h i g h l i g h t sThe paper presents a thermodynamic analysis of A-CAES using packed bed regenerators. The packed beds are used to store the compression heat. A numerical model is developed, validated and used to simulate system operation. The simulated efficiencies are between 70.5% and 71.1% for continuous operation. Heat build-up in the beds reduces continuous cycle efficiency slightly.
a b s t r a c tThe majority of articles on Adiabatic Compressed Air Energy Storage (A-CAES) so far have focussed on the use of indirect-contact heat exchangers and a thermal fluid in which to store the compression heat. While packed beds have been suggested, a detailed analysis of A-CAES with packed beds is lacking in the available literature. This paper presents such an analysis. We develop a numerical model of an A-CAES system with packed beds and validate it against analytical solutions. Our results suggest that an efficiency in excess of 70% should be achievable, which is higher than many of the previous estimates for A-CAES systems using indirect-contact heat exchangers. We carry out an exergy analysis for a single charge-storage-discharge cycle to see where the main losses are likely to transpire and we find that the main losses occur in the compressors and expanders (accounting for nearly 20% of the work input) rather than in the packed beds. The system is then simulated for continuous cycling and it is found that the build-up of leftover heat from previous cycles in the packed beds results in higher steady state temperature profiles of the packed beds. This leads to a small reduction (<0.5%) in efficiency for continuous operation.
Energy storage can help integrate local renewable generation, however the best deployment level for storage remains an open question. Using a data-driven approach, this paper simulates 15-minute electricity consumption for households and groups them into local communities of neighbors using real locations and the road network in Cambridge, MA. We then simulate PV for these households and use this framework to study battery economics in a high PV adoption, high electricity cost scenario, in order to demonstrate significant storage adoption. We compare the results of storage adoption at the level of individual households to storage adoption on the community level using the aggregated community demands. Under the simulated conditions, we find that the optimum storage at the community level was 65% of that at the level of individual households and each kWh of community battery installed was 64-94% more effective at reducing exports from the community to the wider network. Therefore, given the current increasing rates of residential battery deployment, our research highlights the need for energy policy to develop market mechanisms which facilitate the deployment of community storage.
Accurate occupancy is crucial for planning for sustainable buildings. Using massive, passively-collected mobile phone data, we introduce a novel framework to estimate building occupancy at unprecedented scale. We show that, at urban-scale, occupancy differs widely from current estimates based on building types. For commercial buildings, we find typical occupancy rates are 5 times lower than current assumptions imply, while for residential buildings occupancy rates vary widely by neighborhood. Our mobile phone based occupancy estimates are integrated with a state-of-the-art urban building energy model to understand their impact on energy use predictions. Depending on the assumed relationship between occupancy and internal building loads, we find energy consumption which differs by +1% to −15% for residential buildings and by −4% to −21% for commercial buildings, compared to standard methods. This highlights a need for new occupancy-to-load models which can be applied at urban-scale to the diverse set of city building types.
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