# REopt Lite Tutorial: Resilience Outputs (Text Version)

We’ve now added the constraint that the critical load must be met during a grid outage. Remember, the grid outage was specified in the Inputs section. In this case, we selected a 7-day, or 168-hour outage that started at midnight on January 4.

As with a financial analysis, the model will minimize cost through a combination of technologies in order to meet the defined critical load. But in the case of an outage, the grid is not an available technology. In this case that means a 729-kilowatt (kW) PV system and 220 kW of battery storage are necessary.

In a resilience analysis, the net present value can be negative. This happens because the model is adding new or larger technologies to make sure that the critical load can be met during the outage.

In this case, the net present value is now -$107,000. That’s because the model is building a PV and storage system that is larger than what would be cost-optimal if we were only considering the grid-connected benefits.

The section showing your potential resilience shows the modeled probability of meeting the site’s critical load. The benefit of this system is the resilience it can provide to the site. Remember that our 7-day outage starts at midnight on January 4, so the model is making sure that the system can survive this particular outage.

But what about outages occurring at a different time? Here we see that this modeled system has a 72% probability of meeting the site’s critical load during a 7-day outage, at any time of the year.

Next let’s look at how the load is being met in the System Performance Year One section.
The outage event that we specified is highlighted in blue. It’s important to note
that the load is lower during this time-period because we specified that our critical
load during a grid outage is only 75% of the typical load at the site. During this
time-period, the load is being met exclusively by the PV and storage system that REopt
Lite^{®} selected. As soon as the outage ends, the site goes back to purchasing grid electricity.
Because the system is designed to meet the critical load percentage that we specified,
there are times when there is excess PV generation.

An important consideration here is the critical load percentage that we specified. For example, if we estimated the critical load to be only 10% of our typical load, a smaller system would be needed to meet our resilience requirement.

Under the Resilience versus Financial section, we can view the Resilience benefits. This table compares the business-as-usual case and the resilience case with the system that would be recommended without the outage requirements. Notice that the larger sizes in the resilience case lead to a lower net present value, because of the higher capital cost that won’t be completely recuperated during grid-connected operations.

However, the resilient system can, on average, sustain an outage of more than 1,115 hours, compared to the financial system, which can only sustain an outage of 10 hours on average. To calculate this number, 8,760 outage simulations are run—one for each hour of the year—and the Average Resiliency is calculated as the average time survived across all the simulated outages.

Next, let’s simulate an outage to better understand this system’s probability of surviving it, compared to the financial case and the business-as-usual case. Click the “simulate outage” button to do so. View the information by year, by month, or by hour. Let’s first look at the annual values.

The Y axis shows the probability of sustaining an outage of the length shown on the X axis. Here the business-as-usual case only includes the grid, which means we can’t survive any outage. The financial case can survive a 1-hour outage with 91% probability, and a 16-hour outage with 25% probability. For the resilience case, notice how the probability of surviving an outage decreases as the outage duration increases. There is a 75% probability of surviving an outage of 130 hours or less—but as we start extending that outage, the probability of surviving it decreases.

We can view values from month to month, which can vary depending on the load and the generation from the PV system.

Click the “toggle all resilience” button to see how the results vary by month for our system. For example, in the month of May, our system has the highest probability of surviving an outage. If the 7-day outage occurs during this month, we could survive close to 3,000 hours. We can also see that the month we selected, January, has the lowest probability of surviving an outage.

Finally, we can explore the impacts of the timing of the outage.

Similar to the financial evaluation, we can compare the costs and savings for the business-as-usual case, the resilience case, and the financial case under the Results Comparison section. And in the Effect of Resilience Costs and Benefits section, we can gain insight on the value of additional upgrades to our electrical infrastructure, in order to provide power during an outage.

Use the slider under “Microgrid Upgrade Cost” to explore the impact of those upgrades as a percentage of the total installed cost of the system.

The monetary benefit of surviving an outage can be hard to quantify. While this is not included in the optimization, you can use the “Avoided Outage Costs” slider to explore how varying values of avoiding an outage impact the net present value of the system.