Using Simulation to Evaluate Microgrid Design

Learn how to use a model to evaluate energy usage and cost of a microgrid.

According to the United States’ Environmental Protection Agency, the generation of electricity accounts for over a quarter of the greenhouse gas emissions in the United States and is the second leading source of such gases, behind only transportation.  It is well known that GT-SUITE from Gamma Technologies plays a leading role supporting automotive and commercial vehicle OEMs to decrease the greenhouse gases that their cars and trucks release, but what is less known are the capabilities that GT-SUITE has to model and help develop cleaner and more efficient electricity infrastructure.

Microgrids are a relatively new technology that communities, campuses, and governments are investigating to modernize and decentralize their electrical infrastructure.  These microgrids combine renewable energy, energy storage, and advanced controls to give communities and campuses the ability to restructure how they generate, store, transmit, and consume electrical energy.

What is a microgrid?

The U.S. department of energy microgrid exchange group defines a microgrid as “a group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid. A microgrid can connect and disconnect from the grid to enable it to operate in both grid-connected or island-mode.”

In short, microgrids enable communities to integrate decentralized generation (i.e. renewable energy and backup generators), as well as energy storage in batteries into the larger electrical grid.  Additionally, microgrids integrate advanced controls to enable precise energy management within the community and disconnect from the larger grid and operate in an islanding mode.

Why install a microgrid?

A microgrid’s battery backup and islanding feature enable microgrids to continue to operate during power outages, which has the potential to save lives during natural disasters.

Microgrids can also be a good financial investment. Microgrids generally have a high upfront cost to install renewable energy, controls, and energy storage, but over time, this has the potential to return on its investment with the money that is saved by purchasing less electrical energy.

Why model a microgrid?

Because of the steep upfront costs of microgrids, it is important to understand the expected return on investment and payback period before committing to installing a microgrid.  Because the upfront costs are so high, it is likely that zero prototypes will be built.

Additionally, it is beneficial to study the trade-offs between different design decisions in order to optimize the system (How large should the solar array be?  How many wind turbines should be installed?  How large of a battery should be used?). The optimal design will change for every project, based on the load of the system and the weather. For example, perhaps a business park in Arizona can expect a high load due to more air conditioning usage and may invest more into solar generation than wind because of the lack of cloud coverage.

All these factors mean that simulation is paramount for designers to optimize the various trade-offs associated with their microgrid project.

Modeling a Microgrid:

To demonstrate the capabilities and to evaluate the effectiveness of a microgrid, we built a comprehensive model in GT-SUITE. This model reflects the size and topology of a residential microgrid for a neighborhood of 20 homes.  The model includes renewable energy, a battery, the electrical consumption from the homes, and simplified controls to optimize energy usage. This model is parameterized so weather, load, and cost of electricity data can be customized to fit any application.

 

Each source of generation and the loads were converted to a simple power source/sink and a simple circuit that obeys Kirchhoff’s current law was created. This provides a full picture of the power flowing through the system and allows the cost of ownership of a microgrid to be easily calculated.

The power supplied by the renewable energy sources are based upon the weather data and specifications for specific wind turbine and solar panel models.  The model was setup such that new weather data can easily be brought into the model to calculate how the microgrid would behave in any climate.

In addition to differing weather, each microgrid’s electrical load will be unique as well. The load in the demonstration model is meant to represent a series of residential homes, so we have included a dependency on ambient temperature to increase power usage during times when heating or air conditioning are required. The daily energy usage of a home in the microgrid is defined using a piecewise function that defines three zones: heating, comfort, and cooling.  The comfort zone (when neither heating nor cooling is required) is defined as 55-65 degrees F.  Additionally, we have defined a baseline electrical energy usage for a household to be 20 kWh/day.  The piecewise function for daily power usage as a function of ambient temperature is shown below. This is of course flexible and can be altered to represent the electrical load from any system, like a hospital or college campus.

 

As mentioned earlier, another large benefit of microgrids is the increased control over the loads, generation, and connection to the grid. In the demonstration model, we have included some controls to take advantage of fluctuations in the price of electricity by charging the battery when the hourly price is low and discharging the battery when the price is high. Other control schemes can be tried and tested, allowing users to find new ways to save money and energy within their system.

Because the model is simple, we were able to run it with a timestep size of one hour, enabling simulations to be years long but only take seconds to run.  We setup the model to simulate 30 years of operation, which took less than 90 seconds to complete.

To understand the financial viability of the microgrid over the course of time, this demonstration model calculates financial results, including:

  • Upfront cost of the microgrid
  • Cost of electricity with and without the microgrid
  • Net return of the microgrid
  • Return on investment (ROI) of the microgrid
  • Annualized ROI of the microgrid

Data Population

For this demonstration model, weather data from this website was used.  This is a paid service that provides historical weather data for locations around the world.  As an evaluation option, this web service provides historical weather data for Basel, Switzerland for free, which was used to build this demonstration model.  The data includes historical temperature, solar radiation, and wind speeds (all shown in the image below), but much more weather data is available on the website.  Please note that the simulation was setup such that day 0 is January 1st and Day 365 is December 31st.

In this demonstration model, we decided to use hourly pricing based upon historical data found on the website of Commonweath Edison (“ComEd”) Electricity Company.  ComEd is the local supplier of electricity in the Chicago area, which is the home of Gamma Technologies.

ComEd has given the public the ability to download historical prices of electricity here on their website.  For this demonstration model, the price of electricity during calendar year 2019 was used.  Additionally, to account for taxes and other fees, GT has applied multipliers and shifts onto this data.

Model Results:

In the image below, we have included the power generated by both solar and wind generation and the power consumed by the 20 homes in the microgrid over the course of one year.  The model uses a wind system that is rated at 105 kW and a solar system that is rated at 90 kW.  Clearly, the renewable energy sources are not operating at their maximum powers very frequently due to the highly transient weather data used in this simulation.

In addition to the amount of electrical power generated, stored, and consumed, the financial calculations were done over a 30 year period.  Below is a plot showing the cost comparison of the microgrid that was modeled (red line with a high initial investment) and the cost of electricity for the 20 homes if no microgrid was built (blue line).

In addition to the cost calculation, the return on investment (ROI) was calculated as a percentage, calculating the rate of return on the initial investment of the microgrid.  The payback period in this model was roughly 8.7 years and before that point, the ROI was negative.  Additionally, we calculated the annualized ROI, which asymptotes close to a 4% return on investment after 30 years.

Conclusion:

Using the simulation capability of GT-SUITE, we are able to evaluate the long-term power and aspects of installing a microgrid well before the initial investment for such a project would be made.  The results shown above reflect only a single simulation, but many more simulations can be done to experiment with and optimize the sizing of the wind turbines, solar arrays, and batteries.  Additionally, every microgrid will be different, so the electricity usage patterns, electricity price patters, and weather patterns need to be considered for each individual microgrid.  GT-SUITE is a powerful tool to be able to evaluate the economic viability of any microgrid.  Want to evaluate GT-SUITE to study your microgrid?  Contact us!

For those who already have GT-SUITE installed, we’ve uploaded the demonstration model to our secure downloads page.  To download the file, the username and credentials used to log onto our website are required.

Written by Holden Symonds and Joe Wimmer