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Benchmarking stochastic and deterministic MPC: A case study in stationary battery systems
Author(s) -
Kumar Ranjeet,
Jalving Jordan,
Wenzel Michael J.,
Ellis Matthew J.,
ElBsat Mohammad N.,
Drees Kirk H.,
Zavala Victor M.
Publication year - 2019
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16551
Subject(s) - benchmarking , benchmark (surveying) , model predictive control , computer science , constraint (computer aided design) , range (aeronautics) , implementation , battery (electricity) , mathematical optimization , constraint satisfaction , control (management) , artificial intelligence , engineering , mathematics , probabilistic logic , power (physics) , physics , mechanical engineering , geodesy , marketing , quantum mechanics , aerospace engineering , business , programming language , geography
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.