Copyright (c) 2018 Kelvin Say

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The R code in this software was used to simulate the scenarios and generate the 
figures in the Energy Policy journal paper titled:

The coming disruption: the movement towards the customer renewable energy transition

Authors: Kelvin Say (1), Michele Rosano (1), Raymond Wills (2), Roger Dargaville (3) 
1, Sustainable Engineering Group, Curtin University, Bentley 6012, Australia
2, University of Western Australia, Perth 6009, Australia 
3, Monash University, Clayton 3800, Australia

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main.R is used to generate the results from an individual scenario. The scenario
is configured by changing the 'g.scenario_' values. The results are saved into the
'_Output' folder with the 'g.scenario_name' folder name. The load profiles, 
g.scenario_load$Filename, are selected (based on filename) from the 
'_Scenarios/ConsumptionPatterns_SunWiz' directory. The insolation profiles, 
g.scenario_insolation$Filename, are selected (based on filename) from the 
'_Scenarios/Insolation_PVWatts' directory.

To run the scenario simulation, source the main.R file and type in the run() 
command. The software will resample the load and insolation profiles from the
'_Scenarios' directory to the simulation timestep and create temporary 
HouseholdLoad.csv and UnitInsolation.csv files in the '_Inputs' directory. 
These files are used as data sources for the techno-economic simulaion analysis.

The results from each scenario (stored in '_Output') include:
[Figures]
  - PV vs battery combination NPV contour for each year
  - PV and battery systems in the 95th percentile of maximum NPV for each year
  - The change in energy grid dependence, self-consumption and exports for the 
    system with the maximum NPV and also systems within the 95th percentile over
    the forecast period
  - The change in NPV for the system with the maximum NPV and with also systems 
    within the 95th percentile over the forecast period
  - The change in discounted payback period for the system with the maximum NPV and
    with also systems within the 95th percentile over the forecast period
[RDS data]
  - data_technical_data.rds:
  	* detailed technical data for a single year of operation
  - data_technical_summary.rds:
  	* summarised technical data across each forecast year
  - data_economic_summary.rds:
  	* summarised economic data across each forecast year
  - load_insolation_info.rds:
  	* insolation load profile 

posprocessing.R takes each scenario's technical data, technical summary data 
and economic summary data to generate the plots used in the Energy Policy paper.