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Multi-configurational sizing and analysis in a nanogrid using nested integer linear programming

delete2021-11-01
delete6
PRE
AI
A
Ahmed Tijjani Dahiru
C
Chee Wei Tan *
S
Sani Salisu
K
Kwan Yiew Lau
DOI:10.1016/j.jclepro.2021.129159delete
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摘要

摘要

En 中文
Optimization algorithms are tools used in the planning and operations of renewable energy-based distributed power systems. Mixed integer linear programming as a classical optimization method is considered in the literature for sizing nanogrid systems due to simplicity and speed. However, the method has limited capabilities in implementing multi-configurational analysis and requires large formulations. In this paper, nested integer linear programming is proposed to decompose the large formulations and simplify the multi-configurational sizing of residential nanogrid in a semiarid zone. The proposed method is aimed at optimal sizing for energy cost reduction and increased supply availability. The method is implemented in multi-stage hybridization of relaxation and integer methods of linear programming to achieve optimal sizes of the nanogrid components using photovoltaics, wind turbines, and battery. The method realizes $72,343 net present cost and 0.3755 $/kWh levelized cost of energy indicating 33% and 11% reductions compared to mixed integer linear programming and particle swarm optimization. System availability of 99.97% is envisaged to achieve 6677-7782 kWh per capita electricity consumption in residential buildings against the existing 150 kWh. Three configurations analyzed indicated the robustness of the proposed method and the multi-configurational designs clarify options against factors such as space, logistics, or policies.
Keyword:
Renewable energy
Nanogrid
Cost of energy
Optimization
Linear programming
Integer programming

期刊

Journal of Cleaner Production 封面图
Journal of Cleaner Production
IF:
10
论文数:
4.6W
被引数:
36.8W

机构

U
Universiti Teknologi Malaysia
学者数:
1.4W
论文数: 1.1W
被引数: 85
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