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Maven Challenge: Electric Grid Outage Analysis [Challenge Winner]

Tools used in this project
Maven Challenge: Electric Grid Outage Analysis [Challenge Winner]

U.S. Electric Grid Outage Analysis

About this project

Objective:

This project was created as part of Maven's Power Outage Challenge. The objective was to act as a Senior Analytics Consultant hired by the U.S. Department of Energy to clean data and create a report/dashboard that showcases trends and key insights for 20 years worth of event level power outage data.

Key Methods/Insights:

My goal was to create a one page summary report showcasing general trends and highlighting specific areas that the Department of Energy should focus their attention on for further analysis/potential grid improvements. For the majority of metrics shown on the report, I placed the primary focus on the last 10 years of data (2013 - 2022) to provide a more recent/relevant view of the U.S. grid system.

Change Over Time

When looking at the different components of the data, such as event causes and NERC region, I wanted to highlight components that show significantly higher increases over time.

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Comparing States to Identify States With Higher Demand Loss

I wanted to compare each state's total annual demand loss to see which states are affected most by electric grid outages. However, I knew that a direct comparison wouldn't be apples to apples because larger states, with larger grid systems, may naturally have more demand loss compared to smaller states. In order to right size this issue, I used Power BI's DAX measures to first calculate each state's annual total demand loss per 100,000 people, taking into account year over year population changes for each state. I then took the average for each state of the annual total demand loss per 100k people between 2013-2022. I then used Power BI's Esri map visual to map the average.

undefinedundefinedMethod/Assumptions:

The data cleaning for this project was quite intensive. I used a mix of Power Query, Python, and Excel to clean the 20 different tables and combine them into one master table. I also took advantage of Power BI's modeling feature and created reference tables for date and event type.

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Discussion and feedback(6 comments)
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Christopher Whitlock
Christopher Whitlock
6 months ago
Whoa, very well done! Great work!

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Megan Henry
Megan Henry
6 months ago
Very cool to see this broken down! I wonder why such a decrease in the number of customers affected. Due to more having back up power? Or customers spread out over more power houses. Either way it's fascinating to see!

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Vishal Singh  Thakur
Vishal Singh Thakur
5 months ago
I like the detailing of this dashboard . Nicely done .

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An Pham
An Pham
5 months ago
Love all the details of this dashboard and how you broke down all the information. Lovely job!

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Louise Randall
Louise Randall
5 months ago
Great Analysis Katie and insight into the power outage. Thought your dashboard was amazing with helpful date filtering and top 3 event causes. With practice I hope to create something that is as close to your high standard.

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Muhammad Huzaifa
Muhammad Huzaifa
5 months ago
Great work Katie
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