A techno-economic optimization of Renewable energy sources within a smart grid and battery system through evolutionary algorithms
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Keywords:
RESs, MG, PV, WT, MOPSOAbstract
The contemporary economy is partially reliant on the electricity supply. The demand for an
accessible and enduring energy supply is unavoidable. Due to the depletion of conventional energy sources,
energy sectors are transitioning their generation units from traditional sources to renewable energy sources
(RESs). These sources are abundant in nature; nevertheless, energy derived from these sources exhibits
unpredictable patterns, which can present a potential danger to the regular functioning of the system. To
address this problem, this study introduces an innovative method that utilizes heuristic optimization
approaches, such as the multi-objective genetic algorithm (MOGA), to efficiently optimize cost and
emissions in battery energy storage systems (BESSs) scenarios. Genetic Algorithm (GA) stands out among
the several available methodologies since it consistently delivers superior outcomes for many case studies.
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