Dear Reader,

For the 53rd issue of our heatbeat Research Newsletter, we take a look at two recently published papers in the field of district heating network planning and optimal control of the supply plants.

The first paper "Investigating the sensitivity of input parameters on the predicted allowable heat generation costs for district heating supply" by Friebe et al. introduces a new method to investigate the feasibility of district heating networks compared to decentralized, individual solutions. The second paper "Optimizing district heating operations: Network modeling and its implications on system efficiency and operation"

by Friedrich et al. investigates advanced district heating control technologies with day-ahead operational planning.

The first research paper investigates the sensitivity of various input parameters affecting the predicted allowable heat generation costs for district heating (DH) supply. With the growing emphasis on climate neutrality, municipalities face the crucial decision of whether to supply heat through district heating networks (DHNs) or rely on individual building-level heating solutions. Existing planning guidelines primarily focus on heat density as the key factor for economic feasibility, but this study argues that other variables significantly influence the cost-effectiveness of DHNs.

To address this, the paper introduces a new metric: Predicted Allowable Levelized Cost of Heat (PA-LCOH), which defines the maximum cost threshold for DHN heat generation to remain competitive with individual heating solutions. A sensitivity analysis was conducted using a Python-based model to examine 15 key parameters, including energy costs, heating system costs, heat density, piping costs, and the seasonal coefficient of performance (SCOP) of heat pumps.

The results indicate that heat density, plot ratio, and piping costs remain crucial for DHN feasibility, but factors affecting individual heating alternatives, such as electricity prices, SCOP of heat pumps, and biomass fuel costs, exhibit equally high sensitivity. The study finds that SCOP has the highest influence in heat pump scenarios, whereas biomass price dominates for biomass boilers. Additionally, interest rates and network investment costs significantly impact DHN competitiveness.

The findings challenge conventional DHN planning approaches that rely solely on area and network parameters. Instead, a holistic assessment incorporating individual heating costs is essential. Future research should focus on refining PA-LCOH calculations, exploring low-temperature DHNs, and incorporating real-world heat generation costs to enhance economic assessments.

The second research paper explores the optimization of district heating system (DHS) operations using advanced network modeling techniques. As DHSs increasingly integrate renewable energy sources and large-scale heat pumps (HPs), accurate operational planning is crucial to balance efficiency, cost, and technical feasibility. The study compares two network modeling approaches for day-ahead operational planning: a Mixed-Integer Linear Programming (MILP) model, which assumes uniform temperature distributions for computational efficiency, and a Mixed-Integer Non-Linear Programming (MINLP) model, which accounts for temperature-dependent heat losses, mixing of different temperature sources, and non-uniform temperature zones. To validate the models, transient simulations in Modelica were conducted, ensuring real-world applicability.

The results demonstrate that non-linear modeling significantly enhances heat pump efficiency by allowing for flexible temperature regulation. The MINLP model reduces HP supply temperatures by up to 15 °C, leading to higher coefficients of performance (COP) and lower operational costs. By contrast, the MILP model often overestimates required temperatures, resulting in higher energy consumption and reduced efficiency. In networks with mixed heat sources, such as heat pumps and combined heat and power (CHP) units, the non-linear model optimizes temperature mixing, reducing overall energy costs by up to 16 % and increasing network efficiency.

From an environmental perspective, the study finds that lower HP supply temperatures in the MINLP model reduce electricity-related CO₂ emissions, contributing to decarbonization efforts. However, the Carnot allocation method used in the analysis attributes higher emissions to heat at elevated temperatures, leading to increased CO₂ emissions for CHPs when operating alongside HPs. The study also explores the role of heat exchangers in network design, showing that low-temperature subnetworks can improve efficiency without requiring complex non-linear models. This suggests that strategic network topology modifications could serve as an alternative to computationally intensive optimization techniques.

The findings indicate that non-linear modeling is most beneficial in networks with diverse heating demands and flexible supply sources. Future research should focus on scaling non-linear models for large urban heating networks, integrating real-time AI-driven control strategies, and exploring new pricing mechanisms for CHP-HP competition in local heat markets. By leveraging advanced optimization techniques, DHSs can improve cost efficiency, reduce emissions, and enhance energy sustainability, supporting the transition toward low-carbon district heating solutions.

Further Information

As always, we recommend reading the article in full. We would also like to share a little note on our own behalf: In addition to this research newsletter and various blog posts, we have added a monthly feature update to our blog, summarizing important developments and new features in our heatbeat Digital Twin. You can find the first entry at "https://heatbeat.de/en/blog/64/ "

The next issue of our newsletter will be published on April 2, 2025.