Storage Requirements and Costs of Shaping Renewable Energy Toward Grid Decarbonization
By Micah S. Ziegler, Joshua M. Mueller, Gonçalo D. Pereira, Juhyun Song, Marco Ferrara, Y. Chiang, Jessika E. Trancik. Joule, 2019
391 citations recorded by the source corpus.
Summary Deeply decarbonizing electricity production will likely require that low-carbon sources meet energy demand throughout days, years, and decades. Wind and solar energy are possible low-carbon options, but resource variability can limit their reliability. Storage can help address this challenge by shaping intermittent resources into desired output profiles. But can solar and wind energy with storage cost-competitively fulfill this role? How do diverse storage technologies compare? We address these questions by analyzing systems that combine wind and solar energy with storage to meet various demand profiles. We estimate that energy storage capacity costs below a roughly $20/kWh target would allow a wind-solar mix to provide cost-competitive baseload electricity in resource-abundant locations such as Texas and Arizona. Relaxing reliability constraints by allowing for a few percent of downtime hours raises storage cost targets considerably, but would require supplemental technologies. Finally, we discuss storage technologies that could reach the estimated cost targets.
Subfields: Automotive Engineering, Electrical and Electronic Engineering, Energy Engineering and Power Technology
View Paper: https://www.semanticscholar.org/paper/309b6ccbd6a191e82b3ca02968452a28644bd742
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In Electrical and Electronic Engineering
Citations in the corpus
- An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage (2020, background): “Estimating the total stored energy and capacity needed to move to 100% renewable sources is difficult and depends on numerous factors [16, 46, 47].”
- Re-examining rates of lithium-ion battery technology improvement and cost decline (2020, methodology): “ To expand the definition of unit service provided by a lithium-ion cell, we defineits service as the product of its cell-level attributes (gi) weighted by constants (hi) Service per cell = ∏ i gi hi (9) where these attributes can be energy capacity per cell, energy density, cycle-life, etc. Using t”