Exhaust gas. Red line: Electricity. PtG refers to the PtG process.
Having this information and detailed process conditions at 40% LPP operational load it was possible to work out a detailed process scheme of the PtG process linked to the LPP. It is shown in Figure 8. Oxygen expansion (01-02) in a turbine (Tl), C02 compression (C1-C6) in three compressors (CP1-CP3) and mixing with hydrogen (H2) results in the educt stream (Gl) which is preheated in a heat exchanger (HEX3). Between each compression stage streams C2 and C4 are cooled down such that the exit temperature in C4 and C6 does not exceed 250°C. Gl can be preheated to an inlet temperature of 250°C because RXN1 operates at an exit temperature of 260 °C to be able to preheat G2 with a minimum temperature difference of 10°C. After that the pre-cooled product stream from RXN1, G4 is further cooled in order to condensate (in SEP 1) the produced water in Wl. Then the finishing reaction takes place in RXN2, after being preheated again in HEX5. This is achieved in the same way as described before. After a second condensation step, where the remaining water is separated, the produced SNG (Gil) is compressed (in CP4) to the required pressure of the gas grid.
Cooling water is split from the pre-boiler feed water stream (split is behind the pre-boiler feed water pump, see Figure 8). This point was chosen because it is the easiest accessible point in a LPP and does not have influence on the stability of the plant operation. Stream properties were available from data [19]. Starting with a lower pressure as required, the cooling water is first pressurized to 47 bar in order to achieve the steam formation at 260°C (see Slto S2). This stream is pre-heated, beginning at low temperatures (in HEX6), then with increasing temperatures as intermediate cooling of compressed C02 (HEX1 and HEX2). Before steam is produced in the first reactor, it is further pre-heated from the pre-cooled outlet of RXN1 (HEX4). RXN1 and RXN2 are then cooled/kept at temperature by steam formation at 260°C. After each reaction step, steam is separated in a steam drum. S8 and Sll represent the total amount of steam produced in RXN1 and RXN2. Preheated water from RXN1 and RXN2 is
shown in S9 and S12, respectively. In order to know the overall enthalpy of the steam and preheated water, S12 and S13 are mixed to form Smix-
Figure 8: Scheme of detailed Sabatier process for process integration into a LPP. CAP = C02 capture. EL = Electrolysis. PR = Product.
The recuperated energy AHS from the process can be obtained by analyzing the enthalpy change of the inlet (Si) and outlet (Smix) streams according to eqn. (1) and it was found to be +12.3 MWth.
AHS = HSMX -Hsi (!)
Only low pressure steam (~3.6 bars) is required for the amine reboiler in the CO2 scrubbing section. From an energy point of view it can be concluded that S8 could fully be used to supply the heat in the reboiler because its temperature level is high enough. Knowing the specific energy consumption and the required mass flow of CO2, the energy required for the amine reboiler (-5.4 MWth) can be calculated. Overall, 6.9 MWth of surplus energy for re-integration to the LPP was calculated.
Table 6: Overview of energy flows from the PtG process to the LPP. Given in [MWih].
Energy recuperated Energy amine boiler Surplus energy to LPP
+12.3 -5.4 +6.9
At this point of research the re-integration in the LPP was only analyzed holistically. Important is, that all cooling water taken from the pre-boiler feed water must be re-integrated in front of the "Pre-boiler feed tank" by re-integration into the steam condensate or as pre-boiler feed water. This point of re-integration is indicated by ©. In each case the pressure level of S8 has to be adjusted accordingly, enabling the use of another option: the usage of a turbine to generate the required electricity for compressors and pumps. This latter option was considered in a similar project, funded by the Federal ministry of research, Germany (BMBF) [20].
By knowing the thermal power from the amount of SNG produced (Qth), the total power re-integrated (Qre_mt) and the overall electric energy put into this process (Qei), it is possible to estimate an overall efficiency of the PtG plant in combination with the LPP. The calculation of the efficiency is given in eqn. (2).
Qh + Q t
'/PtG q (/j
Respective numbers are given in Table 7.
Table 7: Summary of energy going in and out of the PtG process.
In / [MW] Qel Out / [MW] Qth | Qre-int |
Electrolysis: 80 SNG 2592 kg/h see Table 6
Process units: 0.12 LHV SNG 50 MJ/kg
C02 capture: 0.17
Total: 80.3 36.0 7.0
As a result, the efficiency is 53.5%. This number is slightly higher compared to efficiencies of 51% as mentioned in Müller et al. [21].
The efficiency for the re-electrification of SNG is given by eqn. (3).
V'el->el ~ ^PtoHchem-yel (3)
Where ^Chem-x:i is the efficiency of converting SNG to electricity. Assuming an efficiency for ^Chem->ei of 55% (gas turbine station), ^chem->eiis in this case 29.4%, to be comparedto a28.1 % reference value (Müller et al. [21]).
6 Technical evaluation of combined PtG with LPP
All necessary assumptions for the technical evaluation are given in Appendix B.
6.1 Impact of energy recovery
It is assumed that the amount of surplus energy re-integrated to LPP reduces the thermal duty in the boiler with its equivalent amount. From this information it is possible to make an estimate of possible savings in lignite use and associated CO2 emissions. All of these findings are reported in Table 8 assuming an operating time of the PtG unit of 1000 h per year (13% of time on stream).
Table 8: Saved amount of lignite and C02 for 100 % re-integration of surplus recuperated energy.
Lignite reduction CO2 reduction
[kg/s] / [%] 0.79/0.86 0.66-0.81 /0.76-0.78
[kt] at 1000 h 2.84 2.38-2.92
In case of full re-integration and usage of recuperated energy from the methanation process this only leads to a marginal reduction of 0.9 % of lignite consumption for the process. Nevertheless, this reduction was included in the economic analysis. Due to the fact that the re-integration was only looked at holistically, a statement about the overall efficiency change of the LPP is not possible at this stage. It is expected that this number would also be marginal.
6.2 Impact on flue gas emissions
By having the above mentioned data, it was possible to estimate the amount of flue gas needed for CO2 scrubbing. An overview is given in Table 9. The required amount of CO2 is 7.52 t/h. In case of an operational load of 40 % for an 800 MWei block, 320 MWei are generated, thus emitting 310-384 tco2/h. As a result 2-2.4 % of the emitted CO2 is required for the process. Overall -41.8 t/h of flue gas is required for the operation of the PtG plant (assuming 90 % CO2 capture).
Table 9: Influence on flue gas emissions.
Generated CO2 in LPP [t/h] Required CO2 [t/h] Share Required flue gas [t/h]
310-384 7.52 2-2.4 % 41.8
6.3 Dynamics
A complete study of the dynamic behaviour of the overall system was outside the scope of this study. But several aspects can already be discussed now.
• Electrolysis: In literature start-up times of less than 10 minutes were reported [22].
• CO2 capture: Dynamic operation of a MEA CO2 capture process is a difficult issue, already investigated in literature. Start-up times, depending on the capacity of the plant, were simulated and measured to be between 45-60 min [23-24].
• Sabatier reaction: cyclic experiments with varying CO2 supply in literature show response times of 2 minutes between start-up and full CH4 formation [25]. As a result, start-up of this unit seems possible within the range of minutes.
The current start-up time for LPP is 4-8 hrs and the response time 2.5 % PN/min, with PN the nominal capacity. In this case the response time would be 20 MWe[/min. In this particular case this would mean that you would have 4 minutes to reach a peak-load shaving of 80 MWe[. According to e.g. German law the required primary response time is 4% PN/min [26]. This infers that the PtG process could improve the response time, in case the CO2 capture would be running continuously.
When analyzing the overall process response based on an unit operation level it can be concluded that the CO2 capture is the limiting process. Therefore, different strategies to overcome this problem need to be evaluated, for instance:
• Intermediate CO2 storage, e.g. for one hour of operation (~7.5 tco2)
• Constant operation of the CO2 capture unit
o with flue gas load ^ CO2 storage or purge
o without flue gas load ^ adaptation of solvent concentration in order to reduce energy consumption [27]. Further evaluation was outside the scope of this study. 7. Basic equipment design and economical evaluation
The basic equipment design was made according to the methodology described in Seider et al. [7] to obtain the basic dimensions required for an economical evaluation (mass and energy balance based). Results are presented in Table 13, Appendix C. All numbers are given in 2012's CE Plant Cost Indices and US$.
Total production costs (OPEX) were obtained according to the guidelines given in Table 14, Appendix C. The annual profit/savings are given as the difference of the total sales/savings in OPEX costs and the total production costs. Two cases were evaluated: without (Case I) and with (Case II) taking the benefit of reduced O&M costs (Cred) of the LPP into account. This reduction is achieved by the PtG plant that overtakes surplus operational peak loads of the LPP such that the LPP can run on a fixed load and does not have additional O&M through load following operation.
Higher maintenance costs due to cyclic operations (load following) will occur more likely with an increasing renewable energy share in the grid. Maintenance costs manifest themselves in past and future maintenance and capital replacements. It includes fatigue and corrosion in boiler tubes, turbine parts, valve systems and components.
Per case, two scenarios with different on-stream times (Scenario A: 800 and Scenario B: 1200 h operation/a) were investigated. Most important results are presented in Table 10. Profit/Saving include possible lignite savings through heat re-integration of reaction enthalpy, selling of SNG at market prices (0.50 US$/kgsNG [29]) and compensation for carbon capture and usage (2.4 US$/tC02 at half the price for CO2 certificates [30]). It is likely to obtain further compensations for the production of "green" SNG, yet it is not included in this analysis.
Table 10: Summary of results from the economical analysis for different on-stream times.
Case I: Without Cred Case II: With Cred
Scenario A Scenario B Scenario A Scenario B
(800 h/a) (1200 h/a) (800 h/a) (1200 h/a)
CAPEX [M$] 126 126 126 126
Cred [M$/a] - 35.0 54.0
OPEX [M$/a] 30.8 32.5 30.8 32.5
Profit/Saving [M$/a] -29.75 -30.95 6.25 23.1
ROI [%] (after tax) - - 3.0 11.0
PBP [a] - - 7.6 4.7
Cost price SNG ($/kg) 15 10
A factor method (Guthrie) was applied to estimate the CAPEX in the range of ±30%. It correlates the bare-module costs (CTbm) to the overall investment. The alkaline electrolysis is the biggest asset (~78 % of CAPEX). This also means that it will have a huge impact on the economic performance. OPEX estimation shows that the highest assets are depreciation (~40 %) and
maintenance costs (~33 %) see Table 16, Appendix C.
In Case I (without Cred) it is shown that the PtG process is not viable, the cost price of the produced SNG is ranging from 10 to 15 $/kg. Compared to the benchmark for natural gas prices between 0.16 (Alberta, Canada) and 0.50 (average German import price) $/kgsNG this results in a 20-100 times higher cost price [29]. The production costs are not competitive and hence, as a result compensation for a "less" dynamical LPP needs to be included (Case II). It is assumed that a flexible operation of the LPP increases the annual operation and maintenance costs. Basic O&M of a LPP are 70 M$/a (800 MWel LPP ^ CAPEX: 2200 k$/MWel; O&M: 4 % of CAPEX; price basis 2008 [31]). The increase of O&M due to flexible LPP operation is assumed to amount 50% of basic O&M costs which results in Cred = 35 M$ and 45 k$/h on an hourly basis (Case II, Scenario A). For such a contribution the PtG plant is cost-competitive. Annual profit/saving is ~6 M$ at a return of investment of 3% and a payback time of ~8 years. In case there are a higher number of load-following hours (Case II, Scenario B) and the increased O&M costs per hour stays constant at 45 k$/h the PtG plant pays off even faster. This assumption still has to be verified in a different study -different approaches are already available [28] but it is not fully covered yet. For sure Cred depends on the number of peak-load shaving operating hours.
Thus the PtG plant reduces/saves these costs of the LPP. It can be said that Cred is the most decisive economical parameter for process viability.
As a result, it can be concluded that the PtG in combination with the LPP does not generate a profit in itself (Case I) but is a method to secure the long-term operability of the LPP (Case II) at the expense of a contribution to the PtG plant. This money would otherwise be spent on additional O&M costs due to peak-load shaving. At this point in time it is difficult to judge how these costs will change in the future. However, with decreasing alkaline electrolysis costs (see Sensitivity Analysis) and an increasing share of renewable energies it is likely that the OPEX of the PtG process will become lower than additional maintenance costs due to peak-load shaving operation of the LPP.
It has to be stressed that the main purpose is not to sell the methane but to store the energy (generating an added value to the company's financials). In case the SNG is re-electrified in a gas turbine station the electricity regained from this would be around 23.6 MWei (taking into account the efficiency of re-electrification).
8. Sensitivity analysis
A sensitivity analysis was performed in order to indicate which parameters are important for the process economics. Therefore, the following parameters were varied in a range of ±50% from the base case (Case I and II, Scenario B, 1200 operating hours):
• Cbm of the alkaline electrolysis
• Total CAPEX
• Price of electricity
• Reduction of O&M costs for flexibility (Cred) = Additional O&M costs through load following operation.
• Number of operating hours.
The influence on two economic indicators, the SNG cost price (Case I) and the profit before tax (Case II), was analysed. As it can well be seen in Table 10 that Cred has a strong influence on the profit of the PtG process but the SNG price stays the same. Results from this analysis are shown in Figure 6. As discussed before, the CAPEX of the alkaline electrolysis has a big influence on both indicators, the SNG price and the profit. This can also be seen in comparison with the sensitivity of the total CAPEX. It is worth to mention that a 40 percent reduction of the CAPEX of the alkaline electrolysis is expected to be realised until 2020 [32]. Unfortunately, SNG prices are not competitive towards current prices. Even an investment of 50 % less compared to the base case decreases the SNG price to a number that is still 10 times higher than the benchmark SNG price from the European market. Utilities are insignificant for process economics where the difference is only in the range of 1 $/kgSNG or less than M$ 1 profit. On the other hand the profitability of Case II (see Figure 8, right) is very sensitive to the remuneration for flexibility (Cred)- It is the most important factor besides bare-module costs of the electrolysis (which is almost the total capital investment) to investigate in more detail because the PtG plant viability highly depends on these factors.
Case I
s 15 $ 8
50 100 150 200 LOSS
Percentage from Base Case / [%] Percentage from Base Case / [%]
-CAPEX Alkaline Electrolysis
Total CAPEX
- Price of electricity
• Reduction of maintenance cost
■ Number of operating hours
Figure 9: Sensitivity study for different process parameters influencing process economics. Left: SNG price as a function of asset cost variation (Case I, scenario B). Right: Profit/loss (before tax) as a function of asset cost variation (Case II, scenario B).
Another important parameter is the number of operating hours which has an equivalent influence on the economic performance to the extent of Cred- If the number of peak-load shaving operating hours increased in future (which very likely with increasing renewable energies in the market), this technology could be a good asset for LPP's balance sheet.
9. Results and Conclusions
In this work it was studied whether the PtG process can be used in a LPP environment in a profitable way, to store surplus, renewable electrical energy from the grid by converting it to natural gas, thereby utilizing 10% of the capacity of the LPP (here: 80 MWe[) for hydrogen generation and allowing the LPP to run with less capacity fluctuations or an even more decreased minimum nominal capacity. Various process configurations were evaluated in a flowsheeting study to identify the optimal conceptual process and reactor design of the PtG process. It was found that SNG gas quality H can be produced in two cooled tubular packed bed reactors placed in series, operating at p = 30 bar and an inlet temperatures of 250°C. By utility integration between the PtG and LPP process, synergetic combinations are possible. A process design of the PtG plant was made and required utilities were integrated with those from a LPP (at conditions of minimum operational load).. Produced steam can be used for running the MEA C02 absorption unit and surplus recuperated energy can be re-integrated to the LPP. This enhances the performance of the steam cycle and leads to a lignite and C02 reduction of around one percent. The overall efficiency of the PtG process was determined = 53.4%), which is slightly higher compared to PtG processes discussed in literature [21].
Capital expenditure (CAPEX) was estimated at M$ 126 (3500 $/kWth; referring to the thermal output of SNG) and the major contributor is the alkaline electrolysis (~ 78%). OPEX was estimated in the range of 31-33 M$/a, resulting in a cost price for SNG between 10-15 $/kgSNG, which exceeds current market prices. Profits/savings were identified as well for this process; e.g. via reduction of O&M costs (Cred) of the LPP by substituting the PtG process for peak-shaving operation. This saving was estimated at 45 k$/h which corresponds to a 50% increase in O&M costs of the LPP.
An economical sensitivity analysis indicated that the viability is independent from variable factors (i.e. price of electricity), but it strongly depends on integration benefits from synergy with a LPP. Main parameters are related to reduced O&M costs, Cred, and CAPEX for alkaline electrolysis. Based on the current cost estimates for these, a grass root PtG plant in this configuration is not economically viable, but CAPEX of alkaline electrolysis are expected to substantially decrease in future The already existing distribution network and the large storage capacity are the biggest advantages for this technology which gives the technology an important strategic background. With this, PtG can play a key role during the transition to a 100% renewable power supply. Additionally, when combining PtG with a LPP, economics are improved by substantial savings on maintenance because the LPP can be operated at more constant load. This helps the LPP to extend its operational life time at the cost of investing into the PtG unit.
This part of research was carried out in cooperation with ThyssenKrupp Industrial Solutions AG, Business Unit Process Technology and Vattenfall Europe Generation AG and we would like to thank those who initiated and supported this project.
CAP CP Da H
C02 capture Compressor Shell diameter Enthalpy Heat exchanger constant
Lignite Power Plant
Pump/Capacity
Superficial gas velocity Turbine
Efficiency I Effectiveness factor Adiabatic temperature rise
Subscripts
BM Bare module
chem ^ el chemical to electrical
el Electrical
eq Equilibrium
N Nominal
Num Numerical
R Reaction
RED Reduction of maintenance
re-int Reintegrated
th Thermal
Appendix A. Wobbe Index [33-34]
According to the DVWG (German Technical and Scientific Association for Gas and Water) gas norm there are three different gas families (see Figure 7).
Gas families —-1--
1st Hydrogen rich gases 2nd Methane rich gases (SNG) 3rd Liquid gases
Wobbe Index
Gas content
Group A (Town gas)
Group L (Low)
Group H (High)
Propane/ Butane mixtures
Figure 10: Difference between gas families according to DVWG gas norm.
Within a gas family it is differentiated between gas groups that are divided by a range of Wobbe Indices. The Wobbe-Index is given by eqn (3). for the lower (LHV) as well as the higher heating value (HHV) of the gas mixture. Respective heating values are obtained from eqn. (4). The third gas family (liquid gases) differs from the first and second family where the gas content is only compared.
W _ LHVmix > HHVmix
WuH 4d (3)
In this case d is the relative density of the gas mixture towards air at normal conditions (0°C, 1 atm) which is set to pajj° = 1.293 kg/m3. The density of the gas mixture is calculated from respective volume share of each gas.
LHVmix, HHVmx —nl(LHVl, HHV) + n2(LHV2, HHV2) + ... + ni(LHVi, HHVt) (4)
Where n; is the molar fraction of each gas compound with a heating value.
In this work the focus is on the production of SNG, thus specifications from the second gas family apply to the process. Table 11 gives all specific values for gas classification to Group L and Group H (this is referred to as "Gas quality H" in this work) in this family. In order to be applicable for a specific group both values of WL,Hhave to be in the given range.
Table 11: Specific values for SNG (2nd gas family).
Description Unit Group L Group H
Wobbe-Index
Overall range kWh/m3 10.5.-13.0 12.8-15.7
MJ/m3 37.8-46.8 46.1-56.5
Set point kWh/m3 12.4 15.0
MJ/m3 44.6 54.0
Relative density - 0.55-0.75
Furthermore, there are thresholds for certain gases (see Table 12). According to the norm, the gas pressure should high enough to be pressurized into the gas grid. This set point is 20 mbar above grid pressure.
Table 12: Summary of important thresholds in SNG.
Gas component Threshold
H2 <5 vol%
O2 <3 vol%
CO <3 vol%
CO2 < 6 vol%
Sulfur, overall < 30 mg/m3
H2S_< 5 mg/m3
Appendix B. Technical evaluation of the PtG process
In order to estimate the influence if integrating a PtG plant with a LPP, following data and assumptions were given:
• The LPP is fired with lignite. A lower heating value of 8.8 MJ/kgLigmte has been reported for the open cut mining region of Lausitz, Germany [35].
• Specific C02 emission of a LPP (lignite): 970-1200 kgC02/MWhei [36]
• Overall efficiency from combustion to electricity: 40 % [36]
• Specific lignite consumption: 1022 kgLignite/MWhei
• The surplus energy from Table 7 can fully be re-integrated to the LPP without loss of efficiency.
• Flue gas contains 20 wt% C02.
Appendix C. Basic equipment design and economical evaluation
Table 13: Main results from equipment design of all major PtG process units.
Process unit Unit's specific design aspects
Compressors Type of compressor Power required [kWl Number of stages
CP1 Centrifugal 199 5
Turbine Type of turbine Power generated [kWl Number of stages
T1 Centrifugal -191 3
Pump Tvpeofpump
P1 Centrifugal multistage
Separation Diameter Iml Height Iml
SEP 1 1.1 4.73
SEP 2 0.251 1.83
Heat exchanger Type of heat exchanger Heat exchange area lm2l
HEX1 Shell and tube 8.2
HEX2 | 5.1
HEX3 47.6
HEX4 248
Methanation reactor Wall thickness tube Imml Wall thickness shell Imml Shell diameter Tmrnl
RXN1 1.6 22 1215
Table 14: Detailed overview of total production cost assets [7].
Cost factor Typical factor in SI units
Utilities See Table 15
Direct wages and benefits (DW&B) 52.23 S/operator-hr
Direct salaries and benefits 0.15 x DW&B
Operating supplies and services 0.06 x DW&B
Technical assistance to manufacturing 81,921 $/(operator)-a
Control laboratory 88,748 $/(operator/shift)-a
Wages and benefits (MW&B) 0.035 x Ctdc
Salaries and benefits 0.25 x MW&B
Materials and services 1 x MW&B
Maintenance overhead 0.05 x MW&B
Operating overhead 0.228 x (1.15DW&B+1.25MW&B)
Property insurance and tax 0.02 x CTDC
Depreciation CTDc/years of operation
Cost of manufacture (COM) SUM ABOVE
General expenses
Selling (or transfer) expense No break down
Direct research
Allocated research
Management incentive compensation
Total general expenses (GE) 0.05 x COM [37]
Total production costs GE + COM
Table 15: Required process utilities. A specific electricity price of 48 USS/MWhei was assumed [38].
Utility Required amount [MWhe)] Utility costs per annum [MS]
Electricity Scenario A: 64,859 Scenario B: 97,289 Scenario A: 3.11 Scenario B: 4.67
Table 16: Summarized cost factors for the methanation process (prices in MS) for Scenario A and B, Case II with Cred-
Cost factor Scenario A (800 h) Scenario B (1200 h)
Utilities 3.11 4.67
Operations 0.12 0.17
Maintenance 9.51 9.51
Operating overhead 1.23 1.24
Property insurance and tax 2.41 2.41
Depreciation 12.07 12.07
Raw materials 0.90 0.92
COM 29.35 31.00
GE 1.47 1.55
Total production costs 30.82 32.55
Sales/Cost reduction 37.07 55.61
Profit (before tax) 6.25 23.05
ROI (%) 3.0 11.0
PBP (years) 7.63 4.66
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redpajama_set_name: RedPajamaCommonCrawl


At Taylor Farms, we make healthy fresh foods to nourish your body, so you can enjoy life and do what you love.
This past weekend, Team Taylor once again joined forces with the Big Sur International Marathon team to host the annual Salinas Valley Half Marathon.
Over the past two weeks Team Taylor and their families raised money for this weekend's American Cancer Society's Relay for Life by attending yoga classes at the Taylor Gym!


text:
require 'date'
require 'google/apis/core/base_service'
require 'google/apis/core/json_representation'
require 'google/apis/core/hashable'
require 'google/apis/errors'

module Google
  module Apis
    module GenomicsV1

      class ListDatasetsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Dataset
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Empty
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class UndeleteDatasetRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Operation
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Status
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ListOperationsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class CancelOperationRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ImportReadGroupSetsRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ExportReadGroupSetRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchReadGroupSetsRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchReadGroupSetsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ReadGroupSet
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ReadGroup
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Experiment
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Program
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ListCoverageBucketsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class CoverageBucket
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Range
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchReadsRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchReadsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Read
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class LinearAlignment
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Position
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class CigarUnit
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchReferenceSetsRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchReferenceSetsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ReferenceSet
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchReferencesRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchReferencesResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Reference
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ListBasesResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ImportVariantsRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class VariantSet
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ReferenceBound
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class VariantSetMetadata
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ExportVariantSetRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchVariantSetsRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchVariantSetsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchVariantsRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchVariantsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class Variant
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class VariantCall
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class MergeVariantsRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchCallSetsRequest
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class SearchCallSetsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class CallSet
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ImportReadGroupSetsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ImportVariantsResponse
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class OperationMetadata
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class OperationEvent
        class Representation < Google::Apis::Core::JsonRepresentation; end
      end

      class ListDatasetsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :datasets, as: 'datasets', class: Google::Apis::GenomicsV1::Dataset, decorator: Google::Apis::GenomicsV1::Dataset::Representation

          property :next_page_token, as: 'nextPageToken'
        end
      end

      class Dataset
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :id, as: 'id'
          property :project_id, as: 'projectId'
          property :name, as: 'name'
          property :create_time, as: 'createTime'
        end
      end

      class Empty
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
        end
      end

      class UndeleteDatasetRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
        end
      end

      class Operation
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :name, as: 'name'
          hash :metadata, as: 'metadata'
          property :done, as: 'done'
          property :error, as: 'error', class: Google::Apis::GenomicsV1::Status, decorator: Google::Apis::GenomicsV1::Status::Representation

          hash :response, as: 'response'
        end
      end

      class Status
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :code, as: 'code'
          property :message, as: 'message'
          collection :details, as: 'details'
        end
      end

      class ListOperationsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :operations, as: 'operations', class: Google::Apis::GenomicsV1::Operation, decorator: Google::Apis::GenomicsV1::Operation::Representation

          property :next_page_token, as: 'nextPageToken'
        end
      end

      class CancelOperationRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
        end
      end

      class ImportReadGroupSetsRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :dataset_id, as: 'datasetId'
          property :reference_set_id, as: 'referenceSetId'
          collection :source_uris, as: 'sourceUris'
          property :partition_strategy, as: 'partitionStrategy'
        end
      end

      class ExportReadGroupSetRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :project_id, as: 'projectId'
          property :export_uri, as: 'exportUri'
          collection :reference_names, as: 'referenceNames'
        end
      end

      class SearchReadGroupSetsRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :dataset_ids, as: 'datasetIds'
          property :name, as: 'name'
          property :page_token, as: 'pageToken'
          property :page_size, as: 'pageSize'
        end
      end

      class SearchReadGroupSetsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :read_group_sets, as: 'readGroupSets', class: Google::Apis::GenomicsV1::ReadGroupSet, decorator: Google::Apis::GenomicsV1::ReadGroupSet::Representation

          property :next_page_token, as: 'nextPageToken'
        end
      end

      class ReadGroupSet
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :id, as: 'id'
          property :dataset_id, as: 'datasetId'
          property :reference_set_id, as: 'referenceSetId'
          property :name, as: 'name'
          property :filename, as: 'filename'
          collection :read_groups, as: 'readGroups', class: Google::Apis::GenomicsV1::ReadGroup, decorator: Google::Apis::GenomicsV1::ReadGroup::Representation

          hash :info, as: 'info', :class => Array do
        include Representable::JSON::Collection
        items
      end

        end
      end

      class ReadGroup
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :id, as: 'id'
          property :dataset_id, as: 'datasetId'
          property :name, as: 'name'
          property :description, as: 'description'
          property :sample_id, as: 'sampleId'
          property :experiment, as: 'experiment', class: Google::Apis::GenomicsV1::Experiment, decorator: Google::Apis::GenomicsV1::Experiment::Representation

          property :predicted_insert_size, as: 'predictedInsertSize'
          collection :programs, as: 'programs', class: Google::Apis::GenomicsV1::Program, decorator: Google::Apis::GenomicsV1::Program::Representation

          property :reference_set_id, as: 'referenceSetId'
          hash :info, as: 'info', :class => Array do
        include Representable::JSON::Collection
        items
      end

        end
      end

      class Experiment
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :library_id, as: 'libraryId'
          property :platform_unit, as: 'platformUnit'
          property :sequencing_center, as: 'sequencingCenter'
          property :instrument_model, as: 'instrumentModel'
        end
      end

      class Program
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :command_line, as: 'commandLine'
          property :id, as: 'id'
          property :name, as: 'name'
          property :prev_program_id, as: 'prevProgramId'
          property :version, as: 'version'
        end
      end

      class ListCoverageBucketsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :bucket_width, as: 'bucketWidth'
          collection :coverage_buckets, as: 'coverageBuckets', class: Google::Apis::GenomicsV1::CoverageBucket, decorator: Google::Apis::GenomicsV1::CoverageBucket::Representation

          property :next_page_token, as: 'nextPageToken'
        end
      end

      class CoverageBucket
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :range, as: 'range', class: Google::Apis::GenomicsV1::Range, decorator: Google::Apis::GenomicsV1::Range::Representation

          property :mean_coverage, as: 'meanCoverage'
        end
      end

      class Range
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :reference_name, as: 'referenceName'
          property :start, as: 'start'
          property :end, as: 'end'
        end
      end

      class SearchReadsRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :read_group_set_ids, as: 'readGroupSetIds'
          collection :read_group_ids, as: 'readGroupIds'
          property :reference_name, as: 'referenceName'
          property :start, as: 'start'
          property :end, as: 'end'
          property :page_token, as: 'pageToken'
          property :page_size, as: 'pageSize'
        end
      end

      class SearchReadsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :alignments, as: 'alignments', class: Google::Apis::GenomicsV1::Read, decorator: Google::Apis::GenomicsV1::Read::Representation

          property :next_page_token, as: 'nextPageToken'
        end
      end

      class Read
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :id, as: 'id'
          property :read_group_id, as: 'readGroupId'
          property :read_group_set_id, as: 'readGroupSetId'
          property :fragment_name, as: 'fragmentName'
          property :proper_placement, as: 'properPlacement'
          property :duplicate_fragment, as: 'duplicateFragment'
          property :fragment_length, as: 'fragmentLength'
          property :read_number, as: 'readNumber'
          property :number_reads, as: 'numberReads'
          property :failed_vendor_quality_checks, as: 'failedVendorQualityChecks'
          property :alignment, as: 'alignment', class: Google::Apis::GenomicsV1::LinearAlignment, decorator: Google::Apis::GenomicsV1::LinearAlignment::Representation

          property :secondary_alignment, as: 'secondaryAlignment'
          property :supplementary_alignment, as: 'supplementaryAlignment'
          property :aligned_sequence, as: 'alignedSequence'
          collection :aligned_quality, as: 'alignedQuality'
          property :next_mate_position, as: 'nextMatePosition', class: Google::Apis::GenomicsV1::Position, decorator: Google::Apis::GenomicsV1::Position::Representation

          hash :info, as: 'info', :class => Array do
        include Representable::JSON::Collection
        items
      end

        end
      end

      class LinearAlignment
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :position, as: 'position', class: Google::Apis::GenomicsV1::Position, decorator: Google::Apis::GenomicsV1::Position::Representation

          property :mapping_quality, as: 'mappingQuality'
          collection :cigar, as: 'cigar', class: Google::Apis::GenomicsV1::CigarUnit, decorator: Google::Apis::GenomicsV1::CigarUnit::Representation

        end
      end

      class Position
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :reference_name, as: 'referenceName'
          property :position, as: 'position'
          property :reverse_strand, as: 'reverseStrand'
        end
      end

      class CigarUnit
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :operation, as: 'operation'
          property :operation_length, as: 'operationLength'
          property :reference_sequence, as: 'referenceSequence'
        end
      end

      class SearchReferenceSetsRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :md5checksums, as: 'md5checksums'
          collection :accessions, as: 'accessions'
          property :assembly_id, as: 'assemblyId'
          property :page_token, as: 'pageToken'
          property :page_size, as: 'pageSize'
        end
      end

      class SearchReferenceSetsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :reference_sets, as: 'referenceSets', class: Google::Apis::GenomicsV1::ReferenceSet, decorator: Google::Apis::GenomicsV1::ReferenceSet::Representation

          property :next_page_token, as: 'nextPageToken'
        end
      end

      class ReferenceSet
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :id, as: 'id'
          collection :reference_ids, as: 'referenceIds'
          property :md5checksum, as: 'md5checksum'
          property :ncbi_taxon_id, as: 'ncbiTaxonId'
          property :description, as: 'description'
          property :assembly_id, as: 'assemblyId'
          property :source_uri, as: 'sourceUri'
          collection :source_accessions, as: 'sourceAccessions'
        end
      end

      class SearchReferencesRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :md5checksums, as: 'md5checksums'
          collection :accessions, as: 'accessions'
          property :reference_set_id, as: 'referenceSetId'
          property :page_token, as: 'pageToken'
          property :page_size, as: 'pageSize'
        end
      end

      class SearchReferencesResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :references, as: 'references', class: Google::Apis::GenomicsV1::Reference, decorator: Google::Apis::GenomicsV1::Reference::Representation

          property :next_page_token, as: 'nextPageToken'
        end
      end

      class Reference
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :id, as: 'id'
          property :length, as: 'length'
          property :md5checksum, as: 'md5checksum'
          property :name, as: 'name'
          property :source_uri, as: 'sourceUri'
          collection :source_accessions, as: 'sourceAccessions'
          property :ncbi_taxon_id, as: 'ncbiTaxonId'
        end
      end

      class ListBasesResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :offset, as: 'offset'
          property :sequence, as: 'sequence'
          property :next_page_token, as: 'nextPageToken'
        end
      end

      class ImportVariantsRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :variant_set_id, as: 'variantSetId'
          collection :source_uris, as: 'sourceUris'
          property :format, as: 'format'
          property :normalize_reference_names, as: 'normalizeReferenceNames'
        end
      end

      class VariantSet
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :dataset_id, as: 'datasetId'
          property :id, as: 'id'
          collection :reference_bounds, as: 'referenceBounds', class: Google::Apis::GenomicsV1::ReferenceBound, decorator: Google::Apis::GenomicsV1::ReferenceBound::Representation

          collection :metadata, as: 'metadata', class: Google::Apis::GenomicsV1::VariantSetMetadata, decorator: Google::Apis::GenomicsV1::VariantSetMetadata::Representation

        end
      end

      class ReferenceBound
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :reference_name, as: 'referenceName'
          property :upper_bound, as: 'upperBound'
        end
      end

      class VariantSetMetadata
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :key, as: 'key'
          property :value, as: 'value'
          property :id, as: 'id'
          property :type, as: 'type'
          property :number, as: 'number'
          property :description, as: 'description'
          hash :info, as: 'info', :class => Array do
        include Representable::JSON::Collection
        items
      end

        end
      end

      class ExportVariantSetRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :call_set_ids, as: 'callSetIds'
          property :project_id, as: 'projectId'
          property :format, as: 'format'
          property :bigquery_dataset, as: 'bigqueryDataset'
          property :bigquery_table, as: 'bigqueryTable'
        end
      end

      class SearchVariantSetsRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :dataset_ids, as: 'datasetIds'
          property :page_token, as: 'pageToken'
          property :page_size, as: 'pageSize'
        end
      end

      class SearchVariantSetsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :variant_sets, as: 'variantSets', class: Google::Apis::GenomicsV1::VariantSet, decorator: Google::Apis::GenomicsV1::VariantSet::Representation

          property :next_page_token, as: 'nextPageToken'
        end
      end

      class SearchVariantsRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :variant_set_ids, as: 'variantSetIds'
          property :variant_name, as: 'variantName'
          collection :call_set_ids, as: 'callSetIds'
          property :reference_name, as: 'referenceName'
          property :start, as: 'start'
          property :end, as: 'end'
          property :page_token, as: 'pageToken'
          property :page_size, as: 'pageSize'
          property :max_calls, as: 'maxCalls'
        end
      end

      class SearchVariantsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :variants, as: 'variants', class: Google::Apis::GenomicsV1::Variant, decorator: Google::Apis::GenomicsV1::Variant::Representation

          property :next_page_token, as: 'nextPageToken'
        end
      end

      class Variant
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :variant_set_id, as: 'variantSetId'
          property :id, as: 'id'
          collection :names, as: 'names'
          property :created, as: 'created'
          property :reference_name, as: 'referenceName'
          property :start, as: 'start'
          property :end, as: 'end'
          property :reference_bases, as: 'referenceBases'
          collection :alternate_bases, as: 'alternateBases'
          property :quality, as: 'quality'
          collection :filter, as: 'filter'
          hash :info, as: 'info', :class => Array do
        include Representable::JSON::Collection
        items
      end

          collection :calls, as: 'calls', class: Google::Apis::GenomicsV1::VariantCall, decorator: Google::Apis::GenomicsV1::VariantCall::Representation

        end
      end

      class VariantCall
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :call_set_id, as: 'callSetId'
          property :call_set_name, as: 'callSetName'
          collection :genotype, as: 'genotype'
          property :phaseset, as: 'phaseset'
          collection :genotype_likelihood, as: 'genotypeLikelihood'
          hash :info, as: 'info', :class => Array do
        include Representable::JSON::Collection
        items
      end

        end
      end

      class MergeVariantsRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :variant_set_id, as: 'variantSetId'
          collection :variants, as: 'variants', class: Google::Apis::GenomicsV1::Variant, decorator: Google::Apis::GenomicsV1::Variant::Representation

        end
      end

      class SearchCallSetsRequest
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :variant_set_ids, as: 'variantSetIds'
          property :name, as: 'name'
          property :page_token, as: 'pageToken'
          property :page_size, as: 'pageSize'
        end
      end

      class SearchCallSetsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :call_sets, as: 'callSets', class: Google::Apis::GenomicsV1::CallSet, decorator: Google::Apis::GenomicsV1::CallSet::Representation

          property :next_page_token, as: 'nextPageToken'
        end
      end

      class CallSet
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :id, as: 'id'
          property :name, as: 'name'
          property :sample_id, as: 'sampleId'
          collection :variant_set_ids, as: 'variantSetIds'
          property :created, as: 'created'
          hash :info, as: 'info', :class => Array do
        include Representable::JSON::Collection
        items
      end

        end
      end

      class ImportReadGroupSetsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :read_group_set_ids, as: 'readGroupSetIds'
        end
      end

      class ImportVariantsResponse
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          collection :call_set_ids, as: 'callSetIds'
        end
      end

      class OperationMetadata
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :project_id, as: 'projectId'
          property :create_time, as: 'createTime'
          hash :request, as: 'request'
          collection :events, as: 'events', class: Google::Apis::GenomicsV1::OperationEvent, decorator: Google::Apis::GenomicsV1::OperationEvent::Representation

        end
      end

      class OperationEvent
        # @private
        class Representation < Google::Apis::Core::JsonRepresentation
          property :description, as: 'description'
        end
      end
    end
  end
end

redpajama_set_name: RedPajamaGithub


Chinese New Year or the Lunar New Year for 2009 started today, January 26! It is the Year of the Ox.
To all the Chinese people and others celebrating this auspicious season, John and I would like to wish you and your loved ones a very happy new year filled with much joy and peace, success and prosperity, and good health too! Enjoy your holidays and have great bonding times with family, relatives and friends.
Read more about this Spring Festival at wikipedia and infoplease.