Résumé
Buildings account for 35% of total energy-related carbon dioxide emissions in the U.S. and thereby play an important role in achieving the decarbonization goals set by the administration. Renewable energy such as solar and wind can help reduce grid emissions by displacing fossil fuel; however, the uncertainty and intermittency of renewable energy resources can cause both diurnal and seasonal variability of the electricity carbon intensity. This paper first presents an analysis of the carbon variation patterns across eleven locations in the U.S. A carbon savings potential indicator is introduced to afford first-order estimation of carbon reduction potential based on diurnal variability of the carbon intensity. This analysis is followed by a model predictive control strategy to minimize building carbon emissions through optimized thermal load shifting in response to time varying carbon intensity signals. The control strategy was tested using a commercial building case study subject to hourly marginal carbon emissions of two U.S. electricity markets – the California Independent System Operator (CAISO) and the New York Independent System Operator (NYISO). The test results show that for the NYISO market, the carbon responsive strategy could save 3.4% carbon emissions compared to the energy minimizing strategy, while for the CAISO market, up to 33.5% carbon emission reduction could be achieved because of the more aggressive intro-day variation of the electricity carbon intensity.
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Pages : 9 p.
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Détails
- Titre original : Carbon responsive control of building thermal loads.
- Identifiant de la fiche : 30032936
- Langues : Anglais
- Source : 2024 Purdue Conferences. 8th International High Performance Buildings Conference at Purdue.
- Date d'édition : 15/07/2024
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