# Student Project Presentations – Part 1

On August 10, 2019 by Raul Dinwiddie

The following content is

provided under a Creative Commons license. Your support will help

MIT OpenCourseWare continue to offer high quality

educational resources for free. To make a donation or

view additional materials from hundreds of MIT courses,

visit MIT OpenCourseWare at ocw.mit.edu. PROFESSOR: Final

presentations should be very exciting–

fruits of your labor over the entire

semester in reality. The fundamentals

in the first third, the technologies in the second,

gearing up toward the cross cutting themes in the third. I understand that we’ve

had an accelerated project schedule this semester. We’ve completed

the entire projects over the duration of

about a month and a half, so you are to be congratulated

for your hard work in a very intense period of

time McKenzie tiger team style. For that, I reward you

with doughnuts and coffee over there. I understand many of you

were up late last night, so you’re welcome to ingest

some shortchange carbohydrates and some caffeine. If you would like to

get some, get it now. We’re going to have another

minute of blah, blah, blah before we dive into

the presentations and the real fun begins. I’d like to introduce

our panelists up here in the front who will be

the evaluation criteria a la American Idol style,

except that you’re, of course, a lot smarter and

equally well dressed. Starting from right

to left in front of me we have Dr. Jasmin Hofstetter,

who comes from IES, Spain. That’s the Institute for

Solar Energy in Spain. That’s where she did her

PhD with Antonio Luque. Those who have been studying

intermediate band solar cell materials may know

the name as one of the fathers of the field. She studied under Antonio

Luque’s organization with Carlos de Canizo and is the

winner of presentation awards at scientific

conferences, among others, so Jasmin is welcome here. We have thought that Dr.

Mark Winkler, as well, a PhD in Eric Mazur’s

laboratory at Harvard. Those who are familiar

with femtosecond laser characterization may be familiar

with Eric Mazur, also one of the fathers of the field. Mark started the Harvard

Journal Energy Club, which is Harvard’s version of

the MIT Energy Club– a lot smaller and a lot less

dynamic than MIT’s version, but nevertheless to

be congratulated. And of course, a very

good organization. I kid. There’s a little bit

of MIT Harvard rivalry. And of course, our

very own Joe Sullivan, who has been with you

the entire semester. For those who might

not be familiar as much with this research as you

are with his teaching, Joe is studying intermediate

band solar cell materials here at MIT in the Media Lab

and has been working for the last– what is it now? JOE SULLIVAN: Three and a half. PROFESSOR: Three

and a half years– focused on intermediate

band solar cell materials, coming from a very broad

background in energy from climate science. So with that, I’d like to

welcome our first team down, and the floor is yours. STUDENT 1: Good morning, we are

the PV smart retrofit group, and our project goal was

to assess whether or not the there was an

electrical benefit or loss from retrofitting an

old home with a PV system. Now, in less lofty terms,

it essentially means from an on site

energy perspective, does it make sense to put

PV panels on my house? You’d think that that’s a

kind of an obvious question. We’d all say, well, yeah. We produce energy for

free, except that you have to consider other

things, such as shading from trees that you’d

have to cut down or the color of the roof

that you might be changing by adding a black panel. These would both reduce the

thermal load of your house in normal situation. So by adding a PV panel and

increasing the thermal load, you actually add the

energy to cool your house during the summer. We considered multiple

variables in this project. One was location, which

has an effect on the amount of sunlight you’re receiving. The PV panel’s

presence and its size. We had a couple of

situations where there was no panel as

a kind of a baseline. And then also different sizes

to figure out if a bigger panel had a bigger effect. We looked at two different

colors, black and white. Those are the ends

of the spectrum, and they give us

endpoints to look at, and the color matters

because a darker color will absorb more heat. We looked at roof pitch. The reason for this–

well, first off, roof pitch is the angle of your roof. And we looked at this because

we assumed that our panels were fixed and parallel to

the roof, so this kind of controlled what

angle your solar panel was facing towards the sun. And finally you had

your house footprint, which is the area

that the house covers, and when you combine

that with the roof pitch, you get the area of the

roof, which is really what we’re concerned with. We had five

scenarios, and as you can see from our

cute little diagrams, we have a black

roof, a white roof, a white roof with the tree, a

white roof with a solar panel, and a white roof with a

solar panel and a cut tree so the solar panel is

getting plenty of sunlight. In evaluating the

five scenarios, we had three models of

increasing complexity from left to right, which you

can note from the fact that model one only

covers three of the five, and these models all

had common assumptions. The first was that

you had a single story house in a suburban

locations, so you didn’t have shading from

other buildings, for example. We had a common house size of

about 2000 square feet, or 186 square meters. The roof pitch,

which in construction is set usually at 5/12,

which means 5 inches of rise for 12 inches of travel. We had an unfinished

attic space, which means that it’s

sealed to the outside, but you didn’t make it

livable, a five kilowatt PV system covering

36 square meters, and we chose reflectance values

of 0.08 for black and 0.35 for white, and this, again,

reflects the effect that color has on heat absorption. With that, I will turn

you over to, Jordan. JORDAN: So the first

one that we looked at was basically using

most readily available and simple models there can be. So this is from the

Department of Energy to measure– well,

to get a gauge of how the color of the roof

and the roof properties affect the thermal

loads in the house. This is a simple one

dimensional model where you’ve got an inside

cavity of 65 Fahrenheit, and you basically

input the location, and from that it has a lookup

table of the average insulation on the house, as well as the

number of heating degree days and cooling degree days relative

to that 65 inside temperature. The other parameters

are just at the roof, so we insert the

reflectance, and we’re using values that represent

real tiles for an average house, black and white, as well as the

thermal resistance and the heat absorbance of the tiles–

from this model, the outputs and the thermal loads

in terms of the heating you need to put in, as well

as the cooling energy load. So with the thermal

model assessed, we can assess the

photovoltaic output. And for this, we’re just

using a simple model PVWatts. This basically takes in

the location and the angle of the panels, as well

as a derate factor from converting to AC to DC. It’s quite a simple model,

and output from this is the amount of

kilowatt hours per year that you get from the panels. STUDENT 2: So I’ll be talking

about a couple thermal electric model. This is the model that

we built in our group, and we developed

this model based on two sets of

individual parameters and individual models. I would say that one comprises

of the thermal model, and there is the electric model. So what we need to note

from this model here is it’s a further

step in complexity when compared to the module

one that Jordan just discussed. And it takes into account

various input parameters that the model one

doesn’t take into account. So the basic structure of

this model is as follows. We have a thermal model

which takes into account several input conditions, such

as the insulation and shading, and it outputs a living

space temperature, which is this temperature of

the living room in our house. And then this temperature is fed

into an electric model, which calculates the cost

and energy values, and thus we can compare an

energy production and energy consumption. Going to the thermal

model in detail, I’ve just shown a picture here. So it considers

basically when we start from the top of the house,

we use insulation and shading as the input parameters. We then calculate the

temperature of the PV panel, and then the

temperature of the roof using two different

energy balance models. And these two energy

balance models are pretty robust in the

sense that they consider all these physical phenomenon

which are realistic, such as the

convection, radiation, and PV electrical output. And based on these

energy balance equations, we can calculate the PV

panel temperature and then the roof temperature. And once we get these

two temperatures, we get the heat flux

that goes into the roof and that enters the attic. And once you get

this, we find out the attic temperature,

which then determines what is the ceiling temperature. And then we consider the

convection via ceiling, and then finally we end up with

the living space temperature. And then the couple this

living space temperature with another electric model,

which I’ll discuss now. So the electric model is

basically based on an ideal gas assumption. So it basically–

what it does is it calculates the energy

needed by the AC, which could be the heating

or cooling, in order to maintain the living space

at a particular temperature. And we use this formula,

m dot Cp delta T, which is an ideal

gas formula, which gives out the energy

needed by the cooler. And then so the electrical model

uses the power consumption, and then we know the power

production through PV output. So comparing these

two, we can really assess whether PV installation

is favorable or not. And this is just

the model in making. What we want to signify

here is we actually made this model on

our own, and this is the MATLAB code we wrote. And the model not only just

predicts energy values, but it also can do

a lot more things, such as predicting temperature. And what I’ve shown

here is the PV output, and then the cooling

load that is required, so it can do a lot of

other things, as well. This is the

temperature of the roof in terms of direct sunlight

and diffuse sunlight. So if anyone is

interested, I’d be happy to discuss with them more. Thank you. And I’ll now pass

it on to Heidi. HEIDI: So I take over

from here talking about the third and most

complex model that we used. For this model, we used

two different softwares– one called BEopt from

NREL, and the second, called EnergyPlus,

which you’ll see later, developed by the

Department of Energy. So what we did in this model

was we took into account the 3D effects of these

thermal and electric loads that have been talked about

in the other two models. The first thing we did was to

actually model the 3D house, as you can see right over here. BEopt allows for a very nice

interface, where you can easily model the house and easily

and put a whole bunch of input parameters for the house. And for these input

parameters, we consulted with an experienced

building inspector for the construction inputs

and used BEopt default values for the rest of the inputs. Once this model of

the house was done, we had to actually

export it into EnergyPlus because EnergyPlus gives us

a much more detailed look into all these different

parameters, I guess. And you can actually go in

and modify different things. For the materials, we can

modify every single material property– conductivity,

density, specific heat– and we actually did

that for the roof. And we also removed

a whole bunch of other miscellaneous loads

that BEopt had included. So this is how we modeled

the trees and the panels that we talked about in

our scenarios before. For the trees, we modelled

them as these really large 5 by 20 meter rectangles that

act as shading for the house. So the trees are located on

the south side of the house, and we modelled them

as deciduous trees, so we set up a transmitting

schedule so that they have a higher transmittance in

the winter when the leaves have fallen and a low

one in the summer. And for the case of scenario

E in which the trees are cut, we modelled the trees as

being five meters tall. On the other hand,

for the PV panel, we modeled it as being

fixed on the roof. We actually had to completely

change the model from BEopt, because they modeled

it as being decoupled from the entire system, placed

30 meters away from the house. So we completely changed

that, and we read extensively into EnergyPlus literature and

found this particular object– I guess you could call

it– called the integrated exterior vented cavity object. And what this does is it

models a surface as being, in our case, 0.5 meters

away from the roof, and it models the convection

and radiation between these two surfaces. We also considered

the solar panel to have a solar

absorbance of 0.92 and thermal emissivity of 0.9. STUDENT 3: And so

we actually got a lot of results, as

you might imagine, from all those different

models, but just for the purposes of comparison

for the presentation, we’re just going to show

you– just summarize results. And basically what we’re

showing you here is specifically for Boston, and this

figure that we’re showing is the y-axis is the

relative energy gain. So in order to compare them,

we decided within each model compare it to a

common situation. So we decided to say

that, if we’re in Boston, let’s say we start with

a white roof and a tree, so that’s scenario C there,

so that’s why it’s 0. So everything is in

comparison to that. And right off, we could see

that, as we might expect, putting a PV on makes

sense energetically. And specifically

scenario D, which is where you completely remove

the tree instead of just cutting it, in Boston at least,

is what makes the most sense. And this is for both

models– actually, for all the models– although

model one can’t really model a tree necessarily. So that’s why it basically

doesn’t apply for that. In terms of comparing the

results between the models, model one actually does a

pretty decent job in Boston in getting close to model

three, which is impressive, because model one is

significantly more crude, much simpler than model three, which

required many, many inputs. And the reason we believe the

discrepancy is between models two and three is that

model two– the way that we basically treated the

solar insulation– it doesn’t treat diffuse

sunlight differently, whereas basically the models

PVWatts and EnergyPlus will take all that into

account, so we think that’s a larger

reason for that. And then similar

thing in Phoenix. And basically we see a

slightly different case here. Actually scenario E, where

you just– you install the PV, but you cut the tree

instead of completely removing it– gives you a

slightly better increase in net energy gain. In terms of– and

this is, again, just in terms of energies. There could be rounding errors. This is ignoring the fact

that, if you cut a tree down, the net greenhouse gas emissions

would be changed and altered, and this is ignoring

all those other effects. This is just in terms of net

energy gain of the house. And just to point out the

discrepancy between model one here, we think that model

one is actually, again– that’s using PVWatts to

get your energy output. And that’s basically assuming

peak solar insulation, whereas our models basically

use more empirical formulas to get the estimates

for your PV output. And then the last thing we

did to do the sensitivity– or to compare the models, was do

a sort of sensitivity analysis. So basically on

your y-axis, what you have is your percent change

in your relative energy gain– relative, again, to

that scenario C– divided by the percent

change in your parameters. So we just considered

four parameters. The x-axis is a rough

estimate of how difficult it would be to actually change

that parameter in your house. So the far left

one is the PV size. We just assumed if you went–

instead of a five kilowatt to a 5.5 kilowatt, so that’s

why the price is roughly $3,000. We assumed about a $5 to $6

per watt installation cost for that. So that’s basically

the easiest one to do, and you get various significant

change in your thermal energy gains because of that. And then just

because of time I’m going to rush through these, but

basically a lot of the models follow similar trends in

terms of the sensitivities. The last one is the roof pitch. You obviously wouldn’t

really want to change that. It’s very expensive to do. Luckily, for most

of the models, it’s not actually that

sensitive to it, at least within a close amount

to where you start with. So in conclusion,

in all three models, it makes sense to

install a PV system. It’s kind of what we

expected from the beginning, but it’s nice to get

that sort of conclusion. For models one and

two, we actually were able to get pretty

reasonable results, but they’re limited in

terms of what you can consider within those models. The advantage of looking

at this basically is that, if you have

a user that isn’t as familiar with EnergyPlus

in model three, which is very sophisticated– it

requires a lot of inputs– they can still get a

rough estimate, which is relatively close, using

these much simpler models. And model three–

again, we were taking that to be the more

realistic case, but you’d have to compare

it to real life data and do an empirical analysis

to see how close it actually does correlate. And then just,

again, to summarize the results we got from

model three– in Boston, it makes sense to

install the PV, but completely remove the tree. And your payback period

is about 24 years. In Phoenix, the best scenario

is to install the PV, cut the tree, and

it’s about 51 years. And interestingly, the maximum

of that relative energy gain was essentially

the same in both, even though the

scenarios were different. So we’d just like to

acknowledge Professor Buonassisi for helping assist

us and guiding the direction of the project,

and Bryan Urban at Fraunhofer and other members at Fraunhofer

for giving us guidance. And with that, we would like

to ask you for questions. [APPLAUSE] AUDIENCE: My

question is I grew up in a neighborhood that

has a lot of trees, and so cutting

down all the trees wouldn’t be very practical,

but do you at all consider PV systems that

could handle shading at different times of the day? So somehow decoupling

different parts of it knowing that some of them

will be in sunlight for part of the day, some of them will

be shaded, and that will change? STUDENT 3: So you could

make the model– especially in EnergyPlus, you could make

it as complex as you want. We just did this for simplicity,

just to put boundaries around what are problem is

that we were considering, but you could definitely– yeah,

you could definitely add that to the model if you wanted to. STUDENT 1: Cutting

the tree was not worrying about the output of

the actual cell in terms of whether or not some shading

was going to bring down the rest of the cell. The reason we

would cut the trees is to increase the maximum

amount of sunlight per day hitting the panel. And it’s because we were looking

mostly at endpoints, trying to get the spectrum ends. That was why we went

to such extremes. Cutting down selective trees

and just parts of trees would be kind of in between. It’s a little bit more

difficult to assess. STUDENT 2: Cutting down trees

is actually [INAUDIBLE]. It’s not [INAUDIBLE] because

we found that the shading factor doesn’t play a much

bigger role if you look at the relative [INAUDIBLE]. So you would as

well have increased the– there will be objectively

small loss in the energy gain, but that shouldn’t matter much. AUDIENCE: Sort of a

philosophical question. If the payback period in Phoenix

is 51 years, is it worth it? That’s a long time period

for– I guess economically you could say that you could do

other things with that capital instead that would have

a shorter payback period. JORDAN: Well, 51

years– then the answer is probably not if you’re

looking at the benefit of cost money-wise. We did analysis

about the energy. So we found an estimate for the

embedded energy of the panel. This is from– I can’t

remember the source, but they change quite a bit. But this example says it’s

1,500 kilowatt hours per meter squared, so that equivalents

to nine years payback. Pretty much nine

years in Boston. I think it turned out to

be eight years in Phoenix. So there is a

benefit energy-wise, but in this example,

perhaps not cost-wise– perhaps not the most

advantageous to do per dollar. MARK WINKLER: A related

question, actually. Can you back to your

two slides back maybe? JORDAN: Yeah. MARK WINKLER: Your look

at the net energy gain was quite similar. So why the large difference

in yearly savings and payback period. STUDENT 3: Just the cost of

electricity in each location. We estimated it as about $0.07

per kilowatt hour in Phoenix and $0.17 in Boston

for residential. STUDENT 1: Which is why you

would get a shorter payback period for Boston– is because

the cost of the electricity that you’re [INAUDIBLE]. MARK WINKLER: So I would have

assumed that the generation mix is sort of similar. Is that regulatory,

or– I would assume they’re coal/gas centric

generation mixes. STUDENT 3: Yeah. You mean in terms of

how the houses are– MARK WINKLER: This is a

little outside the scope of what you guys did. I was just curious

if you guys had any sense of why

the big difference in wholesale electricity

price is between Boston– STUDENT 3: I think part of it

is just how plentiful energy is. I guess Boston is at the very

end in the corner of the US. It’s more difficult to get fuel,

oil, gas shipped over here. I think Arizona–

I think they’re relatively close to a nuclear

power plant over there. Oil– I think it’s

just location–wise. JOE SULLIVAN: There’s

a lot of coal there. They actually ship a

lot of the electricity to California because they

can’t [INAUDIBLE] in California. One quick question, though. The relative energy gains

are the same for both. Do you have different

sized panels, or is heating that much? That’s a big deal. STUDENT 3: Heating, yeah. JOE SULLIVAN: OK. So if you– HEIDI: Also for Phoenix. For Phoenix, you can see

this is the cooling over here and heating on the

right over there, and for Phoenix, you can

just look at the values and see that there’s a lot more

cooling than heating compared to the Boston case, where

there’s a lot more heating by many orders of

magnitude more. And so that kind

of balances it out. JOE SULLIVAN: And so that’s

only in a cutting down a tree case if you were already

well shaded or not shaded at all? HEIDI: These are

actually all the curves for all the scenarios, and– JOE SULLIVAN: I see. OK. HEIDI: So it does matter

just because of location. So if you’re not

cutting down a tree, then there’s no

decrease in shading. Or is it just the panel

itself that’s heating up more? STUDENT 3: Well, actually

if you go back to– JOE SULLIVAN: Sorry, I

think I missed something. STUDENT 3: Actually,

in Boston it actually makes more sense to have a

black roof than a white roof. So you actually want–

shading isn’t necessarily good in Boston, just because

there’s so much heating that you need in the winter. It seems to be the

dominant effect in Boston, and in Phoenix,

it’s the opposite. The cooling is the

dominant effect. PROFESSOR: Did you

consider the possibility that snow also

insulates the house once it falls on the roof? STUDENT 3: No. We did not. I don’t know if– is

that built into– I don’t know what would happen. No, I don’t think we did,

but that’s a good point. AUDIENCE: Along

those lines, do you have any intuition

as to why in one case it’s better to cut down a

tree other than remove it, and then the other is better to

remove it rather than just cut it down? Are you expecting it

to grow back and then have to incur more

costs because you’re going to have to cut it down

again, or– what’s going on? STUDENT 3: Well, just

in terms of pure energy, it was very, very

slightly better in this case to have

the tree just cut just in terms of the balance

between heating and cooling [INAUDIBLE]. STUDENT 1: I may be able

to help clarify that. The idea is in Boston

we’re relatively cold most of the year, so the more

sunlight that hits your house is going to add more

heat to your house, and that’s less energy

that you have to pay for. So the reason that it’s

beneficial to cut down the tree completely in Boston is

because it allows more sunlight to hit your house, whereas

in Phoenix, you don’t want the sunlight to hit your house. If you cut down the

tree completely, that’s more you have to

pay for AC in the summer. So the– AUDIENCE: [INAUDIBLE] the

difference between cutting down the tree and removing it? STUDENT 1: So– STUDENT 3: So– go ahead. STUDENT 1: Cutting

the tree is assuming that you’re going to maintain

it at that certain level. So by cutting the tree,

you keep a certain amount of shading on the lower

part of your house, but you still allow sunlight

to hit your solar panel. Cutting down the

tree completely means there’s no shading

on your house at all. AUDIENCE: I guess I have

a philosophical question. So I think there are

a lot of people– motivation for the solar panels

is not just the [INAUDIBLE], but rather the desire

to do something good for the

environment, and to lower carbon emissions, et cetera. But when you cut down trees,

that increases your carbon emission because you’re

reducing the plant, that reduces your carbon output. So given that you won’t have a

tree there for like 50 years, does that offset the

carbon emission gains that you get by– STUDENT 1: Just my two cents. If you really want

to go in depth, you can look at

how much carbon is going to be produced

by the coal power plant to give you the energy that

you’re going to be using for 50 years and compare that

to the amount of carbon that one tree was going to save

you, or you could ask yourself, am I planning to have a child

during those 50 years, which will produce so much more CO2

than that tree will take out? Either way, it’s

relatively a small value. However, we do

acknowledge that there was a lot of

philosophical questions that we argued

amongst ourselves, but we realized we didn’t have

the time to try to evaluate, or the materials, and scope. PROFESSOR: One more

question, and then we’re going to have to switch groups. Jasmin? JASMIN HOFSTETTER: Do

you have any real data to compare your

model results to. From your results,

it seems that it doesn’t make any

sense to install solar panels ins Phoenix. Is that right? That’s the impression? STUDENT 3: Financially. Just purely financially, yeah. In terms of the PV

output, it seemed to be pretty close

in comparison to what we got from other sources. So it seems like the net gain

in energy is roughly right, but obviously people still

install panels there. So either, I’m guessing,

subsidies, or larger installations, or

something else, or just the desire to install it

just for installing it– not necessarily for financial

reason– in Phoenix. JASMIN HOFSTETTER: What was the

temperature that you assumed? I suppose you assumed a constant

temperature in the house that was like the– STUDENT 3: Yes, that

would also change it. JASMIN HOFSTETTER: What

was this temperature? STUDENT 3: The set

points for our model was– the cooling

set point was 71. HEIDI: 76. Yeah, 76. And then the heating

set point was 71. STUDENT 3: Yes. JASMIN HOFSTETTER: Can you

say that again, please? HEIDI: The cooling set point

was 76 degrees Fahrenheit, and the heating was 71. STUDENT 3: For both locations. HEIDI: For both locations. JASMIN HOFSTETTER: Thank you. AUDIENCE: [INAUDIBLE]. JOE SULLIVAN: Are we out of– PROFESSOR: No, that’s it. You guys are done. Congratulations. [APPLAUSE] [INAUDIBLE] coming up, PV grid. What happens when you install

loads, and oodles, and oodles, and oodles of solar

onto the grid? We’re going to hear all about. And take it away. Knock it out of park, guys. IBRAHIM: So as

[? Tony ?] mentioned, we’re the PV grid project. I’m Ibrahim. MARY: I’m Mary. RITA: I’m Rita. ASHLEY: I’m Ashley. JARED: I’m Jared. IBRAHIM: All right,

so I’m just going to start with the motivation

behind our project. So as we discussed in

class, PV installations have witnessed very

significant growth rates over the last few years. Last year alone PV installation

growth rates were around 17%. Around 18 gigawatts

globally were installed. As the cost of PV

approaches grid parity, more investors and

consumers are going to want to adopt PV systems. However, one lingering

or major obstacle preventing the further or

high penetration levels of PV systems is intermittency. So as we discussed

in class, there’s variability in terms

of the solar resource, both on a long-term scale and

a short-term scale seconds to minutes. So on a long-term

scale, we’re talking about the position of the

sun relative to the Earth and so on. So in that respect,

that’s predictable and can be planned for. When we define or talk

about intermittency, it’s the short-term

unpredictable effects that change the power

output significantly. So what we have here is

the fractional change in power output over

the course of one day. So as you can see, between

the two consecutive seconds, the power output

can almost double, and it can at other

times drop by half. So from a system

operator perspective, that’s obviously

a major challenge because demand should

match supply at all times. So again, these effects, or

these intermittency issues, arise due to regional weather

patterns that can be predicted and also due to local

weather patterns that are less predictable. So in our project, what

we tried to address is, can the weather report be

used to predict the power output from an ensemble

of smaller distributive PV systems? That is, can we average out

these local less predictable intermittency effects? I’ll give it to Mary to

discuss our approach. MARY: So our goal

of this project was to design a model

that could quantitatively analyze a PV grid and determine

its robustness in terms of variability. And our main components were

meantime between failure– which is the average

time between two system failures, which Rita

will define and discuss later– number of

systems in the grid, and geographic

dispersion, which we measured through

geometric mean distance. Our data set was from

the Oahu airport, which is part of the National

Renewable Energy Laboratory. There are 17 systems all

within about a kilometer of each other, so it’s a very

small, very dense system, but there was second interval

data for a year, which we used. So there’s a fair amount

of data to give us an estimate of how

intermittency varies over the course of a system

and the number of systems and density. RITA: So our first

step was to define what was a PV system failure. In order to do so, we

accessed the CAISO website– that is, the California

Independent System Operator– and we took that data from

one week of the actual demand and hour hand demand forecast. They give this value

for every hour, so we took the value for

every hour of the week, and then we plotted

in this graph that we have a line for

each day of the week, and can see that both the

magnitudes and the shapes throughout the week

are almost the same. We can also see that the

values are almost all positive. This means that they

usually underestimate. They usually think

that the demand is going to be under

what it really happens. And so what we defined was

that, if this estimation is OK for CAISO, if

they can manage that the grid with this

variation, then they could also manage the grid with

this variation in a PV output. And so we looked at 5:00 PM. That is the hour that

we have the biggest variation between the two,

and we averaged the value, and we got to 6%. So this means that, if our

intermittency is above 6%, we are going to have

a PV system failure. If the variation is below

6%, then the intermittency is not going to be a failure. Then we could define the

mean time between failures– that is, the mean time between

two intermittencies higher than 6%. JARED: OK, now that we

have some context of what the problem is, and we have an

idea of what variability is, and we have a data

set to work with, I’m going to talk about how

we actually solve the problem. We use coding in MATLAB to

handle this huge data set. NREL had 17 systems out

there for every second of an entire year. And so we took all

the files from NREL and put them all

into one huge matrix. You can imagine it

was– it ended up being about 23 fields

by several million, and it’s about 677 megabytes. So actually handling the

data was an issue in itself. I don’t recommend it

with an old computer. And we also the GPS

coordinates for each of those locations

and the variability from the California

ISO, so with that data we could begin to build

our code to figure out a quantitative description

of mean time between failure and our idea of density. So once everything was

loaded into one big matrix that we could work

with, we moved on to use the GPS coordinates. And of those 17

systems, we found every single combination

of 17 choose 2, 17 choose 3– every possible

way that you could connect these systems– and

came up with something like 60,000 different

ways of connecting these, and then for each

possible connection, we had a function

that would calculate this geometric

mean distance that would give you an

idea of the density of that particular connection. And so to compare our

mean time between failure for these systems while

holding the density constant, we then searched through

those possible combinations and found this magic

number that kind of existed for each of those

possible combinations of two, of three, or four,

all the way through. And it kind of lined up

for geometric mean distance of 400 meters. So using that set,

we could then go on and see how increasing

the number of systems helped the mean time

between failure. And then for a given set,

we ended up using eight. 17 choose 8 gave us like 24,000

possible ways to connect them. We searched through and

found varying densities for one set number. Then finally we

wrote a function that calculated the mean time

between failure that went through our data

from NREL and said– looked at the fractional

difference and said, OK, each time it’s about

6%, that’s a failure, and then measured that distance,

took the average of that, and that was our mean

time between failure. And then finally,

once we had all that together, we

crunched all the numbers, took a long time on my computer. We were able to plot it together

and get some very nice trends. One of the great things,

I think, about our code is that it was only 525

lines, and if you’ve ever built a programmer, a

big application, that’s really small. It’s very easy just to go in

and see exactly what’s going on. So it’s very flexible. We could hand it off

to another company, to another research group,

and they go in and adapt it to just about any data set. If you are able to get data in

California, or from Germany, or from somewhere else, and

bring it into our format, it’s very easy just to plug

it in and run the data. Very, very minimal

changes within our code. And then you could

also build on our code to look at other problems. So we have the change in the–

we have the variability data as a function of time. We also have the solar

output as a function of time. So you could conceivably

go in and figure out how your meantime

between failure changes based on the time of day and

change your critical percentage based on the time

of day, and there are several other problems that

you could go, and launch off of our code, and continue on. And if you’re interested

at all, I actually put the code of my public space. There’s the link there. Check it out. It’s pretty cool. And then Ashley is going

to talk about our results. ASHLEY: Cool, so the first

thing that we did in order to try to see the trends in

these huge fields of data was just to plot the data. It was actually a

much bigger task than I thought it

was going to be. The plot on the left is for

one day’s worth of data, and the plot on the right

is one week’s worth of data. The y-axis is power density

in watts per square meter, and the x-axis is

the time in seconds. The blue is all 17 of

our systems together, and the red is just

for one system. So as you would assume,

the power output for all 17 together is clearly

a lot greater than the output from

just one system, but this give us a sense of

being able to see fluctuations within one day, and

also were able to see when the sun rose, and peaked,

and also fell each day. And in order to quantify all

those different fluctuations, we did the fractional change

in power density versus time, once again, for one day,

and then for one week. And red is the one system. Blue is all 17 systems

together, and we can already see just from plotting the

data that having all 17 systems together does

start to average out the fluctuations of

individual systems by a significant amount. So then Rita earlier

mentioned that we use 6% as our cut off for failure. We actually went ahead and

did 6%, 12%, and 18% just to see how sensitive our

analysis was to that threshold value. So here we have

plotted on the left the meantime between failure

versus the number of systems, and on the right,

meantime between failure versus the geometric

mean distance. I also calculated

these values for using a week’s worth of data,

a month’s worth of data, and a year’s worth of data. So the week would give

you more fluctuations, but the year would give you the

more long-term overall system behavior. Relationship between the mean

time between failure and number of systems is quadratic, and

we found a linear relationship between the mean time

between failure and the GMD. So this is four 6% cut off. This is for 12% cut off,

and this is for 18% cut off. And the mean time

between failure increases dramatically

as you go from the 6% cut off to the 18% cut off. So a lot of this makes

sense, but it was really cool to quantify that. RITA: So after

applying those graphs we could take our conclusions

and answer our question. And so the first thing that we

noticed, but we were expecting, is that a big data

sample should be used if conclusions are going

to be used as a design tool. As Ashley said, we used for

a week, a month, and a year. And so we know that the

bigger the data set, it’s going to be–

it’s not going to be influenced by

abnormal things that can happen in a given day. And we also saw that

there is a linear relation between mean time

between failure and GMD. When GMD increases– that

is, when density decreases– we are going to have an increase

in mean time between failure. This was also what

we were expecting because the local

effects will not affect systems that are further apart. We also saw that there

was a quadratic relation between mean time between

failure and number of systems. Number of systems increased. Mean time between

failures also increased. This was also according

to what we expected because we know that the

percentage and the total output is going to be lower. We also saw that the mean time

between failure is very low, even when we can see

the 17 systems together, we have about 900

seconds between failures. This means that

some backup systems should be used in order

to take over the load when we have a failure. And so now we’re

running conditions to ask our first question. And so we conclude

that localized predictable intermittency

do average out and that this effect decreases

as the number of systems and the GMD increase. The data that we used

was for 17 systems, and the biggest distance

between them was one kilometer. So we believe that

it’s important to run our code for a bigger set of

data, because only in this way we can confirm our

conclusions and guidelines for the design of PV

systems can be defined. Thank you, and we’ll be happy

to answer your questions. [APPLAUSE] JOE SULLIVAN: So

a couple things. First of all, you ended

at exactly 15 minutes. I find that remarkable. Additionally, just– sorry. Can you repeat what exactly

a failure mode is defined as? Are you looking at 6%

intermittency varying from second to second? So if you look at the output

from one second to the next, does that change by over 6%? RITA: Mm-hm. JOE SULLIVAN: It wasn’t

average out over an hour. RITA: No, no. It was second by second. JOE SULLIVAN: You got

the 6% from Cal ISO. RITA: We said that if there–

in a given hour, we measured– let me just– JARED: They only had an

hour of data [INAUDIBLE]. RITA: Yeah, they only

gave hourly data. So the difference

between the actual demand and the hour-ahead

demand forecast. So this is what they are

expecting, but the difference between what they are expecting

and what the grid is really asking them. So if they can manage this

difference on a second base, they can also manage this

difference on the PV grid. JOE SULLIVAN: So you

took the worst case. Is that how you got 6%? RITA: Yeah, we took the

average of the worst case. It’s the 5:00 PM. The 5:00 PM is always

the worst hour. It’s always when

they have that peak. And in fact, all of the

base– almost all of the base were around 6%. Our peak was like 6.8%, and

we averaged, and it was 6%. MARK WINKLER: So

that’s essentially their peaking capacity? RITA: Yeah. ASHLEY: Also, so I

actually wrote down the numbers for

6% function, 18%– like our mean time

between failure. For 6%, we had up to 15

minutes between failure. So it’s a pretty low amount

of time between failures. And if you allow 12%

as your intermittency, you can get up to

about half a day. And then for 18% as your cut

off, you get about nine days. So it is still

very intermittent, and you would pretty often have

to have backup systems if you had the small of a system. So if you had a much wider

spread system and a lot more systems in your grid,

then you could definitely significantly increase the

mean time between failure. Yeah, Joe? JOE SULLIVAN: So you

have this awesome graph. So if you go back to

the time between failure number of systems. The interesting takeaway

is how large of an area do you have average over, right? So 300 seconds on

a grid perspective is unacceptable for

widespread PV developed point. We need to be on

the order of years. And so do you have an idea

of what that distance is? ASHLEY: If we just

extrapolate it out? JOE SULLIVAN: If you

extrapolate– this is obviously like we’re taking the very, very

edge of that function and then extrapolating [INAUDIBLE]. ASHLEY: So looking

at the numbers– JOE SULLIVAN: But it looks like

it’s going up exponentially, or do you have an idea

of what that trend is? ASHLEY: For 6% for

the one year, it was almost exactly x squared. It was like x squared plus

50, or 100, or whatever that would be. So if you want, you

could say, mean time how many seconds are in a

year equals number squared. So the square root of however

many number of seconds there are in a

year would give you your number of systems required

for a year between failures. IBRAHIM: But this is for a

given geometric mean distance, so you have two factors. If you sort of

spread them out more, probably going to

require less systems. JARED: And if you

looked that map, that’s all at the end of a

runway at the Honolulu airport. So if you have a huge

field in Arizona, thousands of systems, your

mean time between failure is going to be a lot better. MARK WINKLER: So I’m actually

really surprised that there’s such a huge effect

from adding systems, just because it seems as

though the relevant length scales for weather

should be very large. Do you guys say

anything about that? JARED: I think the idea was

that, for long-term weather, you can predict that. So if you know

there’s going to be a storm front coming through,

you can add natural gas. You can add coal to the system. Back up– ASHLEY: And that would

cover the entire system. JARED: Our kind of variability

we’re talking about is say, if one cloud goes

over, or a flock of birds, or something. So we were thinking

that would be on a few seconds

for a single module for a cloud just go over

shade it for a short distance. So if you add

thousands of modules, the other modules wouldn’t

be shaded while that one is. MARK WINKLER: But

these fields– I mean, 100 meters on the

scale of cloud cover, this still seems like a

somewhat small length scale. Let me rephrase the question. Do you think that the

graph on the right would be a smooth

function of distance, or do you think

there’s some length scale at which the

behavior on that plot changes significantly? JARED: That would be

interesting if we could find– ASHLEY: The assumption

is definitely evenly dispersed in an area. JARED: That would be something

that, if we had another data set that had wider

distances, it would be very easy to plug it in. I think our code’s

really flexible. It would show us

that relationship. MARK WINKLER: What do

you guys think, though? JARED: It’s a good question. ASHLEY: I wouldn’t be surprised

if it was linear still. I guess another

complexity we could do would be you would

have– right now we just have one big field of

systems, but if you had one set of systems that

was spaced x distances apart, and then you had some

number of kilometers away from another

one space– I’m not sure how exactly

would model that, but I think that

at that point I’m not sure what the

curve would look like, but a continuing linear

trend seems reasonable to me. IBRAHIM: So I guess another

thing to keep in mind is we did not take into

account transmission costs, so I guess you’d have to weigh

the cost of failure versus, I guess, the added incurred

cost for transmission lines and so on, so there’s

sort of an optimum point where you want to

space them and have a certain number of

systems where I guess, after a certain point, your

returns diminish and are not equal to, I guess,

the cost of failure. So that’s something

where, I guess, future people can

come in and expand on. AUDIENCE: So all this

is data from Hawaii, which has a very notable

climate and weather. I’ve never been there, but– [LAUGHTER] Do you think that this is

really– your code is flexible, so I understand that, but do

you think the conclusions are really extensible to

other parts of the world with different weather

patterns or climate? RITA: That’s why we think

that the future mark is really to do it for a different place

and for a bigger set of date because we really want to be

sure that the conclusions are going to be applicable, because

we had that same question. We were talking just

about a small place. We said that it’s one kilometer

apart for the distance that we have. So we also want to run

for a bigger set of data and for another place just to

be sure that our conclusions are applicable everywhere. JARED: And I would say the

relationship would probably hold because if you have– say,

if your regional weather is very different, that

wouldn’t show up in the fractional second to

second difference that we had. And so the timescale

that we measured it on I think would be, say, small

clouds or intermittent events that would occur over a wide

range of different climates. The general regional

weather is predictable, and it isn’t investigated

in our study at all. So I would say I think

the relation would hold. ASHLEY: I think

that the big change between different regions

would just be the total output power that you can

get, but I think– I wouldn’t be surprised if the

fluctuation is still the same or is similar. And I think certainly that, as

you increase a number systems and as you decrease the density

with which they’re packed, you should be able to

have a more robust grid. I would be very surprised

if that weren’t the case. AUDIENCE: Do you see shading

for planes at the airport? ASHLEY: There’s no way

for us to determine what causes the shading. The raw data we

have is just output. JOE SULLIVAN: Can you

see how they move? [LAUGHTER] ASHLEY: It’s like

there is this line– IBRAHIM: We actually

did that for one plot. You could see the cloud

moving around the plot. JOE SULLIVAN: That’s cool. IBRAHIM: And you see the

power output for [INAUDIBLE]. ASHLEY: Yeah, it was

on the order of– we had like two billion

data points, I think, which was overwhelming. But yeah, it was really cool. Any more questions? Yeah? AUDIENCE: Can you describe

a little more what these PV system failures entail? And what happens,

and how long does it take to get them

back up and running? What has to be done to do that? ASHLEY: You wanna get that one? JARED: Sure. So basically there

is a certain capacity that the grid would have. Say, you can compensate for a

6% drop in this case, or a 20% drop, or something like that. So if your system is

completely powered by PV, which is not realistic,

and you have, say, a 20% drop and

nothing to compensate that, you have a blackout. And so we investigated

18%, for instance. So that would be,

say, if your grid is a certain percentage

of PV and then has natural gas, or

coal, or something that you can bring

online quickly to compensate a drop in PV. That would be an idea

of what a failure is– if you aren’t able to

compensate that fluctuation AUDIENCE: And how

long [INAUDIBLE]? JARED: How long

would a failure last? It depends. If you can’t meet the demand– AUDIENCE: [INAUDIBLE]. JARED: For a PV system,

I think the problem is the PV system would come back up

right after the cloud was over, but if you can’t

meet power demand, you’ve got all kind

of protection systems that would trip off,

and it would be mess. So I don’t think would

come back very quickly. ASHLEY: That’s a good

question, though. JARED: That’s a good question. PROFESSOR: It’s relevant because

you can envision back up power that could kick in really

quick, but exhaust itself within the period of

the delta t necessary. AUDIENCE: For your

definition of intermittency, did you look at the absolute

value or just the drop? Because the grid can’t deal with

excess power as well, and so I was just wondering if

you had insight on that. Like if you dumped 60%

more power in the demand, there’s no way for you to– JARED: We did the

absolute value. So 6% more, 6% less. JASMIN HOFSTETTER: So I’m

going to ask you for real data. So do you know where

more or less data points would lie for, let’s say,

PV systems on houses that are like– with a

typical distance in some kind of neighborhood. JARED: I think that

would just be you would adjust your

geometric mean distance to whatever the distance

from the houses are. I don’t think our data has to

be a solar farm, for instance. I think it could be houses in

a neighborhood, for instance. So if they’re perfectly

connected to the grid, I think that our code

would account for that. ASHLEY: This was

for eight, right? JARED: Uh-huh. ASHLEY: The right-hand graphic

held the number of systems at eight. And so if you had eight

houses spread apart by an average of 150

meters, then you would– and if you considered a year’s

worth of data– is it like 250? I just can’t see it. So you’d have meantime between

failure of 250 seconds, which is four minutes? Doing math under pressure. JARED: Right, but if you

have a grid to back that up, it’s not big of a deal. AUDIENCE: I’m confused about

the plot on the right here. What it’s suggesting is

that one week you picked was significantly below the

year average [INAUDIBLE], and you could have equally

picked another week that was significantly above. JARED: Right. This was, I think,

just to give the trend. The relationship between the

day, and a week, and a year is just the day

that we– I’m sorry. A week, and a month, and a year

is just the week we picked, the month we picked. I think you see on

some of the other plots that the week and the

month actually shift. It’s just the year was kind

of the average of those. MARK WINKLER: I would

assume that areas, or specifically countries, that

made large investments in solar would have studied this

question in a detailed fashion. Do you know if, for example,

Germany or Spain have looked at this problem when it’s spread

across hundreds of kilometers. ASHLEY: Ibrahim, do

you know that one? I think you might be– IBRAHIM: I was actually

very– we didn’t find a lot of literature actually. For wind, there was a

lot of data out there, I guess, because the high

penetration levels with PV. There were very few studies. Most of them actually

were addressing the US. I didn’t find any, actually,

on Germany or Spain. Probably maybe they’re

in Spanish or German, so I don’t know. JOE SULLIVAN: So

what I find really startling is that,

for a given system, the time between failure

of the 6% intermittency is on the order of a minute. Do you have any the idea– is

that vastly different for wind and what that number is? And this is outside

of your– I’m just wondering if in your

literature searching. JARED: You probably

know the most about it. ASHLEY: You would know from it. JOE SULLIVAN: It seems like

you have this big rotor. There’s some momentum, and

that to slow that thing down requires more time, but

I don’t– as opposed to electrons. JARED: I would say

wind would definitely have a much longer time

scale than solar, I think. There’s a lot of momentum there. RITA: But when wind stops,

the times that you have intermittency is going

to be much bigger. And there’d be

backup systems you need to have to take over

for a long period of time. JARED: And maybe

in high winds you would have more of an issue,

because if the turbine is spinning too fast, you

actually have to stop it. So maybe there you’d run

into issues of variability on the order of minutes. AUDIENCE: So I

think– and this is kind of going back to location

data set– comes from Hawaii, which I would imagine has

mostly direct sunlight. For locations such as

Boston, would the data set change for,

say, diffuse light and would that generally bring

in panels closer together or require more panels at

the same geometric distance to get the same results? ASHLEY: Well, the raw data

that we have doesn’t separate direct and diffuse, so I think

that the first thing would be we’d want to

look at a data set and from whatever

other location you wanted to know about and look at

how diffuse and direct differs. I don’t think we have a

sense here of that effect. Does that answer your

question to some degree? AUDIENCE: Some degree. I don’t know if someone

else wants to add more. JOE SULLIVAN: [INAUDIBLE]

after you respond. JARED: I think it’s

interesting– I was just thinking about this. Something that might be

interesting to investigate is concentrated solar. If it’s easier to

shade, it would look like a denser system. So maybe that would be– maybe

a concentrated solar farm might be a bad idea if you

have lots of little clouds. So that’s something I

think that you could expand into from this project. IBRAHIM: And another thing, I

guess, to add to your point, if you look at, I guess,

solar thermal systems probably because of the diffuse

sunlight, the intermittency I would expect is going

to be probably less. You’re going to have less,

or the mean time to failure is going to be longer,

so you could maybe add a solar thermal system, sort

of balance the power output, and decrease your

intermittency even further. JOE SULLIVAN: Any

last questions? No? All right, let’s

thank our group. [APPLAUSE]

looooooooooooooooong video

2nd person is confirmed Asian may be from Pakistan or India

👍

Interesting

arazona will love the roof cooling

I’m not even presenting and my stomach hurts