While only about 30 percent of California’s usable water storage capacity lies at higher elevations, high-elevation hydropower units generate, on average, 74 percent of California’s instate hydroelectricity.
In general, high-elevation plants have small man-made reservoirs and rely mainly on snowpack. Their low built-in storage capacity is a concern with regard to climate warming. Snowmelt is expected to shift to earlier in the year, and the system may not be able to store sufficient water for release in high-demand periods. Previous studies have explored the climate warming effects on California’s high-elevation hydropower system by focusing on the supply side (exploring the effects of hydrological changes on generation and revenues) but they have ignored the warming effects on hydropower demand and pricing.
This study extends the previous work by simultaneous consideration of climate change effects on high-elevation hydropower supply and demand in California. Artificial Neural Network models were developed as long-term price estimation tools, to investigate the impact of climate warming on energy prices. California’s Energy-Based Hydropower Optimization Model (EBHOM) was then applied, to estimate the adaptability of California’s high-elevation hydropower system to climate warming, considering the warming effects on hydropower supply and demand.
The model was run for dry and wet warming scenarios, representing a range of hydrological changes under climate change. The model’s results relative to energy generation, energy spills, reservoir energy storage, and average shadow prices of energy generation and storage capacity expansion are examined and discussed. The modeling results are compared with previous studies to emphasize the need to consider climate change effects on hydroelectricity demand and pricing when exploring the effects of climate change on California’s hydropower system.