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We propose a multiscale model predictive control (MPC) framework forstationary battery systems that exploits high-fidelity models to trade-offshort-term economic incentives provided by energy and frequency regulation (FR)markets and long-term degradation effects. We find that the MPC framework candrastically reduce long-term degradation while properly responding to FR andenergy market signals (compared to MPC formulations that use low-fidelitymodels). Our results also provide evidence that sophisticated battery modelscan be embedded within closedloop MPC simulations by using modern nonlinearprogramming solvers (we provide an efficient and easy-to-use implementation inJulia). We use insights obtained with our simulations to design alow-complexity MPC formulation that matches the behavior obtained withhigh-fidelity models. This is done by designing a suitable terminal penaltyterm that implicitly captures longterm degradation. The results suggest thatcomplex degradation behavior can be accounted for in low-complexity MPCformulations by properly designing the cost function. We believe that ourproof-of-concept results can be of industrial relevance, as battery vendors areseeking to participate in fast-changing electricity markets while maintainingasset integrity.

Multiscale Model Predictive Control of Battery Systems for Frequency Regulation Markets using Physics-Based Models