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Australian-German Climate and Energy College and the Energy Transition Hub Seminar Optimal hydrogen supply chains: co-benefits for integrating renewable energy sources Fabian Stckl. Wolf-Peter Schill, Alexander Zerrahn September 17, 2019

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Australian-German Climate and Energy College and the Energy Transition Hub Seminar

Optimal hydrogen supply chains: co-benefits for integrating renewable energy sources

Fabian Stöckl. Wolf-Peter Schill, Alexander Zerrahn September 17, 2019

Work in progress – working paper and source code should be available by October 2019

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Background

Optimal hydrogen supply chains

German energy and climate policy targets

  • Strongly increasing use of variable renewable

energy sources

  • Decarbonization of all energy sectors

Sector coupling as a strategy to

  • (i) decarbonize other sectors
  • (ii) provide flexibility to the power sector

 often under-represented in IA models

  • E.g., produce hydrogen with renewable electricity

and use it for mobility, heating, industry, …

Focus here

  • Domestic H2 production and distribution
  • Use of H2 for fuel-cell electric vehicles
  • Research carried out in Kopernikus project P2X, supported by BMBF

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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BMWI, AGEE Stat: Zeitreihen zur Entwicklung der erneuerbaren Energien in Deutschland

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Research questions and contribution

Optimal hydrogen supply chains

We aim to determine least-cost hydrogen supply chains…

  • … considering differences in energy efficiency, investment costs, and storage capabilities
  • … and considering electricity system interactions

This calls for a numerical model

  • We develop an open-source model and apply it to a future (German) power system with high

shares of renewables

Outcomes of interest

  • Hydrogen: optimal technology mix, supply costs, and their drivers
  • Electricity system: effects on capacity and dispatch, costs

What is new?

  • Previous studies often did not account for power sector interactions of flexible hydrogen supply
  • Fully open-source / open data analysis

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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Background

The model

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Model: DIETER

Optimal hydrogen supply chains

Visit DIETER

  • Open-source GAMS code under MIT license
  • www.diw.de/dieter
  • https://github.com/diw-berlin/dieter

Cost minimization

  • Dispatch and investment
  • Hourly resolution over one year
  • Thermal and renewable technologies
  • Different types of electricity storage
  • Demand-side management, reserves
  • Residential heating, electric vehicles

Linear program

  • Deterministic, perfect foresight
  • No transmission constraints

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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Model: extension of DIETER

Optimal hydrogen supply chains

New hydrogen module

  • Two electrolysis technologies
  • Four channels for distributing H2 to fuel stations, including
  • Gaseous H2
  • Liquified H2
  • LOHC
  • Different storage options
  • Follow-up work: reconversion to electricity

Full co-optimization

  • Model decides on optimal capacities and hourly use
  • Given conventional electricity demand and H2 demand for mobility

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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https://commons.wikimedia.org/wiki/File:Dibenzyltoluene_V1.svg

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Overview of hydrogen supply chains in the model

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne Optimal hydrogen supply chains

 We investigate not all channels in one model run, but combinations of each centralized with the decentralized channel

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Data and scenarios

Optimal hydrogen supply chains

Electricity sector

  • Brownfield scenario for 2030
  • Capacities bounded by current grid

development plan (NEP)

  • Maximum investment into thermal

plants, minimum investments into renewables and storage

  • Time series provided by Open

Power System Data & ENTSO-E

  • Exogenous minimum renewables

share of 65%, 70%, 75%, 80%

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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Hydrogen infrastructure

  • Fully „greenfield“
  • H2 demand for mobility: 0, 5%, 10%, 25% of passenger road traffic in Germany (0, 9, 18, 45 TWhH2)
  • General assumptions: each fuel station can only offer H2 from one channel

Lignite; 9.3 GW Hard coal; 9.8 GW CCGT; 17.6 GW OCGT; 17.6 GW Oil; 3.2 GW Other; 4.1 GW Run-of-river; … Biomass; 6.89 GW Wind onshore; 81.5 GW Wind offshore; 17.0 GW PV; 91.3 GW Pumped-hydro storage; 9.5 GW Lithium-ion batteries; 2.0 GW

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Background

Some intuition: potential drivers of results

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Drivers I: Tradeoff between overall efficiency and flexibility

Optimal hydrogen supply chains

 LOHC dominated by GH2 and LH2 (worse in both dimensions in direct comparison)

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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DEC GH2 LH2 LOHC

10 20 30 40 50 60 40 45 50 55 60 65 70

Electricity demand at the filling station after mass storage (kWhel/kgH2) Overall elecricity demand of hydrogen supply chain (kWhel/kgH2) more flexible more energy efficienct

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Drivers II: Fixed investment and transportation capacity costs

Optimal hydrogen supply chains

 Only 3% spread between cheapest and most expensive supply chain  Transportation costs highest for GH2 , low effective load capacity of GH2 trailer

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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5,000 10,000 15,000 20,000 25,000 30,000 DEC GH₂ LH₂ LOHC unweighted fixed costs (€/kW) with(out) transportation 200 400 600 800 1,000 1,200 1,400 GH₂ LH₂ LOHC transportation capacity costs (€/kgH2)

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Drivers III: Storage costs (and losses)

Optimal hydrogen supply chains

  • Substantially lower storage costs for LH2 and LOHC
  • Expensive high pressure storage at the filling station  only buffer storage
  • LH2 also suffers from boil-off (about 20%/week)

 Intuition not so clear  Analysis with numerical optimization model required

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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2 4 6 8 10 12 14 16 18 20 DEC (only HP) GH₂ LH₂ LOHC High Pressure storage costs (€/kg)

filling station

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Background

Results: hydrogen supply

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Results: hydrogen supply chains and H2 supply costs

Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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Low RES share, low H2 demand:

  • Limited renewable surpluses
  • Not much need for additional flexibility
  • Decentralised H2 supply dominant because

high energy efficiency matters most

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Results: hydrogen supply chains and H2 supply costs

Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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High RES share, low H2 demand:

  • Higher renewable surplus generation
  • Temporal flexibility more beneficial
  • LH2 and LOHC allow longer-term storage
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Results: hydrogen supply chains and H2 supply costs

Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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High H2 demand:

  • LH2 or LOHC beneficial
  • High RES: boil-off prevents seasonal storage with LH2
  • Hardly any GH2: high storage and transportation costs
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Background

Results: electricity system

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Effects on generation capacity (vs. respective baseline)

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne Optimal hydrogen supply chains

  • 10

10 20 30 40 50 Res65-Dem5 (DEC) Res65-Dem25 (DEC+LH2) Res80-Dem5 (DEC+LOHC) Res80-Dem25 (DEC+LOHC) Gigawatt Pumped hydro Li-ion Other renewable PV Offshore wind Onshore wind Other conventional Natural gas Hard coal Lignite

 More PV and (a bit) less storage  Less capacity needed in high-RES scenario (better utilization)

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Effects on yearly electricity generation (vs. respective baseline)

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne Optimal hydrogen supply chains

  • 30
  • 10

10 30 50 70 90 Res65-Dem5 (DEC) Res65-Dem25 (DEC+LH2) Res80-Dem5 (DEC+LOHC) Res80-Dem25 (DEC+LOHC) TWh Pumped hydro Li-ion Other renewable PV Offshore wind Onshore wind Other conventional Natural gas Hard coal Lignite

 Storage capability of LOHC and LH2 allows additional integration of wind power

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Effects on renewable curtailment (vs. respective baseline)

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne Optimal hydrogen supply chains

  • 50
  • 40
  • 30
  • 20
  • 10

10 Res65-Dem5 (DEC) Res65-Dem25 (DEC+LH2) Res80-Dem5 (DEC+LOHC) Res80-Dem25 (DEC+LOHC) TWh

 LOHC makes use of renewable electricity that would otherwise be curtailed

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Effects on system LCOE (without fixed H2 costs)

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne Optimal hydrogen supply chains

 Clear renewable integration co-benefit of hydrogen in 80% renewables case

  • 10%
  • 8%
  • 6%
  • 4%
  • 2%

0% 2% Res65-Dem25 (DEC+LH2) Res80-Dem25 (DEC) Res80-Dem25 (DEC+LOHC)

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Sneak preview: what about battery-electric vehicles? Effects on system LCOE (without fixed H2 or BEV-related costs)

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne Optimal hydrogen supply chains

 If BEV are used instead of fuel cell H2 vehicles, also substantial co-benefits  …and lower electricity demand, lower deployment of RES, lower overall cost

  • 10%
  • 9%
  • 8%
  • 7%
  • 6%
  • 5%
  • 4%
  • 3%
  • 2%
  • 1%

0% Res80-Dem25 (DEC+LOHC) Res 80_Dem25 EV (no V2G) Res 80_Dem25 EV (with V2G)

 To be explored in more detail in future work

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Summary and conclusion

Optimal hydrogen supply chains

Tradeoff between energy efficiency and temporal flexibility

  • Energy-efficient decentral electrolysis optimal for lower shares of variable renewables
  • Less energy-efficient centralized electrolysis gains relevance with higher shares of variable

renewables because of storage benefits

Optimal choice of H2 supply chains also needs to consider other factors

  • Space requirements
  • Technology acceptance
  • Perceived / real danger of operations

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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Summary and conclusion

Optimal hydrogen supply chains

Flexible sector coupling

  • …can generate substantial co-benefits for integrating wind and solar energy

 should be considered in energy models

  • …but also requires additional deployment of variable renewables

Limitations

  • Results are driven by renewable surplus generation
  • Surpluses may be over-estimated, as we do not consider competing options for flexibility and

sector coupling  More research on energy system implications of massive sector coupling necessary

Future research

  • Additional or competing flexibility options in the electricty sector
  • Long-term power storage via H2-to-electricity
  • Maybe: how does this compare with Australia?

Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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Thank you for listening

DIW Berlin — Deutsches Institut für Wirtschaftsforschung e.V. Mohrenstraße 58, 10117 Berlin www.diw.de Contact

  • Dr. Wolf-Peter Schill

wschill@diw.de | @WPSchill

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Utilization patterns LOHC (RES75 DEM5)

Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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2 4 6 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

hydrogenation activity (MW) electricity price (€/kWh)

Hydrogenation

1 2 3 0.05 0.1 0.15 0.2 0.25

dehydrogenation activity (MW) electricity price (€/kWh)

Dehydrogenation

0.2 0.4 0.6 0.8 1 1.2 1 517 1033 1549 2065 2581 3097 3613 4129 4645 5161 5677 6193 6709 7225 7741 8257

Filling level (TWh) Hour of the year

LOHC (production site storage)

20 40 60 80 1 517 1033 1549 2065 2581 3097 3613 4129 4645 5161 5677 6193 6709 7225 7741 8257

Filling level (MWh) Hour of the year

LOHC storage at filling stations (per station)

2 4 6 8 10 12 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151

Filling level (MWh) Hour of the year

HP storage at filling stations

 „Flexible“ H2 storage loading

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0.2 0.4 0.6 0.8 1 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151

Filling level (MWh) Hour of the year

HP storage at filling stations

2 4 6 8 10 1 585 1169 1753 2337 2921 3505 4089 4673 5257 5841 6425 7009 7593 8177

Filling level (MWh) Hour of the year

LH2 storage at filling stations (per station)

0.1 0.2 0.3 0.4 0.5 1 517 1033 1549 2065 2581 3097 3613 4129 4645 5161 5677 6193 6709 7225 7741 8257

Filling level (TWh) Hour of the year

LH2 (production site storage)

1 2 3 4 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

liquefaction activity (MW) electricity price (€/kWh)

Liquefaction

Utilization patterns LH2 (RES75 DEM5)

Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

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1 2 3 0.05 0.1 0.15 0.2 0.25

evaporation activity (MW) electricity price (€/kWh)

Evaporation

 Flexibility of storage loading constrained by boil-off