Electricity/power

The electricity market (often described in the USA as the power market) is very
different for one fundamental reason: both storage and transportation are incredibly
expensive.10 Let us briefly describe the nature of the power market from a trading
and risk management perspective.
First, like many products, electricity demand varies significantly throughout the
day, week and year. However, electricity has the same properties as a highly perishable
product in that not only must supply meet demand, but production must meet
demand minute by minute. The result of this unique attribute is that the market
must keep a significant amount of idle capacity in place for start-up when the
demand is there and shut-down when demand recedes. In some cases, this is a
generation station that is already running, ready to meet anticipated customer
demand. In other cases it is plant that may only be asked to start up once every few
years. Typically a local market (or grid system) will keep 15–20% more plant capacity
than it expects to use on the highest hour of demand during a normal year. This will
often represent over double the average demand.
This, coupled with the physical challenges of transporting electricity,11 leads to a
general position of over-supply combined with short periods when the normally idle
plant needs to run. At such peak times, when all the capacity on the system is
needed, spot market prices12 will rise dramatically as plant owners will need to
recover not only their higher cost of running but also their capital costs in a relatively
short period of time. This is exacerbated by the fact that electricity generation is one
of the most capital-intensive industries in the world.
The forward market will, of course, smooth this by assigning probabilities to the
likelihood of the high prices. Higher probabilities are obviously assigned during the
periods when demand is likely to be at a peak and this will generally only occur
during two or three months of the year. In the USA this is generally in the summer.
Thus unless there is a huge over-supply the summer prices will be significantly
higher than the rest of the year. When there is a potential shortage prices will be
dramatically higher, as was seen in the Cinergy market in late June 1998 where
daily prices that trade most of the year at $30/MWh increased to $7000/MWh.
It is these important market characteristics that lead to the extreme seasonality,

jumps, spikes and mean reversion that will be discussed below in the section on
Market Risk. Given the extreme nature of these factors trying to capture them on
one term structure or convenience yield is extremely difficult.
Despite this price uncertainty, a forward market for electricity has developed along
a standard commodity structure. In fact seven exchange contracts, reflecting the
regional nature of power, currently exist at Palo Verde, California/Oregon Border
(COB), Entergy, Cinergy (NYMEX), TVA, ComEd (CBOT) and Twin Cities (M. Grain
Exchange) and PJM. The forward curves for Cinergy is shown in Figure 18.2 compared
to the Henry Hub gas curve.
Figure 18.2 Forward curves for Cinergy compared to the Henry Hub gas curve. (Source: Citizens
Power,June 1999)
Some standard options are traded at the most liquid hubs. These tend to be ‘strips’
of daily European calls based on monthly blocks. However, the bid/ask on such
products are often wide and the depth of liquidity very limited.
Forward curve price discovery – the problems with power
Let us focus on two major differences in power:
Ω Storage First, you have virtually no stack and roll storage arbitrage. In other
words, since you cannot keep today’s power for tomorrow there is no primary
‘arbitrage’ linkage between today’s price and tomorrow’s. There are, of course,
many secondary links. The underlying drivers are likely to be similar – demand,
plant availability, fuel costs, traders’ expectations and general market environment.
But as you move forward in time, secondary links break down quickly and
so does the price relationship. Figure 18.3 shows the forward correlation between
an April contract and the rest of the year. As you can see, almost no relationship
exists between April and October and most of the relationship has evaporated
once you are beyond one month. In other words, the October Cinergy contract
has no more relationship with the April contract than, say, an oil, gas or even
interest rate market.
Ω Transportation Second, you have limited ‘hub basis’ arbitrage. Since there are
numerous logistical limitations on moving electricity it is difficult to arbitrage
between many of the power markets within the USA (never mind internationally).
Even hubs that are relatively close show large variations in the spread between
prices. Figure 18.4 shows some of the correlations between major power hubs in
the USA. Rather than thinking of them as one market, it is more accurate to view
the power market as at least twelve (the final number of hubs is still being
determined by the marketplace) independent markets with some but often little
relationship.
Once you put all these factors together you see a picture similar to the one a global
risk managers in a big bank will have experienced, a huge number of independent
products that need to be combined for risk purposes. Instead of having one forward
curve for US Power, we have up to eighteen independent months for twelve independent
markets, in other words 216 products. This brings both the curse of lack of
liquidity and data integrity for each product and, on the positive side from a risk
perspective, diversity.
Forward curves up to 24 months are traditionally built using daily trader/broker
marks for monthly peak/off-peak prices, with the breakdown, where necessary, into
smaller time periods (down to an hourly profile) using historical prices adjusted for
normal weather conditions. Prices beyond two years are significantly less liquid and
where information is available bid/ask spreads can increase significantly. Forward
price curves (and volatility curves) beyond 24 months thus need to be created through
more of a mark to model rather than mark to market process. As noted above, using
a model to connect price quotes inevitably involved a number of assumptions about
how the market behaves. In power, this involves not just fitting a serious of different
price quotes together, but also filling in the gaps where price quotes are not available.
The price structure will have to make certain model assumptions based on historical
observation about seasonality and the year-to-year transition process. Models
can be bought (such as the SAVA forward curve builder) or, more often, built inhouse.
However, given the developing nature of the market this still tends to be a
relatively manual process to ensure all the relevant market information can be input
into the curve and minimize the error terms.
It needs to be remembered that in making assumptions about the structure of the
curve in this process to estimate the fair value of a transaction that the forward
curve, while objective, unbiased and arbitrage-free, may be unreliable given the
incomplete data sets. Throughout the process it is thus necessary to estimate the
impact of such assumptions and modeling or prudency reserves are likely to need to
be applied against the fair value under these circumstances. The collection of market
data with an illiquid market also becomes a major operational issue with a need to
continuously verify and search for independent data. 534