The Ins and Outs of Managing Financial Risks for Electric Utility Market Participants
Deregulation of the energy markets has changed, for better or worse, the financial risk landscape for utility market participants. The ultimate goal of any utility is to reliably offer its customers the lowest, or most competitive, electricity rates. However, achieving this goal can be a huge challenge, particularly for market participants, due to a number of reasons—chief among them are the uncertainties surrounding the cost of serving load, accurate forecast of customer load levels, reliable performance of generation resources, and a multitude of levied market fees and penalties. Under these conditions, the traditional practice of using Value-at-Risk (VaR) and expected shortfall to manage financial risk is simply inadequate for energy utilities. Achieving low rates for utility customers will require creative use of available hedging tools to effectively manage and/or mitigate financial risks generated from core business operations.
Market Participants Risk Management Challenge
By design, a Regional Transmission Organization (RTO) facilitates a market where generators offer capacity, energy, and ancillary services to serve the load requirements of the market. It’s also a market where Load Serving Entities (LSEs) tender in their demand bids and are charged market-clearing Locational Marginal Prices (LMPs) for the load served at these locations. A reverse auction process, governed by a complex economic optimization algorithm, is employed by the RTO to determine the pecking order for dispatch of generators within the market footprint until total load requirements are met. That means a generator is not guaranteed dispatch, even if it is in the money, and without dispatch there is no revenue gained. In this environment, risks associated with revenue and cost computations are heavily impacted by uncertainty around:
1. Dispatch quantities relative to offers
2. Forecast demand bids relative to actuals
3. Spread between cleared Day Ahead (DA) and Real Time (RT) LMPs.
The interplay of these factors often results in fee assessments and inadequate performance penalties that impair the financial bottom line.
A possible solution are financial engineering tools that can be used creatively to hedge resulting financial risks, but only after the financial risks have been adequately measured or quantified. For this purpose, good financial risk management measures are enterprise-wide risk measures like Revenue-at-Risk (RaR), Cost-at-Risk (CaR), Cash Flow-at-Risk (CFaR), and Earnings-at-Risk (EaR). These account fully for both price and non-price risk factors. By employing the methods and concepts of financial statement modeling under uncertainty, in conjunction with applying stochastics to key RTO-based variables, utility market participants can confidently estimate their RaR, CaR, CFaR, and EaR measures in a correlation-driven Monte Carlo simulation program.
Having quantified the enterprise-wide risk measures, a variety of hedging programs can be developed and deployed to minimize the organization’s financial exposures. Composition of any such hedging program should take into account whether the utility is asset-rich or load-rich. One effective hedging tool is the participation swap, which can be based on price, quantity, temperature, or some other benchmark variable. For example, its structure, based on a defined price threshold, allows protection when the threshold is breached, but foregoes a portion of the upside from favorable price movement. The utility’s price pain point can be derived from the enterprise’s Monte Carlo simulation program and used as the threshold for the participation swap. In addition, Financial Transmission Rights (FTRs) can be acquired to hedge congestion costs embedded in the LMPs. However, doing so only makes sense if the utility is load-rich and concerned about exposure to excessively high prices. It is to be noted that high prices benefit dispatched generator revenues, but hurt load costs. Also, a ladder of call options can be purchased at different strikes with the initial strike handle derived from the price pain point gleaned from the simulation program. Furthermore, structured traditional insurance products tied to a pay-off structure based on well-defined events such as weather, can be used to minimize the downside to the financial bottom line.
About the author:
Geoff Anato-Mensah has more than 35 years of corporate business experience, with the last fifteen focused on energy risk management. Mr. Anato-Mensah is currently Managing Director of Risk at OATI. He has directed, managed, and implemented financial valuation and risk management programs for energy companies such as Split Rock Energy and ConocoPhillips. He has strong quantitative skills in asset valuation, credit risk modeling, market risk modeling, derivatives pricing, options trading, strategic planning, and risk policy development. He has been certified as an Energy Risk Professional (ERP) by the Global Association of Risk Professionals (GARP). He also holds a bachelor’s degree in Economics from MIT, a master’s degree in Applied Math from MIT, and the master’s degree in Management from MIT’s Sloan School of Management. In his current role, Mr. Anato-Mensah is leading the vision for the OATI risk solution for energy market participants.