Unlocking Extra Value from Grid Batteries Using Advanced Models

TL;DR

This study demonstrates that using physics-based degradation models in operational strategies for grid batteries can significantly increase revenue and lifespan, outperforming conventional methods in energy trading applications.

Contribution

The paper introduces a physics-based degradation model integrated into economic optimization for battery operation, showing improved revenue and lifespan in real-world experiments.

Findings

20% increase in revenue compared to existing methods

30% reduction in battery degradation

70% potential increase in lifetime revenue

Abstract

Lithium-ion batteries are increasingly being deployed in liberalised electricity systems, where their use is driven by economic optimisation in a specific market context. However, battery degradation depends strongly on operational profile, and this is particularly variable in energy trading applications. Here, we present results from a year-long experiment where pairs of batteries were cycled with profiles calculated by solving an economic optimisation problem for wholesale energy trading, including a physically-motivated degradation model as a constraint. The results confirm the conclusions of previous simulations and show that this approach can increase revenue by 20% whilst simultaneously decreasing degradation by 30% compared to existing methods. Analysis of the data shows that conventional approaches cannot increase the number of cycles a battery can manage over its lifetime, but the physics-based approach increases the lifetime both in terms of years and number of cycles, as well as the revenue per year, increasing the possible lifetime revenue by 70%. Finally, the results demonstrate the economic impact of model inaccuracies, showing that the physics-based model can reduce the discrepancy in the overall business case from 170% to 13%. There is potential to unlock significant extra performance using control engineering incorporating physical models of battery ageing.