Multi-Objective optimisation of a micro-grid hybrid power system for household application. (1st July 2020)

• Record Type: Journal Article
• Authors: Jaszczur, Marek; Hassan, Qusay; Palej, Patryk; Abdulateef, Jasim
• Is Part Of: Energy. Volume 202(2020)
• Publication Date: 2020-07-01
• Subjects: Renewable sources of energy -- Hybrid systems -- Energy safety -- Optimisation; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.117738
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This research study aimed to design and optimise hybrid renewable energy systems at two optimisation objectives. The economic and environmental optimisation objectives of four hybrid power system scenarios were tested based on a non-dominated sorting genetic algorithm. The selected optimisation objectives were used simultaneously and parallel to sensitivity analysis in order to determine optimum system configuration under specific criteria. The environmental objective targeted the reduction of CO2 emissions, which resulted in a system with high renewable energy fractions. The economic objective targeted the reduction of system cost (net percentage cost), which resulted in a system size that could meet desired electrical loads. The novelty of this work lay in determining the relationship between two different optimisation objectives i.e., environmental and economic. Result showed that the optimisation process under the economic objective recorded the lowest energy cost for all scenarios with and without storage units. By contrast, the optimisation process under the environmental objective demonstrated a higher system cost value compared with that in the case of the economic optimisation objective for all scenarios. The analysis also shows the CO2 emission with and without considering systems components manufacturing primary emissions. Highlights: Micro-grid hybrid renewable system for household application. Simulation-based multi-objective economic and environmental optimisation approach. The utilisation of a non-dominated sorting genetic algorithm. Systems components manufacturing primary emissions. Post-optimisation renewable hybrid system sensitivity analysis.