The Impact of Intermittent Renewable Energy Sources on Wholesale Electricity Prices Prof. Dr. Felix Müsgens, Thomas Möbius USAEE-Conference Pittsburgh, October 27, 2015

Motivation  How will intermittent renewable energy sources (RES) influence wholesale electricity prices, and in particular price volatility?  Relevant for –Traders risk premia structural changes peak/off-peak –Regulators increasing zero variable cost generation peak load pricing and capacity mechanisms –Investors how to finance investment? Brandenburg University of Technology – Felix Müsgens2

Outline and Methodology  Stylized Analysis: –Two thermal technologies (base and peak load) –Exogenous RES capacity changes –Dynamic effects (i. e. start-up costs and minimum load requirements) –Uncertainty of RES feed-in investment planning start-up and dispatch decisions –Implemented with linear optimization electricity market model endogenous optimization of base/peak capacity investments and base/peak/wind dispatch (depending on availability factors) model computes green-field full cost electricity market equilibria electricity price equals marginal of demand constraint (shadow price) 3Brandenburg University of Technology – Felix Müsgens

Outline and Methodology  Stylized Analysis: –Two thermal technologies (base and peak load) –Exogenous RES capacity changes –Dynamic effects (i. e. start-up costs and minimum load requirements) –Uncertainty of RES feed-in Investment planning start-up and dispatch decisions –Load duration curve –Perfect foresight –Implemented with linear optimization electricity market model endogenous optimization of base/peak capacity investments and base/peak/wind dispatch (depending on availability factors) model computes (green-field) full cost electricity market equilibria, electricity price equals marginal of demand constraint. 4Brandenburg University of Technology – Felix Müsgens

Insights from Textbook (Power System) Economics Brandenburg University of Technology – Felix Müsgens5 t [MW] X no RES generation Y RES generation t [MW] X no wind generation

Results – Textbook Brandenburg University of Technology – Felix Müsgens6

Outline and Methodology  Stylized Analysis: –Two thermal technologies (base and peak load) –Exogenous RES capacity changes –Dynamic effects (i. e. start-up costs and minimum load requirements) –Uncertainty of RES feed-in Investment planning start-up and dispatch decisions –Load duration curve –Perfect foresight –Implemented with linear optimization electricity market model endogenous optimization of base/peak capacity investments and base/peak/wind dispatch (depending on availability factors) model computes (green-field) full cost electricity market equilibria, electricity price equals marginal of demand constraint. 7Brandenburg University of Technology – Felix Müsgens

Equivalent (Simple) Electricity Market Model  Objective Function  s. t.  Parameters: –Peak tech: OCGT, base tech: hard coal (numbers: appendix) –Wind: 0, 35 and 70 GW, availability factors from Germany –Hourly load profile: Germany Brandenburg University of Technology – Felix Müsgens8 Variable generation costs Annualized investment costs Lower/upper limit for generation Wind feed-in Energy balance – market clearing

0 GW Wind35 GW Wind70 GW Wind Price Variance 286, ,024 Wind Curtailment [GWh per year] GW Wind35 GW Wind70 GW Wind Price Variance 286, ,024 Results  Simplest model formulation  Variance increases with further increasing wind capacity?  Wind curtailment appears at 70 GW wind capacity! Brandenburg University of Technology – Felix Müsgens9 0 GW Wind35 GW Wind Price Variance 286,000

Results – Textbook with Wind Curtailment Brandenburg University of Technology – Felix Müsgens10 Base- Load Peak- Load t [MW] X Y no wind generation high wind generation Curtail- ment

Outline and Methodology  Stylized Analysis: –Two thermal technologies (base and peak load) –Exogenous RES capacity changes –Dynamic effects (i. e. start-up costs and minimum load requirements) –Uncertainty of RES feed-in Investment planning start-up and dispatch decisions –Load duration curve –Perfect foresight –Implemented with linear optimization electricity market model endogenous optimization of base/peak capacity investments and base/peak/wind dispatch (depending on availability factors) model computes (green-field) full cost electricity market equilibria, electricity price equals marginal of demand constraint. 11Brandenburg University of Technology – Felix Müsgens

Model Extensions – Intertemporal Constraints  Objective Function Brandenburg University of Technology – Felix Müsgens12  Upper bound constraint  Lower bound constraint Variable generation costs Annualized investment costs Start-up Costs Costs at partial load [MW el ] [hour]

Model Extensions – Intertemporal Constraints  Activating start-up costs  Upper limit for started capacity  Wind feed-in  Energy Balance - Clearing the market in every time period  Electricity price estimator: marginal of energy balance constraint Brandenburg University of Technology – Felix Müsgens13

Results  Extended Model - Intertemporal Constraints  Intertemporal constraints generally lead to a higher price variance  Slight increase up from the first 35 GW wind capacity is visible Brandenburg University of Technology – Felix Müsgens14 0 GW Wind35 GW Wind70 GW Wind Without intertemporal constraints Price Variance 286, ,024 Wind Curtailment [GWh per year] With intertemporal constraints Price Variance 286,762286,774286,814 Wind Curtailment [GWh per year] GW Wind35 GW Wind70 GW Wind Without intertemporal constraints Price Variance 286, ,024 Wind Curtailment [GWh per year] 00199

Outline and Methodology  Stylized Analysis: –Two thermal technologies (base and peak load) –Exogenous RES capacity changes –Dynamic effects (i. e. start-up costs and minimum load requirements) –Uncertainty of RES feed-in Investment planning start-up and dispatch decisions –Load duration curve –Perfect foresight –Implemented with linear optimization electricity market model endogenous optimization of base/peak capacity investments and base/peak/wind dispatch (depending on availability factors) model computes (green-field) full cost electricity market equilibria, electricity price equals marginal of demand constraint. 15Brandenburg University of Technology – Felix Müsgens

Results – Volatility under Uncertainty Brandenburg University of Technology – Felix Müsgens16 long term variation of ‚wind years‘ short term wind variation – influences the unit commitment + 10% ± % ‚Investment Decision‘ + 10% ± 0 ­­- ­­­ 10% –Long term uncertainty due to a set of different ‘wind years’ One global investment decision with identical installed capacities for all wind years and short term deviations. –Short term uncertainty with a strong impact at the start-up decision Started capacity is fixed at the second stage of the scenario tree, but has to hold for all variations at the third stage  Integrated ‘single stage’ electricity market model

Model Extensions – Uncertainty Brandenburg University of Technology – Felix Müsgens17 Variable generation costs Annualized investment costs Start-up Costs Costs at partial load

Model Extensions – Uncertainty Brandenburg University of Technology – Felix Müsgens18  s. t. following constraints –Lower/upper limit for generation –Activating start-up costs –Lower/upper limit for started capacity –Wind feed-in –Energy Balance - Clearing the market in every time period  Electricity price estimator: marginal of energy balance constraint

Results – Volatility under Uncertainty  Generally higher values due to lower likelihood for the occurrence of the scarcity hour and thus, a significantly higher value for the scarcity price Brandenburg University of Technology – Felix Müsgens19 0 GW Wind35 GW Wind70 GW Wind Without intertemporal constraints Price Variance 286, ,024 Wind Curtailment [GWh per year] With intertemporal constraints Price Variance 286,762286,774286,814 Wind Curtailment [GWh per year] With intertemporal constraints - Under uncertainty Price Variance 286,7624,282,9144,282,976 Wind Curtailment [GWh per year] 00297

Conclusion  In all considered market equilibria, market prices cover (and must cover) full costs of all thermal technologies. This is true regardless of the amount of wind energy in the system.  Price volatility increases with additional renewable energy sources (RES) capacity.  Driving factors are –RES curtailment –Changes in residual load profile in combination with thermal inflexibility –Uncertainty of RES generation Brandenburg University of Technology – Felix Müsgens20

Thank you very much! Questions?

22 Methodology  Stylized system with two thermal technologies and one intermittent RES technology –Base-Load Technology:High fix and low variable costs –Peak-Load Technology :Low fix and high variable costs  Variable wind generation as the only intermittent RES –Wind capacities exogenously implemented Brandenburg University of Technology – Felix Müsgens TechnologyEfficiency loss at minimum load [% ‑ pt] Base Load132, Peak Load56, Wind-0000

23 Backup I  Comparison of resulting investment decision with and without considering uncertainty at different wind levels  Uncertain wind realization encourages a higher share of peak load plants Brandenburg University of Technology – Felix Müsgens

24 Backup II – Basic Model  Objective Function –Operating at partial load is causing lower efficiency rates and thus, higher variable costs Brandenburg University of Technology – Felix Müsgens [MW] [hour]