RETRACTED: Techno-economic performance analysis in a nanogrid with Hybrid Renewable Energy Sources using classical optimization technique (Retracted article. See vol. 9, pg. 2591, 2023)

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 analysed to indicate the robustness of the proposed method had multi-configurational designs clarify options against factors of space, logistics, or policies. (C) 2021 The Authors. Published by Elsevier Ltd.