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Thermal and Electrical Load Optimization in Building Clusters for Energy Flexibility in Grid Interaction
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Thermal and Electrical Load Optimization in Building Clusters for Energy Flexibility in Grid Interaction
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Journal Article
Thermal and Electrical Load Optimization in Building Clusters for Energy Flexibility in Grid Interaction
2025
Overview
The transition from traditional to smart grid operations necessitates studies that assess buildings potential for providing energy flexibility to the electrical grid. This paper investigates the effects of optimizing thermal and electrical loads in a building cluster supported by a microgrid equipped with photovoltaic panels and battery storage systems. It examines how leveraging building thermal mass can flatten the load profile and support the operation of the connected microgrid. This simulation study employs a data-driven methodology based on Resistance-Capacitance thermal networks and machine learning. Using these techniques, the aggregated thermal and electrical loads of the buildings are evaluated to develop an energy management strategy based on model predictive control. The model predictive control strategy aims to optimize grid interaction by enhancing self-consumption and energy flexibility, quantified in terms of load leveling and flattening. The study includes simulations with variations of key parameters to assess performance under different scenarios. The case study focuses on a virtual community located in Varennes, Québec, Canada, utilizing data from institutional and residential buildings, including the Varennes Library-the first Net Zero institutional building in Canada-and its surrounding buildings. These buildings are equipped with building automation systems and smart thermostats/meters, providing access to real-time operational data. Results for typical days, identified through the k-means clustering technique, demonstrate that the ratio between average daily energy consumption and peak energy consumption of the virtual community can increase by over 80% with the utilization of communal battery storage in the microgrid. Additionally, integrating thermal load management through strategic temperature setpoint variations within buildings can half of the increase of the load factor, potentially reducing the required battery size.
