Reduced-form models for power market risk analysis

The experience of the last fifteen years has illustrated dramatically the emergence of new risks facing power market investors. The volatility of commodity prices, the strategic behavior of competing firms, and regulatory uncertainty all contribute to a challenging investment and operating environment. Traditionally, utilities and power-market investors have used large-scale optimizing production-cost models to analyze the cash flows of power generators. The complexity of these models, particularly when applied on a regional or national scale, is such that computational costs often prohibit extensive analysis of commodity, regulatory, and structural risks. This article demonstrates how a reduced-form modeling approach utilizing neural networks can be used to increase greatly the ability of modelers to use modern simulation-based risk analysis techniques. In particular, several applications relevant to evaluating the cash flow risks of generators, with applications to hedging, are presented. Central to the contributions of this paper is our reduction of complex optimizing models to spreadsheet form, reducing not only their computational complexity, but also their practical user complexity.