Skip to main navigation Skip to search Skip to main content

Consumption modeling based on Markov chains and Bayesian networks for a demand side management design of isolated microgrids

  • Tomislav Roje
  • , Luis G. Marín
  • , Doris Sáez
  • , Marcos Orchard
  • , Guillermo Jiménez-Estévez
  • Universidad de Chile

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

This paper proposes a novel simulator of energy consumption patterns that allows designing demand side management (DSM) strategies without economic incentives. The simulator emulates consumers' patterns with and without installed DSM interfaces, based on both actual consumption measurements and surveys applied to the inhabitants of an existing isolated microgrid (Huatacondo, Chile) that has a particular DSM strategy without economic incentives. The simulator uses Markov chains to generate data characterizing consumption patterns without DSM and Bayesian networks for cases in which the users respond to the DSM strategy. Data obtained from the simulator are used to derive a response model of the consumers to the DSM interface, which can be included for the energy management system design. Results show that the implemented strategy can be effective and can generate savings up to 4.45% in diesel consumption for an ideal case where all the dwellings have the interface installed.

Original languageEnglish
Pages (from-to)365-376
Number of pages12
JournalInternational Journal of Energy Research
Volume41
Issue number3
DOIs
StatePublished - 10 Mar 2017
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Bayesian network
  • Markov chain
  • demand side management
  • microgrid

Fingerprint

Dive into the research topics of 'Consumption modeling based on Markov chains and Bayesian networks for a demand side management design of isolated microgrids'. Together they form a unique fingerprint.

Cite this