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| Main Authors: | , |
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| Format: | Preprint |
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2018
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1806.04003 |
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| _version_ | 1866929564467331072 |
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| author | Ripp, Christopher Steinke, Florian |
| author_facet | Ripp, Christopher Steinke, Florian |
| contents | CO$_2$ emission reduction and increasing volatile renewable energy generation mandate stronger energy sector coupling and the use of energy storage. In such multi-modal energy systems, it is challenging to determine the effect of an individual player's consumption pattern onto overall CO$_2$ emissions. This, however, is often important to evaluate the suitability of local CO$_2$ reduction measures. Due to renewables' volatility, the traditional approach of using annual average CO$_2$ intensities per energy form is no longer accurate, but the time of consumption should be considered. Moreover, CO$_2$ intensities are highly coupled over time and different energy forms due to sector coupling and energy storage. We introduce and compare two novel methods for computing time-dependent CO$_2$ intensities, that address different objectives: the first method determines CO$_2$ intensities of the energy system as is. The second method analyzes how overall CO$_2$ emissions would change in response to infinitesimal demand changes. Given a digital twin of the energy system in form of a linear program, we show how to compute these sensitivities very efficiently. We present the results of both methods for two simulated test energy systems and discuss their different implications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1806_04003 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Modeling Time-dependent CO$_2$ Intensities in Multi-modal Energy Systems with Storage Ripp, Christopher Steinke, Florian Signal Processing CO$_2$ emission reduction and increasing volatile renewable energy generation mandate stronger energy sector coupling and the use of energy storage. In such multi-modal energy systems, it is challenging to determine the effect of an individual player's consumption pattern onto overall CO$_2$ emissions. This, however, is often important to evaluate the suitability of local CO$_2$ reduction measures. Due to renewables' volatility, the traditional approach of using annual average CO$_2$ intensities per energy form is no longer accurate, but the time of consumption should be considered. Moreover, CO$_2$ intensities are highly coupled over time and different energy forms due to sector coupling and energy storage. We introduce and compare two novel methods for computing time-dependent CO$_2$ intensities, that address different objectives: the first method determines CO$_2$ intensities of the energy system as is. The second method analyzes how overall CO$_2$ emissions would change in response to infinitesimal demand changes. Given a digital twin of the energy system in form of a linear program, we show how to compute these sensitivities very efficiently. We present the results of both methods for two simulated test energy systems and discuss their different implications. |
| title | Modeling Time-dependent CO$_2$ Intensities in Multi-modal Energy Systems with Storage |
| topic | Signal Processing |
| url | https://arxiv.org/abs/1806.04003 |