Evaluating the Impact of Model Accuracy for Optimizing Battery Energy Storage Systems

TL;DR

This paper compares simple linear and complex non-linear models for optimizing battery energy storage systems, showing that the more accurate non-linear model improves efficiency and revenue, especially for high-resistance batteries.

Contribution

It introduces a non-linear equivalent-circuit-model-based optimization approach for battery energy storage, demonstrating its advantages over linear models in energy trading scenarios.

Findings

Non-linear model outperforms linear model in accuracy.

Enhanced model improves operational efficiency and revenue.

High internal resistance batteries benefit most from the advanced model.

Abstract

This study investigates two models of varying complexity for optimizing intraday arbitrage energy trading of a battery energy storage system using a model predictive control approach. Scenarios reflecting different stages of the system's lifetime are analyzed. The findings demonstrate that the equivalent-circuit-model-based non-linear optimization model outperforms the simpler linear model by delivering more accurate predictions of energy losses and system capabilities. This enhanced accuracy enables improved operational strategies, resulting in increased roundtrip efficiency and revenue, particularly in systems with batteries exhibiting high internal resistance, such as second-life batteries. However, to fully leverage the model's benefits, it is essential to identify the correct parameters.