The Value of Multi-stage Stochastic Programming in Risk-averse Unit Commitment under Uncertainty

TL;DR

This paper evaluates the benefits of multi-stage stochastic programming in risk-averse unit commitment for power systems, showing it offers increased flexibility and value under high uncertainty at the cost of higher computational effort.

Contribution

It provides theoretical and empirical analysis quantifying the value of multi-stage solutions over two-stage models in risk-averse stochastic unit commitment.

Findings

Value of multi-stage solution increases with uncertainty and number of periods.

Value decreases as decision maker's risk aversion increases.

Multi-stage models offer more flexibility but require higher computational effort.

Abstract

Day-ahead scheduling of electricity generation or unit commitment is an important and challenging optimization problem in power systems. Variability in net load arising from the increasing penetration of renewable technologies have motivated study of various classes of stochastic unit commitment models. In two-stage models, the generation schedule for the entire day is fixed while the dispatch is adapted to the uncertainty, whereas in multi-stage models the generation schedule is also allowed to dynamically adapt to the uncertainty realization. Multi-stage models provide more flexibility in the generation schedule, however, they require significantly higher computational effort than two-stage models. To justify this additional computational effort, we provide theoretical and empirical analyses of the value of multi-stage solution for risk-averse multi-stage stochastic unit commitment models. The value of multi-stage solution measures the relative advantage of multi-stage solutions over their two-stage counterparts. Our results indicate that, for unit commitment models, value of multi-stage solution increases with the level of uncertainty and number of periods, and decreases with the degree of risk aversion of the decision maker.