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Accompanying technical brief demonstrates its use for evaluating the backup power performance of distributed solar+storage systems
Most power interruptions are relatively short, typically lasting minutes to hours, but they are also unpredictable. Assessing the potential of solar photovoltaic (PV) and energy storage systems (PVESS) to mitigate these interruptions requires the ability to account for both the unpredictability of these events as well as typical patterns in when and for how long they tend to occur in any given location.
To address this challenge, Berkeley Lab has developed the Power Reliability Event Simulator TOol (PRESTO) an easy-to-use, publicly available model that can be used to simulate the occurrence of short-duration power interruptions in any county in the continental United States. An accompanying case study shows how PRESTO can be used to analyze the performance of a PVESS in providing backup power during short-duration interruptions.
Berkeley Lab will host a free webinar presenting the tool and case study on November 7th at 11:00 a.m. Pacific Time. Please register for the webinar here: https://lbnl.zoom.us/webinar/register/WN_xpf40e0uQribiqE3zMp_LQ.
What does PRESTO do? PRESTO simulates the occurrence of interruption events over a large number (up to 20,000) of simulation years. In each simulation year, PRESTO produces a time-series of interruption events with stochastic frequency, duration, and timing. The model relies on a set of probabilistic functions trained on historical hourly power interruption data at the county level, obtained from PowerOutage.US for the years 2017-2021. As a result, the patterns and statistical properties (e.g. mean, standard deviation) of simulated power interruption events match the statistical properties of actual historical power interruptions for the selected county.
How to use it. PRESTO is flexible and easy to use. Upon selecting a county for analysis, PRESTO loads default average interruption duration and frequencies based on power reliability data from the Energy Information Administration, which the user can readily use or override. The user can visualize the statistical properties of the produced dataset – including distributions of duration and frequency as well as seasonal and time-of-day profiles, and download the detailed results in a CSV file. In addition to the web-based interface, an Application Programming Interface (API) is also available for batch processing of larger number of counties.
What can you do with it? PRESTO is primarily designed for use in Monte Carlo or other probabilistic analyses of the impacts of short-duration power interruptions. One intended application is to assist in evaluating the backup power capabilities and customer reliability value of onsite PVESS or other strategies for mitigating short-duration power interruptions. To demonstrate its use in this context, Berkeley Lab developed an accompanying case study analysis that explores several key determinants of PVESS backup power performance, for representative single-family homes in three distinct regions of the U.S.
Case study highlights. Among other factors, the case study highlights how PVESS backup performance (defined as the percentage of backup load served), is impacted by how the customer operates the battery under normal day-to-day conditions, which affects the battery’s state of charge whenever the power interruption occurs. In the example shown in Figure 2, backup performance is especially low for interruptions occurring during evening hours. In this case, the customer is assumed to be taking service under the utility’s residential time-of-use rate, which has a peak period extending from 4-8 pm. This incentivizes the customer to discharge its battery during that timeframe and leaves little charge remaining for any power interruption that happens to strike soon thereafter. The case study also illustrates how PVESS backup performance is impacted by system sizing, backup load configuration, whether or not the customer is able to charge from the grid, and regional variations in heating and cooling demand.
Originally published by Lawrence Berkeley National Laboratory
We appreciate the funding support of the U.S. Department of Energy Solar Energy Technologies Office in making this work possible.
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