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BGS Auctions: What Price Is Right?
How to price new load-servicing contracts while incorporating market-risk analysis into such deals.
Why have basic generation service auctions historically been overly competitive given the prevailing market prices at the time? The answer requires an exploration of the concept of "charging" for market risk and then incorporating the existing risk profile of the bidding organization. EnergyCo – a hypothetical yet typical 5,000-MW vertically integrated energy company with a relatively balanced portfolio of generation, customer load, and wholesale trades – will help illustrate these points, showing how an organization can price a new load-servicing contract in isolation and then in conjunction with its existing portfolio.
Generation and customer demand have complicated dynamics. They are not easily understood because they are subject to a number of simultaneous uncertainties. The market-price uncertainty related to power and fuel prices create a spread-option portfolio that could be managed using financial derivative theory. However, generation plants also are subject to a range of operational factors ( e.g., start-up and shut-down costs, ramp rates, forced outages, environmental constraints, etc.) that have a material impact on the plant's performance. Customer demand has a significant level of volumetric uncertainty as well. Much of it can be explained by the region's weather conditions.
A merchant must consider these factors to understand the risk a new trade brings to an existing business. Monte Carlo simulation is one effective way of modeling all of these factors simultaneously. Simulation allows for prices and temperatures to be simulated and fed into generation dispatch and customer load models. In this way, we can generate 1,000 or more scenarios of hourly forecasts of generation, demand, mark-to-market transactions, and settlement.
This article uses Monte Carlo simulation to generate scenarios of net earnings for EnergyCo's current portfolio and a potential new load-serving contract. This information is then used to generate values and risk measures and risk-adjusted return on capital (RAROC).
EnergyCo serves a range of residential, commercial, and industrial customers. It forecasts its production and supply needs using Monte Carlo simulation modeling that incorporates the uncertainty in power and fuel prices, regional temperatures, and customer-load behavior to understand its expected net energy position. This analysis forecasts an expected excess level of supply of 1.5 million MWh during the next two years. Figures 1 and 2 summarize EnergyCo's net position.
At first glance, this portfolio appears to be relatively balanced between generation and load when the trading portfolio is taken into consideration. However, the drivers of this balance ( e.g., market prices, expected forced outage rate, temperature, customer demand behavior, etc.) create significant uncertainty in the actual balance. A significant excess or deficit could lead to enormous volatility in earnings and cash flows. When this balance is evaluated at the monthly level, the uncertainty is even more pronounced. For instance, Figure 3 illustrates the range