Sid Jacobson, a consultant at Hagler Bailly Risk Advisors, explains how to use disaggregation techniques to uncover risks hidden in common energy transactions.

Managing energy risk involves much more than using derivatives and financial instruments. One of the biggest challenges energy companies face is managing the risk characteristics embedded in standard business practices such as owning generation facilities and selling retail energy. These embedded risks, which are frequently unrecognized, can have a far greater impact on a company’s overall risk exposure than a simple myopic accounting of traded instruments and structures.

Over the past few years, large end-users and generation owners have begun to understand the need to become more sophisticated in managing these embedded commodity risks. Highly publicized losses, as well as missed opportunities, have elevated the issue from a trading floor hurdle to a boardroom and enterprise priority.

Effective portfolio and risk management demand some familiarity with the concept of risk disaggregation, or the identification of discrete risk components within transactions, assets and contracts. The concept allows users to decompose the contractual terms of an agreement or the operating characteristics of an asset into their derivative risk equivalents. Energy companies new to risk management can think of these risks in two broad categories: static and dynamic.

Static risks are linear exposures to movements in underlying commodity prices. Static risks have a single measurement equivalent expressed as the derivative measure delta. A delta of 1.0 can be expressed in standard contract equivalents. In electricity, the delta can be reduced to the unit of measure expressed in megawatts (MW). In natural gas, the delta might be reduced to the unit of measure MMBTU or BTU equivalent in a multi-commodity portfolio.

Examples of static risks include fixed-price contracts, basis and transportation contracts, installed capacity requirements, futures and swaps, and physical bilateral contracts.

Identifying dynamic risks, by contrast, requires isolating the embedded optionality found in all nonstandard contracts and mid-merit or peaking generation assets. Many of these commodity risks are masked within contract language typically seen only as commercial terms by some less-sophisticated energy industry participants. Dynamic risks can only be truly quantified and hedged using traditional options theory.

Once identified, the dynamic risks should be quantified as options (swing, European, Asian and so on) and subsequently measured in the relevant Greek exposure. Greek measurements of dynamic risks often react to market forces independent of one another.

Examples of dynamic risks include renewal rights and evergreen clauses, callable and interruptible contracts, injection and release rights, dispatch rights, cheaper-than-fuel decisions, and multiple-location delivery agreements.

Retail exposures present their own problems. In fact, one of the greatest dilemmas in the electricity market is how to price, measure and manage the risk inherent in a retail commodity strategy effectively. As companies migrate from wholesale marketing strategies to retail strategies, the resulting commodity sales drive a risk profile that is subject to unexpected load swings, contract defaults and load shapes that do not mirror standardized hedging instruments.

Typical end-user electricity sales contracts include many dimensions of commodity risk that cannot be perfectly offset by standard Nymex or over-the-counter wholesale contracts. Here’s an example: A retail marketer sells power to an industrial end-user under a fixed-price contract delivered to the meter for a period of one year. The end-user is located in Pennsylvania with electricity delivered to the Duquesne transmission interconnect, which requires two wheels (transmission interconnects) from the Cinergy hub and one wheel from Pennsylvania-New Jersey-Maryland.

At first glance, a derivatives trader might see this transaction solely as a fixed-price swap derived from cost of carry and a weighted-average strip price. The minutia of retail delivery, however, actually allow for greater dimensions of commodity risk.

Retail contracts expose energy providers to a broad set of risks:

Contract terminology stating “full requirements”: translates to an end-user paying a fixed price, and the volumetric obligation is unknown. The dynamic risks associated with usage swings from changes in weather and other external factors are best managed using swing options. When a price for a customer is calculated, the retail commodity volumes are usually based on historical usage or budgeted forecasts, as opposed to actual usage.

Load shape adjustments: standard forward contracts available in the bilateral OTC electricity market or on Nymex are ratable contracts, meaning that electricity is delivered in even streams of volume. The typical end-user exhibits an odd load shape, resulting in different quantities for each given hour of a day during the contract term. Each consumption hour of a specific load shape also has a different price and volatility characteristic.

Least-cost delivery of power: typically, electricity retailers have the ability to deliver electricity into the control area from multiple generation locations or from its own service area and generating assets. Since the electricity market has developed regionally differentiated price reference points, with each exhibiting distinct volatility characteristics, the seller has the option to deliver power from the least-cost supply point.

When disaggregating commodity risks, the retailer should derive the value of each risk component to isolate its risk behaviors. To disaggregate the example posed earlier, the first step is to recognize and separate static and dynamic risk elements.

Retail transactions usually contain the following derivative components:

Short a weighted-average fixed-price swap: derived from an hourly load and price curve not available in the OTC markets.

Identifying dynamic risks requires isolating the embedded optionality found in all nonstandard contracts.

Short a volumetric swing option: unlike the standard daily swing options found in the OTC market, the swing optionality of a retail transaction is a strip of hourly options with a real-time exercise that is not absolutely sensitive to price, as opposed to an exercise notice.

Short residual risks: in the form of partial contract equivalents, based on the volatility-adjusted weighted delta of the forward hourly load shape.

Long a locational basket or rainbow option: an option with the ability to choose the location of delivery from a basket of multiple locations, based on the cheapest to serve.

Generation exposures

As a result of deregulation, generation assets are migrating from regulated rate-of-return business models to at-risk merchant plants. Portfolios of generation assets are currently being valued at a multiple of production cost values or a dollar-per-kilowatt-hour basis. In order to recognize and capture the value enhancement from commodity volatility, generators will be forced to optimize their assets within their overall commodity portfolio.

The typical mid-merit generation plant contains significant optionality that can be recognized and optimized to extract value from the real options associated with flexible operating characteristics. Here’s an example for illustrative purposes: A generator with a dual-fuel combustion turbine (CT) power plant might have modeled a unit based solely on the production or marginal cost of generation. This is one element in the optimization or value-recognition process of an asset.

A portfolio manager or risk manager with a strong financial understanding might value this asset differently, perhaps as a short fixed-price fuel (MMBTU) obligation and long plant output (MWs), or daily call option on MWs. These are both reasonable approaches to value the asset and recognize the optionality from mid-merit generation.

But a more accurate representation of the value-at-risk associated with a mid-merit asset would involve recognizing its dynamic and static risks, and modeling the resulting exposures in the broader portfolio and managing them discretely.

A combustion turbine usually has the flexibility to:

Be ramped up and down with a short period of notice. A CT is flexible enough to be run on demand with minimal time to reach its peak production output.

Switch fuels to the least-cost fuel to supply. A dual-fuel CT can usually convert fuel oil or natural gas to electricity, based on its heat rate.

Deliver the electricity to multiple delivery points through various transmission paths. If located on the border of multiple regions, the dual-fuel CT can be dispatched into a financially optimal delivery location.

When managing a generation portfolio, the risk manager should disaggregate its multiple risks into a portfolio of derivatives characteristics. This combustion turbine plant can be disaggregated into:

Long a compound option that consists of a cheaper-than call on fuel, that is one leg of a “spark spread” option (conversion or spread option, struck at the differential of converting fuel to electricity).

Long a basket or rainbow option that gives the holder the right to deliver power into multiple locations that is struck at the cost of generation plus transmission.

Rolling up the risks

By taking steps to identify the embedded risk types within a portfolio of business activities such as generation and retail, the risk manager has the inputs to develop a tool for portfolio decision making. Once all the risks are dissected they can be reaggregated into their appropriate Greek measurements. The resulting measurements can be rolled up into risk reports that will be both a front-office decision tool and a mid-office reporting tool.

The resulting portfolio can illustrate all the commodity risk components to derive a cross section of views from the beta-neutral BTU or dollar-equivalent portfolio, and to the net delta and gamma exposure of each commodity type. This flexibility provides the ability to perform stress tests or scenario simulations expressed in gamma, vega, theta and rho exposures measuring the changing dynamics of a composite portfolio.

Although there are many different methods and models to derive the value of each one of these derivatives and compound derivative instruments, taking steps to recognize these risks is the common starting point and a major brick in the foundation of effective risk management.

Risk disaggregation is a tool to measure current risk exposure, determine optimal hedging strategies and capture previously unrecognized opportunities. The act of trading then facilitates risk management—trading to execute the hedging and arbitrage strategies, while supporting the necessary supply and balancing functions.

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