Título Optimal sizing for a hybrid power system with wind/energy storage based in stochastic environment
Autores Abd el Motaleb, Ahmad Mohamed , Bekdache, Sarah Kazim , ALVARADO BARRIOS, LÁZARO
Publicación externa No
Alcance Review
Naturaleza Científica
Cuartil JCR 1
Cuartil SJR 1
Impacto JCR 8.05
Impacto SJR 2.998
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955617357&doi=10.1016%2fj.rser.2015.12.267&partnerID=40&md5=d5b17a3c24dec6ff6236d566395515be
Fecha de publicacion 01/06/2016
ISI 000371948400082
Scopus Id 2-s2.0-84955617357
DOI 10.1016/j.rser.2015.12.267
Abstract For isolated power networks supplied by intermittent energy sources, several doubts have emerged regarding the impact of the uncertainties on the networks reliability. This paper performs optimal sizing for a hybrid power system with wind/energy storage sources based on stochastic modeling of historical wind speed and load demand. The autoregressive moving average is used to stochastically model the uncertainty of the load demand/wind speed and, the sequential Monte Carlo simulation is performed to chronologically sample the system states. The contribution of the paper can be summarized as follows: (1) an objective function based on self-adapted evolutionary strategy in combination with the Fischer Burmeister algorithm is proposed to minimize the one-time investment and annual operational costs of the wind/energy storage sources; and (2) the effect of the cycle efficiency and charging/discharging rate of different energy storage units on the system cost is investigated under different reliability and load shifting levels. The computational performance of the proposed optimization solver is proven in order to obtain the minimum possible investment cost. The presented case studies in this paper provide the decision makers with the flexibility to choose the suitable capacity installation at different values of reliability and load shifting levels. (C) 2016 Elsevier Ltd. All rights reserved.
Palabras clave Distributed generation (DG); Load shifting; Stochastic modeling; Hybrid energy systems; Wind turbines; Energy storage sources
Miembros de la Universidad Loyola

Change your preferences Gestionar cookies