The rapid rise of electric mobility and decentralized renewable energy has fundamentally reshaped modern power systems, introducing both complexity and opportunity. As Electric Vehicles (EVs) evolve into active participants in the energy ecosystem, their flexible charging and discharging capabilities can be strategically harnessed to improve microgrid performance if coordinated effectively. However, the variability of EV behaviors, renewable output, and load demands poses significant optimization challenges. To address these issues, this paper proposes a novel multi-objective scheduling framework that optimizes the daily energy dispatch of such microgrids. The system incorporates Redox Flow Batteries (RFBs) as the primary energy storage technology due to their long cycle life, thermal stability, and the ability to decouple power and energy capacities. A key technical contribution of this work is the development of a modified slime mould algorithm enhanced with a dynamic weight adaptation strategy. The optimization model considers multiple objectives, including the minimization of microgrid operating costs and electric vehicle charging payments, while ensuring demand-supply balance and grid interaction constraints. Simulation results demonstrate that the proposed DW-SMA algorithm outperforms conventional SMO, PSO, and HHO in convergence speed, cost reduction, and stability. With RFB integration, the system achieves up to 58% lower daily operating cost, 18.3% less grid energy dependence, and 25.7% higher EV discharge utilization compared to baseline cases. The DW-SMA algorithm converged 27% faster and achieved a 14.6% lower objective function value than standard methods.
Cheng et al. (Sun,) studied this question.