Sensitivity Analysis of Performance-Based Partitioning in District Heating Networks

TL;DR

This study analyzes how various system parameters affect the optimal partitioning of district heating networks, demonstrating that well-designed partitions are generally robust with minimal cost increase under perturbations.

Contribution

It introduces a sensitivity analysis framework for performance-based partitioning in district heating networks using a learning-enhanced branch and bound method.

Findings

Nominal partitions increase costs by only 2.8% on average under perturbations.

In 3 out of 12 cases, the nominal partition remains globally optimal despite parameter changes.

Sensitivity of the optimality loss metric highlights the importance of tuning cost functions and initial conditions.

Abstract

The paper presents a sensitivity analysis of the factors affecting the optimal partitioning of a district heating network for distributed control. Leveraging a physics-based, distributed model predictive control framework and a performance-based partitioning method, this work studies the relationship between variations in system parameters and the resulting optimal partition, providing insight into the robustness of a nominally designed partition to perturbed operating conditions. The enabling methodology is a learning-enhanced branch and bound method that culls the search space, reducing the number of partitions evaluated for each case. The sensitivity of the nominally optimal partition is characterized across twelve parameter variations, including supply temperature, operating season, building flexibility, pipe characteristics, and building type. This simulation study shows that a well-designed nominal partition exhibits an average cost increase of only 2.8% relative to centralized control across eleven of the twelve cases, with three cases identifying the nominal partition as globally optimal under the perturbed conditions. The robustness study is followed by an analysis of the sensitivity of the optimality loss metric (OLM), revealing that, in five of twelve cases, the case-specific OLM-minimizing partitions underperform the nominally optimal one due to shifts in the relative magnitude of heat loss versus flexibility costs. This indicates that proper tuning of cost function weights and initial conditions for the performance optimization problem is essential for reliable partition selection, and that seasonal repartitioning is warranted when demand profiles deviate substantially from the nominal, as observed in the November operating case.