The value of energy storage in decarbonizing the electricity sector
By F. D. Sisternes, Jesse D. Jenkins, Audun Botterud. 2016
442 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/034d945a9a723c7d92dc0cbcded3df2249ae22dd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Yury Dvorkin
- Leander Kotzur
- Miguel Ortega-Vazquez
- M. Hoffmann
- Detlef Stolten
- Jip Kim
- Mushfiqur R. Sarker
Citations in the corpus
- The rise of electric vehicles—2020 status and future expectations (2021, unknown): “…stabilization and flexibility and can improve resilience and efficiency for power systems in general, especially for critical services or when a high share of variable power generation (e.g. from solar or wind) is expected (Divya and Østergaard 2009, Denholm et al 2013; De Sisternes et al 2016).”
- Optimal Carbon Taxes for Emissions Targets in the Electricity Sector (2018, result): “In addition to higher average LMPs, there is also an increase in the variability of LMPs, which improves the value proposition for grid-scale energy storage devices, consistent with findings in [46].”
- Burden of proof: A comprehensive review of the feasibility of 100% renewable-electricity systems (2017, background): “To bet the future on such breakthroughs is arguably risky and it is pertinent for policy makers to recall that dependence on storage is entirely an artefact of deliberately constraining the options for dispatchable low-carbon generation [127,128].”
- Molten Salts for Sensible Thermal Energy Storage: A Review and an Energy Performance Analysis (2021, background): “Electricity generation is one of the main contributors to greenhouse gas (GHG) emissions due to CO2 being released from fossil fuels; additionally, electricity is also one of the energy vectors upon which many applications will be transformed in the near future [1,2].”
- The Pareto-optimal temporal aggregation of energy system models (2021, methodology): “…popular, which was either used to quantify the difference between the original and aggregated time series [31, 38], an example of which is depicted in the left half of Figure 6, or between the respective duration curves [15, 31, 67, 68, 72, 81, 85-88], as depicted in the right half of Figure 6.”