cfsotago/evanalysis

Analysis of EV usage and charging data

Code used to analyse various EV performance & power flow datasets as part of the Centre for Sustainability's interest in energy demand in and around the home.

Sources of data

• Sample of charging data collected by FlipTheFleet Black Boxes in 2018 - 2019

Analysis

Best viewed via github pages.

Reports and publications

The analysis above has contributed to:

• Daniel Myall, Henrik Moller, Dima Ivanov, Hannah Gentle, Joe Barnett, Ben Anderson, Rafferty Parker , Michael Jack and Donald Love, (2019) ‘Accelerating Electric Vehicle Uptake in New Zealand: an information gap analysis’ Flip the Fleet Report No. 4 (Comissioned report to Parliamentary Commissioner for the Environment / Te Kaitiaki Taiao a Te Whare Pāremata).
• Ben Anderson, Rafferty Parker, Daniel Myall, Henrik Moller, and Michael Jack (2020) ‘Will Flipping the Fleet F**k the Grid?’ In IAEE Asia-Oceania 2020: Energy in Transition. Auckland, New Zealand.

Structure

• plots -> saved plots (not the ones found in reports)
• R -> various R scripts and useful code
• Reports -> various data exploration .Rmd & notebooks
• docs -> analytic reports - best viewed via github pages.

evanalysis's Issues

review of interpretation needed

see comment ref

master branch protected

@zenourn @raffertyparker this is to avoid accidental code breakages. You'll now need to create your own branch and commit changes to your own branch before making a pull request.

double-check exactly what the pre-delivery filter/data processing was

what it says...

need for data imputation

We currently only have observations for when EVs are being driven or are charging. This means there is a lot of 'missing' data when it comes to calculating 'population' average kW charging demand etc. We need to impute missing 1 minute observations (given them 0 kW charging) and re-calculate sample means. Obviously we can't impute state_of_charge - although we cold assume some decay curve between the last and next real observation?