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
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.
- class idaes.apps.grid_integration.tracker.Tracker(tracking_model_object, n_tracking_hour, solver)¶
Wrap a model object to track the market dispatch signals. This class interfaces with the DoubleLoopCoordinator.
Formulate the tracking optimization problem by adding necessary parameters, constraints, and objective function.
Returns the last delivered power output.
Record the operations stats for the model.
kwargs – key word arguments that can be passed into tracking model object’s record result function.
- track_market_dispatch(market_dispatch, date, hour)¶
Solve the model to track the market dispatch signals. After solving, record the results from the solve and update the model.
market_dispatch – a dictionary that contains the market dispatch
track. (signals that we want to) –
date – current simulation date
hour – current simulation hour
This methods writes the saved operation stats into an csv file.
path – the path to write the results.