  This paper presents an analytical method for calculating the operational
value of an energy storage device under multi-stage price uncertainties. Our
solution calculates the storage value function from price distribution
functions directly instead of sampling discrete scenarios, offering improved
modeling accuracy over tail distribution events such as price spikes and
negative prices. The analytical algorithm offers very high computational
efficiency in solving multi-stage stochastic programming for energy storage and
can easily be implemented within any software and hardware platform, while
numerical simulation results show the proposed method is up to 100,000 times
faster than a benchmark stochastic-dual dynamic programming solver even in
small test cases. Case studies are included to demonstrate the impact of price
variability on the valuation results, and a battery charging example using
historical prices for New York City.
