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

TL;DR

This paper presents a scalable, integrated model for long-term multi-objective optimization of district heating networks, balancing economic and environmental goals, demonstrated through a case study of Berlin.

Contribution

It introduces a novel network-flow-based mixed integer linear programming model suitable for large-scale urban district heating planning, combining unit commitment and investment decisions.

Findings

The model effectively balances cost and CO2 emissions.

Case study reveals diverse feasible transformation pathways.

The approach provides decision-makers with valuable strategic insights.

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 CO2 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.