Fundamental of Energy Systems II

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86025_3

• Fundamental of Energy Systems II

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Energy Systems Constraints Integration Demand -
Supply

• Physical
• Matching form value
• Matching spatial scales
• Matching temporal scales
• Societal - Availability of
• Capital
• Information
• Incentives
• Policy attention

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Energy Constraints

• Matching form value need (and limits) of
  conversion (e.g. radiant?mechanical energy)
• Spatial mismatch supply-demand World trade in
  fuels gt1000 Billion (2003 data)
• Temporal mismatch supply-demand (load curves)
  Need for storage interconnection (capital
  intensive)
• Magnitude mismatch supply-demand Power
  densities, e.g. renewables vs. urban energy use

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Energy Constraints I Space

• Fossil fuels Deposits determined by nature
• Extremely uneven distribution of reserves
  OilltCoalltGas
• Transport costly ElectricityltLNGltGasltCoalltOil
• Inventory (storage) minimization increases
  vulnerability (only 90 days oil use in strategic
  reserves)
• Renewables Land availability as major spatial
  constraint

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http//www.bp.com/centres/energy2002/gas/trademove
ment.asp
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US Gas Pipeline Transport Flows
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World Oil Trade in 2004(net trade of crude and
oil products)
Million Tons Billion US
USA -590.8 -165.3
Europe -524.0 -147.1
Japan -254.0 -42.0
China -149.7 -40.1
Other importers -862.2 -242.1

Middle East 959.7 269.4
Africa 319.7 89.8
Ex-USSR 314.3 88.2
Latin America 197.7 55.5
Other exporters 589.3 165.4

World Trade 2380.7 668.4
Source BP Statistical Review of World energy 2005
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How Much Do Fuels Costs the World?

• The power of back-of-the envelope calculations
• World crude oil trade 2.6 109 tons
• 1000 109
• World oil use 3.8 Gtoe
• World energy use 10 Gtoe
• World GDP 45 1012
• Rough (upper) estimate is ?

2005 data from BP Stat. Review 2006 and IMF
2006using (high) oil prices lt10using avg.
energy costs and long-term average oil prices
3-5 of GWP
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Energy Constraints II TimeWhy Electricity Load
Curves Matter

• Electricity cant be stored at reasonable costs
  storage of other energy forms also costly
• Therefore Electricity must be generated
  whenever demand arises
• Therefore Need enough installed generation
  capacity to meet peak demand (plus reserve
  margin), even though system peaks very rarely
  (few hrs/yr)
• Result Some plants run only a few hours per year
  (economics! efficiency!)
• Peak load versus average load Times 3
• Reserve margin 10-30 of peak load
• Fractality Daily, weekly, monthly, yearly load
  curves

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Cum. Annual Electricity Load Curveakin load
duration curve (US)
Pumped hydro, gas turbines
Gas combined cycle, coal
MEDIUM LOAD PLANTS
Power demand
Hydropower (rivers), nuclear, coal
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Heat Load Curve of an Austrian Hotel with
Electricity Cogeneration(stacked boilers due
to inefficiency of low capacity
utilizationNever design a heating system based
on peak load!)
x
kWth
electric boiler 50 kWth
hours per year
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Daily Load Curves Tokyo
Source Mogouro et al., 2002
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Linking Space and Time in Tokyo Power Density of
Demand
Source Mouguro et al., 2002
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Tokyo Electricity Demand vs. Solar Energy
Supply
kWh
100000
Electricity demand
10000
Solar radiation
1000
Solar radiation converted to electricity
100
10
km2
1
0
1000
2000
3000
Source TEPCO NIES, 2002
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Spatial Power Densities of Energy Production and
Consumption
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Energy Density Example IHambach Lignite Mine
Germany
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Large Scale Opencast Brown Coal
Mining Germany New West Havenfor scale
comparison
FORTUNAGARSDORF
BERGHEIM
HAMBACH
NuclearResearchCentre
INDEN
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An IE Perspective on Hambach

• The 1 TW hole
• 3000 billion tons lignite reserves 1 BTCE 1
  TWyr 30 EJ
• 8500 ha mined between 1980-2040(all reclaimed)
• Largest man-made machines in the world(240,000
  m3/day bucket wheel excavators)
• 2004 40 million tons lignite500 million
  tons overburden removed600 million tons water
  pumped1 ton of lignite (2 bbls of oil) 30
  tons of material handling

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Energy/Carbon Densities Example IIC
sequestered by fuel substitution vs. forest sinks
and sources
Source Science 317(17 August 20907)902
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Power Densities II

• Spatial mismatch between demand and supply
  requires imports (domesticinternational)
• gt80 of world energy use in urban high demand
  density areas
• Power density mismatch biggest for renewables
  (except large hydro)
• Hence Renewables best suited for niche markets
  low population/energy density areas (rural),

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Europe Power Density of Demand (W/m2) Grey
areas indicate where biomass or wind can satisfy
local energy demand (lt 0.5 W/m2)
England Energy demand footprint larger than
country area
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Orders of Magnitude

• 1 W/m2 upper energy yield of
  biomass/wind
• 10 kWh/m2 resulting annual energy yield
• 30 MJ/m2
• 300 GJ/ha
• 10,000 liters/ha liquid fuel with
  100 conversion efficiency
• 1,000 gal/acre (for the non-metric inclined)
• 10 toe/ha tons oil equivalent
  yield (max. yield, no losses!) 3 toe/ha
  realistic yield incl. conversion losses
• US transport energy use 600 Mtoe 200
  million ha 100 of all cropland
• World energy use (PE) 10 Gtoe 3000 million
  ha 200 of cropland area, or 75 of forests

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The Economics of Land-use ConflictsBioenergy
and Agricultural Crop Yields(typical, rounded
values)
Crop Yield per ha Producer Price Yield /ha
Wheat Brazil 2 t 110 /ton 220
Soybeans Brazil 2.5 t 150 /ton 380
Rapeseed Germany 3.5 t 170 /ton 600
Sugarcane Brazil 68 t 10 /ton 680
Wheat France 7 t 100 /ton 700
Cotton USA 2 t 420 /ton 840
Rice China 6 t 140 /ton 840
Tobacco India 1.5 t 560 /ton 840
Tobacco USA 2.4 4200 /ton 10,000
Low yield/price biomass 3 t (10 GJ/t) 3 /GJ 90
Med. yield/price biomass 10 t (14 GJ/t) 4 /GJ 560
High yield/price biomass 18 t (18 GJ/t) 6 /GJ 2,000
Rapeseed EU biodiesel 1300 l 0.6 /litre 850
Sugarcane Ethanol Brazil 6000 l 0.2 /litre 1,200
Palmoil Indonesia 6000 l 400-600 /Klitre 2,400-3,600
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Choice of Energy Systems and Technologies

• Need to satisfy first all energy systems
  constraints
• Need to satisfy demand for energy services rather
  than fuels
• Economics not all (invisible costs, convenience,
  social visibility, etc.)
• Choices available inverse of scale (family home,
  plant, vs. planet)
• Analysis needs large system boundaries

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Energy Chains and Analysis

• For MEMs LCA

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Energy Chain AnalysisExample of IIASA CO2DB

• Broad coverage (end-use to extraction, 2000
  technologies)
• Comprehensiveness (technological, economic,
  emissions characteristics)
• Multiple entries (uncertainties, regional
  differences)
• No single best guess (reflecting dynamicsin
  time, process variation, heterogeneity)
• Analysis (queries, energy chain analysis)

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The Cost of Lighting/k-lumen-yr
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CO2 Emissions of Lighting(kg C/k-lumen-yr)
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Cheapest and 2nd cheapest chains
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4
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1
5
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Energy Chain LCA Analysis

• Easy comparison at investment margin
• Analytical simplicity
• Data sharing
• Good for project-specific analysis(GEF
  additionality)
• Imports can be considered
• Representativeness of examples under
  proliferation of combinations (xn!)
• Largely static analysis (whats the investment
  margin?)
• Reconciliation of multiple criteria(costs,
  emissions)
• System aspects Diffusion potentials and
  constraints (capital, vintage structure,
  environment, relative shares of various chains)

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I-O Input-Output Analysis

• Basically a matrix of monetary flows across
  sectors of an economy
• Info one unit of output of sector i needs how
  much () inputs from other sectors (j..n)
• Based on detailed (but lagged) nationally
  reconciled sectorial statistics
• Complemented by physical flows(e.g. energy, CO2
  emissions)

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US- Energy per Value Added (TJ per Million ,
energy embodiment, 1992 I-O data)Source
Carnegie Mellon Univ. www.eiolca.net
Direct energy
Indirect energy
Note product and value orientation Energy
embodied in car vs. total energy use over
lifetime of car Energy per VA industry vs.
services (energy price differences)
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I-O Tables for Energy and Environmental Analysis

• Comprehensive national accounting
• Widely available (mostly in OECD however)
• Basically only data source for indirect
  energy and rucksack environmental impacts
  (things happening outside the sector of
  consideration but linked to it)
• Possibility to combine with physical I-O info
• Static and often delayed (-5 to -10 yrs) snapshot
• Average sectorial picture (difference to marginal
  investments)
• Little end-use (consumption) detail
• Constrained by national border systems boundary

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B-U Engineering Modeling

• Representation of conversion technologies linking
  I-O
• Simulation or optimization (LP) based
• Dynamic (back-and forecasting)
• LPs Clear, simple decision rule (discounted)
  cost minimization under constraints
• Trade explicitly considered
• Data rich

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Energy Flows in MESSAGE Model
1990 -- 2020
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B-U Engineering Models

• technology detail
• multi-criteria analysis
• environmental constraints explicitly considered
• dynamic, systems view
• Extremely data intensive
• Decision rule simplistic(global cost
  minimization)
• Consumer choices poorly modeled(rational
  choice assumed)
• Linkage to other sectors only captured if
  coupled with macro-economic models (complex)