Depreciation Cost is a Poor Proxy for Revenue Lost to Aging in Grid Storage Optimization

Abstract

Dispatch of a grid energy storage system for arbitrage is typically formulated into a rolling-horizon optimization problem that includes a battery aging model within the cost function. Quantifying degradation as a depreciation cost in the objective can increase overall profits by extending lifetime. However, depreciation is just a proxy metric for battery aging; it is used because simulating the entire system life is challenging due to computational complexity and the absence of decades of future data. In cases where the depreciation cost does not match the loss of possible future revenue, different optimal usage profiles result and this reduces overall profit significantly compared to the best case (e.g., by 30–50 %). Representing battery degradation perfectly within the rolling-horizon optimization does not resolve this-in addition, the economic cost of degradation throughout life should be carefully considered. For energy arbitrage, optimal economic dispatch requires a tradeoff between overuse, leading to high return rate but short lifetime, vs. underuse, leading to a long but not profitable life. We reveal the intuition behind selecting representative costs for the objective function, and propose a simple moving average filter method to estimate degradation cost. Results show that this better captures peak revenue, assuming reliable price forecasts are available.