Flexibilizing Demand and Renewable Supply

Transcription

1 Flexibilizing Demand and Renewable Supply Experiences & Challenges Dr Maximilian Kloess oekostrom Handels GmbH Laxenburger Straße Wien 1

2 Overview About oekostrom AG Flexibilizing renewable supply Hydro power Wind power Flexibilizing demand Large consumers Small consumers summary and outlook 2

3 About oekostrom AG 3

4 about oekostrom AG Pioneer in renewable power in Austria Founded in 1999 First to provide 100% renewable power to end consumers (2000) First to buy excess feed-in from PV producers (2003) Pioneer in wind generation in Austria 2015: market introduction of Plug-in PV Module 4

5 about oekostrom AG oekostrom AG today customers in Austria 1200 small excess feed-in suppliers (mostly PV) 30 MW of wind power in Austria and neighbouring countries 2 MW large-scale PV 1500 simon Plug-in PV Modules sold to customers in Europe 5

6 about oekostrom AG oekostrom AG (public limited company) Production Trading Sales oekostrom Produktions GmbH oekostrom Handels GmbH oekostrom GmbH homemade energy GmbH simon 6

7 Flexibilizing renewable supply 7

8 Flexibilizing renewable supply challenges of renwable supply integration: 16% 14% Hydro Feed-In Seasonal fluctuations Daily fluctuations Spot price interaction (merit-order-effect) Forecasting errors Balancing costs 12% 10% 8% 6% 4% 2% 0% 12% 10% 8% 6% 4% Base jan feb mar apr may jun jul aug sep oct nov dec Wind Feed-In Base 2% 0% jan feb mar apr may jun jul aug sep oct nov dec 8

9 Flexibilizing renewable supply challenges of renwable supply integration: Seasonal fluctuations Daily fluctuations wind forecast vs. generation: Forecast Generation Spot price interaction (merit-order-effect) Forecasting errors Balancing costs [MW]

10 Flexibilizing renewable supply challenges of renwable supply integration: Seasonal fluctuations Daily fluctuations Spot price interaction (merit-order-effect) Forecasting errors Balancing costs 10

11 The effect of wind & PV on the power price Generation & Load Spotmarket-Price EEX Phelix Quelle: Agora Energiewende [ /MWh]

12 wind site & market value correlation with total wind feed-in determines market value high correlation low value Geringe Korrelation high value Sites in Austria are less correlated than sites in Germany Regional differences within Austria Differences due to different turbine types Wind-centre Germany Installierte capacity: Germany: >40 GW Austria: 2,4 GW Profile value wind: Germany: ca. 90% of Base Austrian: ca. 95% of Base Wind-centre Austria Source: Fraunhofer IWES Quelle: IG Windkraft 12

13 Flexibilizing renewable supply challenges of renwable supply integration: Seasonal fluctuations Daily fluctuations Spot price interaction (merit-order-effect) Forecasting errors Balancing costs 13

14 Wind Forecasting & Balancing Energy Costs Balancing Costs DA ID Balancing Cost Wind IST Prognose day ahead Prognose intraday IST [ /day] Forecast vs. Feed-in Forecast vs. Feed-In [MW] Balancing Price AE Preis Balancing AE Preis Energy Price -200 [ /MWh]

15 Flexibilizing Wind Wind parks have to be linked to a control centre Live data is used to correct forecasting errors on the intraday market minimizing the lead time is crucial! in order to reduce balancing costs wind feed-in can be curtailed curtailment is based on the short-term forecasting error and the balancing price forecast generation Erzeugung forecast Prognose Leistung load t 1 curtailment Abregelung t 2 Zeit time 15

16 Flexibilizing Hydro hydro power plants have to be linked to a control centre Live data is used for forecasting in order to reduce balancing costs in the balancing group hydro feed-in can be curtailed Pooling to offer control reserve Tertiary control reserve Secondary control reserve 16

17 PV power in Austria Small-scale PV feed-in standard load profile no smart meters PV standard load profile 1,2 1 0,8 0,6 0,4 0, Large-scale PV feed-in: feed-in tarifs Have to be marketed by central semi-public company not available for commercial portfolios 17

18 Flexibilizing demand 18

19 Status Quarterly meter data available Large consumers forecast vs. load 1400 Forecast 1200 Load Market-based pricing is standard futures market Spot market balancing market Load shifting and curtailment is allready applied kw Load is pooled for the control reserve market Barriers to flexibilization: no real time data provided by the distribution grid operator Seperate metering devices with data connection required Economically feasible only for large consumers with shiftable loads (heating & cooling-processes) 19

20 small consumers Current satus Standard load profiles forecast vs. load 0,14 0,12 0,1 Standard Business peak business Household only few smart meters rolled out so far in Austria 0,08 0,06 0,04 0,02 Barriers to flexibilization: no economic incentive to shift loads due to standard load profiles no real time data available High effort: - metering devices for live data - variable tarif structure complex billing Low load shifting potential: - mainly heating 20

21 Summary & Outlook Renewable Supply: Challenges: - seasonal & daily fluctuations - balancing costs (wind) Flexibilization: - economically feasible and largely applied Demand: Challenges/Barriers: - standard load profiles (small consumers) - lack of smart meters (small consumers) - high effort & small potential (small consumers) Flexibilization: - economically feasible and applied for large consumers - economically infeasible for small consumers 21

22 Thank you for your attention! Dr Maximilian Kloess oekostrom Handels GmbH Laxenburger Straße Wien T F Maximilian.Kloess@oekostrom.at oekostrom.at 22