Measuring Performance of Long-Term Power Generating Portfolios - Green Energy and Efficiency: An Economic Perspective - page 391

binding greenhouse gas (GHG) emission constraints. Note that the UK Government
has announced a oor price for carbon in the power sector from 1 April 2013 with

an initial value around 16

₤ /tCO2 to target a price for carbon of 30 ₤ /tCO2 in 2020

and 70 /tCO2 in 2030. Each generation portfolio is exogenously given but is

optimally managed by changing input fuel and electricity output as required.
The paper is organized as follows. Section 2 introduces the theoretical model.
Upon the distinction between the physical environment and the economic envi-
ronment it presents the optimal dispatch problem. Then Sect. 3 shows a heuristic
application to four dynamic generating portfolios assumed to provide a range of
potential paths of Great Britain over the period 2012

₤

2032. A section with our main

-

ndings concludes.

2 The Model

We propose a model for evaluating the performance of time-varying generation
portfolios. The performance depends on factors that change over time, e.g. network
topology, market structure, fuel and electricity prices, energy policy, environmental
and climate policies, etc. Our valuation model rests on solving an optimization
problem. At any time it minimizes the total costs of electricity generation and
delivery; in this sense it draws on Bohn et al. [ 10 ]. A distinctive feature of our
model is that the optimization process is subject to the behavior of the stochastic
variables (e.g. load, fuel prices); thus we deal with a problem of stochastic optimal
control, which is similar to that in Chamorro et al. [ 11 ]. We allow for the possibility
that a fraction of the demand is unserved, but this has a non-negligible cost (thus,
with the exception of extreme cases, in practice load is always served). Regarding
market power or strategic bidding by power generators, we account for these issues
through the pro

) technology. 2
The model allows for random failures in physical facilities. Uncertainty stems
also from load, wind generation, and hydro generation. We assume these follow
stochastic processes with suitable properties (for example, seasonality or stationa-
rity) that can be estimated from of

t margin of the electricity price-setting (or ' marginal '

cial statistics. Stochastic processes similarly
govern the economic sources of uncertainty (fossil fuel prices and allowance pri-
ces). For estimation purposes, the ideal market data are composed of futures prices;
this is important because (assuming the required liquidity/maturities are met) they
enable us to estimate parameter values in a risk-neutral setting. 3
Our model does not address the question of the optimal time to alter the gen-
eration portfolios. We ignore in ciency targets at this stage. We

abstract from access-pricing problems for new generators. The model allows a
number of questions to be modeled and answered. Thus, in our base case climate

ation and ef

2

See Chamorro et al. [ 11 ], Appendix C.

3

This does not mean that investors are risk neutral.