Optimal Online Peak Minimization Using Energy Storage

TL;DR

This paper develops an online algorithm for energy storage allocation that minimizes peak demand costs, achieving optimal competitive ratio and adaptive performance, with real-world simulations showing significant demand reduction.

Contribution

It introduces a novel online algorithm for peak minimization with energy storage, achieving optimal competitive ratio and adaptive performance in a challenging online setting.

Findings

Achieves the optimal competitive ratio for online peak minimization.

Develops an anytime-optimal online algorithm with adaptive performance.

Reduces peak demand by 19% in simulations compared to baselines.

Abstract

The significant presence of demand charges in electric bills motivates large-load customers to utilize energy storage to reduce the peak procurement from the grid. We herein study the problem of energy storage allocation for peak minimization, under the online setting where irrevocable decisions are sequentially made without knowing future demands. The problem is uniquely challenging due to (i) the coupling of online decisions across time imposed by the inventory constraints and (ii) the noncumulative nature of the peak procurement. We apply the CR-Pursuit framework and address the challenges unique to our minimization problem to design an online algorithm achieving the optimal competitive ratio (CR) among all online algorithms. We show that the optimal CR can be computed in polynomial time by solving a linear number of linear-fractional problems. More importantly, we generalize our approach to develop an \emph{anytime-optimal} online algorithm that achieves the best possible CR at any epoch, given the inputs and online decisions so far. The algorithm retains the optimal worst-case performance and attains adaptive average-case performance. Trace-driven simulations show that our algorithm can decrease the peak demand by an extra 19% compared to baseline alternatives under typical settings.