  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
trade-off 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.
