Nonparametric Kernel Regression for Coordinated Energy Storage Peak Shaving with Stacked Services

TL;DR

This paper introduces a nonparametric kernel regression framework for coordinating energy storage in commercial buildings, enabling peak shaving and energy arbitrage without relying on forecasts, leading to significant cost savings.

Contribution

It presents a novel two-stage, data-driven control strategy with theoretical guarantees for peak shaving and energy arbitrage in energy storage systems.

Findings

Achieves 1.3x performance improvement over forecast-based methods

Reduces electricity costs and extends battery life in case studies

Operates effectively without relying on demand predictions

Abstract

Developing effective control strategies for behind-the-meter energy storage to coordinate peak shaving and stacked services is essential for reducing electricity costs and extending battery lifetime in commercial buildings. This work proposes an end-to-end, two-stage framework for coordinating peak shaving and energy arbitrage with a theoretical decomposition guarantee. In the first stage, a non-parametric kernel regression model constructs state-of-charge trajectory bounds from historical data that satisfy peak-shaving requirements. The second stage utilizes the remaining capacity for energy arbitrage via a transfer learning method. Case studies using New York City commercial building demand data show that our method achieves a 1.3 times improvement in performance over the state-of-the-art forecast-based method, achieving cost savings and effective peak management without relying on predictions.