The potential for a federal renewable energy standard (RES) and carbon regulation, considered with the effect of state-imposed renewable energy standards, is fueling a strong, but challenging,...
Business & Money
various states of the market without having any discontinuities or inconsistencies. Finally, enormous computational and data storage hurdles must be overcome to deliver hourly risk metrics in real time. A viable solution needs to meet the following minimum requirements:
- The market's forecast of future price distributions must be calibrated using historic weather data, forward curves, and market option quotes.
- The hourly valuation of every trade and supply contract in the portfolio must be distributed and optimized, utilizing intelligent valuation shortcuts to reduce brute-force calculations by more than 99 percent.
- Keen attention must be placed on how users will view computational results and what dimensions are to be used in monitoring positions. Reporting structures must be optimized for fetch operations, thereby supporting real-time updates, detailed drill-down functionality, and custom run-time display, sorting, and grouping.
Wall Street typically has used one-factor, short-term interest rate models to drive its fixed-income valuation models. In the power market, the most important factor to consider is weather-the single strongest determinant of price, load, generation utilization, and transmission availability. Higher temperatures increase load, which in turn is served by higher marginal cost generation. As the load/available generating capacity ratio approaches 1.0, price-stack-based economics give way to scarcity-pricing, gaming, and other factors that are possible in an environment characterized by inelastic supply and demand curves. If modeled properly, a position estimation and valuation model will transition between various states of the market without having any discontinuities or inconsistencies.
Calibration Is the Key
Models must be continually recalibrated to existing market conditions. The goal of econometric modeling has always been to design models that use as few observable parameters as possible to accurately describe real-world conditions. A properly calibrated energy pricing model generates option prices that are identical to observed market prices of all options, regardless of whether they are in or out of the money. (Contrast this to the Black-Scholes model, which uses a "smile" function to imply that price distribution varies at different price levels.) The basic premise of a weather/option-calibrated model is that weather is the driving force defining the general shape of price distribution, but that the market continually reveals its estimate of the market level, amplitude, and skewness of price distributions based on changing forward markets and option quotes.
Simulations of price, load, and generation are not independent and should share a common weather pattern within each simulation. If temperatures are high, we generally expect load, prices, and generation to be high as well. If one treated these variables independently, the true cost of unforced outages and transmission constraints would be grossly understated.
Just as importantly, all commodities and regions should have simulations that are derived from fully integrated weather data. Common sense tells us that it's unlikely for temperatures to simultaneously be 10 degrees above normal in the Midwest and 10 degrees below normal in the East. This underscores the need for our estimates of weather, prices, and loads to be plausibly related on a geographical basis. If position and valuation models are not driven by common weather patterns across regions, then the