Uncertainty quantification and scenario generation of future solar photovoltaic price for use in energy system models
Young-Hwan Ahn and
Dong Gu Choi
Energy, 2019, vol. 168, issue C, 370-379
Recently, researchers have recognized the necessity of incorporating uncertainties into energy system models. This has led to the development of stochastic programming-based models. Such models require values of input parameters in the form of a scenario tree to handle the uncertainties. However, there are limited studies that have generated scenario trees for technical factors based on historical data and quantitative methods. This study shows that a scenario tree for a technical factor can be constructed based on quantitative methods and historical data. More specifically, the main contribution of this study is that it proposes an approach to not only quantify the uncertainty of future solar photovoltaic module price by considering the uncertainty in learning rate but also make it into a scenario tree. The approach comprises three steps: (1) stochastic process model estimation, (2) scenario tree generation, and (3) uncertainty quantification. In conclusion, an estimated multivariate autoregressive model can efficiently represent the uncertainty of solar photovoltaic module price. The moment matching method can be applied to generate an appropriate scenario tree for the price. The proposed approach can be applied to other technical factors, and it can help policy makers and practitioners to develop persuasive scenarios for technical factors.
Keywords: Energy system model; Uncertainty quantification; Scenario generation; Solar PV; Price projection (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:168:y:2019:i:c:p:370-379
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Haili He ().