Optimal indoor temperature flexibility for thermal peak shaving in buildings connected to the district heating network
Keywords:Thermal peak shaving, Indoor temperature flexibility, Model Predictive Control (MPC), Data driven model
AbstractBuildings are currently non optimally controlled, using a weather compensation controller that depends only on external temperature. A rich amount of real-time data is available and can be used for better control. This work is focused on developing a general and dynamic model for utilizing the building as an energy storage for a peak-shaving control strategy.A dynamic grey-box model is developed using industry standard operators’ data from a multifamily building, Building A, located in Västerås, Sweden. The training period is set to 408 hours, and the prediction horizon to 48 hours. The model is verified in 4 steps: prediction ability on the historic data, parametric verification on the time constant, simulation of heat supply separated from the historic data and model generality by implementing the model on a second multifamily building, Building B. The modelling errors over a two-month simulated period are 8% for Building A and 9% for Building B. To demonstrate the utilization possibilities, an optimizer is constructed to evaluate a peak shaving control strategy. Different flexibilities for the indoor temperature have been examined yielding heat load peak shaving between 30 to 45%. Flexibility paves the way for improvement in pricing models for the heating sector. This work demonstrates the potential for utilizing building heat storage capacity to reduce peak consumption and costs.
Copyright (c) 2023 Mathilda Cederbladh, August Dahlberg, Stavros Vouros, Konstantinos Kyprianidis, Costanza Saletti, Mirko Morini
This work is licensed under a Creative Commons Attribution 4.0 International License.