An evaluation of robust controls for passive building thermal mass and mechanical thermal energy storage under uncertainty

• Published in: Applied energy Vol. 111; pp. 602 - 623
• Main Author: Kim, Sean Hay
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.11.2013; Elsevier
• Subjects: Applied sciences; buildings; demand; Demand control; Energy; Exact sciences and technology; inventories; MPC; Risk; TES; Thermal mass; Uncertainty; MPC; Risk; Uncertainty; TES; Demand control; Thermal mass; Robust control; Evaluation; Buildings; Thermal energy storage
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2013.05.030

•Building thermal mass and TES are substantial demand-side control instruments.•MPC is proven to enhance their control performance and thus bring economic advantages.•Uncertainty in certain operating conditions could diminish their control effectiveness.•A robust MPC in which relevant uncertainty sources are compiled is proposed.•Robust MPC presents a stable performance in varied and non-indigenous conditions. Passive building thermal mass and mechanical thermal energy storage (TES) are known as one of state-of-the-art demand-side control instruments. Specifically, Model-based Predictive Control (MPC) for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field, its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This study pursues improvements of the control performance of both thermal inventories under uncertainty by proposing a robust MPC in which relevant uncertainty sources are compiled; therefore, it is designed to perform more stable than traditional MPCs under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls include: (i)Controls are developed based on the a priori uncertainty assessment, such that a systematic modeling approach for uncertainty was taken according to characteristics and classifications of uncertainty.(ii)The robust MPC reduces the variability of performance under varied and non-indigenous conditions compared to the deterministic MPC, and thus can avoid the worst case situation.