Stochastic Optimal Design of Household-Based Hybrid Energy Supply Systems Using Sample Average Approximation

Abstract
In terms of energy production, combining conventional and renewable energy sources prove to be more sustainable and cost-e ective. Nevertheless, e cient planning and designing of such systems are extremely complex due to the intermittency of renewable sources. Many existing studies fail to capture the stochasticity and/or avoid detailed reliability analysis. is research proposes a practical stochastic multi-objective optimization tool for optimally laying out and sizing the components of a grid-linked system to optimize system power at a low cost. A comparative analysis of four state-of-theart algorithms using the hypervolume measure, execution time, and nonparametric statistical analysis revealed that the nondominated sorting genetic algorithm III (NSGA-III) was more promising, despite its signi cantly longer execution time. According to the NSGA-III calculations, given solar irradiance and energy pro les, the household would need to install a 5.5 (kWh) solar panel tilted at 26.3° and orientated at 0.52° to produce 65.6 (kWh) of power. e best battery size needed to store enough excess power to improve reliability was 2.3 (kWh). e cost for the design was $73520. In comparison, the stochastic technique allows for the construction of a grid-linked system that is far more cost-e ective and reliable.
Description
This article is published by Hindawi 2022 and is also available at https://doi.org/10.1155/2022/9021413
Keywords
Citation
Hindawi Mathematical Problems in Engineering Volume 2022, Article ID 9021413, 19 pages
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