Battery systems play an increasing role in powering electric grids, but their widespread deployment is hindered by the inability to determine how energy stored in a battery evolves with different operating schedules. Conventional approaches to determine how much energy is available from a battery system typically rely on information that grid operators may not have, such as battery temperature, direct current (DC) efficiency, and inverter efficiency. Even when such information is available, conventional linear approaches for determining these metrics result in overly simplistic approximations that can be inaccurate.

Researchers at Pacific Northwest National Laboratory developed an innovative, nonlinear modeling approach to estimate the energy available in a battery with different planning schedules. Based on actual performance of the battery system, the model identifies periods of charging or discharging and uses them to estimate expected battery performance. Grid operators can then use this estimate to issue operating instructions for each battery system, using it to peak efficiency.

The model runs on any computer system and is effective regardless of the data available (such as battery chemistry and capacity). Calculations accurately determine performance at different states of charge, operating temperatures, and/or output. The resulting estimate accounts for conditions, such as operating mode, power, state of health, state-of-charge range, and temperature. Because it is a self-learning model that uses data directly from the battery itself, the estimate is more reliable than other, indirect methods. Knowing expected battery performance offers flexibility on its use, which can improve economic viability.

APPLICABILITY

Armed with an accurate estimate of battery performance, grid operators can

• Maximize revenue during market operations, by understanding optimal charging rates and finding operational “sweet spots” that minimize energy losses
• Use grid-deployed battery systems for “peak shaving,” in which the battery systems are brought online to provide extra capacity during high demand
• Store energy generated by renewables and provide a time shift from when the renewable energy is generated to when the energy is needed (for example, store energy generated by solar panels during the afternoon and provide the energy to the grid during high-demand evening hours).