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 Or when lumpy investments are made or withdrawn.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.