A characteristic-oriented strategy for ranking and near-optimal selection of phase change materials for thermal energy storage in building applications

Publication Name

Journal of Energy Storage


This paper presents a methodological approach for characteristic-based selection of phase change materials (PCMs) for thermal energy storage in building applications. Unlike previous studies that were mainly focused on applying Multi-Criteria Decision Analysis (MCDA) to rank PCMs without using a rational ranking strategy, this study presents a weighted product method (WPM) based ranking strategy by incorporating qualitative and quantitative attributes of PCMs to rank and determine near-optimal PCMs for given applications. Qualitative attributes were converted into quantitative factors by using the standard Saaty scale. A weight assignment process was introduced to handle multiple characteristics of PCMs by using priority-based clusters in the pair-wise matrix of the analytical hierarchy process (AHP). The effectiveness of this strategy was tested and evaluated by performing two case studies. In the first case study, a sample dataset of PCMs was used to rank PCMs for different building applications based on the four major thermodynamic properties of the PCMs, whereas in the second case study, several qualitative and quantitative characteristics of PCMs including cost were used to select a near-optimal PCM from a list of eight PCMs for a thermal energy storage system integrated with a ground source heat pump system. The ranking results of the first case study were also quantitatively analyzed by using data analytic techniques. Two PCMs from the ranking list were selected to verify the effectiveness of the strategy by implementing the PCMs into a thermal energy storage system via simulations. The capacity of each PCM to provide energy flexibility to a Heating, Ventilation and Air-conditioning (HVAC) system was calculated. The cost-based flexibility potential by using the better-ranked PCM thermal energy storage system was 37.1 %, while that for the lower-ranked PCM thermal energy storage system was 27.5 %, and both PCMs provided around 73 % flexibility in terms of the peak load reduction of the HVAC system. The simulation results were consistent with the ranking results. The second case study also proved the effectiveness of this method to select near-optimal PCMs for thermal energy storage systems.

Open Access Status

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Article Number


Funding Sponsor

Higher Education Commission, Pakistan



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