Battery Storage Co-Optimization in Day-Ahead and Real-Time Markets with Bayesian Optimization

2026-08-01

AI Summary

This work addresses the coordinated scheduling of battery energy storage systems in day-ahead and real-time electricity markets by proposing the ARBO-DART algorithm, which jointly optimizes day-ahead bidding curves and real-time dynamic dispatch policies without requiring explicit gradients or scenario approximations. The method embeds Bayesian optimization within a black-box stochastic control solver and incorporates an adaptive time-partitioning mechanism that dynamically refines the day-ahead schedule resolution during critical periods. By integrating a mean-reverting electricity price model with piecewise-linear state-of-charge dynamics, ARBO-DART efficiently recovers economically meaningful bidding structures under real-world price data. The algorithm achieves several-fold computational speedups compared to fixed-resolution approaches and represents the first gradient-free, closed-loop framework for integrated day-ahead and real-time market coordination.

Abstract

We propose Adaptive Refinement Bayesian Optimization for Day-Ahead and Real-Time (ARBO-DART) markets, an algorithm for BESS intraday dispatch co-optimization in which day-ahead (DA) commitment profiles are optimized against value of real-time (RT) recourse computed by a black-box stochastic control solver. In our framework, the DA price curve is taken as exogenous and RT prices evolve as a mean-reverting process around it. The RT recourse layer performs dynamic closed-loop control while accounting for the piecewise-linear state-of-charge dynamics and the DA-driven feasible control set. By wrapping Bayesian Optimization (BO) around the RT solver, ARBO-DART jointly optimizes DA commitments and dynamic RT flexibility without requiring analytic gradients, closed-form value functions, or finite-scenario approximations. To overcome the curse of dimensionality in fixed-resolution DA profiles, ARBO-DART starts from a coarse partition of DA commitments and progressively refines charge and discharge blocks where additional temporal resolution is most needed, as judged by the corresponding RT policy. Numerical experiments across realistic DA price curves reveal the effectiveness of ARBO-DART in recovering economically meaningful DA bidding structures while being several times faster relative to fixed-resolution

Problem

• Battery Energy Storage System
• Day-Ahead Market
• Real-Time Market
• Co-optimization
• Stochastic Control

Innovation

• Bayesian Optimization
• Battery Energy Storage System (BESS)
• Day-Ahead and Real-Time Markets
• Adaptive Refinement
• Stochastic Control