Tracker

Production Cost Models (PCM) computes the time-varying dispatch schedules for each resource using simplified models. Emerging resources including IESs and industrial demand response need to determine the optimal operations strategy to track their market dispatch signal. The Tracker formulates these decisions as a model predictive control (MPC) problem. The figure below shows an example of the optimal tracking from an integrated energy system which consists of a thermal generator and an energy storage. The figure shows that to track the dispatch (load) the energy system can optimally use power output from charging and discharging cycle.

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class idaes.apps.grid_integration.tracker.Tracker(tracking_model_object, n_tracking_hour, solver)[source]

Wrap a model object to track the market dispatch signals. This class interfaces with the DoubleLoopCoordinator.

formulate_tracking_problem()[source]

Formulate the tracking optimization problem by adding necessary parameters, constraints, and objective function.

Parameters

None

Returns

None

get_last_delivered_power()[source]

Returns the last delivered power output.

Parameters

None

Returns

None

record_results(**kwargs)[source]

Record the operations stats for the model.

Parameters

kwargs – key word arguments that can be passed into tracking model object’s record result function.

Returns

None

track_market_dispatch(market_dispatch, date, hour)[source]

Solve the model to track the market dispatch signals. After solving, record the results from the solve and update the model.

Parameters
  • market_dispatch – a dictionary that contains the market dispatch

  • track. (signals that we want to) –

  • date – current simulation date

  • hour – current simulation hour

Returns

None

write_results(path)[source]

This methods writes the saved operation stats into an csv file.

Parameters

path – the path to write the results.

Returns

None