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

•Multi-objective optimal sizing of HRES is implemented considering resource uncertainties to obtain more realistic results.•A novel method in using CCP is proposed to estimate the expected value of the objective function affected by uncertain values.•Proposed method reduces the evaluation time of th...

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Vydáno v:International journal of electrical power & energy systems Ročník 74; s. 187 - 194
Hlavní autoři: Kamjoo, Azadeh, Maheri, Alireza, Dizqah, Arash M., Putrus, Ghanim A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.01.2016
Témata:
Chance constrained programming
Design under uncertainties
Multi-objective optimisation
NSGA-II
Reliability
Standalone hybrid wind-PV-battery
Design under uncertainties
NSGA-II
Reliability
Chance constrained programming
Standalone hybrid wind-PV-battery
Multi-objective optimisation
ISSN:0142-0615, 1879-3517
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Shrnutí:•Multi-objective optimal sizing of HRES is implemented considering resource uncertainties to obtain more realistic results.•A novel method in using CCP is proposed to estimate the expected value of the objective function affected by uncertain values.•Proposed method reduces the evaluation time of the design candidate and consequently the run time of the NSGA-II program. 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.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2015.07.007