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High performance of HVAC connected Building Integrated Photovoltaic/Thermal (BIPV/T) systems relies on appropriate control. However, optimal control is often overlooked, resulting in systems that operate inefficiently. This paper investigates how model predictive control (MPC) can improve the operation of open loop air-based BIPV/T systems connected to multiple thermal applications. The BIPV/T system at the first institutional net-zero energy building in Canada, the Varennes library, is used as an archetype. The BIPV/T covers 16% of the south-facing roof and operates under a simple rule-based control strategy. The developed control and design strategies consider variations of this system, to achieve higher thermal utilization efficiency. A control-oriented BIPV/T model is developed and calibrated using monitored data. The BIPV/T airflow is regulated through MPC for simultaneous heat supply to an Energy Recovery Ventilator and air-to-water heat pump. The BIPV/T air flow is efficiently controlled, considering the connected thermal applications, environmental conditions, and PV temperature. Model-based control for BIPV/T systems can increase the amount of useful heat and reduce PV overheating. The MPC controller for the examined system reduced the building energy consumption compared to the business-as-usual operation by 40% and together with increased BIPV/T area can supply excess heat to adjacent buildings. [ABSTRACT FROM AUTHOR]

This article presents the results of a 182 day implementation of a model predictive controller (MPC) in a test house in Ottawa, Canada. The MPC controls the timing of hydronic floor heating and cooling within the highly glazed house (13.5% south-facing window-to-floor ratio). The objectives of the MPC are to optimally control the on/off times of active heating and cooling to minimize energy use while also minimizing air temperature violations outside of a prescribed range. It was found that 71% of the days in the experimental period saw successful operation of the MPC towards these goals. Days that experienced overheating were found to be concentrated in January–February and days that experienced overcooling were found to be concentrated in March–May. Problematic decision making by the MPC was found to be rooted in 4 main issues: (1) global horizontal irradiance (GHI) is a bad indicator of passive solar on its own; (2) weather forecasting (specifically GHI forecasting) is critical to MPC success in highly glazed buildings; (3) the GHI history regressor used in the prediction model regularly influenced predictions illogically; and (4) the MPC lacked decision-making logic on the timing of hydronic floor cooling. • A model predictive controller was implemented in a highly glazed, full-scale, test house. • Performance of the model predictive controller was evaluated over 7 months. • The design of the MPC is simple and practical for implementation in a real building. • A recommendation is made on what a model predictive controller could look like for a highly glazed house. [ABSTRACT FROM AUTHOR]

• A novel hybridization scheme using both stochastic and deterministic techniques. • Achieves an optimum operational trajectory under forecast uncertainties. • Simplified and computationally efficient problem formulation. • Reduced dependence on frequently updated forecasts. • Proof-of-concept is depicted on a remote off-grid PV-Diesel-Battery power system. With the precipitated integration of intermittent renewable sources, operation optimization employed with uncertainty management strategies is imperative to ensure a reliable, cost-effective, and secure energy supply for the remote off-grid power systems. This paper presents a two-stage operation optimization process embedded in a model predictive control framework. The first stage decision variables are derived in a deterministic optimization framework to achieve minimum operational costs and emissions. The second stage uses a stochastic optimization framework to refine the first stage decision variables to achieve a feasible operation considering several candidate scenarios in a computationally inexpensive manner. Several measures are integrated to the proposed framework in order to address the operational requirements pertaining to remote off-grid power systems. The effectiveness of the proposed framework is demonstrated through numerical experiments for an isolated remote power system in Northern Canada for both summer and winter seasons. Quality of the obtained results as well as the computation efficiency of the overall framework has been verified compared to the existing energy management techniques. The overall result confirms the applicability of the proposed method in achieving a cost-effective and environmentally friendly operational trajectory while effectively accounting for the underlying uncertainties with a reduced computational burden. [ABSTRACT FROM AUTHOR]