The German residential building sector plays a crucial role in achieving net greenhouse gas neutrality by 2045. However, current renovation rates remain insufficient to meet climate targets. To improve the efficiency of the sector's transformation, transformation pathways can be utilized, providing plans that specify renovation actions, timings, and locations. Despite their potential, existing literature lacks holistic and exact optimization frameworks for determining such pathways.

To address this gap, the author introduces a problem formulation and a MILP model to optimize transformation pathways for residential building stocks. To accelerate the solution process, he explores multiple Benders decompositions of the original MILP model. Additionally, he implements acceleration techniques, including constraint modifications, valid inequalities, strengthened Benders optimality cuts, a construction heuristic, and an in-out method. The Benders cut separation process is integrated into Gurobi's branch-and-cut algorithm.

About the author

Roman Delorme holds master's degrees in Energy Engineering and in Management, Business, and Economics from RWTH Aachen University, Germany. He is currently a research associate at the university's Chair of Operations Research, where he focuses on developing and refining solution methods for mixed-integer optimization models in the context of energy-related challenges.

Autorentext

Roman Delorme holds master’s degrees in Energy Engineering and in Management, Business, and Economics from RWTH Aachen University, Germany. He is currently a research associate at the university's Chair of Operations Research, where he focuses on developing and refining solution methods for mixed-integer optimization models in the context of energy-related challenges.

Inhalt

Introduction.- Theoretical Background.- Problem and Model Development.- Model Decompositions.- Solution Approach and Implementation.- Computational Studies.- Conclusion.