Nonparametric Correlative-Probabilistic Microgrid Power Energy Management Based Sine-Cosine Algorithm

In modern power systems, microgrids play a pivotal role with several economical, technical, and environmental benefits. However, there are still challenges that need to be properly addressed, including: i) accurate modeling of the uncertain parameters behavior, ii) considering the correlation betwee...

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Autores principales:	Soheil Moradi Nezhad, Hadi Saghafi, Majid Delshad, Ramtin Sadeghi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Microgrid energy management improved kernel density estimator (IKDE) sine-cosine optimization algorithm stochastic optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/c412a3e0db4c46839a02f98f668bd023
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spelling	oai:doaj.org-article:c412a3e0db4c46839a02f98f668bd0232021-12-01T00:01:34ZNonparametric Correlative-Probabilistic Microgrid Power Energy Management Based Sine-Cosine Algorithm2169-353610.1109/ACCESS.2021.3123981https://doaj.org/article/c412a3e0db4c46839a02f98f668bd0232021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9592783/https://doaj.org/toc/2169-3536In modern power systems, microgrids play a pivotal role with several economical, technical, and environmental benefits. However, there are still challenges that need to be properly addressed, including: i) accurate modeling of the uncertain parameters behavior, ii) considering the correlation between the random variables, and iii) find the optimal solutions with low computational burden. To address these issues, this paper proposes a nonparametric-correlative stochastic framework for microgrids (MGs) energy management. The proposed method imposes no assumption on the probability density function of renewable generations and electrical load consumption. To this end, an improved kernel density estimator (IKDE) is presented to estimate the probability density function (PDF) of uncertain parameters, e.g., renewable generations and load. To account for the correlation between the uncertain parameters, Cholesky decomposition is utilized. Furthermore, a multi-objective MG energy management problem considering reliability has been reformulated, and to solve the problem, a sine-cosine optimization algorithm (SCOA) is developed. Numerical results demonstrate the effectiveness and superiority of the proposed stochastic framework through comparison with several optimization algorithms by reducing the total cost of MG more than 11% in comparison with several metaheuristic algorithms and stochastic frameworks with less than 1% error.Soheil Moradi NezhadHadi SaghafiMajid DelshadRamtin SadeghiIEEEarticleMicrogridenergy managementimproved kernel density estimator (IKDE)sine-cosine optimization algorithmstochastic optimizationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156323-156336 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microgrid energy management improved kernel density estimator (IKDE) sine-cosine optimization algorithm stochastic optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Microgrid energy management improved kernel density estimator (IKDE) sine-cosine optimization algorithm stochastic optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971 Soheil Moradi Nezhad Hadi Saghafi Majid Delshad Ramtin Sadeghi Nonparametric Correlative-Probabilistic Microgrid Power Energy Management Based Sine-Cosine Algorithm
description	In modern power systems, microgrids play a pivotal role with several economical, technical, and environmental benefits. However, there are still challenges that need to be properly addressed, including: i) accurate modeling of the uncertain parameters behavior, ii) considering the correlation between the random variables, and iii) find the optimal solutions with low computational burden. To address these issues, this paper proposes a nonparametric-correlative stochastic framework for microgrids (MGs) energy management. The proposed method imposes no assumption on the probability density function of renewable generations and electrical load consumption. To this end, an improved kernel density estimator (IKDE) is presented to estimate the probability density function (PDF) of uncertain parameters, e.g., renewable generations and load. To account for the correlation between the uncertain parameters, Cholesky decomposition is utilized. Furthermore, a multi-objective MG energy management problem considering reliability has been reformulated, and to solve the problem, a sine-cosine optimization algorithm (SCOA) is developed. Numerical results demonstrate the effectiveness and superiority of the proposed stochastic framework through comparison with several optimization algorithms by reducing the total cost of MG more than 11% in comparison with several metaheuristic algorithms and stochastic frameworks with less than 1% error.
format	article
author	Soheil Moradi Nezhad Hadi Saghafi Majid Delshad Ramtin Sadeghi
author_facet	Soheil Moradi Nezhad Hadi Saghafi Majid Delshad Ramtin Sadeghi
author_sort	Soheil Moradi Nezhad
title	Nonparametric Correlative-Probabilistic Microgrid Power Energy Management Based Sine-Cosine Algorithm
title_short	Nonparametric Correlative-Probabilistic Microgrid Power Energy Management Based Sine-Cosine Algorithm
title_full	Nonparametric Correlative-Probabilistic Microgrid Power Energy Management Based Sine-Cosine Algorithm
title_fullStr	Nonparametric Correlative-Probabilistic Microgrid Power Energy Management Based Sine-Cosine Algorithm
title_full_unstemmed	Nonparametric Correlative-Probabilistic Microgrid Power Energy Management Based Sine-Cosine Algorithm
title_sort	nonparametric correlative-probabilistic microgrid power energy management based sine-cosine algorithm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/c412a3e0db4c46839a02f98f668bd023
work_keys_str_mv	AT soheilmoradinezhad nonparametriccorrelativeprobabilisticmicrogridpowerenergymanagementbasedsinecosinealgorithm AT hadisaghafi nonparametriccorrelativeprobabilisticmicrogridpowerenergymanagementbasedsinecosinealgorithm AT majiddelshad nonparametriccorrelativeprobabilisticmicrogridpowerenergymanagementbasedsinecosinealgorithm AT ramtinsadeghi nonparametriccorrelativeprobabilisticmicrogridpowerenergymanagementbasedsinecosinealgorithm
_version_	1718406163437977600

Nonparametric Correlative-Probabilistic Microgrid Power Energy Management Based Sine-Cosine Algorithm

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