Energy Systems and Technologies for the Coming Century ...

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Figure 9: The annual mean power density at 50 m for the Columbia Gorge test regionbased on annual mean wind-speed and variance modelling. Winds derived fromgeostrophic winds from CFSR data.Figure 10: The annual mean power density at 50 m for the top 10-percentile (windiest1/10 th of the area) for the Columbia Gorge test region based on annual mean wind-speedand variance modelling. Winds derived from geostrophic winds from CFSR data.To apply the estimated spatial variance of wind speed, wind data pertaining to the largetest areas is required. For this the purpose Climate Forecasting System reanalysis(CFSR) data was used, Saha et al (2010). The data is available at 0.5 degree resolutionand hourly. For this exercise geostrophic winds were calculated for year 2000 using 6-hourly data, for each of the 50 x 50 km blocks. The winds were transformed to 50 mabove surface level winds, using the geostrophic drag law and mean surface roughnessfor each 50 x 50 km block.Wind power density is given by[Eq. 1]Where u is wind speed and ρ is density. Performing Reynold’s decomposition for thetime and space variation of wind speed, one obtains a time and space mean wind powerdensity given by[Eq. 2]Risø International Energy Conference 2011 Proceedings Page 222
The [] represent spatial averaging and the overbar represent temporal averaging, and theprime and * represent temporal and spatial perturbations respectively. Such that[Eq. 3]When the mean wind speed is used (i.e. just the first term in Eq. 2] then the calculatedannual mean wind power density for the large test area is given by Figure 7. This is aserious underestimate as time variance contribution and spatial variance contributions aremissing.When the mean of the cube of the 6-hourly wind speed is used then the calculated annualmean wind power density is given by Figure 8. This estimate included the time variancepart at the coarse resolution that was missing in Figure 7. However the contribution towind power density from the spatial variance of wind speed within each 50 x 50 kmblock is still missing.Figure 9, shows the calculated wind power density using the mean of the 6-hourly windspeed, plus the contributions temporal and spatial variance of wind speed. The temporalvariance comes from a Weibull distribution fitted to the 6-hourly time series of winds;given by a scale factor and a shape factor. The temporal variance can be expressed interm of the shape factor. The spatial variance comes from sum of orography androughness variance contributions, as described here. This estimate shows somewhatincreased wind resources in areas of heterogeneous elevation and roughness lengthcompared to the estimate based on the mean of the cube of wind speed (Figure 8).Another feature of the method making use of the spatial variance of wind speed over the50 x 50 km blocks, is that the distribution of wind power density can be estimated, basedon an assumed distribution of wind speed distribution. In Badger et al (2010) a Gaussiandistribution of wind speed was assumed and found to suitable for most on-land cases,though less appropriate for coastal cases. Figure 10 shows the mean wind power densityfor the windiest 10-percentile of each 50 x 50 km block. Using this map we can see thatthe mountainous areas have high wind power density areas, compared to the blocks’mean power density. Whereas in the plain and offshore areas, the wind power density ofthe windiest sites is not much greater than the blocks’ mean wind power density.Such knowledge of the distribution of the wind power density is of great value, as itgives information about wind resources in a manner suitable for the exploitation of wind.Wind turbines are not randomly distributed. They are sited at the most favourable sites interms of wind resources and after consideration of relevant constraints. Unlike themethod described here, direct application of coarse resolution global wind datasets doesnot provide the distribution of the wind resource at a spatial scale smaller than the dataresolution.5 ApplicationThe datasets comprising the Global Wind Atlas will be created to suit the needs of thepolicy makers and energy planners. Through dialogue with the Integrated AssessmentModel (IAM) community, the required specifications of the Global Wind Atlas datasetswill be determined.The datasets will give both spatial and temporal variation of wind resource. Temporalvariation of wind resource can be of particular importance when consideration of energyRisø International Energy Conference 2011 Proceedings Page 223
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Energy Systems and Technologies for
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ContentsSessions overview 1Programm
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sessions overview08:00-09:00Coffee
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11:00 - 12:30 session 6A - Bioenerg
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ContactSustainable Energy, Technica
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Penetration of new energy technolog
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how decisions are made and by whom
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(9) is the logaritimic expansion of
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(16)we may finally write Eq(14) in
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Table 1: Fast track cases for new r
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Peterka V. Macrodynamics of technol
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Growth in clean and reliable energy
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Conclusion - an ambitious vision an
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"Spurring investments in renewable
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Integrating climate change adaptati
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transport, and finance. Energy prov
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northern South America, the Caribbe
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Table 1: Energy system adaptation s
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More generally, a number of no- or
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6 ReferencesADB, 2005. Climate proo
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Sailor, D.J., Smith, M., Hart, M.,
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Factors affecting stakeholder perce
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Another study conducted in Japan wa
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4 The effect of information on stak
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Furthermore, communication to stake
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Shackley S, McLachlan C, Gough C (2
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Figure 1: Potential CO 2 storage si
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The Upper Jurassic - Lower Cretaceo
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The majority of the individual stru
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Development. Project no. ENK6-CT-19
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Section 5 summarises the key issue
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Table 1. Efficiencies for new large
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In addition, district heating syste
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6.4 Model results from EFDA-TIMESIn
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Maisonnier, D., et al. (2007), Powe
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1Efficiency and effectiveness of pr
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3Stromerzeugung [TWh/a]250200150100
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54 Country-specific Lessons learned
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7100%90%80%quota fullfilment (%)70%
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[5] Haas R., et al: Efficiency and
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The development and diffusion of re
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a. Offshore wind in DenmarkDenmark
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applicants are invited to submit a
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nies that provide upstream and down
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for both technology users and produ
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Trade Disputes over Renewable Energ
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treatment to imported renewable ene
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In Canada, the federal government i
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grid access, subsidies and land off
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In 2010, China’s total wind insta
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Source: Renewables 2010 Global Stat
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Source: REN 21, Renewables 2010 Glo
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Source: adapted from REN21, 2010It
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their precise scope and nature diff
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60 daysBy 2 nd DSBmeetingConsultati
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discriminatory manner on domestic a
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the European Union, the North Ameri
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ended. China lost this case because
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As most countries in the world are
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Session 3A - Energy SystemsRisø In
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1 IntroductionWith the introduction
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occur. This is an undesirably burea
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This curve could be used as a new t
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ut for the total consumption, and i
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This gives the LBR an opportunity t
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concludes that the subsurface conta
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to constrain the evaluation. In add
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Fig. 2 SW-NE trending seismic profi
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Fig. 5. Petrophysical evaluation of
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ConclusionsThe Danish subsurface co
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Performance of Space Heating in aMo
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[5],[10],[11],[16],[18],[20],[26].
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2/3 Sun1 Coal1/3 ElPower plantHP1 H
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25002000Surplus(min(wind,prod-cons)
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160CO 2 emission per unit heat prod
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The calculations have been based on
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Session 3B - smart gridsRisø Inter
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2 Myopic Investments2.1 The Balmore
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Moreover, this model is formulated
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Reducing Electricity Demand Peaks b
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3 Event Driven Scheduling Algorithm
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5 Mapping Teletraffic Theory to Ene
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6.2 Results and Performance Metrics
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Energy Efficient Refrigeration and
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Virtual Power PlantAggregatorSuperm
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C p,rC p,fT rT fW cφ sT a(UA) raHe
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Total PowerP.G. #1P.G. #2C.R. #1201
Page 176 and 177: the availability payment.Simulation
Page 178 and 179: The constraint in Eq. (16) has the
Page 180 and 181: The FlexControl concept - a vision,
Page 182 and 183: can react on requests for power reg
Page 184 and 185: 3 ControlThe concept is based solel
Page 186 and 187: one hour to the next - correspondin
Page 188 and 189: 4 ProtectionThe protection of the p
Page 190 and 191: Session 4 - Efficiency Improvements
Page 192 and 193: the product portfolio point of view
Page 194 and 195: Figure 4: Principle flow sheet of t
Page 196 and 197: process information needed are seld
Page 198 and 199: AIRFINE®(Reference)MEROS®Ca(OH)2M
Page 200 and 201: 4 Conclusion & DiscussionThe Eco-Ca
Page 202 and 203: 1 Energy policy and EU directivesTh
Page 204 and 205: iomass CHP, heat pumps, electric bo
Page 206 and 207: Fig. 1: How to supply a growing hea
Page 208 and 209: Session 5A - Wind Energy IRisø Int
Page 210 and 211: The average cumulative growth rate
Page 212 and 213: turbines is expected to create a st
Page 214 and 215: O&MGridWindturbineSubstructureFigur
Page 216 and 217: Another idea is to harvest energy f
Page 218 and 219: 9. Shimon Awerbuch, Determining the
Page 220 and 221: engaged in wind energy assessment,
Page 222 and 223: treated in such a way to make them
Page 224 and 225: Figure 5: Map showing the estimated
Page 228 and 229: mix is required. For example diurna
Page 230 and 231: Session 5B - Wind Energy IIRisø In
Page 232 and 233: TABLE ISOME OF THE WIND TURBINE MAN
Page 234 and 235: China, has prompted all parties, in
Page 236 and 237: to the high shaft speed. Furthermor
Page 238 and 239: Superconductors must be kept cold b
Page 240 and 241: an annual increase in demand of 6%,
Page 242 and 243: Improved High Temperature Supercond
Page 244 and 245: our study to a single set of deposi
Page 246 and 247: 3.2 Yttrium-enriched YBCO thin film
Page 248 and 249: Fig. 5. AC-susceptibility dependenc
Page 250 and 251: The j C (B) dependence at 50 K for
Page 252 and 253: Session 6A - Bioenergy IRisø Inter
Page 254 and 255: The European politicians are faced
Page 256 and 257: The fast growing demand for biomass
Page 258 and 259: Figure 2: Avedøre Power Plant in C
Page 260 and 261: Figure 5: Concept for a sustainable
Page 262 and 263: The role of biomass and CCS in Chin
Page 264 and 265: 2.2.1 CCS Potential for ChinaCCS is
Page 266 and 267: 80%70%60%China's share ofglobal CO
Page 268 and 269: In general, if the CCS technology d
Page 270 and 271: with high implementation of CCS Chi
Page 272 and 273: The European Biofuels Policy: from
Page 274 and 275: aimed to promote agriculture-based
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(EC, 2009b), and internationally vi
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Session 6B - Bio Energy IIRisø Int
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The commercial scale production of
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the production process parameters,
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NutrientWaterAlgal cultureproductio
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Schenk, P.M., Thomas-Hall, S.R., St
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Liquid biofuels from blue biomassZs
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Even though final ethanol yields we
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Integrated Gasification SOFC Plant
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2.1 Modelling of SOFCThe SOFC model
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where g 0 , R an T are the specific
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hand side y-axis corresponds to eff
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The moister content for these three
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Use of Methanation for Optimization
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power production from utilizing the
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2.1 Model DescriptionThe developed
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Figure 3: Flow sheet of methanation
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efficiency and turbine inlet temper
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[14] DNA - A Thermal Energy System
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On the effectiveness of standards v
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educing new car emissions to 120 g
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In addition energy consumption E an
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coefficient γ for the impact of fu
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400200Change in energy (PJ)01980 19
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Figure 9 depict scenarios for the f
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What are customers willing to pay f
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Randomly, continuous up to ±50%Acc
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The likelihood for the model is giv
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eally means that this factor and no
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Table 5-1 Parameter estimates from
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Table 6-2 Willingness to pay for 10
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Session 12 - Energy for Developing
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Risø International Energy Conferen
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existing renewable energy technolog
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useful energyend energysecondary en
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parts or the means of enforcing war
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The development of micro-hydro proj
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Table 1. Electric power generation
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5 ReferencesBajgain, S., Shakya, I.
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IntroductionElectricity has become
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of 2008, 43.6% of the total populat
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Hilmand provinces have comparativel
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….. eq. 5LCOE is the net present
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case of Nepal, it is 5% (Trading ec
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ing down the cost of solar PV modul
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3.3 Serving the rural areas with na
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The levelized costs of electricity
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Figure 4. Variation in LCOE with si
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The analysis reveals that fuel cost
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In case of DG, we have looked with
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Gross, R., Blyth, W., Heptonstall,
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Mode of Transport to Work, Car Owne
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power parity (PPP) basis, GDP per c
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83,385 to 109,108 while the number
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other purposes so that reduced fare
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emissions studies is that they were
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where I (·) is the indicator funct
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increases, individuals acquire more
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while that of other means of transp
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into CO 2 . For all oil and oil pro
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impact. In general, income is found
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7 ReferenceAlperovich, G., Deutsch,
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Table 1: Definitions of Variables a
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Session 13 - Energy StorageRisø In
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2 Storage demand and resulting stor
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compressed air storages, demand sit
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Fig. 3: Options for underground sto
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3.5 Geological potential in EuropeF
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Sensitivity on Battery Prices and C
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3.1 Base caseA northern European en
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4 ResultsThe model is run on a comp
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Furthermore, a slight decrease in t
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Night time charging increases with
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Lithuanian Energy Institute, PSE In
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atteries, Compressed Air Energy Sto
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negative net load, can be expected
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25%Pct. of the year20%15%10%5%0.5-0
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800006000040000MWh200000Netload-200
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[4] B. V. Mathiesen and H. Lund, "C
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Compressed Air Energy Storage in Of
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compressed air caverns, corrosion-r
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Figure 3: Distribution of salt depo
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Figure 6: EWEA's 20 year Offshore N
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spinning reserves can be provided b
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7 Discussion and conclusionsThis pa
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Risø DTU is the National Laborator
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