Storage technology optimization

Energy storage can help synchronize energy demand and supply, whether from an intermittent renewable resource or a vehicle charging station. Here we analyze energy consumption to determine lab-scale performance targets for storage technologies.

Stationary, large-scale energy storage optimization: We are studying patterns of electricity consumption and comparing them to changes in solar and wind availability throughout the day (Figure 2). From this analysis we are able to identify optimal properties for future storage materials and devices, and compare current technologies to these performance targets.

Figure 2. Price, energy resource dynamics, and the impact of storage. A) Cumulative distributions of hourly solar and wind capacity factors, and electricity prices. B) The frequencies and amplitudes of resource and price peaks. C) Energy resource and price time series, and the impact of storage in shifting generation to times of peak price (dotted to solid lines).
  • Braff WA, Mueller JM, Trancik JE, Value of storage technologies for wind and solar energy, in review.

Design rules for electric vehicle batteries: By studying patterns of travel and energy consumption in the transportation sector, we are identifying requirements for electric vehicle batteries. In addition, we have uncovered several surprising patterns in commuting across cities of various sizes, shown in Figure 1.

Figure 1. Comparison of driving patterns between four major US metro areas. Driving patterns, as characterized by the distance and duration of vehicle trips, are more similar than anticipated between cities with disparate population densities and road networks. Because of this, identifying broad performance requirements for electric vehicle batteries may be possible. Black dots represent the nation-wide distribution of trip distances, red dots represent the city listed. “M/M/M” corresponds to mean, median, and mode values, “n” is the survey sample size. Data has been filtered to reduce rounding bias in survey data. Data from 2009 US National Household Travel Survey.