Intermittent Renewable Energy Sources and Wholesale Electricity Prices

Transcription

1 Intermittent Renewable Energy Sources and Wholesale Electricity Prices Felix Müsgens, Thomas Möbius FERC and TAI Workshop Washington, October 30, 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? 2

2 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) 3 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. 4

3 Results Textbook [MW] X no wind generation Installing wind capacity leads to: Variation in installed thermal capacities (usually more peak, less base) However, identical number of hours for each price level with and without wind Hence, identical variance: Result: Insights from Textbook (Power System) Economics Two thermal technologies (base load and peak load) Characteristics:,,,, t [MW] no RES generation Only three price levels occur 1 hour with (equal to ) t hours with (equal ) hours with (equal to ) solves, X Y RES generation 5 Appearance of RES,

4 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. 7 Equivalent (Simple) Electricity Market Model Variable generation costs Annualized investment costs Objective Function Lower/upper limit for generation Wind feed-in Energy balance market clearing 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 8

5 [MW] X Y no wind generation high wind generation Base- Load Peak- Load Curtailment t 10 Results Simplest model formulation 0 GW Wind 35 GW Wind 70 GW Wind Price Variance 286, , ,024 Wind Curtailment [GWh per year] Variance increases with further increasing wind capacity? Wind curtailment appears at 70 GW wind capacity! 9 Results Textbook with Wind Curtailment Curtailment price occurs: For moderate wind increase Decreasing amount of base load hours:, equals number of hours with negative residual load.

6 [MW el] [hour] Variable generation costs Annualized investment costs Start-up Costs Costs at partial load Upper bound constraint Lower bound constraint 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. 11 Model Extensions Intertemporal Constraints Objective Function 12

7 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 13

8 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. 15 Results Volatility under Uncertainty Long term uncertainty due to a set of different wind years % ± 0-10% 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 Investment Decision 2014 long term variation of wind years short term wind variation influences the unit commitment + 10% ± % 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 16

9 Model Extensions Uncertainty Model Extensions Uncertainty s. t. following constraints Lower/upper limit for generation Activating start-up costs Lower/upper limit for started capacity Wind feed-in Objective Function Energy Balance - Clearing the market in every time period Set extension for wind years () and wind realization scenario () Electricity price estimator: marginal of energy balance constraint 18 Scaling of startup and investment costs by likelihoods and which do not vary within its set 17

10 Results Volatility under Uncertainty Without intertemporal constraints With intertemporal constraints With intertemporal constraints - Under uncertainty 0 GW Wind 35 GW Wind 70 GW Wind Price Variance 286, , ,024 Wind Curtailment [GWh per year] Price Variance 286, , ,814 Wind Curtailment [GWh per year] Price Variance 286,762 4,282,914 4,282,976 Wind Curtailment [GWh per year] Generally higher values due to lower likelihood for the occurrence of the scarcity hour and thus, a significantly higher value for the scarcity price 19 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 20

11 Technology Efficiency loss at minimum load [% pt] Base Load 132, Peak Load 56, Wind Thank you very much! Questions? 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 Technology Annual fixed costs [ /MW*a] Variable production costs [ /MWh] Start-up costs [ / ΔMW] Minimal load [%] Efficiency loss at minimum load [% pt] Base Load 132, Peak Load 56, Wind Variable wind generation as the only intermittent RES Wind capacities exogenously implemented 22

12 [MW] [hour] 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 23 Backup II Basic Model Objective Function Operating at partial load is causing lower efficiency rates and thus, higher variable costs 24