Online optimization of a multi-conversion-level DC home microgrid for system efficiency enhancement. (November 2017)

• Record Type: Journal Article
• Authors: Boscaino, V.; Guerrero, J.M.; Ciornei, I.; Meng, L.; Riva Sanseverino, E.; Zizzo, G.
• Is Part Of: Sustainable cities and society. Volume 35(2018)
• Page Start: 417
• Page End: 429
• Publication Date: 2017-11
• Subjects: DC microgrids -- Active droop control -- Current sharing -- Genetic algorithm -- Master-slave control -- Power conversion efficiency -- Peak shaving; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2017.08.014
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: An on-line optimization of the global efficiency of the DC distribution network based on Genetic Algorithm is proposed. The GA generates the optimal current sharing ratios of concurrent power converters to maximize the conversion efficiency of the overall system. A two conversion stage and three voltage level DC home distribution system is used as the case study. Simulation results considering several case studies are presented and discussed. Abstract: In this paper, an on-line management system for the optimal efficiency operation of a multi-bus DC home distribution system is proposed. The operation of the system is discussed with reference to a distribution system with two conversion stages and three voltage levels. In each of the conversion stages, three paralleled DC/DC converters are implemented. A Genetic Algorithm performs the on-line optimization of the DC network's global efficiency, generating the optimal current sharing ratios of the concurrent power converters. The overall DC/DC conversion system including the optimization section is modelled using MATLAB/Simulink. Thanks to the implemented online algorithm, considering the system modelling as dynamic master/slave configuration, reliability, robustness and flexibility of the whole conversion system is enhanced. Since an online algorithm is implemented, several variables are accounted for, such as: components loss parameters, components ageing, load currents, switching frequency and input voltage. Simulation results considering several case studies are presented and the benefits brought by the optimization algorithm in terms of power saving are widely discussed for each case study.