(303h) Learning-Based Economic Model Predictive Control for Energy Storage Systems Under Imperfect Forecast Information
AIChE Annual Meeting
Tuesday, November 9, 2021 - 2:43pm to 3:02pm
It has recently been shown that an EMPC scheme can be tuned to deliver the optimal policy of the real system even when using the wrong model (Gros and Zanon, 2019). This suggests that EMPC can be used as a function approximator within RL. In this work, we use this idea to design an EMPC scheme where the stage cost, terminal cost, and constraints are carefully parametrized. These parameters are then adjusted online to improve performance under imperfect forecast information using reinforcement learning techniques such as Q-learning. We show in closed-loop simulations how the RL-tuned MPC is able to improve performance under imperfect forecast information.
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