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Multi-objective evolutionary optimization with genetic algorithm for the design of off-grid PV-wind-battery-diesel system
Rathish R.J., Mahadevan K., , Booma J.
Published in Springer Science and Business Media Deutschland GmbH
Volume: 25
Issue: 4
Pages: 3175 - 3194
With the increasing hazard to the environment and progressions in renewable energy technologies, hybrid renewable energy systems can meet the energy demand. Total dependability can be attained by adding battery banks to hybrid systems. This paper aims to optimally design a multi-source grid isolated hybrid generation system for future smart cities in the state of Tamil Nadu, India. This off-grid power generation consists of wind turbine generators, photovoltaic panels, inverters, diesel generators, and batteries. The design intents at minimizing Net Present Cost, Unmet Load, and CO2 emissions which are conventionally contradictory to each other. This paper analyzes the optimal off-grid combination of components and control strategies, utilizing improved Hybrid Optimization using the Genetic Algorithm. The results obtained portrays that a mix of hybrid renewable energy generators at off-grid locations without diesel generators can be a cost-effective choice of new smart cities, and it is sustainable, environmentally viable, and techno-economically feasible. Subvention is imperative to take on the large-scale system due to the high capital cost. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
About the journal
JournalData powered by TypesetSoft Computing
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH
Open AccessNo