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.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Springer Fachmedien Wiesbaden GmbH
Zielgruppe
Illustrationen
1
18 farbige Abbildungen, 1 s/w Abbildung
XX, 144 p. 19 illus., 18 illus. in color. Textbook for German language market.
Maße
Höhe: 210 mm
Breite: 148 mm
Dicke: 10 mm
Gewicht
ISBN-13
978-3-658-47465-2 (9783658474652)
DOI
10.1007/978-3-658-47466-9
Schweitzer Klassifikation
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.
