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

机译:改进的多目标进化算法的混合可再生能源系统多目标优化

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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.
机译:由于常规能源的匮乏和温室效应,可再生能源得到了越来越多的关注。本文提出了孤岛和并网两种模式下混合可再生能源系统(HRES)多目标优化设计的方法。在每种模式下,最佳设计旨在在HRES中找到合适的光伏(PV)面板,风力涡轮机,电池和柴油发电机组的配置,以使系统成本和燃料排放最小化,并且系统可靠性/可再生能力(对应于不同的模式)。为了有效地解决这一多目标问题,提出了一种基于局部罚分边界交集(LPBI)的基于分解的多目标进化算法(MOEA / D)。在HRES模型以及一系列基准测试中,被证明为MOEA / D-LPBI的算法优于竞争对手。而且,它有效地获得了帕累托最优HRES配置的良好近似。通过进一步考虑决策者的偏好,可以确定HRES最满意的配置。

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