Hierarchical MPC for building energy management: Incorporating data-driven error compensation and mitigating information asymmetry

Jens Engel, Thomas Schmitt, Tobias Rodemann and Jürgen Adamy

Applied Energy, 2024, vol. 372, issue C, No S0306261924011632

Abstract: The increasing adoption of renewable energy sources (RESs) in public power grids has led to a demand for more intelligent energy management systems (EMSs) in large-scale buildings. A common approach for controlling EMSs for buildings is Model Predictive Control (MPC). For large-scale buildings, hierarchical MPC schemes have been proposed, offering the advantage of scalability through problem decomposition into multiple layers. However, hierarchical schemes often suffer from information mismatch due to information asymmetry between layers, leading to suboptimal control performance. This issue is worsened by model errors inherent to the models underlying the MPC controllers. To address these challenges, we propose a hierarchical MPC approach, which includes data-driven error compensation. Additionally, to mitigate information mismatch, a one-iteration communication step is introduced between the hierarchical layers. The proposed approach comprises two layers: an aggregator layer that controls overall energy flows of the building, and a distributor layer that allocates thermal energy to individual temperature zones. The distributor may request additional thermal budget by providing the aggregator with an otherwise expected performance loss, which it can trade off accordingly. The approach is evaluated in a software-in-the-loop (SiL) simulation using a physics-based digital twin model of a multi-zone commercial building, showing notable improvements in overall control performance in comparison to a naive hierarchical baseline and similar performance to a monolithic baseline.

Keywords: Energy management system; Model predictive control; Data-driven residual estimator; Disturbance prediction; Digital twin; Information asymmetry (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123780

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