Microgrid planning based on fuzzy interval prediction models of renewable resources

R. Morales, D. Sáez , L.G. Marín, Alfredo Nunez

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)
17 Downloads (Pure)

Abstract

Microgrids are sustainable solutions for electrification of rural zones that can make use of their local renewable resources. In this paper, we propose a new method for microgrid planning which includes the effect of the uncertainties of the renewable resources explicitly. Fuzzy interval models are used because they can capture nonlinearities and systematically represent the uncertainties associated with renewable resources at a certain confidence level. Relying on interval fuzzy models and by considering a set of possible scenarios for the renewable resources, the solution to the microgrid planning problem is given through the optimal sizing and topology of the microgrid. This information, particularly the optimal sizes of generators and the economic analysis, is useful for the design phase of a microgrid project. The proposed methodology is applied to the microgrid planning of the rural Mapuche community, José Painecura, in Chile.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Subtitle of host publicationVancouver, Canada
EditorsO. Cordon
PublisherIEEE
Pages336-343
Number of pages8
ISBN (Print)978-1-5090-0625-0
DOIs
Publication statusPublished - 2016
EventInternational Conference on Fuzzy Systems 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016

Conference

ConferenceInternational Conference on Fuzzy Systems 2016
Abbreviated titleFUZZ 2016
CountryCanada
CityVancouver
Period24/07/1629/07/16

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    Morales, R., Sáez , D., Marín, L. G., & Nunez, A. (2016). Microgrid planning based on fuzzy interval prediction models of renewable resources. In O. Cordon (Ed.), 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE): Vancouver, Canada (pp. 336-343). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2016.7737706