Energy Systems and Technologies for the Coming Century ...

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The constraint in Eq. (16) has the form of a second order cone and the solution to theoptimization problem constrained by Eq. (16) can be computed using Second Order ConeProgramming (SOCP).SimulationUsing Yalmip [26] we have simulated the scenario from section 4.1 but with the additionof uncertainties. The constraints on the cold room temperature and on balancing supply anddemand are formulated as probability constraints and implemented with SOCP. A simulationscenario is provided in Fig. 8. From the figure we note the confidence intervals shownas shaded areas around each of the trajectories. The solid lines are the expected outcomewhile the shaded areas are created by 10,000 simulations with random instances of the noisedescriptions. The 95% percentile was used both in the SOCP formulation and for plottingthe shaded areas. It is easily seen how the amount of back-off from the boundaries is justenough to account for the 95% confidence interval of the uncertainty descriptions for thesystem. Particular this can be seen in Fig. 8(b) where the total production is above the totalconsumption, T cr stays within the boundaries specified and T e ≤ T cr is satisfied. All with 95%probability. The solution here is less optimal compared to the one where we are allowed togo strictly to the boundaries but as this solution handles the always present uncertainties itis crucial for real life implementation.6 ConclusionTo enable more use of renewable energy flexible consumption must be established. UsingEconomic Optimizing MPC schemes for systems with thermal storage capabilities wedemonstrated both cost savings and the ability to deliver crucial services to the SmartGrid. Significant savings have been revealed e.g. 33% for heat pumps and around 10% insupermarket refrigeration. For the investigation of regulating power our perspective was seenfrom the refrigeration system but as it was demonstrated the involvement in the balancingmarket can be economically beneficial for the system itself while delivering crucial servicesto the Smart Grid. Different strategies for controlling the loads are available and startingwith simplified setups we have demonstrated their ability to efficient control of a VPPsetting. The cases have been expanded with realistic scenarios and inclusion of predictionand model uncertainties. The results showed how variations in outdoor temperature areutilized to minimize power consumption whereas exploiting variations in electricity pricestend to increase overall power consumption but at a lower cost. Hence, the goal is notsolely to minimize energy consumption but merely to use energy when it is available. Future(a) Power productions / consumption. P.G. #1 and 2 showthe power productions from the two power plants (blue) andtheir power set-points (red). C.R. #1 is power consumptionin the cold room.(b) ”Total Power” shows total power production (blue)versus the reference consumption (with (black) andwithout (red) the refrigeration). C.R. #1 shows thetemperature in the cold room T cr and the control signalT e .Fig. 8. Simulation of Power Management scenario. α = 0.5, H i ∼ N( ¯H i ,0.0055 2 ), r k ∼ N(¯r k ,0.7071 2 ), T amb ∼N( ¯T amb ,1.7321 2 ). The shaded bands show the 95% confidence interval from 10,000 random instances.Risø International Energy Conference 2011 Proceedings Page 174
work includes simulation of more units with realistic forecasts in a VPP framework andimplementation of the methods on real supermarket controllers.References[1] K. Richardson, D. Dahl-Jensen, J. Elmeskov, C. Hagem, J. Henningsen, J. A. Korstgård, N. B. Kristensen,P. E. Morthorst, J. E. Olesen, and M. Wier, Green energy - the road to a Danish energy system without fossilfuels. Danish Commission on Climate Change Policy, 2010.[2] Energinet.dk, “Potential and opportunities for flexible electricity consumption with special focus on individualheat pumps (in Danish),” Energinet.dk, The Danish TSO owned by the Danish Climate and Energy Ministry.,Tech. Rep., 2011.[3] S. L. Andersson, A. K. Elofsson, M. D. Galus, L. Göransson, S. Karlsson, F. Johnsson, and G. Andersson,“Plug-in hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany,” Energypolicy, vol. 38, no. 6, pp. 2751–2762, 2010.[4] H. Saele and O. S. Grande, “Demand response from household customers: Experiences from a pilot study innorway,” IEEE Transactions on Smart Grid, vol. 2, no. 1, pp. 90–97, 2011.[5] S. Han, S. Han, and K. Sezaki, “Development of an optimal vehicle-to-grid aggregator for frequencyregulation,” IEEE Transactions on Smart Grid, vol. 1, no. 1, pp. 65–72, 2010.[6] A. Molina-Garcia, M. Kessler, J. A. Fuentes, and E. Gomez-Lazaro, “Probabilistic characterization ofthermostatically controlled loads to model the impact of demand response programs,” IEEE Transactionson Power Systems, vol. 26, no. 1, pp. 241–251, 2011.[7] D. S. Kirschen, “Demand-side view of electricity markets,” Power Systems, IEEE Transactions on, vol. 18,no. 2, pp. 520–527, 2003.[8] R. Bush and G. Wolf, “Utilities load shift with thermal storage.” Transmission & Distribution World, p. 12,2009.[9] F. Oldewurtel, A. Parisio, C. N. Jones, M. Morari, D. Gyalistras, M. Gwerder, V. Stauch, B. Lehmann, andK. Wirth, “Energy Efficient Building Climate Control using Stochastic Model Predictive Control and WeatherPredictions,” in Proc. of American Control Conference 2010, 2010, pp. 5100–5105.[10] S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive control technology,” Control engineeringpractice, vol. 11, no. 7, pp. 733–764, 2003.[11] D. Sarabia, F. Capraro, L. F. S. Larsen, and C. de Prada, “Hybrid nmpc of supermarket display cases,” ControlEngineering Practice, vol. 17, no. 4, pp. 428–441, 2008.[12] K. Edlund, J. D. Bendtsen, and J. B. Jørgensen, “Hierarchical model-based predictive control of a power plantportfolio,” Control Engineering Practice, p. accepted, 2011.[13] J. B. Rawlings and R. Amrit, “Optimizing Process Economic Performance Using Model Predictive Control,”Nonlinear Model Predictive Control: Towards New Challenging Applications, pp. 119–138, 2009.[14] K. Edlund, “Dynamic Load Balancing of a Power System Portfolio,” Ph.D. dissertation, The Faculty ofEngineering and Science, Automation & ControlAutomation & Control, 2010.[15] M. Stadler, W. Krause, M. Sonnenschein, and U. Vogel, “Modelling and evaluation of control schemes forenhancing load shift of electricity demand for cooling devices,” Environmental Modelling & Software, vol. 24,pp. 285–295, 2009.[16] P. Nyeng, “System Integration of Distributed Energy Resources,” Ph.D. dissertation, DTU Electrical Engineering,2010.[17] T. G. Hovgaard, K. Edlund, and J. B. Jørgensen, “The Potential of Economic MPC for Power Management,”in 49th IEEE Conference on Decision and Control, 2010, 2010, pp. 7533–7538.[18] T. G. Hovgaard, L. F. S. Larsen, and J. B. Jørgensen, “Flexible and Cost Efficient Power Consumption usingEconomic MPC - A Supermarket Refrigeration Benchmark,” in 50th IEEE Conference on Decision and Control,2011, 2011, p. submitted.[19] R. Halvgaard, N. K. Poulsen, H. Madsen, and J. B. Jørgensen, “Economic Model Predictive Control for ClimateControl of Buildings in a Smart Energy System,” in 50th IEEE Conference on Decision and Control, 2011,2011, p. submitted.[20] M. J. Skovrup, “Thermodynamic and thermophysical properties of refrigerants - software package in borlanddelphi.” Department of Energy Engineering, Technical University of Denmark, Tech. Rep., 2000.[21] T. G. Hovgaard, L. F. S. Larsen, M. J. Skovrup, and J. B. Jørgensen, “Power Consumption in RefrigerationSystems - Modeling for Optimization,” in 4th International Symposium on Advanced Control of IndustrialProcesses, 2011, 2011, p. accepted.[22] H. Madsen and J. Holst, “Estimation of continuous-time models for the heat dynamics of a building,” Energyand Buildings, vol. 22, no. 1, pp. 67–79, 1995.[23] K. K. Andersen, H. Madsen, and L. H. Hansen, “Modelling the heat dynamics of a building using stochasticdifferential equations,” Energy and Buildings, vol. 13, pp. 13–24, 2000.[24] F. Oldewurtel, A. Ulbig, A. Parisio, and G. Andersson, “Reducing Peak Electricity Demand in BuildingClimate Control using Real-Time Pricing and Model Predictive Control,” in Conference on Decision andControl (CDC), Atlanta, GA, USA, 2010, pp. 1927–1932.[25] T. G. Hovgaard, L. F. S. Larsen, and J. B. Jørgensen, “Robust Economic MPC for a Power ManagementScenario with Uncertainties,” in 50th IEEE Conference on Decision and Control, 2011, 2011, p. submitted.[26] J. Löfberg, “Modeling and solving uncertain optimization problems in yalmip,” IFAC Proceedings Volumes(IFAC-PapersOnline), vol. 17, no. 1, 2008.Risø International Energy Conference 2011 Proceedings Page 175
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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
Page 128 and 129: ut for the total consumption, and i
Page 130 and 131: This gives the LBR an opportunity t
Page 132 and 133: concludes that the subsurface conta
Page 134 and 135: to constrain the evaluation. In add
Page 136 and 137: Fig. 2 SW-NE trending seismic profi
Page 138 and 139: Fig. 5. Petrophysical evaluation of
Page 140 and 141: ConclusionsThe Danish subsurface co
Page 142 and 143: Performance of Space Heating in aMo
Page 144 and 145: [5],[10],[11],[16],[18],[20],[26].
Page 146 and 147: 2/3 Sun1 Coal1/3 ElPower plantHP1 H
Page 148 and 149: 25002000Surplus(min(wind,prod-cons)
Page 150 and 151: 160CO 2 emission per unit heat prod
Page 152 and 153: The calculations have been based on
Page 154 and 155: Session 3B - smart gridsRisø Inter
Page 156 and 157: 2 Myopic Investments2.1 The Balmore
Page 158 and 159: Moreover, this model is formulated
Page 160 and 161: Reducing Electricity Demand Peaks b
Page 162 and 163: 3 Event Driven Scheduling Algorithm
Page 164 and 165: 5 Mapping Teletraffic Theory to Ene
Page 166 and 167: 6.2 Results and Performance Metrics
Page 168 and 169: Energy Efficient Refrigeration and
Page 170 and 171: Virtual Power PlantAggregatorSuperm
Page 172 and 173: C p,rC p,fT rT fW cφ sT a(UA) raHe
Page 174 and 175: Total PowerP.G. #1P.G. #2C.R. #1201
Page 176 and 177: the availability payment.Simulation
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 226 and 227: 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
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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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