Long-Term Multi-objective Optimization for Integrated Unit Commitment and Investment Planning for District Heating Networks

Stephanie Riedmüller (), Fabian Rivetta and Janina Zittel ()
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Stephanie Riedmüller: Zuse Institute Berlin
Fabian Rivetta: Zuse Institute Berlin
Janina Zittel: Zuse Institute Berlin

A chapter in Operations Research Proceedings 2024, 2025, pp 235-241 from Springer

Abstract: Abstract The need to decarbonize the energy system has intensified the focus on district heating networks in urban and suburban areas. Therefore, exploring transformation pathways with reasonable trade-offs between economic viability and environmental goals became necessary. We introduce a network-flow-based model class integrating unit commitment and long-term investment planning for multi-energy systems. While the integration of unit commitment and investment planning has been applied to multi-energy systems, a formal introduction and suitability for the application of long-term portfolio planning of an energy provider on an urban scale has yet to be met. Based on mixed integer linear programming, the model bridges the gap between overly detailed industrial modeling tools not designed for computational efficiency at scale and rather abstract academic models. The formulation is tested on Berlin’s district heating network. Hence, the challenge lies in a large number of variables and constraints and the coupling of time steps, for example, through investment decisions. A case study explores different solutions on the Pareto front defined by optimal trade-offs between minimizing costs and CO $$_2$$ 2 emissions through a lexicographic optimization approach. The resulting solution catalog can provide decision-makers valuable insights into feasible transformation pathways, highlighting distinctions between robust and target-dependent investments.

Keywords: multi-objective optimization; multi-energy systems; investment planning; unit commitment (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-92575-7_33

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