Using Design of Experiments and Distributed Computing to Optimize Microgrid Design

Learn how to use GT’s advanced productivity tools to explore a microgrid’s design space.

As mentioned in a previous blog posting, GT-SUITE is a powerful tool for evaluating the economics of a microgrid.  However, in that previous blog post, only one combination of input variables was selected to be analyzed; for instance, the wind generation system was sized to be 105 kW and the solar system was sized to be roughly 90 kW.  One of the main challenges of planning a microgrid is that the design space is very large, and the sizing of every component should be optimized, including the wind turbine system, the solar array, and the battery.  Changing the sizes of these subsystems results in different upfront cost and power generation metrics for microgrids; ultimately, these will develop into different payback periods, returns on investment, and maximum battery backup time for islanding mode.

To accurately quantify how the sizing of the subsystems in a microgrid affect the economics of a microgrid, we will use GT’s integrated Design of Experiments (DOE) and Distributed Computing capabilities to quickly explore the design space of the microgrid.

Model Setup

The first step to exploring a design space using GT’s DOE is to define the inputs and responses of the system.  The tables below summarize the system inputs and the system responses.

System Inputs

System Responses

Next, to properly explore the design space of the microgrid, GT’s DOE tool was used to setup a full factorial of simulations that varied the four system inputs.  To do this, each input requires a minimum, a maximum, and a # of levels (to determine how finely or coarsely to search each system input) to be defined.  The image below shows the settings used for the microgrid deign of experiments.  As shown in the image below, this configuration resulted in 10,368 experiments to be setup.

As mentioned in the previous blog, each 30 year simulation takes rough 90 seconds to complete.  This means that the 10,368 simulations would take over 10 days to complete if run on a single core.  With this in mind, we utilized GT’s ability to distribute this job using a high-performance computing (HPC) cluster and 50 simulations at a time, meaning this DOE could be run in less than 6 hours.

Post-Processing Model

Post-processing and understanding over 10,000 cases of simulation is no easy task; however, with GT’s DOE Post-processor, we are able to setup an easy-to-use and highly visual interface to quickly explore the design space of the microgrid.

In GT’s DOE Post-processor, users create metamodels that are trained by the large number of experiments that are run.  After the metamodels are created, Case predictions are made in the visual interface that is shown below.  This interface allows sliders to quickly adjust the values of the input variables (number of wind turbines, solar panel area, battery sizing) and see how these changes affect the results in plots on the right that are dynamically updated based upon the input values selected.  The response which is plotted is determined by which response is selected in the “Metamodel Tree” on the left side of the screen.

The image above shows a series of “single factor response” plots where the responses are plotted as functions of only changes in a single input variable.  Two factor response plots are also automatically created by GT’s DOE Post-processor.  Examples of these are shown in the image below, which show how the responses vary across two different input variables.

With the plots from the previous two images, we can conclude that as we increase the size of the battery, the annualized ROI of the system decreases This lines up with our expectations because the batteries are expensive and their primary purpose is to provide sufficient amounts of backup power, they’re not installed for profitability.  We can also see that increasing the number of wind turbines and solar panels will increase the annualized ROI, which lines up with our expectations because the wind turbines and solar panels are what will allow a microgrid to save money on the cost of electricity.

In addition to annualized ROI, these intuitive interfaces can be constructed to explore the design space of a micro grid and understand how changes in the design affect other responses like the payback period, net return, initial cost, and islanding time, among many other possible outputs.  For a more interactive demonstration, we’ve created the video below to show how this can be done.

Conclusion

With the microgrid model introduced in a previous blog, GT’s integrated Design of Experiments (DOE), and GT’s Distributed Computing capabilities, GT-SUITE is a powerful tool to explore the design space and optimize the design of any microgrid.  Want to evaluate GT-SUITE to study your microgrid?  Contact us!

Written by Holden Symonds and Joe Wimmer