LCA-based Environmental Impact Assessment of Industrial Decarbonisation Systems via Development of Scalable Life-Cycle Inventory (LCI) Models

Project Description

As renewable energy systems evolve toward higher efficiency, dispatchability, and hybridization, a persistent challenge in life-cycle assessment (LCA) is the lack of transparent, scalable Life Cycle Inventories (LCIs). Current LCIs for photovoltaics (PV), heat pumps (HPs), thermal energy storage (TES), and power cycles are generally static, assuming fixed component sizes and material compositions. However, in real techno-economic energy system design, component size and configuration vary widely, leading to significant inaccuracies when static LCIs are applied.

To address this gap, this thesis aims to develop a dynamic LCI scaling methodology for a hybrid renewable system consisting of a PV field, high-temperature heat pump, thermal energy storage (TES), and an sCO₂ power cycle. The system represents a next-generation dispatchable renewable configuration in which PV electricity charges a TES via an electric heat pump, and stored heat can be discharged either to a power cycle for electricity generation or to a thermal load.

The core objective of the thesis is to establish scaling relationships—linking engineering design parameters (e.g., PV area, compressor capacity, TES volume, PCM mass, power block size) to the corresponding LCI elements (materials, manufacturing processes, energy inputs). These models will enable automatic generation of system-level inventories once techno-economic sizing results are available.

To ensure academic rigor and fulfil MSc requirements, the developed scaling models will be applied to a representative hybrid system use case. Using the dynamically generated inventories, the student will perform an LCA comparing different system sizes or configurations, demonstrating how scalable LCIs influence environmental results relative to traditional static LCIs.

This project directly contributes to ongoing research on hybrid renewable energy systems at KTH’s Heat and Power Division.

Research Questions

• How can engineering design parameters (e.g., PV area, HP thermal capacity, TES energy capacity, sCO₂ turbine rating) be systematically translated into scalable LCI models?

• How do scaled LCIs differ from conventional static inventories across components such as PV, HP, TES, and sCO₂ power cycle?

• What environmental hotspots emerge in a hybrid dispatchable PV–HP–TES–sCO₂ system when using dynamically generated inventories?

• How sensitive are LCA results to system size, configuration, and TES charging/discharging operation?

Main Deliverables

• Final thesis report and presentation, including the system description, literature review, development of the LCI scaling methodology, and full LCA results for the selected hybrid system configuration.

• Python-based LCI scaling framework that links engineering design parameters (PV size, heat pump capacity, TES characteristics, sCO₂ cycle size) to dynamically generated life-cycle inventories.

Duration

The project should start in January 2026 the latest, and should not extend for more than 6 months. Specific earlier starting date to be discussed.

Location

KTH – Heat and Power Technology Division, Energy Technology Department.

Main Supervisors

Contact person

Examiner