Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming

Azadeh Kamjoo, Alireza Maheri, Arash Dizqah, Ghanim Putrus

Research output: Contribution to journalArticle

69 Citations (Scopus)

Abstract

The optimum design of Hybrid Renewable Energy Systems (HRES) depends on different economical, environmental and performance related criteria which are often conflicting objectives. The Non-dominated Sorting Genetic Algorithm (NSGA-II) provides a decision support mechanism in solving multi-objective problems and providing a set of non-dominated solutions where finding an absolute optimum solution is not possible. The present study uses NSGA-II algorithm in the design of a standalone HRES comprising wind turbine, PV panel and battery bank with the (economic) objective of minimum system total cost and (performance) objective of maximum reliability. To address the uncertainties in renewable resources (wind speed and solar irradiance), an innovative method is proposed which is based on Chance Constrained Programming (CCP). A case study is used to validate the proposed method, where the results obtained are compared with the conventional method of incorporating uncertainties using Monte Carlo simulation.
Original languageEnglish
Pages (from-to)187 - 194
Number of pages8
JournalInternational Journal of Electrical Power Energy Systems
Volume74
Early online date5 Aug 2015
DOIs
Publication statusPublished - Jan 2016

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Wind turbines
Genetic algorithms
Economics
Costs
Uncertainty
Optimum design
Monte Carlo simulation

Keywords

  • design under uncertainties
  • standalone hybrid wind-PV-battery
  • reliability
  • Multi-objective optimisation
  • NSGA-II
  • Chance constrained programming

Cite this

Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming. / Kamjoo, Azadeh; Maheri, Alireza; Dizqah, Arash; Putrus, Ghanim.

In: International Journal of Electrical Power Energy Systems, Vol. 74, 01.2016, p. 187 - 194.

Research output: Contribution to journalArticle

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