Experience curves for electrical energy storage technologies.

This dataset compiles cumulative capacity and product price data for electrical energy storage technologies, including the respective regression parameters to construct experience curves.
 

The dataset can be used to:

  • project future investment cost ranges for storage technologies

 

  • determine investment requirements in technology deployment to achieve cost targets

 

  • analyse economic competitivenss of storage technologies in distinct applications

Renewables.ninja allows you to run simulations of the hourly power output from wind and solar power plants located anywhere in the world. This tool makes scientific-quality weather and energy data available to a wider community.

The ninja works by taking weather data from global reanalysis models and satellite observations:

The Renewables.ninja is a collaboration between Stefan Pfenninger and Iain Staffell, who both research the effects of integrating renewable technologies into our energy systems.

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The Storage.ninja enables you to determine the levelized cost of storage (LCOS) and annuitized capacity cost (ACC) for any technology in any application. You can also choose from pre-defined technologies and applications. 

The ninja uses a peer-reviewed model to calculate LCOS and ACC, but let's you decide on the input parameters. The respective publication is here:

Schmidt, O., Melchior, S., Hawkes, A., & Staffell, I. (2019). Projecting the Future Levelized Cost of Electricity Storage Technologies. Joule, 3, 1–20. 

Oliver Schmidt

Centre for Environmental Policy (CEP)

​South Kensington Campus

Imperial College London

London SW7 2AZ, UK

tel: +44 79 345 487 36

e-mail: o.schmidt15@imperial.ac.uk

LinkedIn: www.linkedin.com/in/oliver-schmidt/