Storage modelling

munich-2017
breakout-group

#1

Modelling nonlinearities:

  • Losses (either charge/discharge efficiency or self-discharge rate) in batteries can be approximated as linear for some applications, e.g. LiIon for residential or grid (but not so big)

  • How does that apply to batteries: depending on battery system
    § LFP Li-Ion is quite linear
    § Lead Acid is highly non-linear
    § Degradation being one of the main problems
    § Offering of data by Holger Hesse

  • Vehicles to grid - there the nonlinearity does count → you can linearise

  • TUM-EES building a detailed battery model - open source release to come - www.ees.ei.tum.de/simSES
    Rainfall algorithm: state of the art approach and matches experimental results well (but no histeresys effects accounted for)

  • PYPSA linked to a battery model

  • The multitude of influencing parameters makes it hard to interpret results on the effect of degradation and other stuff (VIBESUM)

  • Mention of study, that concludes not to use current LiIon for large energy storage, since the degradation is too prevelant (Seattle)

Long-term, seasonal storage:

  • PSR Brazil 1979, Pereira SDDP - Stochastic dual dynamic programming
  • Concrete: how to manage the problem of rolling horizon with taking the cumulated storage capacity to next interval
  • Remention of minima at end of interval with framework to move data from one interval to next
  • How to make the model choose between short- and long-term storage? Assigned higher virtual emissions to the least efficient one (long-term storage)

Load shifting as short-term storage; problem is lack of data; there’s one implementation with OSeMOSYS; UK market

  • Open EGo: Acatech

Cost of Storage - Data sources for prices of different storage technologies:

  • JRC ETRI, 2014
  • Deloitte, Electricity storage, technologies, impacts and prospects

Heat storage: Group in TU Wien - probably energy economics group


Do-a-thon: Collect approaches to thermal energy systems