Dragana Pilipovic, CEO of SAVA Risk Management, explains how to use options to value generation assets.

The hot topic in energy is real options. The idea is to value physical generating assets as their equivalent financial derivatives—and book them into an enterprise-wide risk management portfolio. The concept of assets as options solves a major need and may well spark the interest of traditional utility managers to boot.

In the old world of regulated electricity, there was no price risk. Historically, utility managers simply had to justify their prices to regulators using a cost-plus framework. As a result, generators applied a business strategy of cost-minimization to plant-running operations. But deregulation is forcing these same managers to move from marking-to-cost to marking-to-market.

Based on the experience of the last several years of over-the-counter trading, the wholesale side of the electricity markets can tell you that, while the valuation of traded electricity products remains a factor, the demand side brings many issues into the price equation. Cost-plus frameworks are no longer relevant in the price discovery process except as a lower-bound benchmark. Similarly, valuing generation plants outside the market perspective leaves out the marked-to-market value of a plant. Ultimately, the marked-to-market plant valuation will cause nonefficient plants to be seen as unprofitable, since the cost cannot simply be passed on to the user.

Finally, the new FASB 133 accounting standards will force the utilities to justify treating market products on their trading books as hedges (instead of investments) only if they can prove that they are indeed that. This means that they must be proven to be offsets to their existing long electricity positions. And this, ultimately, leads to the inclusion of the utility industry’s generation plants in the marked-to-market portfolio for valuation and risk reporting purposes.

To understand the marked-to-market valuation of a generation asset as a real option, we must understand the decision-making process involved in running a plant. Every day—in fact, every hour—the plant manager must decide on whether or not to run the plant at all, and, if so, at what capacity. The problem is really one of option exercise: If it is worth it, run the plant; otherwise, turn it off. Of course, there are operational questions to consider, such as, What type of a plant are we talking about? For a coal-fuel plant, for example, the turning on and off requires a number of days, because of the physical limitations of such a plant. A natural gas plant is on the extreme side of the flexibility spectrum and allows for quick turnaround times.

For our current purposes, we’ll consider the case of a gas peaker plant, which utilities typically turn on and off to satisfy peak load needs. By making simplifying assumptions, we can model this kind of asset as a “spark-spread” option. In this case, we are dealing with the following exercise problem:

where q = The expected quantity of MWh produced
f = The market price of electricity per MWh
h = The heat-exchange rate
c = The market price of natural gas per MMBTU, and
k = some fixed cost expressed in per MWh.

The problem is difficult in that we need to understand the variables involved—which is not a trivial task—as well as solve the problem mathematically and hopefully under no market arbitrage. The latter two are also nontrivial and nonobvious procedures.

The expected quantity of MWh produced, qt, is defined historically via a linear programming optimization process that needs to be updated to incorporate profit optimization in a marked-to-market world. This quantity also has some probability of becoming zero as a result of unexpected plant downtime. Finally, it has minimum and maximum boundaries when the plant is in use. This valuation problem can be simplified by assuming a behavior for the quantity, an example of which is shown in Figure 1.

Graph 1

Graph 2

Graph 3

The market price of electricity per MWh, ft, must be characterized as consistent with the traded forward prices and implied daily settled volatility. An example of a market-implied daily forward price curve for delivery into Cinergy 5x16 is shown in Figure 2. It also has a negative correlation to the plant-generated quantity. The correlation magnitude is a function of the relative magnitudes the plant produces vs. the local market depth. For example, in a shallow marketplace with only a few plants, if one of these plants cannot produce, the market price would increase significantly.

The heat rate, h, is a function of the quantity of electricity generated, and it provides the relationship between the electricity generated and the natural gas fuel. In solving this problem, an approximation is often made, setting the heat rate to a constant, and thus avoiding the nonlinearity of quantity in the problem statement. In addition to the fuel cost, there is some fixed cost expressed in per-MWh terms, k, which can be thought of like a strike price.

Finally, the natural gas price, ct, is generally readily available from the OTC natural gas spreads to a New York Mercantile Exchange Henry Hub delivery point (although it must be specific to the delivery point of the fuel, which may be illiquid). It also must be characterized as consistent with the traded natural gas markets, on the forward price as well as the volatility side (see Figure 3). Finally, it also might have some nonzero correlation to the electricity price as well as the quantity. The correlation magnitude will be significantly influenced by the level of use of natural gas as a fuel within that electricity market, and by the respective seasonality behaviors of electricity and natural gas within the local region. (Natural gas and electricity, for example, may exhibit zero correlation during the summer months when electricity prices spike, while natural gas markets are off-season.)

When the quantity, and thus the heat rate, is assumed to be fixed, the above problem statement reduces to a spark-spread option, in which the spark spread is defined by the electricity vs. natural gas price heat-rate relationship, and the strike is given by the fixed cost per MWh. As such, it can be valued as a basis-spread option. (This is similar to the oil market’s “crack-spread” option.)

Our example of the gas peaker plant is just the beginning. The assets as options concept can be extended to other power generation types (coal-fired, nuclear, hydro and so on) as well as to retail electricity contracts (including base-load and load-curtailment). Applications can also be made to other commodities such as natural gas and oil. The secret will be, first, to value properly the financial contracts and then to apply these energy-specific models to their physical equivalents. Ultimately, the generation optimization ought to be done at the mark-to-market level, replacing the current generation optimization models and incorporating the financial asset valuation—so, in the end, it can provide optimal generation quantity as well as market hedges.

Dragana Pilipovic is CEO of Winnetka, Ill.-based SAVA Risk Management Corp. and author of Energy Risk: Valuing and Managing Derivatives (McGraw-Hill, 1997), on which this article is based.

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