Reachability Guarantees for Energy Arbitrage

TL;DR

This paper presents a unified stochastic framework for battery energy arbitrage that guarantees terminal state-of-charge constraints with high confidence, using probabilistic methods and online threshold policies to improve operational reliability.

Contribution

It introduces a novel probabilistic dynamic programming approach with explicit thresholds and a distribution pruning method for energy arbitrage under uncertainty.

Findings

Improves estimation of state-of-charge evolution.

Supports high-confidence chance-constraint satisfaction.

Demonstrates effectiveness on real price data.

Abstract

This paper introduces a unified framework for battery energy arbitrage under uncertain market prices that integrates chance-constrained terminal state-of-charge requirements with online threshold policies. We first cast the multi-interval arbitrage problem as a stochastic dynamic program enhanced by a probabilistic end-of-horizon state-of-charge (SoC) constraint, ensuring with high confidence that the battery terminates within a prescribed energy band. We then apply a $k$-search algorithm to derive explicit charging (buying) and discharging (selling) thresholds with provable worst-case competitive ratio, and compute the corresponding action probabilities over the decision horizon. To compute exact distributions under operational limits, we develop a probability redistribution pruning method and use it to quantify the likelihood of meeting the terminal SoC band. Leveraging the resulting SoC distribution, we estimate the minimum stopping-time required to satisfy the SoC chance constraint. Computational experiments on historical real price data demonstrate that the proposed framework substantially improves the estimation of SoC evolution and supports chance-constraint satisfaction.