Representing Energy Price Variability in Long- and Medium-Term Hydropower Optimization

Abstract

Representing peak and off-peak energy prices is often difficult in hydropower modeling because the time scale of price variability (hours or less) is much shorter than that needed for many operations planning models (days to months). This work extends and examines the reliability of an existing approximate method to incorporate hourly energy price information into revenue functions used in hydropower reservoir optimization models with larger time steps (weekly or monthly). The method assumes constant head, an exogenously known frequency distribution for hourly prices during each modeled time period (day, week, or month), and a revenue-maximizing operational strategy that allocates hydropower releases in order of decreasing hourly price. The method is extended to the case with minimum instream flow requirements. The reliability of the method was tested for the cases with and without minimum instream flow requirements. Revenue estimates for a hypothetical hydropower site were compared with the exact optimal revenue from solving the hourly optimization problem within one week, and showed less than 1% error by using a finely discretized price-frequency curve.