Bridging the Gap in Asset Risk Management by Commodity Point

A CommodityPoint Whitepaper

Bridging the Gap in Asset Risk Management Sponsored by:

CommodityPoint a division of UtiliPoint International, Inc. 19901 Southwest Freeway, Suite 121 Sugar Land, Texas 77479 www.commodity-point.com October 2012

Introduction Commercially supported Energy Trading and Risk Management (ETRM) software has become the most widely used tool in midsized and large energy trading shops around the globe.

However, in

CommodityPoint's 2009 survey of risk management capabilities deployed within these companies, more Figure 1: Does your system adequately incorporate risk arising from physical assets?

than half of the respondents indicated that their current risk management systems did not adequately

incorporate

the

uncertainties

associated with their physical assets. This result should not be particularly surprising as most of the vendors of these products have generally entered the market from one of two different directions – either they started by creating a product that serviced the needs of wholesale Source: CommodityPoint – Risk Management Study and Report, 2009

energy traders, or they initially created a

product to service the financial trading industry. Either way, the larger of these software companies have now created products that generally service the needs of both the physical and financial traders of energy commodities. Given this genesis, it's not surprising that, while very adept at capturing, valuing and managing the financial risks of standard energy product commodities, the abilities of ETRM systems to effectively manage the physical and financial risks associated with power generation, gas storage, or retail energy consumption are somewhat inchoate. Most ETRM systems have applied concepts and models that were originally developed on Wall Street. These useful tools manage risks associated with financial products or even physical commodities prior to actual delivery/generation, and are capable of providing a forward-looking view into, and helping to manage, out-months portfolio risks in terms of standard financial instruments. Unfortunately, these models do not accurately reflect the complex nature of those production/generation assets or retail obligations. The representation of generation assets as financial instruments results in a systematic bias and yields inconsistent valuations and risk measures. These modeling limitations are exposed because of the mismatch between the physical attributes of generation and retail load, and their representation as financial instruments combined with the use of forward prices instead of spot prices.

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In order to accurately capture and assimilate the physical attributes of these assets (which is necessary to effectively manage the real-time needs of modeling), many asset holders have acquired specialized software systems from non-ETRM vendors that are essentially highly granular operational models. Commonly known as Production Cost Models or PCMs, these models have proven effective at managing and/or optimizing assets to forecasted spot market prices for wholesale generators and integrated utilities. PCMs can augment the capabilities of ETRM systems, providing improved decision support capabilities around asset operations and real-time portfolio optimization/management; however, these capabilities usually exist in isolation, both in terms of the total asset portfolio and hedge positions.

Differing Views Create a Gap Production cost models are very useful tools for providing an asset-specific view into "what am I doing?" Unfortunately, this focused capability comes with certain limitations when attempting to incorporate such models into a broader portfolio view that relates to financial management: •

PCMs have limited capability to include hedge positions

•

PCMs are generally run as deterministic scenarios and do not simulate forward market conditions, removing the value of hedges to reduce uncertainty

•

PCMs are unable to calculate delta positions or value nearly all hedge instruments

However, the primary analytic gap results from PCMs’ exclusive focus on future spot market conditions without evaluating the uncertainty in the foundation of hedge instruments—forward market prices. The limitation of missing forward market conditions means PCMs only subsume the operational views associated with expected spot prices and not the impact of hedge contracts, creating a chasm between prior-to-delivery market expectations of financial models and during-delivery expectations of the PCMs. Asset holders relying exclusively on traditional ETRM systems are utilizing financial-centric risk models that are adept at modeling forward and option positions but do not accurately reflect the physical and operational attributes of those assets, such as unit outages, ramp rates, heat rates and retail risk related to weather, load, and spot market prices. Given these limitations, these ETRM systems provide a biased and inconsistent view of position exposure and portfolio risks. While a financial-centric model is generally functional for baseload facilities (excluding the impact of unplanned operational upsets), such as coal-fired plants, managers of assets with operational flexibility or retail load obligations cannot rely on such models to provide a true and accurate portfolio view of

their risks and exposures. Removing the modeling limitations of ETRM solutions can yield a more accurate representation of assets, but under most paradigms they endure an additional set of limitations.

Bridging the GAP In order to fully address the risks associated with generation assets and retail loads, risk management tools need to tightly integrate the operational and market risks of spot delivery exposure by employing hybrid models. By bridging the physical and financial, hybrid models move risk management from monitoring and control to a more adept analytic construct that synthesizes operational constraints and complexities, providing for more accurate hedging strategies and more appropriate risk valuation. A hybrid model supports estimation of both value at risk (VaR) and gross margin at risk (GMaR) under a unified simulation framework that transcends the prior-to-delivery of VaR and the during-delivery elements of traditional GMaR. When such a hybrid model is applied to a portfolio of generation facilities, retail loads, or an integrated utility, a critical analytic gap can be bridged; and, in doing so, the model can materially improve a company’s ability to realize value through: •

Understanding the match of hedge instruments to financial performance of assets

•

Developing hedging strategies so realized cash flows are consistent with budgeted cash flows

•

Leveraging the latent value of assets and natural position exposure to recognize additional gains

•

Identifying unrealized risks and managing portfolio risks toward acceptable risks

•

Bridging uncertainty in forward market prices from today’s marks to expiration, and then during-delivery spot price conditions

Additional benefits in credit analysis can be gained in bridging the gap: •

Valuing counter-party exposures for tolling contracts

•

Maintaining consistency of modeling credit risk with portfolio management analytics

By bridging the analytic gap of traditional software modeling paradigms, energy portfolio managers can realize tangible benefits through improved management of market and operational risks with less uncertainty.

In exploring this issue, we visited with Dr. Gary Dorris, President of Ascend Analytics, whose company focuses on producing solutions that provide unique capabilities to "bridge the chasm between the financial construct of ETRM solutions and the pure physical paradigm of analysis of production modeling." According to Dr. Dorris, in crossing this chasm, Ascend has “created a new analytic construct that integrates the financial perspective of forward market expectations with the physical dimension of asset operations and spot market conditions. The analytic chasm exists because ETRM solutions use a financial construct of modeling both hedges, assets, and load as financial instruments, when the value and realized position from these components depends on during-delivery spot market conditions and hourly operations. Alternatively, production cost modeling reflects hourly attributes of assets relative to spot, but fails to integrate market expectations. If the financial world of ERTM lives in the prior-todelivery world of monthly forward markets, then the production cost modeling lives in the world of the physical spot market." According to Dr. Dorris, bridging this gap requires an analytic bridge between the financial and physical world, bringing portfolio management to actionable information so that the physical attributes of power generation, retail load, and gas storage may be managed against the financial dimensions of hedge contracts. By integrating the financial payoffs of forward and option hedges with the during-delivery exposure for both retail contracts and generation dispatch, that analytic bridge is realized. This metaphorical bridge crosses the gulf of forward market expectations with the real-time relationships of weather, load, spot prices and asset operations, supporting the appropriate balancing hedge positions to reduce uncertainty in cash flows and manage physical position exposure. For retail energy providers, the ability to capture the structural relationship between weather, load, and spot market prices is essential to evaluating the financial and physical position exposure associated with their retail energy offerings. Utilizing an integrated modeling framework enables retail providers to optimize hedge instrument selection to mitigate uncertainty in future cash flows. Traditionally, retailers have managed position exposure through balancing net position; however, the landscape of retail losses suggests that a risk analytics gap exists which necessitates a modeling bridge. For wholesale/merchant generators, maximizing the value of generation in today’s competitive energy markets involves pairing an asset’s natural physical attributes with a proportioned mosaic of hedge instruments to fully realize the latent value of generation. The value of generation assets consists of an extrinsic component related to forward market prices and an intrinsic component related to spot prices

and the flexibility of a generator to respond to 1) changing market price signals and 2) the ability to secure additional revenue from the ancillary services. Capturing the intrinsic and extrinsic value component though applying the metaphorical analytic bridge enables generators to fully leverage the value of generation assets through hedge instruments to either minimize uncertainty in future cash flows or increase the potential value of generation. Integrated utilities that operate in a competitive market or ISOs face the challenge of balancing budgets with uncertainty emanating from commodity prices, weather conditions, asset performance, and load growth. Operators of these utilities need to be able to integrate financial and physical risk factors with the physical dynamics of asset operations, load, structured transactions, and hedge instrument payoffs. The value of more robust portfolio management for integrated utilities can be best realized via applying the analytic bridge as a modeling framework for decision analysis.

Conclusion Asset-heavy companies that utilize traditional ETRM software systems simply cannot rely on the financial-centric models that are core to most of those systems when seeking to maximize their portfolios of capital intensive assets, physical transactions, and financial hedges. Such models do not appropriately reflect near-term and real-time market influences, and therefore they cannot capture the real costs or market potential of those assets as delivery nears. These unvalued exposures may not be particularly significant in a relatively static market; however, during periods of market volatility brought about by extreme or unseasonable weather, operational upsets or fuel constraints, the marginal value of an asset can change rapidly and significantly. In order to address this obstacle and achieve a lucid and precise understanding of the values and exposures within their portfolios, asset-heavy companies need to incorporate hybrid technologies that can bridge the prompt-month to long-term financial view provided by ETRM systems and the now commonly deployed production cost models that can capture the operational characteristics and constraints of those assets. Utilizing such a bridging technology will provide the ability to gain a complete and accurate portfolio view, across all time horizons, of the risks and values inherent in hard assets.

About Ascend Analytics Ascend Analytics is an innovative software service company focused on energy analytics. Energy portfolios and markets have increased in complexity, making decision analysis more challenging. Ascend’s solutions provide the core analytic infrastructure to streamline processes, enhance understanding, and support decisions for industry leaders. Founded in 2002, Ascend is an employee-owned software service company based in Boulder, Colorado. Ascend’s management team has over 60 years of direct energy experience and works with many of the world’s leading energy companies to achieve efficient processes and superior decision analysis. Ascend has incorporated the latest technologies and modeling techniques to allow energy analysts and risk managers to meet today’s challenges. The PowerSimm software solution is a complete analytics platform for energy portfolio, risk management and hedge analysis. PowerSimm provides the analytic and data infrastructure to support decision analysis from the balance of month to the next 10 years. Making better models requires starting with correct details in order to accurately represent the physical and financial dynamics of energy supply. For energy portfolio managers that requires a comprehensive analytical framework. Our software models allow energy companies to increase cash flow certainty, improve position analysis, optimize hedge design, and improve price forecasting and asset valuation. Colorado Headquarters 1877 Broadway, Suite 706 Boulder, Colorado 80302 Phone Number: 303.415.1400 Fax Number: 303.223.9141 www.ascendanalytics.com

About CommodityPoint CommodityPoint is the industry leader in providing Commodity Trading & Risk Management (CTRM) research, analysis and advisory services. Our services bring insight into business issues, trends, processes and technology, to utilities, energy companies, banks, brokers, funds, investors and vendors that enhance their competitive position and support critical business decisions around the wholesale commodity trading markets. Our team provides expert analysis of market trends and, in particular, the technologies and applications supporting those that participate in regional or global commodity markets. With offices in Europe and the US, and backed by an experienced research team, our organization provides an unparalleled view of the marketplace. CommodityPoint is a division of leading energy and utilities analyst and consulting firm, UtiliPoint International, Inc.

Other Resources The CTRM Blog – http://www.ctrmblog.com CommodityPoint – http://www.Commodity-Point.com TRM Products and Services Online Directory – http://www.trmdirectory.com

Other CommodityPoint Reports CommodityPoint research reports are available at http://www.Commodity-Point.com For Additional Information regarding this report or any CommodityPoint product or service, please contact Patrick Reames at preames@utilipoint.com or Mark Tredway at mtredway@utilipoint.com.

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