A multivariable optimal energy management strategy for standalone DC microgrids

Dizqah, Arash M, Maheri, Alireza, Busawon, Krishna and Kamjoo, Azadeh (2015) A multivariable optimal energy management strategy for standalone DC microgrids. IEEE Transactions on Power Systems, 30 (5). pp. 2278-2287. ISSN 0885-8950

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Abstract

Due to substantial generation and demand fluctuations in standalone green microgrids, energy management strategies are becoming essential for the power sharing and voltage regulation purposes. The classical energy management strategies employ the maximum power point tracking (MPPT) algorithms and rely on batteries in case of possible excess or deficit of energy. However, in order to realize constant current-constant voltage (IU) charging regime and increase the life span of batteries, energy management strategies require being more flexible with the power curtailment feature. In this paper, a coordinated and multivariable energy management strategy is proposed that employs a wind turbine and a photovoltaic array of a standalone DC microgrid as controllable generators by adjusting the pitch angle and the switching duty cycles. The proposed strategy is developed as an online nonlinear model predictive control (NMPC) algorithm. Applying to a sample standalone dc microgrid, the developed controller realizes the IU regime for charging the battery bank. The variable load demands are also shared accurately between generators in proportion to their ratings. Moreover, the DC bus voltage is regulated within a predefined range, as a design parameter.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Dynamics, Control and Vehicle Research Group
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA0168 Systems engineering
T Technology > TA Engineering (General). Civil engineering (General) > TA0174 Engineering design
Depositing User: Arash Moradinegade Dizqah
Date Deposited: 21 Mar 2019 11:00
Last Modified: 01 Jul 2019 18:15
URI: http://sro.sussex.ac.uk/id/eprint/82679

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