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Revisiting the techno-economic analysis process for building-mounted, grid-connected solar photovoltaic systems: Part two - Application
KTH, School of Industrial Engineering and Management (ITM), Energy Technology.ORCID iD: 0000-0002-2603-7595
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.ORCID iD: 0000-0001-7354-6643
2017 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 74, 1394-1404 p.Article, review/survey (Refereed) Published
Abstract [en]

Part One in this two part paper identified Monte Carlo analysis as an improved approach over traditional deterministic techno-economic methods for solar PV prosumers in deregulated markets. In this paper a novel Monte Carlo methodology is described and demonstrated through a case study for the Swedish residential sector, which includes a review of relevant market, climate, and policy conditions, their use in determining inputs, and the probabilistic results. The probability of profitability (PoP) is introduced as an indicator in conjunction with result distributions. The results show that under current policy conditions, Swedish PV investors with well positioned buildings have a 71% chance of making a 3% real return on investment, and virtually no chance of losing their original investment. Without subsidies the PoP drops to 8%. In none of the simulated cases was any of the original investment lost. The PoP is most sensitive to the capital subsidy and the uncertainty of market based, long-term support is less critical to the chances of a successful investment. Given the current market conditions, Swedish PV prosumers can expect a return on investment. The decision to install will also depend on the probability of achieving their desired profitability, which Monte Carlo analysis quantifies well.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2017. Vol. 74, 1394-1404 p.
Keyword [en]
Solar PV, Techno-economic, Monte Carlo, Investment analysis, Probabilities, Sweden
National Category
Energy Systems Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-208715DOI: 10.1016/j.rser.2017.03.010ISI: 000401492900097ScopusID: 2-s2.0-85015440142OAI: oai:DiVA.org:kth-208715DiVA: diva2:1108447
Note

QC 2017-06-12

Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2017-06-19Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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More styles
Language
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