Unraveling the Black Box of Power Market Models

Valeriya Azarova and Mathias Mier

No 357, ifo Working Paper Series from ifo Institute - Leibniz Institute for Economic Research at the University of Munich

Abstract: Detailed numerical models of power markets with millions of variables and equations are often perceived as black boxes, because differences in results cannot be traced back to single equations or assumptions, respectively. We unravel parts of those black box by determining the impact of different investment cost specifications including the role of varying discount and interest rates. We further expand our analysis to the impact of simplifications strategies (foresight, spatial resolution, temporal resolution) that are applied to contain numerical feasibility of such models. The choice of investment cost modeling (and related discount and interest rates) has the highest impact on results. Increasing or decreasing, respectively, complexity in turn, has only minor impacts. Our findings questions the current focus of the literature on complexity of power market models neglecting the most relevant factor, which is the choice of handling investment costs.

Keywords: Energy system modeling; power market modeling; investment behavior; firm behavior; spatial resolution; temporal resolution; decarbonization (search for similar items in EconPapers)
JEL-codes: C61 C68 Q40 Q41 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-ene and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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