Decision making using a multi-criteria approach in a wholesale electrical power market

In this paper, we formulate a multi-criteria decision problem using a reproducing kernel Hilbert space approach to stochastic processes applied to a wholesale electrical power market. Our approach provides an alternative to stochastic, robust optimization commonly used in recent years in that we consider a range of uncertainty rather than just the most likely case or worst case scenario. In particular, we model operation of the electricity market of the ISO New England, an independent Regional Transmission Organization serving the New England area, using hourly zonal data for the last five years. Alternative investments in production or infrastructure must be evaluated in an environment of risk and Knightian uncertainty. Assuming that performance is modeled as a random function of deterministic but uncertain demand allows risk to be measured by value at risk curves over a range of uncertain demand. The preferred investment is determined by a preference rule defined on the set of value at risk curves, a standard multi-criteria approach.