Business & Money
A spate of proposed U.S. tax rule changes soon may open a window of opportunity for certain utilities.
In the mid-1990s, before...
Business & Money
Business & Money
Obtaining a position measurement in energy markets has become more complex and has increased financial risks for integrated utilities.
"What's your position?" The answer to that simple question in today's energy markets is anything but simple. In fact, answering this question may be the single most difficult challenge faced by a fully integrated energy firm in its efforts to manage risk. Position measurement, and therefore risk management, in today's deregulated energy market is complicated by the fact that weather, fuel costs, outages, transmission availability, embedded optionality, and a host of other interrelated factors all dramatically affect position on a real-time basis.
Without an accurate, granular, and unbiased estimate of position, risk management and generation optimization are impossible. Moreover, without consistent and timely position discovery, hedging programs cannot safely be implemented-and, of course, the efficiency and performance of the trading and marketing team cannot be evaluated.
Discovering, estimating, and valuing one's position requires a rigorous methodology and a computational architecture to back it up.
In late 2000, Cinergy Corp., the Cincinnati-based utility and one of the largest electricity trading companies in the United States, undertook a comprehensive review of its risk management methodology and systems. Cinergy was fully aware of the volumetric risks and embedded optionality in its merchant energy portfolio. However, its internally developed client-server system was not sufficiently up to the task of handling all of the complex data capture, valuation, storage, and presentation nuances of its complex deregulated portfolio.
After conducting an exhaustive review of trading systems in late 2000, Cinergy concluded that no silver bullet existed. Available systems were incapable of managing and pricing positions with hourly granularity. The best systems still relied upon valuation models derived for financial markets that lacked the unique volatility characteristics of the power markets.
Worse, the products required non-vanilla deals to be valued outside the core system and plugged back into the core portfolio, creating the classic apples-and-oranges dilemma. Finally, the dated technical architecture of all available alternatives precluded the ability to scale to meet Cinergy's requirement to calculate and report portfolio results on a real-time basis. In short, the perfect system didn't exist and would need to be built.
On the surface, meeting such an objective for any deregulated utility seems relatively straightforward. Deal terms and conditions are clearly spelled out in contractual documents. The vast majority of deals are for standard blocks. Utilities have detailed historic records of loads, and pricing in forward markets is becoming increasingly transparent. Finally, advances in computing power have enabled more rigorous analytical modeling to handle complex deals with variable load- and path-dependent characteristics.
However, a more thorough review of the dynamics of the power market reveals an additional layer of complexity that cannot be ignored if one is to get a true estimate of position and risk: Position measurement is complicated by the fact that weather, fuel costs, outages, transmission availability, embedded optionality, and a host of other interrelated factors all dramatically affect position on a real-time basis. Moreover, any position estimation and valuation model also needs to transition between