The Top 3 by E3

An Introduction to Photovoltaic (PV) Solar Modeling and Three-Part Overview of the PVsyst Model

• E3 Consulting • Season 3 • Episode 4

Share episode

Send us a text

Join Daniel Tarico and Frances Willberg Plourde as they kick off a discussion regarding the range of PV solar modeling programs available today. In this introductory episode, they explore the key differences between tools, setting the stage for a deep dive into PVsyst, the industry-standard software for PV system modeling. In a series of three following episodes, they provide an overview of PVsyst's capabilities, focusing on essential features that can help you design a customized model tailored to your specific PV project.  Follow-on episodes in the series include:


Series Episode 2: PVsyst – Climate Data Sources
Series Episode 3: PVsyst – Interpreting Reports
Series Episode 4: PVsyst – System Parameters



Daniel Tarico:

Welcome to the Top 3 by E3, a podcast about the intersection between engineering, energy and project finance. I'm Dan Tirico, director of Renewables at E3, and I'll be here as your host. I'm here with Francis Wilberg-Plourd, a project manager here at E3. And Francis is an electrical engineer with a background in solar energy production and analysis. This is the first in a series of podcasts where Francis and I will be discussing several topics related to photovoltaic system modeling and energy estimation. This first podcast is intended to introduce the topics, and our later conversations will cover them in greater detail. Welcome, Frances.

Francis Plourde:

Thank you, Dan. It's great to be here discussing this with you today. Accurate PV system modeling is really crucial to understanding efficient and profitable PV plant operation. Profitable PV plant operation the calculations performed by these models are used to determine how much electricity a PV system can and should produce when it's operating properly, and errors or inaccurate assumptions in these models can typically give incorrect estimated production values. And this has two effects First, there can be impacts to the electric grid if PV systems aren't producing as much power as they should be, and second, it can be really bad for financial estimations and projections if projects aren't producing as much revenue from energy production as they should be, and so investment returns for owners can be affected if plant production is far off of estimated values can be affected if plant production is far off of estimated values.

Daniel Tarico:

Right, yeah, you know, performance modeling has definitely become very important to the solar industry. The designers use it when they're optimizing plant designs. The developers use it to select sites, to see if a site's going to be productive enough to justify building a plant there and for their contractual offtake commitments. The financers absolutely rely on these for modeling long-term cash flows, understanding the uncertainties and making financial decisions. You know, without credible production modeling, most projects couldn't get built these days. Now maybe we could back up a little production modeling. Most projects couldn't get built these days. Now maybe we could back up a little.

Daniel Tarico:

When I started with the solar power project nearly 20 years ago, we didn't have good performance estimation models. There were two out there that were widely known. They were PVWatts and PVSyst. Pvwatts is a free online program from the National Renewable Energy Laboratory, also known as NREL. It's a pretty basic program with a few options for module types and array configuration, but doesn't have a lot of options for detail, and it uses meteorological data from nearby ground-based stations which are less accurate than the satellite-based data that we use for most modeling these days.

Daniel Tarico:

Pvwatts was adequate for the early solar projects, but it hasn't been developed much in the past 15 years and it doesn't allow for the detailed definition of system parameters that PVSyst does. So PVSyst has emerged as a widely accepted standard for production modeling. It's far more advanced. It allows for detailed modeling of the project with a range of inputs for the physical configuration, for shading, for detailed module parameters and inverter parameters and for insulation, which is the sunlight input into the plant, both on an hourly and now even sub-hourly basis, weather parameters like temperature, operational conditions like soiling, things like that. So it really takes a lot of information in to generate that estimate. Frances, could you give a quick summary of what goes into the modeling and what we might expect, since you're the one who's typically running this?

Francis Plourde:

Certainly. There's definitely a lot of background around the production modeling layout and procedure and how it's evolved as the solar energy industry has continued to develop. These days, performance modeling is incredibly intricate and has become its own technical specialization, so people make entire careers out of designing and working with PVsyst models. Solar projects tend to be very large and very costly, and so it's essential for site owners and developers to know how these systems are going to perform long-term, and so there's a lot of money riding on the accuracy of these forecasts coming out of these models.

Francis Plourde:

Obviously, we can't go through it all in detail, but we can give you a quick overview about how this modeling is done. So PVsyst models usually begin with defining the location where the system will be installed. That definition is made through geographic coordinates or selecting the location on a map. That definition is made through geographic coordinates or selecting the location on a map. And then the user can then select a source of climate data that they want to use to run the performance simulations for the project. We'll be discussing the specific options and sources of climate data in a subsequent podcast, but there are several that are available at different price points and different accuracy levels. So, after the location is defined, the user can then design the system according to the desired layout, the as-built plans of the site as per it's defined by the site designer. So that includes the layout where the rows of P modules are going to be installed, things like row spacing, tilt, angle, orientation, things like that, everything that goes into actually defining how a solar system is installed. Shade scene, which models how nearby objects such as vegetation or buildings or other objects cast shade onto a PV system area at different times of the day, to make sure that you're not getting large amounts of shading from nearby trees or nearby buildings that are going to lead to decreased system output over time.

Francis Plourde:

Gotcha, the electrical behavior of the equipment that is going to be installed in a system is so specifically focusing on, the modules and the inverters used are defined by specific types of files. So for modules these are called pan files, (. PAN) files, and for inverters these are called O-N-D, (. ond) files, and these contain information about the electrical specifications and performance characteristics of those specific pieces of equipment and these are usually available from the manufacturers of that equipment. So the On top of that, PVSyst allows for the specification of several different assumptions and design parameters, which can model causes of production loss that can impact system performance. M anufacturers will provide access to those files for use in PVsyst models when you purchase those pieces of equipment or you're looking into purchasing those pieces of equipment. We're going to get more into detail on some of those parameters and assumptions in a later podcast, but those can include things like soiling loss, shading loss, ohmic loss, things like that anything that can lead to potential areas of loss or lowered performance on both the equipment level and the system level.

Francis Plourde:

So after the PVsyst model has run and has calculated the estimated production capacity of the system. The end result is what we call a PVsyst report, which contains a summary of the model, inputs and parameters and then the results of the model. The results of the model are presented in the estimated energy, what's called an energy waterfall, which quantifies how each system parameter, as part of your input into the program, each system parameter impacts system production and loss at both the DC and the AC levels. We will go over PVsyst reports in a later podcast, but it breaks it down into specific loss categories and then also specific estimations of annual system energy production.

Daniel Tarico:

Okay. Well, that's a lot to think about. Frankly, I think it's good we're planning to break this up. It's a complicated set of parameters for each project. I, I'd say many, pretty many of our clients are financial institutions that are investing in or outright owning large projects or portfolios of projects and, for a solar project, energy sale is typically the only source of revenue for the project and that's what drives value and investment returns. So, beside the uncertainty about total expected revenue, which is somewhat captured by the PVsyst model, annual and seasonal production variability increase the risk to the project investors. Can you comment on how that's modeled and what uncertainties there might be, say within a given year, month to month or seasonally, as we would expect? And to be clear, I'm distinguishing, say that, monthly, seasonal variation within a production year versus the year-over-year uncertainties that the financiers have to deal with, which include long-term plant degradation and other long-term trends?

Francis Plourde:

Mm-hmm, absolutely so. A model is just that it's a model, it's our best estimate. Something my dad likes to say is that all models are wrong, but some are useful, and so we want our model to be as useful as possible. But, as you note, there are some inherent uncertainties in PV system production, both within a year and year over year, so PV systems can exhibit what we call seasonality, which is differing performance based on time of year.

Francis Plourde:

So it's probably obvious to most listeners that PV systems perform better in the summer here in the northern hemisphere, where we have higher irradiance levels.

Francis Plourde:

But then there are other aspects of seasonal climate or weather patterns that can impact PV system production at different times of year, and areas that are located further from the equator have greater differences in their climate and weather patterns between summer and winter, and so it's not necessarily going to be an even production all throughout the year.

Francis Plourde:

You might have drastically more production in the summer, drastically lower production in the winter, and that's something that site designers need to incorporate into both their models and the rest of their system design to make sure that the system is going to be adequate to cover energy production needs year long, even with those seasonality impacts.

Francis Plourde:

And, additionally, most model parameters are based on an averaging of historical data. So we're going to get into this in our climate data podcast, but most of the data that we use in order to predict system production comes from an averaging of previous historical data climate data from that location, which is the best way of determining an average climate in that set location, but that doesn't necessarily correspond with any particular one year of system performance and behavior, and so you might have an abnormal year that is not necessarily in line with what the average is, but that doesn't mean that the average is not still a useful metric, and so that's why you have to look at kind of a year over year historical averaging rather than just each one specific year on its own to account for differences in annual patterns and things like that. So in our models we really strive to take account of all of the items that we know will impact system production, even if our precision at certain times in certain areas may be low.

Daniel Tarico:

Sure, yeah, that makes sense. I mean, I am familiar with the modeling approach and it is a very long list and we're really trying to distill a lot of data down to something that gives us a good sense of what's going to happen down the road. Obviously, we can't discuss that all here today.

Francis Plourde:

Yeah, absolutely, and that's why we're breaking this up into several podcasts, being able to dive more deeply into specific topics that we think are going to be most important for site developers and owners and purchasers to understand when it comes to PV system modeling. So this is the introduction to those discussions and that's why we wanted to record this series that will cover several different topics relating to PV system modeling and how those are used to predict and interpret site behavior. So we'll break it down so the individual topics are a bit more manageable and digestible.

Francis Plourde:

So, our first discussion is going to cover the selection of climate data programs that are used for PV system modeling. As I said briefly before, there are many different sources of this available data. Some are free to the user and some require payment or purchase, and they also tend to vary in terms of where they're getting their data, how they're interpreting it data, how they're interpreting it, and then what the accuracy of that data is at the end stage and what that impact has on PVsyst modeling accuracy as a whole, and so that's something that we really want to make sure that we're using the most accurate data available to us in creating these models. Our second discussion is going to focus on some of the parameters and assumptions that are used within PVsyst to quantify impacts to system production and different areas of system loss Verified that are reasonable in terms of estimating loss from different areas of system or equipment function, and these can be focused on either specific system characteristics, such as the specific equipment you're using, or they can be industry accepted best practice, so what the overall industry has experienced and noticed as a reasonable assumption of loss from a certain characteristic, and so we're going to be focusing on about four specific parameters that we've noticed to be the most important in ensuring accurate system models.

Francis Plourde:

And then our third discussion is going to cover how PV system models present their results, both in terms of the PVsyst report that we discussed earlier, but then also how they use what we call p-values, which represent different areas of system production, which can then be correlated to revenue projections, and these values are very commonly used in long-term financial projections of PV system performance, and so they're very important to understand, from the financial side of system revenue calculations, how we get those values and how to ensure that those values are as close to what we're actually going to be producing from a system as possible in order to ensure long-term profitability.

Daniel Tarico:

Yep, that makes sense. So let me make sure I got this right. It sounds like we got a good approach here. First, we'll talk about the climate data that are the input into the models, how that's gathered, what are the different data sets? That's kind of the energy coming into the solar field. Really that's going to get converted to electricity. Second, we'll talk about the physical features of the PV system the modules, the inverters, the wires, the layout, all those things that actually impact the wires, the layout, all those things that actually impact how good that PV array is at converting the sunlight into electricity and how much of it can get delivered. And third, we'll talk about the outputs from these production models that go into the financial models, which is why we're doing all this, and that's really the p-values. That are measurements of expected performance and also measurements of uncertainty, which is very important in the financial modeling as well.

Francis Plourde:

I think that's a very good summary. So we'll begin by talking about the climate, which we measure but we don't have control over. Then, next, we'll move into discussing the system that we design, test and build, that we do have a certain level of control over and can change aspects of to improve performance and revenue, and then, finally, the model results, which we interpret and reflect the expectations and uncertainties that guide financial decisions relating to PV systems.

Daniel Tarico:

Sounds good. I think this is a good start on opening that black box that is the PVsyst model, which people discuss but often don't understand, and I'm looking forward to this series of continuing discussions. Thank you, Frances.

Francis Plourde:

Oh, it's my pleasure, Dan. I'm really looking forward to the rest of this series.

Daniel Tarico:

Great. Well, if you have any questions for me or Francis or the rest of the E3 team, or if you have a suggestion for a future podcast topic, please feel free to reach out to us at e3co@e3co. com. We look forward to hearing from you.