# Introduction

This work supports the Authority's consultation paper "SRAM: principles, options and pass-through". 
The workbook "SRAM impact assessment.xlsx" published on the Authority's website compares settlement residual rebates in 2020/21 ($80.3m total) with 
indicative settlement residual rebates customers would receive under two options considered in the Authority’s consultation paper (assuming the same $80.3m are allocated)

The modelling files published on the EMI  website produces the results for one of the options. It produces the 
indicative settlement residual rebates customers would receive under "simple BB" option considered in the Authority’s consultation paper.
These results are included in sheet 'Input-simple BB' of the workbook 'SRAM impact assessment.xlsx'.



# Overview

Each SPD branch has been mapped to a simple method BBC region (as per the  indicative pricing that supported the Authority's consultation on the proposed new TPM published in October 2021). 

The rentals from each branch are then rebated to transmission customers using the customer level allocators for simple method regions.


# Key output

The modelling produces the following  file:

* rental_attribution_to_customer_simpleBB_py2021.csv

This file provides an estimate of how settlement residual rebates would be allocated across simple method investment regions 
from the indicative pricing that the Authority published alongside its consultation on the proposed new TPM in October 2021.
 
Note that the modelling uses the residual LCE scale factor to scale the rentals associated with each branch. 
While this produces rebates for the a similar total amount ($80.6m vs actual LCE for 20/21 of $80.3m)as the actual rebates in pricing year 2020/21, this is not accurate at a branch level. 



# Content

The modelling files are in three folders
Input
Output
Model

The file "List of files.xlsx', alongside this 'About.txt' file, lists all files that are being released and explains their purpose/contents.



# Installation

This uses R 3.6.3. Install these versions of [R](https://cran.r-project.org/bin/windows/base/old/3.6.3/) and [Rtools](https://cran.r-project.org/bin/windows/Rtools/history.html).
Then run the following to install dependencies.

```
source('renv/activate.R')
renv::restore()
```

# Setup

Configure the following environment variables before running

* `EMI_SAS` - SAS for EMI datasets. See the [EMI forum](https://forum.emi.ea.govt.nz/thread/accessing-emi-datasets-with-azure-storage-explorer/) for the latest SAS to use.

To run the calculation after installation and setup run `source('main.R')`