Kies, Alexander

Medjroubi, Wided

Schlott, Markus

Schyska, Bruno U.

von Bremen, Lüder

Institute of Transport Research:Publications

German Aerospace Center (DLR), Institute of Networked Energy Systems The Sensitivity of Power System Expansion Models on Meteorological Parameters Bruno U. Schyska, Alex Kies, Markus Schlott, Lueder v. Bremen, Wided Medjroubi Climate Forecasting for Energy Workshop 2020-12-04 DLR.de/ve • Chart 1 > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence. Introduction • Power system expansion models are a widely used tool to address research questions in the context of the energy transition.  • Based on their results far-reaching political and societal discussions are made.  • Models are optimization problems and rely on different socio-economical, technical and meteorological parameters:  Demand  Cost  Capacity factors  Efficiencies, emissions, ramping abilities DLR.de/ve • Chart 2 > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence. Parameter Uncertainty Example: Cost of Capital DLR.de/ve • Chart 3 Homogeneous reference Inhomogeneous scenario > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence. DLR.de/ve • Chart 4 ‒ Accumulation of generation capacity in countries with favourable conditions:  Higher share of wind power (esp. onshore)  Lower share of solar PV Overall decrease in LCOE  Increasing inequality in expenditures for electricity between central Europe and the periphery Relative change in expenditures for electricity [%] Schyska and Kies (2020), Applied Energy, preprint on arxiv Parameter Uncertainty Example: Cost of Capital > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence. Introduction • Power system expansion models are a widely used tool to address research questions in the context of the energy transition.  • Based on their results far-reaching political and societal discussions are made.  • Models are optimization problems and rely on different socio-economical, technical and meteorological parameters:  Demand  Cost  Capacity factors  Efficiencies, emissions, ramping abilities  • Often, parameters are derived from Future projections, expansion is planned on historical data from different data sources (see poster of Alex Kies for a comparison of meteorological data sources).  • The choice of (uncertain) parameters affects the model results. But how? DLR.de/ve • Chart 5 > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence.  Systematically describe the effect of different parameter choices on the simulation results   measure the sensitivity Defining a Sensitivity Metric DLR.de/ve • Chart 6 Define a metric which:  • quantitatively measures the sensitivity of power system expansion problems on specific parameter scenarios,  • allows to compare a great(er) number of scenarios,  • is symmetric about the choice/the order of the scenario,  • is greater equal zero     • Two scenarios 𝛼𝑖 and 𝛼𝑗    • Two unconstrained solutions for the two scenarios  • Two solutions for the two scenarios constrained with the respective other scenario Schyska et al. (2020), to be submitted, preprint on arxiv > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence. Computing the Metric DLR.de/ve • Chart 7 Result from scenario 𝛼𝑖  Result from scenario 𝛼𝑗  Simulate scenario 𝛼𝑖  Simulate scenario 𝛼𝑗  Simulate again Simulate again Constrain Result from 𝛼𝑖with constraint 𝛼𝑗  Result from 𝛼𝑗with constraint 𝛼𝑖  + M Difference Difference Sum > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence. DLR.de/ve • Chart 8 Hom. reference Inh. scenario Difference LCOE unconst. [EUR / MWh] 72,2 70.8 -1,4 LCOE constr. [EUR / MWh] 74,7 72,8 -1,9 Difference 2,5 2,0 M = 4.5 EUR/MWh Levelized Cost of Electricity: LCOE =  𝐶𝑜𝑠𝑡 𝑓𝑜𝑟 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑎𝑛𝑑 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝐷𝑒𝑚𝑎𝑛𝑑 𝑚𝑒𝑡 Computing the Metric: Cost of Capital Example Schyska et al. (2020), to be submitted, preprint on arxiv > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence. DLR.de/ve • Chart 9 The Sensitivity to Meteorological Parameters: Data • RCP 8.5 climate projections from the CNRM-CERFACS-CNRM-CM5 • Scaled down within EURO-CORDEX  • Near-surface wind speed extrapolated to hub-height (90m) using the logarithmic wind profile • Surface downwelling shortwave radiation and near-surface air temperature for solar PV • Total runoff for hydro power  • 5 time slices:   Historical (HIS): 1970 – 1976  Begin of century (BOC): 2000 – 2006  Mid of century (MOC): 2038 – 2044  End of century (EOC): 2094 - 2101 Schlott et al. (2018), Applied Energy, preprint on arxiv > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 • PyPSA-Eur (one node per country)  • Including:   Load time series  Renewable potentials  Cost  Technologies  • Onshore + offshore wind, solar PV, OCGT, hydro, batteries, hydrogen storage  • 95% CO2 reduction scenario Hörsch et al. (2018), Energy Strategy Reviews, arxiv preprint Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence. DLR.de/ve • Chart 10 Schyska et al. (2020), to be submitted, preprint on arxiv Capacity Factor Changes towards the End of the Century Onshore wind Solar PV Relative change in 2094-2101 compared to 2000-2006 [%] > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence. DLR.de/ve • Chart 11 Schyska et al. (2020), to be submitted, preprint on arxiv Differences in optimal Capacity Expansion > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Difference in optimal capacity expansion compared to BOC [GW] Copyright © 2020, Bruno Schyska, DLR, Institute of Networked  Energy Systems This work is licensed under a CC-BY-4.0 licence. DLR.de/ve • Chart 12 The Sensitivity to Meteorological Parameters • Same order of magnitude as sensitivity to cost of capital  • Combinations with 2000 – 2006 exhibit highest sensitivities, e.g. higher as HIS + EOC  • 2000-2006 leads to extraordinary solution -> not representative  • Comparably low sensitivity on scenarios HIS, MOC and EOC   • Choosing the right weather data is as important as choosing the right cost.  • Better do not choose 2000-2006. You might get unrepresentative results.  > Sensitivity on Met Parameters > Bruno Schyska > 2020-12-04 Schyska et al. (2020), to be submitted, preprint on arxiv Thank you! Conclusions 

The Sensitivity of Power System Expansion Models on Meteorological Parameters

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The Sensitivity of Power System Expansion Models on Meteorological Parameters

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