Good-Deal Investment Valuation in Stochastic Generation Capacity Expansion Problems

Generation capacity expansion models have a long tradition in the power industry. Designed as optimization problems for the regulated monopoly industry, they can be interpreted as equilibrium models in a competitive environment. While often written as deterministic problems, they can be adapted to accommodate the wide range of uncertainties that currently assail the industry. We consider a stochastic optimization version of the capacity expansion model where risk is assessed through risk functions. In order to combine both the criteria of coherence and time consistency while at the same time sacrificing nothing in terms of computational tractability and economic interpretation, we formulate the model using the so-called "good deal" risk function. We show that the resulting model takes the form of a conic optimization program that can be interpreted as a multistage hedging optimization problem in an incomplete market.