Robust Rule-Based Sizing and Control of Batteries for Peak Shaving Applications

TL;DR

This paper demonstrates that stochastically tuned rule-based controllers outperform deterministic model predictive control in battery sizing and control for peak shaving, offering more realistic cost estimates and improved operational performance.

Contribution

It introduces a stochastic tuning approach for rule-based controllers that enhances battery sizing and control for peak shaving applications.

Findings

RBCs achieve better performance than MPC in tests.

Stochastic tuning provides more realistic LCOE estimates.

Method validated on real meter data for peak shaving.

Abstract

As the cost of batteries lowers, sizing and control methods that are both fast and can achieve their promised performances when deployed are becoming more important. In this paper, we show how stochastically tuned rule based controllers (RBCs) can be effectively used to achieve both these goals, providing more realistic estimates in terms of achievable levelised cost of energy (LCOE), and better performances while in operation when compared to deterministic model predictive control (MPC). We test the proposed methodology on yearly profiles from real meters for peak shaving applications and provide strong evidence about these claims.