Approximate Dynamic Programming for Degradation-aware Market Participation of Battery Energy Storage Systems: Bridging Market and Degradation Timescales

TL;DR

This paper introduces an approximate dynamic programming method that effectively manages battery degradation and market participation by separating long-term health dynamics from short-term market actions, enabling real-time decision-making.

Contribution

The paper develops a novel value function approximation that decouples degradation and market timescales, allowing integration of detailed physics-based models with real-time control.

Findings

Policy outperforms benchmark strategies in backtests

Decoupling timescales improves computational tractability

Offline degradation modeling enhances long-term decision quality

Abstract

We present an approximate dynamic programming framework for designing degradation-aware market participation policies for battery energy storage systems. The approach employs a tailored value function approximation that reduces the state space to state of charge and battery health, while performing dynamic programming along a pseudo-time axis encoded by state of health. This formulation enables an offline/online computation split that separates long-term degradation dynamics (months to years) from short-term market dynamics (seconds to minutes) -- a timescale mismatch that renders conventional predictive control and dynamic programming approaches computationally intractable. The main computational effort occurs offline, where the value function is approximated via coarse-grained backward induction along the health dimension. Online decisions then reduce to a real-time tractable one-step predictive control problem guided by the precomputed value function. This decoupling allows the integration of high-fidelity physics-informed degradation models without sacrificing real-time feasibility. Backtests on historical market data show that the resulting policy outperforms several benchmark strategies with optimized hyperparameters.