Enhanced multi-objective marine predator algorithm for dynamic economic-grid fluctuation dispatch with plug-in electric vehicles

Abstract

The optimization of multi-objective dynamic economic-grid fluctuation dispatch (MODEGD) involves simultaneous optimization of two competing objectives, fuel cost and grid fluctuation, while fulfilling constraints. In this paper, an improved version of the multi-objective marine predator algorithm (IMOMPA), is proposed for solving MODEGD considering plug-in electric vehicles. The IMOMPA algorithm improves upon the existing multi-objective marine predator algorithm in three key ways. Firstly, the initial population's diversity is enhanced using opposition learning combined with chaotic mapping. Secondly, a nonlinear convergence factor is introduced to speed up the convergence. Finally, the spiral search mechanism from the whale optimization algorithm (WOA) is incorporated to improve the algorithm's global search ability. By testing the performance of IMOMPA on ZDT series and DTLZ series multi-objective benchmark functions, IMOMPA shows strong local search capability, better robustness, and better convergence speed and convergence accuracy. Moreover, connecting plug-in electric vehicles (PEVs) to the grid (V2G), can mitigate grid fluctuations and balance peak demand periods. To validate the feasibility of the proposed IMOMPA, numerical experiments are performed on multi-objective benchmark functions and generation units of varying scales. The results demonstrate the superiority of the IMOMPA algorithm compared to other alternatives.