Multi-configurational sizing and analysis in a nanogrid using nested integer linear programming. (10th November 2021)

• Record Type: Journal Article
• Authors: Dahiru, Ahmed Tijjani; Tan, Chee Wei; Salisu, Sani; Lau, Kwan Yiew
• Is Part Of: Journal of cleaner production. Volume 323(2021)
• Publication Date: 2021-11-10
• Subjects: Renewable energy -- Nanogrid -- Cost of energy -- Optimization -- Linear programming -- Integer programming; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.129159
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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. Highlights: Nested integer linear programming algorithm is proposed for nanogrid sizing. The method improved the reference algorithm through multi-configurational designs. Levelized costs of energy were reduced by 7.7–39.7% in the three configurations. The net present costs were reduced by 11.2–46.4% in two configurations. The method optimized the nanogrid's capacity by 54.8–68.1%.