You could look for something that can provide a more relevant reference set for your situation. For example, you point out that power outages have different probability distributions based on your location in time and space.
You could also look at others’ revealed preferences.
My direct answer your question of estimating an outage for the average person in the US over the next 10 years is that the question is not very useful.
I’ll flesh this comment out with a specific example to show what I meant with the first two paragraphs as I have time to do so. By publishing the comment early, I intend to provide some quick value and prevent computer bugs from eating the post.
Goal:
p(>3d outage for a residential location in the next 10 years in downtown San Diego, CA)
Thought Process:
Sources of power outage information:
Potential Primary Sources:
Angry articles and social media posts about extended power outages in the last 10 years
Other studies of power outages in San Diego County, the City of San Diego, the ZIP code containing downtown San Diego, or the census tract(s) that comprise downtown San Diego
Raw counts of power outages in San Diego County, the City of San Diego, the ZIP code containing downtown San Diego, or the census tract(s) that comprise downtown San Diego
The “[fuel] tank shall hold 3 days of diesel fuel storage at 70% generators load” for the “two Owner Supplied Trailer Mounted 2MW Caterpillar XQ2000 backup generators”.
If we could find the power consumption of the Penasquitos Sewer Pump Station, then we could calculate (3 * amount of fuel consumed at 70% load of a 2MW Caterpillar XQ2000 generator * 2) / (daily power consumption) to find the # of expected consecutive blackout days.
Let’s pretend that # is “3” and simplistically assume that it corresponds to 3 days of doom per year. We’ll also multiply by 2 as an extra safety factor because the City can probably get fuel easier than normal people.
I don’t quite understand how your analysis fits together, but notes on two pieces:
Assuming that “>3 day outage” = a MED, then we can do a little algebra.
“MED” is “Major Event Day” (definition, context). My stats aren’t that great, but I think what they’re doing is assuming a log normal distribution of outages and then counting any day that’s 2.5 standard deviations worse than average as a “Major Event Day”. So roughly two days a year (0.6% of days) should count as MEDs. What this means is that if you have a very reliable power company you will have a lower threshold for declaring an MED than someone with a less reliable one.
3 days of diesel fuel storage … the City can probably get fuel easier than normal people.
My interpretation is that they will be able to get their generator tanks refilled within three days even in most emergencies. This does not guarantee that the blackout has ended, though.
You could look for something that can provide a more relevant reference set for your situation. For example, you point out that power outages have different probability distributions based on your location in time and space.
You could also look at others’ revealed preferences.
My direct answer your question of estimating an outage for the average person in the US over the next 10 years is that the question is not very useful.
I’ll flesh this comment out with a specific example to show what I meant with the first two paragraphs as I have time to do so. By publishing the comment early, I intend to provide some quick value and prevent computer bugs from eating the post.
Goal:
p(>3d outage for a residential location in the next 10 years in downtown San Diego, CA)
Thought Process:
Sources of power outage information:
Potential Primary Sources:
Angry articles and social media posts about extended power outages in the last 10 years
Other studies of power outages in San Diego County, the City of San Diego, the ZIP code containing downtown San Diego, or the census tract(s) that comprise downtown San Diego
Raw counts of power outages in San Diego County, the City of San Diego, the ZIP code containing downtown San Diego, or the census tract(s) that comprise downtown San Diego
Reports of telecom outages
Reports of datacenter or server outages
Likely Locations of the Information:
Local newspapers (searchable via databases)
Reddit.com (searchable via Reddit and Google)
Patch.com (Google and site-search)
Local online-only newspapers (searchable via Google and site-search)
San Diego Gas & Electric
California Public Utilities Commission
California Air Resource Board
Just ask the data source directly
Revealed Preferences:
Type of power redundancy in use by self-interested entities that want to always have power
City of San Diego
County of San Diego
Local public safety agencies
Local datacenters
Telecommunications companies
Hospitals
Transit agencies
Jails
Sources of information about the revealed preferences:
Satellite imagery
RFPs, purchase orders, and financial statements
Just ask them
From the CPUC:
SDG&E had 68.64 SAIDI minutes in 2019 (data is available going back to 1997) excluding MED. There were 122.96 SAIDI minutes including MED.
Assuming that “>3 day outage” = a MED, then we can do a little algebra.
Given:
SAIDI = Total minutes every customer was without power due to sustained outages / total number of customers
SAIDI without MED = Total minutes every customer was without power due to sustained outages—total MED minutes / total number of customers
68.64 = (Min—Med) / C
122.96 = Min/C
MED minutes = 1358 * # of customers / 25
Since we want to know the p for a given customer, then we further calculate:
MED minute / customer in a year = 1358⁄25 * # of customers / # of customers
= 54.32 minutes
Therefore:
p = 1.033E-4 that any particular minute will be without power during a major power outage in San Diego, CA
Over the span of 10 years, you’d expect to sit through just over 9 hours of lonely extended darkness.
From a sewage treatment plant RFP:
The “[fuel] tank shall hold 3 days of diesel fuel storage at 70% generators load” for the “two Owner Supplied Trailer Mounted 2MW Caterpillar XQ2000 backup generators”.
If we could find the power consumption of the Penasquitos Sewer Pump Station, then we could calculate (3 * amount of fuel consumed at 70% load of a 2MW Caterpillar XQ2000 generator * 2) / (daily power consumption) to find the # of expected consecutive blackout days.
Let’s pretend that # is “3” and simplistically assume that it corresponds to 3 days of doom per year. We’ll also multiply by 2 as an extra safety factor because the City can probably get fuel easier than normal people.
p = 3 days / 365 days * 2
= 0.016
I don’t quite understand how your analysis fits together, but notes on two pieces:
“MED” is “Major Event Day” (definition, context). My stats aren’t that great, but I think what they’re doing is assuming a log normal distribution of outages and then counting any day that’s 2.5 standard deviations worse than average as a “Major Event Day”. So roughly two days a year (0.6% of days) should count as MEDs. What this means is that if you have a very reliable power company you will have a lower threshold for declaring an MED than someone with a less reliable one.
My interpretation is that they will be able to get their generator tanks refilled within three days even in most emergencies. This does not guarantee that the blackout has ended, though.