# Operational Valuation for Energy Storage under Multi-stage Price Uncertainties

arXiv: https://arxiv.org/abs/1910.09149 · HTML: https://mlsanity.com/paper/1910.09149/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Energy Storage Price Arbitrage via Opportunity Value Function Prediction](https://mlsanity.com/paper/2211.07797/) (2022)
- [Transferable Energy Storage Bidder](https://mlsanity.com/paper/2301.01233/) (2023)
- [Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets](https://mlsanity.com/paper/2410.11180/) (2024)

## Abstract (arXiv)

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.