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Duck Curves and April Showers of Sunlight
Photo by Avery Morrow on Unsplash
Recently, ISO New England issued a dataset showing Solar Production for 2018 in New England, which provided me a wonderful opportunity to look for useful insights about the region’s solar power. We know what a duck curve looks like from load curves posted by the CAISO, which is directly caused by solar generating supply resources on nice, bright, sunny days. Step one; whip up an R script to graph the solar production data to see the inverse duck curve, i.e. Solar Production Supply Curve, by hour, over all of 2018. Sure enough it’s just what I was expecting:
Step two; Create a second graphic showing Solar Production by Month:
What’s this: April had the highest hourly Solar Power output for all of 2018! I surely expected June to be the peak power output winner with the Summer Solstice and all, but it did finish in second place. July takes first place for total energy produced, not too surprising. And, what’s the story behind those outliers in November and February.
Next steps: Examine weather and outage data to see if that might provide some useful insights into why April beats June and July in the race to produce the highest solar power output. As Mark Twain once said, “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so”. Maybe it’s the April showers of bright sunshine that bring the May flowers, based on this chart, unlike what the Song tells us. But, as we all know, Causality is difficult to establish in statistics.
Thank Richard for the Post!
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Richard -
1) There's going to be a Part II to this story, right?
2) What's an R script?
Bob,
I've been informed by an EE colleague that solar energy efficiency is negatively impacted by higher temperatures, which would explain why a nice cool bright April day would produce higher power than a nice hot bright June day.
A1. Yes, if I can track down all the data
A2. R description
I remember this fact from the fun on Twitter the other week when anti-solar people tried to laugh at solar being installed in winter locations, only to be informed that lower temperatures can actually increase the efficiency!
Matt, assuming (simplistically) air temperature is a function of latitude, you'd lose in solar irradiance whatever you gained in photoelectric efficiency (or the glass-half-full version: you'd gain in PE whatever you lost in SI).
Thanks, wish I had known about R a week ago. I'm working on a calculator to visually demonstrate how Zero Emission Credits compensate nuclear plants (and solar/wind farms) for the social cost of carbon - would have come in handy.
There's a power conference at Haas / Berkeley next week, you (or Matt) might be interested. Three of the papers to be presented:
"Market Power and Incentive-based Capacity Payment Mechanisms"
Shaun McRae (Instituto Tecnológico Autónomo de México) and Frank Wolak (Stanford University)
"Setting with the Sun: The Impacts of Renewable Energy on Wholesale Power Markets"
James Bushnell (University of California, Davis) and Kevin Novan (University of California, Davis)
2:15pm-3:00pm: Session 3
"The Welfare Costs of Misaligned Incentives: Energy Inefficiency and the Principal-Agent Problem"
Josh Blonz (Resources for the Future)
https://ei.haas.berkeley.edu/events/power-conference.html
Bob, thanks for pointing out this conference at Berkeley.
This session: "Market Power and Incentive-based Capacity Payment Mechanisms", looks particularly interesting to me, based on its abstract. I see alignment with AOCE in this statement: "we propose an energy-contracting approach to long-term resource adequacy".
Regarding R; I find the supportive community network of R users and developers of packages to be it's most valuable asset. I can usually get an answer to my R needs/questions within minutes using Google, but usually, no more than a few hours in the worst case.
Several people have contacted me asking why the Month graphic (x axis) is in alphabetic order, as opposed to chronological order. That's a reasonable question; here's the answer. The source data provided by ISO New England showing solar production by hour contains Month name abbreviations. I'm a firm believer in maintaining fidelity to the original source data, unless there's a compelling reason not to do so. Therefore, I retained the original source data as Month abbreviation instead of applying a transform to convert the name to it's chronological month number. I hope that helps.