Measuring Performance of Long-Term Power Generating Portfolios - Green Energy and Efficiency: An Economic Perspective - page 388

Keywords Risk-return trade-off Electricity planning Mean-variance analysis

Stochastic processes Futures markets Optimal power ow Monte carlo

1 Introduction

Investments in power generation usually entail two types of effects: (i) portfolio
effects, i.e. the interplay between a new power plant and the existing eet of plants

owned by a utility or located in a country; and (ii) option value effects, e.g. the

exibility to run on particular technologies at a higher or lower rate over time as
uncertainty about the future unfolds. It has long been recognized that a proper
valuation of investments in power generation needs to capture both effects [ 7 ].
In other words, if the optimal degree of fuel mix diversity is to be identi

ed, we
need valuation approaches that trade-off the expected returns and risks of increased
portfolio diversi cation, in both a static and dynamic perspective.

Mean-Variance Portfolio (MVP) theory is well suited for the

rst task [ 25 ]. The
standard framework envisages an investor that is confronted with a ( nancial)

portfolio selection problem. As long as information about asset average returns,
variances, and covariances is available, it is possible to map the whole set of assets
and portfolios of assets on a risk/return diagram. Hence, provided the investor
dislikes risk and likes return, it is possible to delineate the ef cient frontier , i.e. the
set of asset portfolios that either minimizes risk for a given level of expected return,
or maximizes the latter for a given level of the former. Thus MVP theory allows
investors to identify the range of ef

cient choices. Then it is up to the investor to
identify the particular portfolio that best matches her/his individual preferences
regarding expected return and risk (the optimal portfolio ). MVP theory thus
improves decision making in two ways: (i) by simplifying the portfolio selection
problem (narrowing down the choice along the ef cient frontier), and (ii) by

sticking a number to the reduction of risk that diversi cation brings about.

MVP theory has been applied to real assets such as power plants with the aim of
identifying the optimal portfolio of generation assets for a utility or a country [ 2 4 ,

8 , 20 , 21 , 32 ]. Bazilian and Roques [ 7 ] provide a brief review of this literature
alongside a number of state-of-the-art applications of MVP theory for electric
utilities planning. Early MVP applications mostly took a national or societal per-
spective; they were based on power generating cost and concentrated on fuel price
uncertainty. Some recent studies have instead adopted the viewpoint of private
investors. Therefore they also take account of a broader set of risks: electricity
price, emission allowance price, the co-movement of fuel, electricity, and carbon
prices, among others.
In dynamic, uncertain environments the availability of a broad range of gener-
ation technologies and the

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exibility to run on them at different rates are particularly
valuable. However, this value is elusive. The Real Options approach (ROA) aims to