Two-stage models have been the main tools to derive the optimal capacity mix of a wholesale electricity market. In the first stage, independent power producers (IPPs) maximize the expected profits of their capacity investments in generation technologies like photovoltaic (PV) systems and combined cycle gas turbines (CCGTs). In the second stage, interactions among IPPs result in the market's short-run equilibrium prices and output levels for the first stage's determination of the market's long-run optimal capacity mix. However, solving such models necessitates simplifying assumptions about the market's structure that has become more detailed and complex. This study assesses the effect of demand uncertainty on the market's optimal capacity mix by comparing the solutions based on stochastic demand functions to those based on deterministic demand functions. While demand uncertainty can impact the market's optimal capacity mix, we use real-world data to document that its effect is numerically negligible. Hence, deterministic modelling, which is much easier to implement than stochastic modelling, can provide a good approximation to the optimal capacity mix when assessing future electricity market scenarios. This finding's policy implication is that demand uncertainty is a less important driver of a wholesale electricity market's capacity mix than other drivers like capacity and fuel costs. • Assessing the effects of demand uncertainty on the market's optimal capacity mix. • The effect of demand uncertainty on the market's optimal capacity mix is negligible. • The main drivers of the market's optimal capacity mix are fuel and capacity costs. • Deterministic model provides a good approximation to the optimal capacity mix. • Stochastic model is desired for capturing the hourly variation in electricity price.
Milstein et al. (Wed,) studied this question.