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A sizing approach for stand-alone hybrid photovoltaic-wind-battery systems: A Sicilian case study

Author:
Giallanza, Antonio, Porretto, Mario, Puma, Gabriella Li, Marannano, Giuseppe
Source:
Journal of cleaner production 2018 v.199 pp. 817-830
ISSN:
0959-6526
Subject:
batteries, case studies, electric energy consumption, fuzzy logic, probability, wind power, wind turbines, Sicily
Abstract:
Solar and wind energy are the two most available renewable energy resources in the world. In this paper, a high-resolution analysis that allows sizing a hybrid photovoltaic-wind turbine-battery banks has been carried out. The analysis aims to minimize the annualized cost of the systems satisfying two reliability constraints. The solution has been obtained numerically by means of an iterative technique. The decision variables are the photovoltaic area, wind turbine radius, and battery capacity. A high-resolution model, based on fuzzy logic inference system, has been developed to evaluate the number of active occupants and the domestic electricity consumption. In order to allow a more accurate sizing of the system, a new reliability parameter named seasonal loss of load probability ratio that takes into account the seasonality of data has been defined. Seasonal loss of load probability ratio has been used in the iterative process in addition to the most common loss of load probability. Compared with traditional processes, the obtained results demonstrate that the introduction of the new parameter to iterative process causes a meaningful improvement of the system's reliability and a slight increase of its cost on the other hand. The simulation, conducted in MATLABĀ® environment, has been carried out to supply power for a domestic dwelling located in three different locations of Sicily. Compared to reliability values arising from the traditional procedure, the obtained results show that a reliability improvement of 75% is reached by using the new sizing procedure. Therefore, the proposed methodology gives an important advancement on the current state of the art since it allows at designing renewable plants in a more efficient way.
Agid:
6121023