Optimal Online Algorithms for Peak-Demand Reduction Maximization with Energy Storage

TL;DR

This paper introduces an optimal online algorithm for energy storage management that maximizes peak-demand reduction under uncertainty, achieving near-optimal performance and significant improvements over baselines.

Contribution

It develops a novel online algorithm with the best competitive ratio for peak-demand reduction, including an adaptive version that improves average-case results.

Findings

Achieves up to 81% peak reduction of the offline optimal

Adaptive algorithm improves peak reduction by at least 20% over baselines

Optimal competitive ratio can be computed efficiently using linear programs

Abstract

The high proportions of demand charges in electric bills motivate large-power customers to leverage energy storage for reducing the peak procurement from the outer grid. Given limited energy storage, we expect to maximize the peak-demand reduction in an online fashion, challenged by the highly uncertain demands and renewable injections, the non-cumulative nature of peak consumption, and the coupling of online decisions. In this paper, we propose an optimal online algorithm that achieves the best competitive ratio, following the idea of maintaining a constant ratio between the online and the optimal offline peak-reduction performance. We further show that the optimal competitive ratio can be computed by solving a linear number of linear-fractional programs. Moreover, we extend the algorithm to adaptively maintain the best competitive ratio given the revealed inputs and actions at each decision-making round. The adaptive algorithm retains the optimal worst-case guarantee and attains improved average-case performance. We evaluate our proposed algorithms using real-world traces and show that they obtain up to 81% peak reduction of the optimal offline benchmark. Additionally, the adaptive algorithm achieves at least 20% more peak reduction against baseline alternatives.