Smart integration of renewable energy and electric vehicles using multi-objective adaptive artificial hummingbird optimization and decision-making methods

Electric vehicles (EVs) and renewable energy sources (RESs) are among the most promising applications because of their environmental advantages, less reliance on conventional energy sources, and reduced total cost of ownership. This research involves numerous critical components that necessitate development: The multi-objective adaptive artificial hummingbird algorithm (MOAAHA) is introduced to remedy its exploitation deficiencies and improve convergence. This is achieved by employing balanced control energy, improved axial and omnidirectional flights, enhanced guided and territorial foraging, and adjusted migration foraging. Secondly, the MOAAHA method is compatible with well-established algorithms and is verified utilizing multi-objective benchmark functions. Additionally, a novel energy management strategy is suggested for hybrid standalone RES integrated with an EV system, using 3 conflicting criteria: loss of load probability (LLP), wasted energy, and life cycle cost (LLC). The MOAAHA method is then addressed to conduct a set of optimum Pareto front solutions using actual 10-step minutely meteorological data from 2017 to 2018 gathered in Najaf, Iraq. Finally, a new hybridization of multi-criteria decision-making procedures is introduced to allocate weights to competing objectives, rank optimal solutions, and choose the most appropriate design. The experimental outcomes indicated that the optimum configuration is composed of 207 wind turbines, 567 photovoltaic modules, and 300 battery storage units at zero of LLP, 361,704.11 (kWh) of wasted energy, and 351,581.67 ($) of LLC. Overall, the proposed MOAAHA approach for optimal design of the PV-WT-BS-EV system has considerable potential to promote practical investment plans for the energy sector and consumers, while providing extensive insights into the integration of RES via the use of the EV system.

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