Establishing best practices for modeling long duration energy storage in deeply decarbonized energy systems
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909183123652608 |
|---|---|
| author | Mantegna, Gabriel Ricks, Wilson Manocha, Aneesha Patankar, Neha Mallapragada, Dharik Jenkins, Jesse |
| author_facet | Mantegna, Gabriel Ricks, Wilson Manocha, Aneesha Patankar, Neha Mallapragada, Dharik Jenkins, Jesse |
| contents | Long duration energy storage (LDES) may become a critical technology for the decarbonization of the power sector, as current commercially available Li-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. LDES is difficult to model in existing energy system planning models (such as electricity system capacity expansion models), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling LDES in these planning models; however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessarily to accurately capture the full value of LDES, in the context of a continent-scale capacity expansion model. We use the results to draw conclusions and present best practices for modelers seeking to accurately model LDES in a macro-energy systems planning context. Our key findings are: 1) modeling LDES with linked representative periods is crucial to capturing its full value, 2) LDES value is highly sensitive to the cost and availability of other resources, and 3) temporal resolution is more important than spatial resolution for capturing the full value of LDES, although how much temporal resolution is needed will depend on the specific model context. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17474 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Establishing best practices for modeling long duration energy storage in deeply decarbonized energy systems Mantegna, Gabriel Ricks, Wilson Manocha, Aneesha Patankar, Neha Mallapragada, Dharik Jenkins, Jesse Systems and Control Optimization and Control Long duration energy storage (LDES) may become a critical technology for the decarbonization of the power sector, as current commercially available Li-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. LDES is difficult to model in existing energy system planning models (such as electricity system capacity expansion models), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling LDES in these planning models; however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessarily to accurately capture the full value of LDES, in the context of a continent-scale capacity expansion model. We use the results to draw conclusions and present best practices for modelers seeking to accurately model LDES in a macro-energy systems planning context. Our key findings are: 1) modeling LDES with linked representative periods is crucial to capturing its full value, 2) LDES value is highly sensitive to the cost and availability of other resources, and 3) temporal resolution is more important than spatial resolution for capturing the full value of LDES, although how much temporal resolution is needed will depend on the specific model context. |
| title | Establishing best practices for modeling long duration energy storage in deeply decarbonized energy systems |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2404.17474 |