Energy Systems and Technologies for the Coming Century ...

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Table 1: Definitions of Variables and Summary StatisticsVariables Definition %Dependent:CarBus (Ref.)OthersMotor VehiclesCompany VanWalkingTaxisCompany CarPrivate CarsBus owned by private and public individualsOther means of transport27.9456.0915.9712.371.801.200.400.20Independent:Ln (Age)FemaleMale (Ref.)Income:Below Rs. 10,000Rs. 10,001 – Rs. 15,000Rs. 15,001 – Rs. 30,000Rs. 30,001 – Rs. 45,000Rs. 45,001 – Rs. 65,000Above Rs. 65,000 (Ref.)RuralUrban (Ref.)Education:Secondary (Ref.)DiplomaBachelor DegreeOther QualificationsLn (Household Size)EbenePort Louis (Ref.)Travel ExpensesLn (Time To Work)Source: ComputedNatural logarithmic of an individual’s ageGenderIndividuals’ income groupResidential locationAcademic QualificationNatural logarithmic of household sizeWorking placeWeekly travel expensesDuration of trip from house to work10038.1261.8816.9734.1338.727.981.201.0024.8575.1539.5217.9631.9410.5810015.5784.43100100Risø International Energy Conference 2011 Proceedings Page 404
Table 2: MNL Model of Mode of Transport to WorkVariableCarOthersCoefficient Standard Error Coefficient Standard ErrorConstantLn(Age)FemaleMale (Ref.)Income:Below Rs. 10,000Rs. 10,001 – Rs. 15,000Rs. 15,001 – Rs. 30,000Rs. 30,001 – Rs. 45,000Rs. 45,001 – Rs. 65,000Above Rs. 65,000 (Ref.)RuralUrban (Ref.)Education:Secondary (Ref.)DiplomaBachelor DegreeOther QualificationsLn (Household Size)EbenePort Louis (Ref.)Travel ExpensesLn (Time To Work)13.447560.932767-0.2836241-14.41533-13.89378-13.90392-13.27501-12.63246-0.1431156-0.41163150.24890880.77748490.01625420.74275470.0067411-1.8874413.618034*0.75892970.34023511.115669*1.067715*0.9314132*1.076855*1.249028*0.37218160.48792790.40161720.65538760.46715840.343072**0.0015152*0.2677421*1.4616862.456675-2.277146-1.127713-1.664311-1.739124-1.152838-16.70235-0.44614870.9234647-0.23319490.9237205-0.38657391.4686-0.0092009-1.9664883.0887610.6800624*0.5997164*1.5040471.6078041.466481.5719391.973758*0.60974390.51148950.5979318*0.99111860.53390640.6037197**0.0028367*0.4924752*Number of Observations501Log PseudolikelihoodWald χ 2 (30)Prob > χ 2Pseudo R 2-190.69571042.930.0000*0.6090Source: Computed. Note: The baseline category for the mode of transport is bus. Ref. symbolizes the referencecategory. * and ** denote 1% and 5% significance level respectively. Standard errors are computed with theHuber/White/sandwich robust variance estimates.Table 3: OLS Model of natural logarithmic of CO 2 Emissions per GallonVariable Coefficient Standard ErrorConstantLn(Age)Income:Below Rs. 10,000Rs. 10,001 – Rs. 15,000Rs. 15,001 – Rs. 30,000Rs. 30,001 – Rs. 45,000Rs. 45,001 – Rs. 65,000Above Rs. 65,000 (Ref.)DieselGasoline (Ref.)Ln (Distance)4.969187-0.2729997-0.1296833-0.2654535-0.0838857-0.012245-0.05835660.45406710.28248110.6234562*0.1328992**0.35355280.34402290.32996150.33122990.33924380.1433961*0.0619785*Number of Observations140F(8, 131)4.29Prob > FR 20.0000*0.2368Source: Computed. Note: The baseline category for the mode of transport is bus. Ref. symbolizes the referencecategory. *, and ** denote 1% and 5% and significance level respectively. Standard errors are computed with theHuber/White/sandwich robust variance estimates.Risø International Energy Conference 2011 Proceedings Page 405
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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
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the availability payment.Simulation
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The constraint in Eq. (16) has the
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The FlexControl concept - a vision,
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can react on requests for power reg
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3 ControlThe concept is based solel
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one hour to the next - correspondin
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4 ProtectionThe protection of the p
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Session 4 - Efficiency Improvements
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the product portfolio point of view
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Figure 4: Principle flow sheet of t
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process information needed are seld
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AIRFINE®(Reference)MEROS®Ca(OH)2M
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4 Conclusion & DiscussionThe Eco-Ca
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1 Energy policy and EU directivesTh
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iomass CHP, heat pumps, electric bo
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Fig. 1: How to supply a growing hea
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Session 5A - Wind Energy IRisø Int
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The average cumulative growth rate
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turbines is expected to create a st
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O&MGridWindturbineSubstructureFigur
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Another idea is to harvest energy f
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9. Shimon Awerbuch, Determining the
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engaged in wind energy assessment,
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treated in such a way to make them
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Figure 5: Map showing the estimated
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Figure 9: The annual mean power den
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mix is required. For example diurna
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Session 5B - Wind Energy IIRisø In
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TABLE ISOME OF THE WIND TURBINE MAN
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China, has prompted all parties, in
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to the high shaft speed. Furthermor
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Superconductors must be kept cold b
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an annual increase in demand of 6%,
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Improved High Temperature Supercond
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our study to a single set of deposi
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3.2 Yttrium-enriched YBCO thin film
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Fig. 5. AC-susceptibility dependenc
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The j C (B) dependence at 50 K for
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Session 6A - Bioenergy IRisø Inter
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The European politicians are faced
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The fast growing demand for biomass
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Figure 2: Avedøre Power Plant in C
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Figure 5: Concept for a sustainable
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The role of biomass and CCS in Chin
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2.2.1 CCS Potential for ChinaCCS is
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80%70%60%China's share ofglobal CO
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In general, if the CCS technology d
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with high implementation of CCS Chi
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The European Biofuels Policy: from
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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
Page 358 and 359: Table 1. Electric power generation
Page 360 and 361: 5 ReferencesBajgain, S., Shakya, I.
Page 362 and 363: IntroductionElectricity has become
Page 364 and 365: of 2008, 43.6% of the total populat
Page 366 and 367: Hilmand provinces have comparativel
Page 368 and 369: ….. eq. 5LCOE is the net present
Page 370 and 371: case of Nepal, it is 5% (Trading ec
Page 372 and 373: ing down the cost of solar PV modul
Page 374 and 375: 3.3 Serving the rural areas with na
Page 376 and 377: The levelized costs of electricity
Page 378 and 379: Figure 4. Variation in LCOE with si
Page 380 and 381: The analysis reveals that fuel cost
Page 382 and 383: In case of DG, we have looked with
Page 384 and 385: Gross, R., Blyth, W., Heptonstall,
Page 386 and 387: Mode of Transport to Work, Car Owne
Page 388 and 389: power parity (PPP) basis, GDP per c
Page 390 and 391: 83,385 to 109,108 while the number
Page 392 and 393: other purposes so that reduced fare
Page 394 and 395: emissions studies is that they were
Page 396 and 397: where I (·) is the indicator funct
Page 398 and 399: increases, individuals acquire more
Page 400 and 401: while that of other means of transp
Page 402 and 403: into CO 2 . For all oil and oil pro
Page 404 and 405: impact. In general, income is found
Page 406 and 407: 7 ReferenceAlperovich, G., Deutsch,
Page 410 and 411: Session 13 - Energy StorageRisø In
Page 412 and 413: 2 Storage demand and resulting stor
Page 414 and 415: compressed air storages, demand sit
Page 416 and 417: Fig. 3: Options for underground sto
Page 418 and 419: 3.5 Geological potential in EuropeF
Page 420 and 421: Sensitivity on Battery Prices and C
Page 422 and 423: 3.1 Base caseA northern European en
Page 424 and 425: 4 ResultsThe model is run on a comp
Page 426 and 427: Furthermore, a slight decrease in t
Page 428 and 429: Night time charging increases with
Page 430 and 431: Lithuanian Energy Institute, PSE In
Page 432 and 433: atteries, Compressed Air Energy Sto
Page 434 and 435: negative net load, can be expected
Page 436 and 437: 25%Pct. of the year20%15%10%5%0.5-0
Page 438 and 439: 800006000040000MWh200000Netload-200
Page 440 and 441: [4] B. V. Mathiesen and H. Lund, "C
Page 442 and 443: Compressed Air Energy Storage in Of
Page 444 and 445: compressed air caverns, corrosion-r
Page 446 and 447: Figure 3: Distribution of salt depo
Page 448 and 449: Figure 6: EWEA's 20 year Offshore N
Page 450 and 451: spinning reserves can be provided b
Page 452 and 453: 7 Discussion and conclusionsThis pa
Page 454: Risø DTU is the National Laborator
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