Wind Speed and Solar Irradiance Variation Simulation Using ARMA Models in Design of HybridWind-PVBattery System

Kamjoo, Azadeh; Maheri, Alireza; Putrus, Ghanim

doi:10.7763/jocet.2013.v1.4

Cited by 14 publications

(9 citation statements)

References 13 publications

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“…System security relates to the ability of the system to withstand credible contingencies without violating the normal operating limits and is generally in the context of the system's reaction to perturbations. System adequacy on the other hand is the existence of sufficient capacity within the system to meet demand [27].…”

Section: Performance Indexmentioning

confidence: 99%

“…Kamjoo et al [27] recognized the uncertainty in maintaining a high quality of supply as the main challenge in standalone hybrid renewable energy systems. They considered the Loss of Power Supply Probability as a reliability measure for their optimization.…”

Section: Loss Of Load Indicesmentioning

confidence: 99%

“…The result of this approach is usually represented as a probable ration between curtailed energy and energy needed by the system. This ratio, a very small figure, is termed the energy index of unreliability [27].…”

Section: Loss Of Energy Indicesmentioning

confidence: 99%

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A Review of Techniques in Optimal Sizing of Hybrid Renewable Energy Systems

Odero¹,

Okinda²

2015

IJRET

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This paper presents a review of techniques usedin recent published works on optimal sizing of hybrid renewable energy sources. Hybridization of renewable energy sources is an emergent promising trend born out of the need to fully utilize and solve problems associated with the reliability of renewable energy resources such as wind and solar. Exploitation of these resources has been instrumental in tackling or mitigating present day energy problems such as price instability for fossil based fuels, global warming and climate change in addition to being seen as way of meeting future demand for power. This paper targets researchers in the renewable energy space and the general public seeking to inform them on trends in methods applied in optimal sizing of hybrid renewable energy sources as well as to provide a scope into what has been done in this field. In reviewing previous works, a two prong approach has been used focusing attention on the sizing methods used in the reviewed works as well as the performance indices used to check quality by these works. In summary there is a clear indication of increased interest in recent years in optimal sizing of hybrid renewable energy resources with metaheuristic approaches such as Genetic Algorithms and Particle Swarm Optimization coming out as very interesting to researchers. It has also been observed that resources being hybridized are those with complementary regimes on specific sites.

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Section: Performance Indexmentioning

confidence: 99%

Section: Loss Of Load Indicesmentioning

confidence: 99%

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A Review of Techniques in Optimal Sizing of Hybrid Renewable Energy Systems

Odero¹,

Okinda²

2015

IJRET

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“…Lujano-Rojas [13] and Ji [25] used a time series analysis to model wind speed and solar irradiance variations. To obtain accurate modelling of wind speed and solar irradiance variations, two methods are used to correlate historical data to known distributions and time series analysis using autoregressive moving average models (ARMA) [26]. Based on the location of the desired site, the The flowchart of the implemented NSGA-II using adopted methods in evaluation the objective functions affected by uncertainties are explained in sub-section (III-A) and (III-B) is presented in Figure 2.…”

Section: B Monte Carlo Simulationmentioning

confidence: 99%

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

Kamjoo

Maheri

Dizqah

et al. 2016

International Journal of Electrical Power & Energy Systems

203

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Citation: Kamjoo, Azadeh, Maheri, Alireza, Dizqah, Arash and Putrus, Ghanim (2016) Multiobjective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming. International Journal of Electrical Power & Energy Systems, Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

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“…However, the transformed data was not checked the strongly stationary [1,10] or instead of checking the strongly stationary of transformed data, normal distribution is only considered by comparing with corresponding normal probability density [3,4,8] or testing non-stationary of transformed data by Duckey-fuller test [9]. These papers assumed that transformed data fitted to a p th order autoregressive process AR(p) [1] or the transformed data fitted to a p th order autoregressive process AR(p) by autocorrelation, partial autocorrelation analysis in identification process but there was no using Bayesian information criterion (BIC) to select an accurate class of ARMA(p,q) [3] or ACF, PACF and BIC were analyzed but the AR(p) was selected too soon after ACF and PACF analysis [8], instead of analyzing ACF, PACF and BIC together or using only AIC [9,10].…”

Section: Introductionmentioning

confidence: 99%

Hourly Average Wind Speed Simulation and Forecast Based on ARMA Model in Jeju Island, Korea

Duy-Phuong¹,

Lee²,

Choi³

2016

Journal of Electrical Engineering and Technology

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-This paper presents an application of time series analysis in hourly wind speed simulation and forecast in Jeju Island, Korea. Autoregressive -moving average (ARMA) model, which is well in description of random data characteristics, is used to analyze historical wind speed data (from year of 2010 to 2012). The ARMA model requires stationary variables of data is satisfied by power law transformation and standardization. In this study, the autocorrelation analysis, Bayesian information criterion and general least squares algorithm is implemented to identify and estimate parameters of wind speed model. The ARMA (2,1) models, fitted to the wind speed data, simulate reference year and forecast hourly wind speed in Jeju Island.

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Wind Speed and Solar Irradiance Variation Simulation Using ARMA Models in Design of HybridWind-PVBattery System

Cited by 14 publications

References 13 publications

A Review of Techniques in Optimal Sizing of Hybrid Renewable Energy Systems

A Review of Techniques in Optimal Sizing of Hybrid Renewable Energy Systems

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

Hourly Average Wind Speed Simulation and Forecast Based on ARMA Model in Jeju Island, Korea

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