Optimal Battery Bidding under Decision-Dependent State-of-Charge Uncertainties
For operators of LFP battery storage systems participating in electricity markets, this work provides a method to mitigate SOC estimation errors that cause delivery failures.
This paper shows that neglecting state-of-charge (SOC) uncertainty in battery bidding leads to significant delivery failures, and proposes three constraint-tightening optimization approaches. The uncertainty-aware formulation outperforms simpler methods, maximizing revenue while ensuring reliable frequency reserve provision.
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.