We use Monte Carlo analysis to examine the potential of increased renewable generation to provide a hedge against variability in energy prices and costs. Fuel costs, electricity demand and wind generation are allowed to vary and a unit commitment and economic dispatch algorithm is employed to produce costminimising generation schedules under different levels of installed wind capacity. Increased wind capacity reduces the mean and the variance of production costs but only the variance of electricity prices. Wind generators see their market revenues increase while consumer payments and fossil generator profits do not considerably vary as wind capacity increases. Risk aversion is captured by considering the Conditional Value-at-Risk for both consumers and producers. The optimal level of wind generation increases as risk aversion increases due to the potential of wind to act as a hedge against very high electricity prices in high fuel price scenarios.
electricity generation Monte Carlo analysis utility framework Conditional Value-at-Risk