Optimization-based decision support for designing industrial symbiosis district energy systems under uncertainty

In the face of climate change, energy efficiency has become an urgent global concern. Exchanging energy between industrial sectors and urban buildings offers a promising avenue to enhance efficiency, mainly through energy exchange. This study establishes a generic method to facilitate designing optimal energy usage for districts with residential and industrial/commercial stakeholders in close proximity. Specifically, it focuses on harnessing industrial excess heat to meet the space heating needs of neighboring buildings. Given pervasive uncertainty in numerous parameters, a thorough global sensitivity analysis demonstrates the significant influence of specific factors, enabling the selection of more robust solutions by considering uncertainty in the most impactful parameters. This work validates the approach using a real case study in Montréal, Canada and underscores the advantages of district energy systems. Notably, this approach demonstrates the potential for a remarkable 96% reduction in emissions compared to current emission levels. The results clearly illustrate that disrupting traditional energy supply arrangements can play an essential role in reducing emissions within urban environments, thus taking critical steps toward achieving net-zero cities and society. •Introducing a generic optimization model for urban heating system retrofitting.•Decarbonizing building heat demand through industrial excess heat recovery.•Performing global sensitivity analysis to prioritize model parameters.•Optimizing decisions in uncertain conditions.•Demonstrating the potential for 96% annual emissions reduction in a real case study.