Robust stochastic optimal control via variance penalization: application to energy management systems
Abstract
This paper addresses a class of robust stochastic optimal control problems. Its main contribution lies in the introduction of a general optimization model with variance penalization and an associated solution algorithm that improves out-of-sample robustness while preserving numerical complexity. The proposed variance-penalized model is inspired by a well-established machine learning practice that aims to limit overfitting and extends this idea to stochastic optimal control. Using the Douglas–Rachford splitting method, the authors develop a Variance-Penalized Progressive Hedging Algorithm (VPPHA) that retains the computational complexity of the standard PHA while achieving superior out-of-sample performance. In addition, the authors propose a three-step control framework comprising (i) a random scenario generation method, (ii) a scenario reduction algorithm, and (iii) a scenario-based optimal control computation using the VPPHA. Finally, the proposed method is validated through simulations of a stationary battery Energy Management System using ground-truth electricity consumption and production measurements from a predominantly commercial building in Solaize, France. The results demonstrate that the proposed approach outperforms a classical Model Predictive Control strategy, which itself performs better than the standard PHA.
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BibTeX
@article{malisani2026robust,
title = {{Robust stochastic optimal control via variance penalization: application to energy management systems}},
author = {Paul Malisani and Adrien Spagnol and Vivien Smis-Michel},
journal = {Computational Optimization and Applications},
year = {2026},
month = jun,
doi = {10.1007/s10589-026-00802-3},
url = {https://ifp.hal.science/hal-05732626v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Robust stochastic optimal control via variance penalization: application to energy management systems
AU - Paul Malisani
AU - Adrien Spagnol
AU - Vivien Smis-Michel
PY - 2026
DA - 2026/06/20
JO - Computational Optimization and Applications
DO - 10.1007/s10589-026-00802-3
UR - https://ifp.hal.science/hal-05732626v1
AB - This paper addresses a class of robust stochastic optimal control problems. Its main contribution lies in the introduction of a general optimization model with variance penalization and an associated solution algorithm that improves out-of-sample robustness while preserving numerical complexity. The proposed variance-penalized model is inspired by a well-established machine learning practice that aims to limit overfitting and extends this idea to stochastic optimal control. Using the Douglas–Rachford splitting method, the authors develop a Variance-Penalized Progressive Hedging Algorithm (VPPHA) that retains the computational complexity of the standard PHA while achieving superior out-of-sample performance. In addition, the authors propose a three-step control framework comprising (i) a random scenario generation method, (ii) a scenario reduction algorithm, and (iii) a scenario-based optimal control computation using the VPPHA. Finally, the proposed method is validated through simulations of a stationary battery Energy Management System using ground-truth electricity consumption and production measurements from a predominantly commercial building in Solaize, France. The results demonstrate that the proposed approach outperforms a classical Model Predictive Control strategy, which itself performs better than the standard PHA.
SN - 0926-6003
SN - 1573-2894
ER -