Optimal hydrogen supply chains: co-benefits for integrating - PowerPoint PPT Presentation

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

1 Background 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, … BMWI, AGEE Stat: Zeitreihen zur Entwicklung der erneuerbaren Energien in Deutschland Focus here • Domestic H 2 production and distribution • Use of H 2 for fuel-cell electric vehicles • Research carried out in Kopernikus project P2X, supported by BMBF Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

1 Research questions and contribution 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 Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

1 Background The model

2 Model: DIETER 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 Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

2 Model: extension of DIETER New hydrogen module • Two electrolysis technologies • Four channels for distributing H 2 to fuel stations, including • Gaseous H 2 • Liquified H 2 • 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 H 2 demand for mobility https://commons.wikimedia.org/wiki/File:Dibenzyltoluene_V1.svg Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

2 Overview of hydrogen supply chains in the model  We investigate not all channels in one model run, but combinations of each centralized with the decentralized channel Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

2 Data and scenarios Lithium-ion batteries; 2.0 GW Lignite; 9.3 GW Electricity sector Hard coal; 9.8 GW Pumped-hydro storage; 9.5 GW • Brownfield scenario for 2030 CCGT; 17.6 GW • Capacities bounded by current grid PV; 91.3 GW OCGT; 17.6 GW development plan (NEP) Oil; 3.2 GW • Maximum investment into thermal Other; 4.1 GW plants, minimum investments into renewables and storage Run-of-river; … • Time series provided by Open Power System Data & ENTSO-E Biomass; 6.89 GW • Exogenous minimum renewables share of 65%, 70%, 75%, 80% Wind offshore; Wind onshore; 81.5 GW 17.0 GW Hydrogen infrastructure • Fully „greenfield“ • H 2 demand for mobility: 0, 5%, 10%, 25% of passenger road traffic in Germany (0, 9, 18, 45 TWh H2 ) • General assumptions: each fuel station can only offer H 2 from one channel Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

1 Background Some intuition: potential drivers of results

3 Drivers I: Tradeoff between overall efficiency and flexibility 60 more energy Electricity demand at the filling station DEC efficienct 50 after mass storage (kWh el /kg H2 ) more flexible 40 30 20 LOHC 10 GH 2 LH 2 0 40 45 50 55 60 65 70 Overall elecricity demand of hydrogen supply chain (kWh el /kg H2 )  LOHC dominated by GH 2 and LH 2 (worse in both dimensions in direct comparison) Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

3 Drivers II: Fixed investment and transportation capacity costs 30,000 unweighted fixed costs (€/kW) 1,400 transportation capacity costs (€/kg H2 ) with(out) transportation 25,000 1,200 1,000 20,000 800 15,000 600 10,000 400 5,000 200 0 0 GH₂ LH₂ LOHC DEC GH₂ LH₂ LOHC  Only 3% spread between cheapest and most expensive supply chain  Transportation costs highest for GH 2 , low effective load capacity of GH 2 trailer Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

3 Drivers III: Storage costs (and losses) 20 18 16 14 storage costs (€/kg) filling station 12 10 8 6 4 2 0 DEC GH₂ LH₂ LOHC High (only HP) Pressure • Substantially lower storage costs for LH 2 and LOHC • Expensive high pressure storage at the filling station  only buffer storage • LH 2 also suffers from boil-off (about 20%/week)  Intuition not so clear  Analysis with numerical optimization model required Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

1 Background Results: hydrogen supply

4 Results: hydrogen supply chains and H 2 supply costs Low RES share, low H 2 demand: • Limited renewable surpluses Not much need for additional flexibility • • Decentralised H 2 supply dominant because high energy efficiency matters most Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

4 Results: hydrogen supply chains and H 2 supply costs High RES share, low H 2 demand: • Higher renewable surplus generation • Temporal flexibility more beneficial • LH 2 and LOHC allow longer-term storage Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

4 Results: hydrogen supply chains and H 2 supply costs High H 2 demand: • LH 2 or LOHC beneficial • High RES: boil-off prevents seasonal storage with LH 2 • Hardly any GH 2 : high storage and transportation costs Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

1 Background Results: electricity system

4 Effects on generation capacity (vs. respective baseline) 50 Gigawatt 40 Pumped hydro Li-ion 30 Other renewable PV 20 Offshore wind Onshore wind 10 Other conventional Natural gas 0 Hard coal Lignite -10 Res65-Dem5 Res65-Dem25 Res80-Dem5 Res80-Dem25 (DEC) (DEC+LH2) (DEC+LOHC) (DEC+LOHC)  More PV and (a bit) less storage  Less capacity needed in high-RES scenario (better utilization) Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

4 Effects on yearly electricity generation (vs. respective baseline) 90 TWh 70 Pumped hydro Li-ion 50 Other renewable PV 30 Offshore wind Onshore wind 10 Other conventional Natural gas -10 Hard coal Lignite -30 Res65-Dem5 Res65-Dem25 Res80-Dem5 Res80-Dem25 (DEC) (DEC+LH2) (DEC+LOHC) (DEC+LOHC)  Storage capability of LOHC and LH 2 allows additional integration of wind power Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

4 Effects on renewable curtailment (vs. respective baseline) Res65-Dem5 Res65-Dem25 Res80-Dem5 Res80-Dem25 (DEC) (DEC+LH2) (DEC+LOHC) (DEC+LOHC) 10 TWh 0 -10 -20 -30 -40 -50  LOHC makes use of renewable electricity that would otherwise be curtailed Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

4 Effects on system LCOE (without fixed H 2 costs) Res65-Dem25 (DEC+LH2) Res80-Dem25 (DEC) Res80-Dem25 (DEC+LOHC) 2% 0% -2% -4% -6% -8% -10%  Clear renewable integration co-benefit of hydrogen in 80% renewables case Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne

Sneak preview: what about battery-electric vehicles? 4 Effects on system LCOE (without fixed H 2 or BEV-related costs) Res80-Dem25 (DEC+LOHC) Res 80_Dem25 EV (no V2G) Res 80_Dem25 EV (with V2G) 0% -1% -2% -3% -4% -5% -6% -7% -8% -9% -10%  To be explored in more detail in future work  If BEV are used instead of fuel cell H 2 vehicles, also substantial co-benefits  …and lower electricity demand, lower deployment of RES, lower overall cost Optimal hydrogen supply chains Stöckl, Schill, Zerrahn. September 17, 2019, Melbourne