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Spatial and temporal balancing effects

Spatial and temporal balancing effects. Reducing the Variability of Wind and Solar Power Generation in Europe. Stockholm, 22 June 2011 | Cosima Jaegemann. Motivation. Common Knowledge:. Subject of Investigation: Balancing effect of aggregating wind and solar power.

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Spatial and temporal balancing effects

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  1. Spatial and temporal balancing effects Reducing the Variability of Wind and Solar Power Generation in Europe • Stockholm, 22 June 2011 | Cosima Jaegemann

  2. Motivation • Common Knowledge: Subject of Investigation: Balancing effect of aggregating wind and solar power Wind and solar power are variable-output sources Geographical dispersion and aggregation of wind farms results in a smoothing of short-term wind power fluctuations Short-term wind power fluctuations balance each other out, as they are not perfectly positively correlated

  3. Outline • Data • Model ofMean-Variance-Optimization • Results • Conclusion

  4. 38 Data 12 53 10 13 52 • Analysis is based on 4-year (2007-2010) hourly wind speed and solar radiation data for • 54 onshore and 12 offshore wind power regions • 54 solar power regions throughout the EU27 plus NO and CH (EU27++). • Hourly power output (and capacity factor) per region and technology was calculated for a • 5MW onshore/ offshore wind turbine and • 200W solar photovoltaic panel. • Resource potential limitations were considered by the formulation of capacity constraints per region. 37 6 11 51 32 21 3 10 33 26 9 2 5 27 9 28 8 39 18 35 22 1 36 40 3 19 7 4 14 8 34 45 20 1 2 44 25 54 4 15 43 16 46 12 29 17 6 47 5 11 30 41 48 49 31 23 42 50 24 7

  5. MethodologyModel ofMean-Variance-Optimization • wi*: optimal share of total portfolio capacity installed in region i σP := Portfolio standarddeviation ρij:= Correlationcoefficientofhourly power output [-1 ≤ ρij ≤ 1] RP := Meanportfoliocapacityfactor ci:= Capacityconstraintin % of total capacity a) Wind portfolios: 85 GW (EU27++ 2010) b) Wind and solar power portfolios: 100 GW(EU27++ 2010) • Efficient frontier

  6. MethodologyEfficientfrontier Set of optimal portfolios (Efficientfrontier) MeanCapacityFactor Space ofpossibleportfolios Standard Deviation

  7. Efficient Frontier – Optimal Wind Power PortfolioBalancing effect of geographical aggregation Output-variability can be reduced by an aggregation of wind power on a continental rather than national scale

  8. Efficient Frontier – Optimal Wind and Solar Power PortfoliosBalancing effect of geographical and technological aggregation Output-variability be further reduced by aggregating wind and solar power beyond what is possible for wind power alone

  9. Efficient frontier – Optimal Wind and Solar Power PortfoliosOptimal vs. Actual and Planned Portfolios Actual (2010) and planned portfolios (2020) of wind and solar power in the EU27++ do not belong to the set of optimal portfolios

  10. Load-Duration-Curve – Wind powerBalancing effect of geographical aggregation (4 x 8760 h) Flattening of the Load-Duration-Curve

  11. Load-Duration-Curve – Wind and Solar Power Balancing effect of geographical and technological aggregation Further flattening of the Load-Duration-Curve

  12. Conclusion Geographic dispersion of wind and solar photovoltaic capacities throughout Europe can smooth out fluctuations in wind and solar power generation and reduce the associated cost of balancing and reserve power Prerequisite Well-interconnected grid Aim of this analysis Illustration of balancing effects of geographical and technological aggregation No overall electricity-system optimization

  13. Thankyouforyourattention. Do youhaveanyquestionsorsuggestions? Cosima Jaegemann Institute ofEnergy Economics atthe University of Cologne (EWI) Alte Wagenfabrik Vogelsanger Str. 321 50827 Cologne, Germany Tel. +49 – 221 27729 300 E-mail: cosima.jaegemann@uni-koeln.de

  14. Back-up

  15. Literature • Drake, B., Hubacek, K. (2007): What to expect from a greater geographic dispersion of wind farms?- A risk portfolio approach. Energy Policy, Vol. 35, No 8, 3999-4008. • Markowitz, H. (1952): Portfolio Selection. Journal of Finance, Vol. 7, No 1, 77-91. • Milligan, M., Artig, R. (1998): Reliability Benefits of Dispersed Wind Resource Development; National Renewable Energy Laboratory Report No. CP-500-24314. • Palmintier, B. L, Hansen, L., Levine, J. 2008. Spatial and Temporal Interactions of Solar and Wind Resources in  the Next Generation Utility. Proceedings of the American Solar Energy Society (ASES) Solar 2008 Conference. • Roques, F., Hiroux, C., Saguan Marcelo (2010). Optimal wind power deployment in Europe: a portfolio approach. Energy Policy, Vol. 38, No 7, 3245-3256. • Fürsch, M., Gollig, C., Nicolosi, M., et al., April 2010, EUROPEAN RES-E POLICY ANALYSIS – A model-based analysis of RES-E deployment and its impact on the conventional power market, EWI • EEA (2009): Europe's onshore and offshore wind energy potential. An assessment of environmental and economic constraints, EEA Technical report, No 6/2009.

  16. Example Optimal Wind Power Portfolio (85 GW)

  17. Example Optimal Wind and Solar Power Portfolio (100GW)

  18. Wind Regions OnshoreOffshore 1 2 4 3 1 38 6 5 12 8 7 9 2 53 10 3 10 11 13 52 13 12 37 17 16 14 15 6 4 11 20 18 19 51 5 22 21 32 21 6 3 24 23 10 33 26 9 25 7 2 5 27 28 27 26 9 28 8 39 18 31 30 29 35 22 8 1 36 40 32 3 19 7 4 33 14 8 34 45 34 20 1 2 44 25 35 36 54 4 15 43 16 37 38 46 12 9 29 39 40 10 17 6 47 5 42 41 11 30 41 44 43 11 48 49 31 45 23 42 46 50 24 50 49 48 47 7 51 53 52 12 54

  19. Solar PhotovoltaicRegions PV 1 2 3 4 5 6 12 7 8 37 9 51 52 10 13 11 13 12 36 15 14 17 16 11 50 19 20 18 32 22 21 21 10 23 24 23 26 9 25 27 28 18 28 26 27 34 22 38 39 35 29 31 30 3 19 7 32 14 4 8 44 33 20 1 2 25 34 35 42 53 15 43 45 36 37 29 16 39 38 6 46 5 17 41 40 30 40 43 42 48 47 31 23 44 41 45 49 24 49 46 48 47 51 50 52 53

  20. MethodologyModel Framework • Ri : = capacity factor of a 5MW onshore/offshore wind turbine / 200W solar PV panel in region i [%] • wi: = proportion of total portfolio capacity in region i [%] • RP: = ∑wiRi = portfolio mean capacity factor • σi : = standard deviation of the hourly power output in % of the nominal capacity in region i • σ p: = portfolio standard deviation • ρij : = correlation coefficient of hourly power output in % of the nominal capacity between region i and j • ci : = capacity restriction expressed as % of total portfolio capacity

  21. Wind Power Performance Curve

  22. Solar power Assumptions: • Capacity : 200 W • Required space: 0,007 m²/W • Module efficiency: 15 % Performance ratio: 85 %

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