Ee w09.1 m_ ee_ 4. renewables and the electricity industry (climate change) & organization of electricity markets_ liberalisation, competition
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Semelhante a Ee w09.1 m_ ee_ 4. renewables and the electricity industry (climate change) & organization of electricity markets_ liberalisation, competition
Semelhante a Ee w09.1 m_ ee_ 4. renewables and the electricity industry (climate change) & organization of electricity markets_ liberalisation, competition (20)
Mais de Silvester Van Koten
Mais de Silvester Van Koten (20)
Ee w09.1 m_ ee_ 4. renewables and the electricity industry (climate change) & organization of electricity markets_ liberalisation, competition
- 3. • Literature – Taylor, G., Tanton, T. 2012. The hidden cost of wind electricity. American tradition institute. http://www.atinstitute.org/wp-content/uploads/2012/12/Hidden-Cost.pdf – Hirth, L. 2013. The optimal share of variable renewables. How the variabiity of wind and solar power affects their welfare-optimizing deployment. FEEM Working Paper 90.2013. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2351754 – Kirchen. Chapter 1
- 4. • How expensive are renewables after all?
- 5. The technology behind crystalline silicon solar cells has profited from extensive developments in the multi- billion-dollar microelectronics industry. About 20 years ago, a kilowatt of solar energy cost about 50 euro cents ($0.69) to produce, today in Germany it's about 10 euro cents - while in sunny regions it's between 5 and 8 euro cents. So worldwide, we're totally competitive with, and often even cheaper than, fossil fuels. http://www.dw.de/at-the-floodgates-of-a-solar-energy-boom/a-17259267 Professor Eicke R. Weber is the Director of the Fraunhofer Institute for Solar Energy Systems ISE and professor of physics/solar energy at the Department of Mathematics and Physics and the Department of Engineering respectively at the University of Freiburg, Germany. http://www.ise.fraunhofer.de/en/about-us/director-and-division-direc Can this be true? Why give subsidies still?
- 9. 2013 €1-2/W Capital costs of PV have fallen with 85%-90% since 1998 (from $12 -> $1-2 per Wpc)
- 12. • Refinements: 1. Explicitly model intermittent (solar+wind) + backup 2. Model the value of electricity produced by intermittent generation
- 13. 1. Explicitly model intermittent (solar+wind) + backup
- 14. • Wind and solar should better be seen as: – Wind and solar + gas backup (round 90%) – Wind and solar + coal backup (round 90%)
- 15. • Taylor, G., Tanton, T. 2012. The hidden cost of wind electricity. American tradition institute. http://www.atinstitute.org/wp- content/uploads/2012/12/Hidden-Cost.pdf
- 16. Source: Energy Information Administration 2012 Annual Energy Outlook
- 20. Source: Energy Information Administration 2012 Annual Energy Outlook
- 21. • This report has shown that the cost wind electricity is not approaching parity with conventional sources, and is unlikely to reach parity – unless the price of natural gas, the price of coal and the capital cost of nuclear facilities were all to increase dramatically.
- 22. • Study applies to US – 3% of energy generated by wind • Germany – Has higher wind penetration – 7~8% of energy generated by wind • What is the effect of an increase in wind penetration on costs?
- 23. 2. Model the value of electricity produced by intermittent generation
- 24. • Hirth, L. 2013. The optimal share of variable renewables. How the variabiity of wind and solar power affects their welfare- optimizing deployment. FEEM Working Paper 90.2013. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2351754
- 26. Levelized costs: Levelized value: Where (exact): Over lead times Over timesOver places Approximation
- 28. • We define profile costs as the price spread between the load- weighted and wind-weighted day-ahead electricity price for all hours during one year. Profile costs arise because of two reasons. On the one hand, demand and VRE generation are often (positively or negatively) correlated. A positive correlation, for example the seasonal correlation of winds with demand in Western Europe, increases the value of wind power, leading to negative profile costs. • On the other hand, at significant installed capacity, wind “cannibalizes” itself because the extra electricity supply depresses the market price whenever wind is blowing. In other words, the price for electricity is low during windy hours when most wind power is generated. Fundamentally, profile costs exist because electricity storage is costly, recall physical constraint i). A discussion of profile costs and quantitative estimates are provided by Lamont (2008), Borenstein (2008), Joskow (2011), Mills & Wiser (2012), Nicolosi (2012), Hirth (2013), and Schmalensee (2013).
- 29. • wind “cannibalizes” itself because the extra electricity supply depresses the market price whenever wind is blowing
- 30. Remember how wind was cannabalized in the model of lecture W07.2F
- 31. • Profile cost: wind produces most when the price is low
- 34. • Profile cost: wind produces most when the price is low • Balancing cost: forecasting errors – wind produces is “out-of-balance”, produces more or less than promised – Cycling costs of plants
- 35. • Profile cost: wind produces most when the price is low • Balancing cost: forecasting errors – wind produces is “out-of-balance”, produces more or less than promised – Cycling costs of plants
- 36. European Climate Foundation 2050: • Increase from 34 GW to 127 GW • 400% increase The future of the EU transmission network Authors wont model the losses due to location (grid costs) were not modeled
- 37. • Profile cost: wind produces most when the price is low • Balancing cost: forecasting errors – wind produces is “out-of-balance”, produces more or less than promised – Cycling costs of plants • Grid costs: wind produces far away from demand – Cost of additional transmission
- 38. • Cost change with penetration (cannabilization effect)
- 41. • So far are theoretical models, what do the numbers tell us? – Use of a dispatch model, feeding realistic data – Northwestern Europe: Germany, Belgium, Poland, The Netherlands, and France
- 42. 3% 20% Wind
- 43. Wind
- 44. If wind blew constantly If wind was variable but perfectly predictable True situation Note that losses due to location (grid costs) were not modeled LCoE use the simplification that wind blows constantly. This simplification seems to explains the wide gap in the debate on the usefulness of wind.
- 45. Assuming 30% fall in wind cost wrt today
- 46. • What is remarkable about this curve? • Optimal wind share with doubling of: – Coal price -> increases – Gas price -> decreases
- 47. • Doubling coal prices -> optimal wind up by 5% points • Halving gas prices (“shale gas”) -> optimal wind down • Doubling gas prices -> optimal wind down.
- 48. • Doubling coal prices -> optimal wind up by 5% pointsfive percentage points (Figure 17). • Lowering gas prices by half (“shale gas”) has a similarly expected effect,dramatically lowering optimal wind deployment. • Surprisingly however, doubling gas prices reduces the optimal wind share. – As in the case of CO2 pricing, the reason for this seemingly counterintuitive result can be found in the capital stock response to the price shock. Higher gas prices induce investments in hard coal, which has lower variable costs, reducing the value of wind power and its optimal deployment.
- 49. Solar • Even at 60% cost reduction, the optimal solar share is below 4% in all but very few cases. • Reason: the marginal value of solar power drops steeply with penetration, – Even more so than wind. Why? – Because solar radiation is concentrated in few hours • In line with earlier studies (Nicolosi 2012, Mills & Wiser 2012, Hirth 2013). Now: €1-2/W Hirth + 60% cost reduction:€0.6/WHirth uses€1.6/W
- 50. 2013 €1-2/W
- 56. • Coal supplies more than 5 percent of energy – 1840 • fossil fuels (coal) surpasses use of biomass (wood and charcoal) – 1885 USA – 1875 France – 1901 Japan – 1930 U.S.S.R – 1965 China – 1970 India
- 57. • Oil supplies more than 5 percent of energy – 1915 • Oil surpasses use coal – 1964
- 59. • Organization of electricity markets 1. Liberalization 2. Transmission pricing (Nodal versus Zonal)
- 60. • Organization of electricity markets 1. Liberalization Kirchen. Chapter 1
- 62. Allow Independent Power Producers (IPP) (US: PURPA law 1978)
- 65. Add retail competition Close to the present model in EU (but wholesale market and transmission system are separate)
- 66. Unbundling was a process over several years S. van Koten, A. Ortmann / Energy Economics 30 (2008) 3128–3140
- 69. Unbundling was a process over several years S. van Koten, A. Ortmann / Energy Economics 30 (2008) 3128–3140
- 70. Unbundling was a process over several years S. van Koten, A. Ortmann / Energy Economics 30 (2008) 3128–3140 http://www.cerge-ei.cz/pdf/pb/PB13.pdf
Notas do Editor
- Figure 2: From the average electricity price to wind’s market value (illustrative). At high penetration, timing and location as well as forecast errors typically reduce the market value
- Figure 2: From the average electricity price to wind’s market value (illustrative). At high penetration, timing and location as well as forecast errors typically reduce the market value
- • This graph from Aptech Engineering Services shows the different types of load cycles (megawatts versus time) that a unit could be exposed to and the relative damage that occurs each cycle. • Three different low load cycling points LL1, LL2 and LL3 are defined on this slide. Each point affects the degree of thermal cycle transient experienced during a load following event because the metal incurs larger temperature changes. • Three on/off cycles are defined based on hours off-line (hot, warm and cold starts) with the worst damage occurring during a cold start cycle. • Definition of Equivalent Hot Start – Standardized in a 1985 EPRI study of Haynes Unit 5 (Supercritical 350 MW unit) • Load follows each have relatively low damage costs but because there are so manyof them, the cumulative impact of manyload follows leads to the ypy damage of an equivalent hot start.
- Figure 2: From the average electricity price to wind’s market value (illustrative). At high penetration, timing and location as well as forecast errors typically reduce the market value
- Figure 2: From the average electricity price to wind’s market value (illustrative). At high penetration, timing and location as well as forecast errors typically reduce the market value
- Figure 2: From the average electricity price to wind’s market value (illustrative). At high penetration, timing and location as well as forecast errors typically reduce the market value
- Figure 3: Average electricity price and market value as a function of the quantity of wind power in the system. At low penetration, the wind market value can be higher than the average power price, because of positive correlation between generation and load.
- Figure 6: Wind’s market value falls with penetration. The intersection between LEC and market value gives the optimal share (section 2.4). At LEC of 68 €/MWh the optimal share is around 3%; if generation costs fall by 30%, the optimal share is about 20%.
- Figure 7: The optimal share of wind power in total electricity consumption as function of wind power cost reduction under benchmark assumptions. In Northwestern Europe, the share increases from 2% to 20%
- If wind generation was constant, its optimal share would rise above 60%. The impact of forecast errors is much smaller: switching off the reserve requirement and balancing costs increases the optimal share by only eight percentage points. This endorses previous findings that temporal variability is significantly more important for welfare analysis than uncertainty-driven balancing
- Figure 17: The effect of fuel price shocks. As expected, lower gas prices reduce and higher coal prices increase the optimal wind share. However, higher gas prices reduce the optimal share. The reason is the investments in baseload technologies triggered by high gas prices.