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Benchmarking stochastic and deterministic MPC: A case study in stationary battery systems

Funding information National Science Foundation; Johnson Controls Abstract We present a computational framework that integrates forecasting, uncertainty quantification, and model predictive control (MPC) to benchmark the performance of deterministic and stochastic MPC. By means of a battery management case study, we illustrate how off-the-shelf deterministic MPC implementations can suffer significant losses in performance and constraint violations due to their inability to handle disturbances that cannot be adequately represented by mean (most likely) forecasts. We also show that adding constraint back-off terms can help ameliorate these issues but this approach is ad hoc and does not provide performance guarantees. Stochastic MPC provides a more systematic framework to handle these issues by directly capturing uncertainty descriptions of a wide range of disturbances.