Efficient Multi-Energy Generation
Portfolios for the Future
Florian Kienzle
Fourth Annual Carnegie Mellon Conference on the Electricity Industry
Pittsburgh, March 11, 2008
Pittsburgh, March 11, 2008
Motivation
ƒ Increasing worldwide demand for energy: How to satisfy this?
ƒ Uncertainty and risk factors affecting energy system planning:
ƒ
How will prices of primary energy carriers evolve?
ƒ
Future control on carbon emissions? Future CO2 price?
ƒ
Risks introduced by the restructuring of energy markets
ƒ
Growing number of emerging technology options:
Which mix to choose?
ƒ Physical links between different energy systems are becoming stronger
ƒ Potential synergies between different energy carriers
Ö Application of portfolio theory to multi-energy generation portfolios
Î Method for long-term investment planning of future multi-energy systems
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Pittsburgh, March 11, 2008
Portfolio Theory Fundamentals
ƒ
ƒ
ƒ
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A portfolio is composed of securities
Security: decision affecting the future
Portfolio: the totality of such decisions
Estimates of the future performance of securities are “fuzzy”
Probability distribution
35
30
Probablity [%]
25
20
15
10
5
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Rate of return [%]
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Pittsburgh, March 11, 2008
Portfolio Theory Fundamentals
ƒ Portfolio theory uses two quantities to characterize the probability
distribution of a portfolio’s rate of return
ƒ
Expected return: Weighted average of all possible outcomes,
with each outcome weighted by its likelihood
m
E = ∑ pi ⋅ ri
i =1
ƒ
Standard deviation
σ=
m
∑ p (r − E )
i =1
2
i
i
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Pittsburgh, March 11, 2008
Portfolio Theory Fundamentals
Two-securities portfolio
Expected value of rate of return
100% Security A
Efficient frontier
Minimum
risk
portfolio
100% Security B
Standard deviation
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Pittsburgh, March 11, 2008
The Multi-Energy Portfolio Model
ƒ Mean returns and covariance matrix are not computed with historical
data
ƒ Instead: Consideration of scenarios to take into account uncertainties
about external drivers that can alter the future economic
performance of a technology
ƒ Examples for external drivers
ƒ
Environmental concern with respect to climate change and resulting
CO2 price
ƒ
Geopolitical tensions with effect on prices of fossil fuels
ƒ Different states of external drivers (e.g. ‘high’ or ‘low’)
Î differences between scenarios
ƒ All possible combinations of external drivers result in a set of scenarios
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Pittsburgh, March 11, 2008
The Multi-Energy Portfolio Model
ƒ Costs of technologies will differ according to the scenario
ƒ
Overall cost matrix Ctot
Scen. 1 . . .
Scen. s
Tech. 1
.
.
.
[Cts ] =
USD
MWh
Tech. t
ƒ When having a number of α energy outputs:
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Pittsburgh, March 11, 2008
The Multi-Energy Portfolio Model
ƒ Inverting the values in Ctot gives the overall return matrix Rtot
[ Rts ] =
MWh
USD
ƒ Individual probabilities can be assigned to the scenarios
RP σ P
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Pittsburgh, March 11, 2008
The Multi-Energy Portfolio Model
ƒ For a technology i with a conversion efficiency ηik with respect to
the kth output and a total efficiency of ηi,tot, the share of output k in
the total output is simply:
ƒ Output ratio matrix Γ:
ƒ Share of each
output in the
total output:
- Share of each technology in the
portfolio
Pittsburgh, March 11, 2008
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Application: An Electricity and Heat Portfolio
ƒ Technologies with electricity as output
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T1: Wind
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T2: Photovoltaics (PV)
ƒ Technologies with electricity and heat as output
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T3: Biogas engine
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T4: Natural gas fired engine
ƒ Technologies with heat as output
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T5: Solar (thermal)
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T6: Gas boiler
ƒ Three major external drivers:
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D1: Environmental concern regarding climate change
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D2: Energy-related research efforts
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D3: Geopolitical tensions
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Pittsburgh, March 11, 2008
Application: An Electricity and Heat Portfolio
ƒ Equal probabilities for all scenarios:
ƒ Output ratio matrix:
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Pittsburgh, March 11, 2008
Application: An Electricity and Heat Portfolio
ƒ Cost matrices for the electricity and heat output:
All cost values in USD/MWh and mainly taken from the
NEA/IEA publication “Projected costs of generating electricity”, 2005.
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Pittsburgh, March 11, 2008
Application: An Electricity and Heat Portfolio
Electricity share in the combined electricity and heat
portfolio as function of risk and return
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Pittsburgh, March 11, 2008
Application: An Electricity and Heat Portfolio
Heat share in the combined electricity and heat
portfolio as function of risk and return
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Pittsburgh, March 11, 2008
Application: An Electricity and Heat Portfolio
Efficient frontier of the combined electricity and heat portfolio
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Pittsburgh, March 11, 2008
Application: An Electricity and Heat Portfolio
Shares of electricity and heat along the efficient frontier
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Pittsburgh, March 11, 2008
Application: An Electricity and Heat Portfolio
Shares of all technologies along the efficient frontier
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Pittsburgh, March 11, 2008
Summary
ƒ General extension of portfolio theory to multi-energy portfolios with
an arbitrary number of output energy carriers
ƒ Uncertainty factors are taken into account using a set of several
possible scenarios with individual probabilities of occurrence
ƒ System planners can determine a portfolio being the best answer
to this set of scenarios instead of having to choose a portfolio
being only efficient for one single scenario
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Pittsburgh, March 11, 2008
Outlook
ƒ Apply the model to a “real” case, e.g. to the generation portfolio of
a municipal or regional utility
ƒ Analyze interdependences between investments in transmission
infrastructure and investments in generation facilities
kienzle@eeh.ee.ethz.ch
www.future-energy.ethz.ch
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Pittsburgh, March 11, 2008