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What is FCASpays data

FCASpays utilises and provides access to the following data sets:Causer pays 4 second data

  1. Restructured so that variable numbers have been transposed to column labels,
  2. Split into four tables for the four element types, those being units (duids), system measurement point (e.g. NemSouth), Interconnector and Load. 
  3. New derived variables,
    1. trajectory - the basepoint or linear trajectory between snapshot of scada output and generation target at the end of the dispatch interval,
    2. trajectory_ramp_mw_per_min:  the required ramp rate per minute in order to reach the generation target
    3. utilisation - the sum of response divided by the enablement of a regulation service
    4. effective_enablement:  the volume of regulation service available which is limited by AGC control logic
  4. Normalised variables such as RaiseReg+Totalcleared 
  5. Aggregate 5min variables including averages, standard deviations, maximums and minimums
    1. e.g. maximum_participation_factor 

Summary of FCASpays services

FCASpays is a web service accessible via Chrome only.  The aim of FCASpays is to help you inspect 4 second causer pays data and to help you maximise value in the FCAS markets; primarily the regulation FCAS markets.  FCASpays includes the following applications;

Financial reports

Monitor the real cost and revenue to provide regulation FCAS.  Shows a breakdown of actual regulation FCAS gross margin and reports on all FCAS revenue. 

Physical data

A visualisation tool to inspect 4 second causer pays data, includes our restructured tables and enhanced data fields such as a unit's Basepoint. 

SQLpad

Write your own PostgresSQL queries to our 4 second causer pays data base.

Under Construction

Bid Optimiser 

Generates bids using the objective to maximise value.  This includes co-optimising bids between regions and applying our algorithms that predict the utilisation of regulation services and price elasticity.  

Bid Benchmarking 

Shows the value created by Bid Optimiser by benchmarking actual gross margin against the gross margin that would have been gained with your original bid.  Also compares the predicted gross margin of these two scenarios.

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