Tag: Jennifer Newman

Employee Spotlight on Jennifer Newman, VP of Atmospheric Science Research

Jennifer Newman

“I found anecdotally that a lot of meteorologists also play instruments.”

Jennifer Newman, Vice President, Atmospheric Science, REsurety, standing in front of a wind turbine.

“I grew up in the Boston area, and my dad was a sportswriter and my mom works in medical book publishing. So not really all that science-related. But I’ve always loved blizzards and snowstorms and thunderstorms. I still absolutely love snow. Growing up in New England, we definitely got a wide variety of weather. I was always fascinated by all of it and loved being out in it. 

“I have a younger brother who’s a software developer out in L.A. Sometimes we chat about agile development and things like that. We have this common vernacular now.

“Back then I took dance classes, I was in chorus, I played clarinet in the band, I acted. I was into the humanities, but I had this inclination for science and math. Everything appealed to me, so I went in as an undeclared major at Cornell University

“One draw for Cornell was its marching band. I did marching band throughout high school and all four years in college. I played the clarinet. We did every home and away game, and we also did a couple NFL games, and a Canadian Football League game. Rehearsals were three times a week. One of them was Tuesdays until 11 pm, which now I can’t imagine!

“Someone in the band was in the Meteorology Department, and he became known as the band meteorologist. I had never really found an outlet for all the math and physics, but once I saw, you can apply it to something that I really loved – the weather – that’s when things started to click. I found anecdotally that a lot of meteorologists also play instruments. 

Jennifer Newman, Vice President, Atmospheric Science, REsurety, with a weather balloon.

“I did an internship with the University of Rhode Island, sending up weather balloons with instruments to measure ozone. Then the summer after my junior year, I went to the University of Oklahoma and got into more severe weather research, and ended up going there for grad school too. 

“My thesis was on how to better detect tornadoes with current weather radar systems. I did a lot of storm-chasing down there. It took me a couple of years of going out driving around dirt roads in Kansas, but I did eventually get to bag a couple of tornadoes. You end up running into all kinds of people, like a crowd of people on a dirt road in Kansas or Oklahoma. Now that I own a house, I have to say I don’t think I’d be thrilled if there was a tornado coming through or hail, knowing I would have to pay to replace my roof. I think I’m good with an occasional minor thunderstorm.

“While taking a renewable energy class during the last semester of my Master’s program, I realized I really loved learning about wind energy and the meteorology applications. That’s when I decided to stay for a PhD so that I could learn more. During my PhD, I got to set up meteorological instruments at some operational wind farms and analyze the data, which gave me a great understanding of how important accurate measurements are for wind energy. After finishing my PhD, I did a postdoc with the National Renewable Energy Laboratory in Boulder, Colorado.

“I’ve always thought I liked working in industry more than academia, and I wanted to move back to the Boston area, because my family is still here. I started reaching out to my network, and was connected with REsurety. It was a smaller company then, about 10 or 11 employees, and they were looking to hire some kind of research scientist, so my skill set matched really nicely. 

“I was able to look in-depth into the challenges we were facing and improvements we wanted to make with our generation modeling. What I bring is figuring out what we’re doing well, where we can improve, and working with the engineering team to make those changes to our wind and solar models.

“Math and physics tend to be male-dominated fields. Having two female co-advisors in graduate school was very impactful in my life. Seeing that they had to work hard to be heard always inspired me to speak up and be confident. There was only one other female when I got here, and so I started Women of REsurety. I want the females here to have a connection to other women working at the company.

“I had a daughter five months ago, so my hobby right now is child rearing!”

Jen’s full bio.

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Friends don’t let friends use 8760s

says Jennifer Newman, VP of Atmospheric Science Research, REsurety

As featured in POWER Magazine

Any company embarking on a new project must do its research to ensure that it calculates the proceeds based on the right financial information. With so much data now readily available, it’s more important than ever to use the right data, and make accurate calculations. 

REsurety's Jennifer Newman, VP of atmospheric science research, talks about 8760s.

Amid the boom in demand for renewable power plants, that is not always happening when backers go to measure the value of the energy they will generate. Here’s why, according to Jennifer Newman, Vice President of Atmospheric Science Research at REsurety, the Boston-based renewable industry data and analytics company. 

Q: What is an 8760 and how is it used in the renewable energy industry?

A:  An 8760 (sometimes referred to as a typical meteorological year or TMY) consists of hourly generation values for a wind or solar project for all 8,760 hours of a typical year. Importantly, 8760s are almost always used to represent average generation for a renewable energy project in a given hour. 

Q:  And what’s the problem? Why shouldn’t 8760s be used to estimate the value of power generation being produced by renewable energy projects? 

A:  An 8760 isn’t bad on its own – it’s a perfectly acceptable way of representing average generation. The issue is when a generation 8760 is paired with hourly power prices to produce either a revenue backcast (an estimate of the revenue a project would have made given historical prices) or a revenue forecast (an estimate of how much revenue a project could earn in the future). 

The problem with a backcast is that hourly renewable generation influences power prices during each hour. And that’s because wind and solar tend to be very inexpensive sources of electricity. So an hour where there’s a lot of wind or solar on the grid will tend to be associated with lower power prices, particularly in markets with high renewable penetration. When you use an 8760 instead of actual generation values during each timestamp, you aren’t able to capture that impact of hourly generation on hourly power prices.

And when analysts are using a model to predict future power prices, it’s a mistake to assume that conditions in the future will be similar to  an “average weather year”. Abnormal weather conditions can cause drastic price changes, as we all saw in Texas during February 2021.

Q: So what should be used to accurately calculate the value of renewable power generation?

A: There’s an abundance of rich datasets we can use to inform our decisions on whole new levels. For a backcast analysis, we should be using concurrent generation and price time series data to make these calculations and avoid errors (i.e. the generation volume that is used for 7:00 am on January 13th, 2019 should reflect the same weather conditions that generated the price that was observed in that same hour). In a forward-looking scenario, you should use a variety of different potential weather conditions beyond just an average year. Would you want to use a typical Texas February to project possible gains and losses, now that you know that Texas in February of 2021 is possible?  

Q: Where does a company turn then, to ensure it’s using the right information?

A:  At REsurety we offer the REmap tool, which models hourly generation for every wind and solar project in the United States, and will soon look forward at hypothetical situations to allow for future planning. REmap also offers data for synthetic situations – what-if planning for potential future sites – including historical modeled generation, observed power prices, and the combination of generation and power prices to estimate revenue. 

Getting beyond 8760s can not only steer a company to site a new renewable project in one location versus another, it can also provide guidance on the financial risk associated with a range of potential weather conditions.

Learn more, download the white paper.

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PODCAST: SunCast episode 403 – Friends Don’t Let Friends Use 8760s with Dr. Jennifer Newman


A recent white paper from REsurety, with contributions from HASI (fka Hannon Armstrong), a leading investor in climate solutions, offers an in-depth analysis into how using an “8760” energy model can lead to significant errors in revenue modeling — topping 30% in some high renewable penetration markets.

Suncast Podcast featuring Jennifer Newman

Despite their widespread use in the renewable energy industry, using an 8760 to project financial performance can lead to significant errors in revenue models. In particular, revenue models that pair an 8760 with historical prices miss the impact of hourly renewable energy generation on hourly power prices. Because wind and solar plants are relatively inexpensive sources of generation, there tends to be a negative correlation between generation and power price in markets with high renewable penetration.

A recent white paper from REsurety, with contributions from HASI, a leading investor in climate solutions, offers an in-depth analysis into how using an “8760” energy model can lead to significant errors in revenue modeling — topping 30% in some high renewable penetration markets.

An “8760” (also known as a “typical meteorological year,” or “TMY”) is the average generation expected for a given wind or solar project for each of the 8,760 hours in a non-leap year. As implied by its “typical meteorological year” moniker, an 8760 contains average generation values reflecting typical seasonal and diurnal weather patterns. The problem with using an 8760 is that “typical” weather isn’t actually all that common, and high prices almost always coincide with extreme weather.

In today’s episode, Nico discusses the findings with the whitepaper’s author, Dr. Jennifer Newman, VP of Atmospheric Science at REsurety.

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