Optimal Battery Bidding under Decision-Dependent State-of-Charge Uncertainties

TL;DR

This paper develops and compares three optimization strategies for battery bidding that explicitly account for the decision-dependent uncertainties in State of Charge estimation, improving reliability and revenue.

Contribution

It introduces an uncertainty-aware optimization model that explicitly considers decision-dependent SOC uncertainties, outperforming simpler methods in revenue and reliability.

Findings

The uncertainty-aware model maximizes revenue while ensuring reliable frequency reserves.

Neglecting SOC uncertainty can cause significant delivery failures.

All proposed methods improve robustness against SOC estimation errors.

Abstract

Lithium Iron Phosphate (LFP) Battery Energy Storage Systems (BESSs) are a key enabler of the energy transition. However, they are known to exhibit significant inaccuracies in the estimation of their State of Charge (SOC). Such estimation errors can directly impact the participation of BESSs in electricity markets. In this work, we demonstrate that neglecting SOC uncertainty in battery bidding can lead to significant delivery failures, including the inability to meet promised frequency reserves. To address this risk, we investigate bidding strategies that account for SOC uncertainty. We propose three constraint-tightening optimization approaches of increasing complexity: (i) a fixed-margin formulation, (ii) an adaptive-margin optimizer, and (iii) an uncertainty-aware optimization model. The latter explicitly accounts for the decision-dependent nature of the uncertainty. Numerical results demonstrate that while all three approaches robustify against SOC uncertainty, the uncertainty-aware formulation outperforms the others in maximizing revenue while ensuring reliable frequency reserve provision. This highlights the significance of treating SOC uncertainty as an endogenous process within the operational strategy.