Modelling district heating flexibility in a scalable marketplace environment: A data-driven and agent-based simulation

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master_Ciechanski_Rafal_2026.pdf (5.82 MB)

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School of Science | Master's thesis
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Date

2025-12-24

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Computer Science

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Degree programme

Master's Programme in Computer, Communication and Information Sciences
Master's Programme in Computer, Communication and Information Sciences
Master's Programme in Computer, Communication and Information Sciences

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en

Pages

55

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Abstract

The increasing usage of renewable energy sources and distributed energy resources fuels the need for flexibility solutions capable of balancing power grids at a local level. District heating constitutes a major potential source of flexibility, due to its large share in total energy consumption and thermal energy storage capability. This thesis addresses two complementary objectives: estimating building-level thermal flexibility using historical thermal energy consumption data, and evaluating the scalability of a flexibility trading platform operating under realistic urban conditions. A data-driven pipeline is developed to model thermal flexibility for buildings connected to a district heating network using historical heat consumption and outdoor temperature data. By exploiting data mining techniques and equations derived from Newton’s law of cooling, building-specific heat loss coefficients and thermal capacities are estimated and translated into hourly flexibility profiles. These profiles serve as inputs for an agent-based simulation of flexibility trading. The second contribution evaluates the scalability of the GLocalFlex marketplace, a flexibility trading platform. Using findings from the first objective, a set of trading agents offering thermal flexibility of buildings was created and subsequently used to facilitate system-wide load and performance tests. The evaluation was designed to expose bottlenecks hampering the ability of the marketplace to handle increasingly large incoming loads. An iterative process is applied where the system behaviour is inspected and evaluated after each test run. The results indicate that the showcased thermal flexibility estimation pipeline is capable of producing consistent and usable flexibility profiles for the vast majority of buildings in the studied district heating network. Additionally, the scalability tests facilitated the identification of concrete limitations in the deployment of the trading platform. Altogether, the findings of this thesis prove the usability of combining data-driven flexibility estimation with agent-based scalability testing as a foundation for evaluating the behaviour of flexibility marketplaces before their real-life deployment.

Description

Supervisor

Korpi-Lagg, Maarit

Thesis advisor

Mehnert, Stephan

Keywords

thermal flexibility, flexibility trading, data mining, scalability testing, load testing, district heating

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https://urn.fi/URN:NBN:fi:aalto-202601221831

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[dipl] Perustieteiden korkeakoulu / SCI