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Bibliographic Details
Main Authors: Nespoli, Lorenzo, Medici, Vasco
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2511.21619
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author Nespoli, Lorenzo
Medici, Vasco
author_facet Nespoli, Lorenzo
Medici, Vasco
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2511_21619
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Robust Rule-Based Sizing and Control of Batteries for Peak Shaving Applications
Nespoli, Lorenzo
Medici, Vasco
Systems and Control
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.
title Robust Rule-Based Sizing and Control of Batteries for Peak Shaving Applications
topic Systems and Control
url https://arxiv.org/abs/2511.21619