Two-Stage Planning for Smart Buildings With Flexible Heating Load Considering Climate Change Induced Heat Waves

Abstract

Building energy planning is significantly challenged by climate change, particularly the increasing frequency of heat waves impacting heating and cooling demands. Current planning methodologies neglect the impacts of heat waves on energy consumption and do not accurately model the temperature-dependent performance of heat pumps (HPs). This paper addresses the critical issue of designing energy-efficient and climate-resilient buildings through optimal resource configuration under uncertain weather conditions. A two-stage stochastic optimization model for building energy system planning is proposed. In the first stage, the capacities of energy resources are optimized; in the second stage, operational strategies under various weather scenarios are determined. A novel long-term load forecasting method using morphing techniques is developed to generate scenario trees accounting for both normal conditions and heat waves, capturing the impact of climate change on energy demand. Additionally, a temperature-dependent HP model with finite partial output levels is introduced, improving upon existing fixed coefficient of performance models to reflect practical operational characteristics. Simulation results on a real educational building in Stockholm demonstrate the effectiveness of the approach, showing an 8.33% reduction in heating capacity requirements and a 62.14% decrease in solution time, enhancing both resilience and computational efficiency.