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Energies 2017, 10(5), 674; doi:10.3390/en10050674

Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm

College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China
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Academic Editor: K.T. Chau
Received: 1 March 2017 / Revised: 26 April 2017 / Accepted: 5 May 2017 / Published: 11 May 2017
(This article belongs to the Section Electrical Power and Energy System)
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Abstract

Due to the scarcity of conventional energy resources and the greenhouse effect, renewable energies have gained more attention. This paper proposes methods for multi-objective optimal design of hybrid renewable energy system (HRES) in both isolated-island and grid-connected modes. In each mode, the optimal design aims to find suitable configurations of photovoltaic (PV) panels, wind turbines, batteries and diesel generators in HRES such that the system cost and the fuel emission are minimized, and the system reliability/renewable ability (corresponding to different modes) is maximized. To effectively solve this multi-objective problem (MOP), the multi-objective evolutionary algorithm based on decomposition (MOEA/D) using localized penalty-based boundary intersection (LPBI) method is proposed. The algorithm denoted as MOEA/D-LPBI is demonstrated to outperform its competitors on the HRES model as well as a set of benchmarks. Moreover, it effectively obtains a good approximation of Pareto optimal HRES configurations. By further considering a decision maker’s preference, the most satisfied configuration of the HRES can be identified. View Full-Text
Keywords: hybrid renewable energy system (HRES); power grid; multi-objective optimization; multi-objective evolutionary algorithm (MOEA); penalty-based boundary intersection method hybrid renewable energy system (HRES); power grid; multi-objective optimization; multi-objective evolutionary algorithm (MOEA); penalty-based boundary intersection method
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ming, M.; Wang, R.; Zha, Y.; Zhang, T. Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm. Energies 2017, 10, 674.

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