Recommendation for Intermittent Solar vs. Baseload Fossil Generation Financial Analysis

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#1

Hello Openmod Community,

As my background is in international development and energy project finance, my experience in energy systems modeling is zero (my excel skills are high, though) but I am looking to seek your recommendations on the right tools for what I am trying to accomplish. As an American, given the current skeptical political environment surrounding renewables, I would like to explore arguments for solar generation and, specifically, the ideal target for solar (or other intermittent power) penetration in a developing country based on current costs and fixed PPA generation charges. The argument being that solar can be economically helpful as a fuel saving technology paired with natural gas or other fossil fuel generation sources, and support the economic development of developing countries.

Specifically, I would like to calculate the optimized solar (or other) generation capacity in a limited grid (I typically work on projects in developing countries) at various price points vs. the fixed capacity charge and variable fuel cost for dispatchable power.

For instance, in a hypothetical island nation with 100% generation from oil with a variable fuel charge of 13 cents/kWh, solar generation offered at 14 cents/kWh would result in a 0% economically optimized solar generation mix, assuming no peak pricing issues. Should solar generation be priced at 7 cents/kWh, obviously the cost savings of solar would merit reducing oil generation, but given the intermittent nature of solar generation this would not result in the economic viability of 100% solar generation given the need for dispatchable power plus a reserve margin. It seems to me that, assuming a lack of storage and a need for dispatchable power, there is a theoretical economic limit to the amount of solar capacity that should be introduced, with this limit increasing as the cost of solar falls.

I would like to create a model that would find the economically optimized solar capacity limit in this system, that would account for the fixed capacity cost of a dispatchable power plant, the variable fossil fuel costs, and fixed solar generation costs (assuming power is delivered via a long-term fixed price PPA). I had been exploring building a 15-minute interval data annual model, witch logic to account for intermittent generation to get an 8760 annual profile, but wanted to seek other opinions before starting the effort.

Any thoughts on this would be greatly appreciated, and could help serve to motivate additional renewable energy investment in a climate where fossil fuel generation sources are preferred.

Is Excel the best tool for the job, or is there a better solution?

Thank you,


#2

Hi BWhiteside,

I think I can definetly provide you with useful information, as i deveoped an application that is called micrOgridS, based on the Open Energy Modelling Framework (Oemof) for the purpose of hybrid Mini-Grid design. I also tested in in a case study and compared it to Homer. Hence, i would obviously argue that there iis a great alternative to Excel;) If you are interested i would be happy to provide you with further information. What about a skype call in the next week?


#3

Hi B!

I would agree with @sar_b (and so would many on this forum) that you’ll find a more powerful solution available to you once you leave the realms of Excel :wink: In fact, the model you’re thinking of building is exactly the kind of thing we’re working on in many of our Python/GAMS-based linear optimisation models.
Most of the optimisation tools associated with openmod will likely meet your needs. If you want to model AC power flow in some detail, I would recommend PYPSA. If the complexity of your power grid is less important then the likes of Oemof, URBS and Calliope would be worth a look - they can all get you to where you want to be. If you primarily have experience with Excel, so don’t want to have to deal with the learning curve of Python, then my (biased?) opinion would be to opt for Calliope. It has the added benefit that I am currently modelling the effect of an intermittent grid power on optimal technology portfolios in Indian cities, so know that it is perfectly capable at answering your particular questions!

Most energy model maintainers are happy to help you get on your feet with their tool, so the best is to just contact any one of us directly if you decide to go down that route :slight_smile:


#4

Hello and welcome. I know you mentioned power purchase agreements (PPA) but very few energy system models support bilateral contracts and contract formation (I wrote one that supported the former). So I’ll comment instead on LCOEs (levelized cost of electricity): typical yet ever evolving LCOE figures are of limited use for system design purposes, although they may be helpful for some types of broad-brush public policy analysis.

Most energy systems exhibit strong network externalities and significant network effects, both operational and structural (terminology as per Outhred and Kaye 1996). Technological learning is likewise typically present and multi-factor. And technological surprise may also occur but is not normally amenable to prediction and characterization.

Network externalities are the net benefits of belonging to various energy supply grids as apposed to autonomous supply (in some local sense at least). Network effects occur when system elements hit capacity or some other kind of bound during runtime. Or when lumpy investments are made or withdrawn. Economists often treat network effects and network externalities as synonyms, but it is useful for energy modelers to view these as separate and distinct concepts. Strategic behavior is also facilitated by network effects and is sometimes unambiguously evident (Enron in California being the prime example in this regard).

In addition, the cross-correlations between various time-series are important and need to be maintained. For instance, between weather and demand (unless the cross-correlations can be determined exogenously and suitably represented during runtime). This issue is particularly important when considering systems with short-haul storage.

All of which is why this community has largely settled on high-resolution modeling and scenario analysis as its preferred approach.

Network effects are also found in electoral systems: swing states and swing counties under first-past-the-post (FPP) and the 5% threshold under mixed-member-proportional (MMP). As an aside, FPP is more open to manipulation than MMP, ranging from gerrymandering to Cambridge Analytica style targeting.

Also of note is that most high-resolution modeling projects are fundamentally scale and scope independent. Which is why the same model framework (aka modeling environment or model generator) can be used for islanded, single-site, municipal, national, and supra-national systems. Naturally issues of problem dimensionality intrude, as do other issues like the need to represent spot markets and possibly bilateral contracts as one increases in scope.

While none of the above precludes spreadsheet modeling, it does lend itself rather well to either algebraic modeling languages (GAMS and MathProg) or object-oriented programming (C++, Java, julia, python, and others). Spreadsheets in contrast are prone to buggy code (Hermans and Murphy-Hill 2014), difficult to version control, and not particularly suited to collaborative development. HTH, Robbie

References

Hermans, Felienne and Emerson Murphy-Hill (2014). Enron’s spreadsheets and related emails: a dataset and analysis — Report TUD-SERG-2014-021. Delft, The Netherlands: Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology. ISSN 1872-5392.

Outhred, Hugh R and R John Kaye (1996). Electricity transmission pricing and technology. In Michael A Einhorn and Riaz Siddiqi. Electricity transmission pricing and technology. Boston, Massachusetts, USA: Kluwer. ISBN 978-94-010-7304-2. doi:10.1007/978-94-009-1804-7.


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