Operating conditions of lead-acid batteries in the optimization of hybrid energy systems and microgrids

Lujano-Rojas, J. ; Dufo-López, R. (Universidad de Zaragoza) ; Atencio-Guerra, J. ; Rodrigues, E. M. G. ; Bernal-Agustín, J. L. (Universidad de Zaragoza) ; Catalão, J. P. S.

Resumen: The promotion and deployment of storage technologies in autonomous and grid-connected systems plays a relevant part in the massive integration of renewable power sources required for the worldwide development of a sustainable society. In this regard, analyzing the behavior of electrochemical storage devices such as lead-acid batteries installed on hybrid energy systems and microgrids in terms of their lifetime and economic profitability is an important research topic. Since renewable generation is characterized by its random nature, lead-acid batteries typically work under stress conditions, which directly influence their lifetime in a negative way by increasing the net present cost. Due to the fast growing of renewable sources as a consequence of governmental policies and incentives, the number of manufacturers to be considered worldwide is becoming really high, so that optimization techniques such as genetic algorithms (GAs) are frequently used in order to consider the performance of a high number of manufacturers of wind turbines, photovoltaic panels and lead-acid batteries subject to the environmental conditions of the location under analysis to determine a cost-effective design. In this paper, GA method combined with weighted Ah ageing model is improved by including expert experiences by means of stress factors and the categorization of operating conditions, as a new contribution to earlier studies. The effectiveness of the proposed method is illustrated by analyzing a hybrid energy system to be installed in Zaragoza, Spain, resulting in a near-optimal design in a reduced computational time compared to the enumerative optimization method.

Idioma: Inglés

DOI: 10.1016/j.apenergy.2016.07.018

Año: 2016

Publicado en: Applied Energy 179 (2016), 590-600
ISSN: 0306-2619

Factor impacto JCR: 7.182 (2016)

Categ. JCR: ENGINEERING, CHEMICAL rank: 4 / 135 = 0.03 (2016) - Q1 - T1
Categ. JCR: ENERGY & FUELS rank: 6 / 92 = 0.065 (2016) - Q1 - T1

Factor impacto SCIMAGO: 3.011 - Building and Construction (Q1) - Civil and Structural Engineering (Q1) - Energy (miscellaneous) (Q1) - Nuclear Energy and Engineering (Q1) - Fuel Technology (Q1) - Management, Monitoring, Policy and Law (Q1) - Mechanical Engineering (Q1) - Energy Engineering and Power Technology (Q1)

Financiación: info:eu-repo/grantAgreement/EC/FP7/309048/EU/Smart and Sustainable Insular Electricity Grids Under Large-Scale Renewable Integration/SINGULAR
Financiación: info:eu-repo/grantAgreement/ES/MINECO/ENE2013-48517-C2-1-R

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Ingeniería Eléctrica (Dpto. Ingeniería Eléctrica)