*Article* **Study of an Optimized Micro-Grid's Operation with Electrical Vehicle-Based Hybridized Sustainable Algorithm**

**Muhammad Shahzad Nazir 1,\* , Zhang Chu <sup>1</sup> , Ahmad N. Abdalla <sup>2</sup> , Hong Ki An <sup>3</sup> , Sayed M. Eldin <sup>4</sup> , Ahmed Sayed M. Metwally <sup>5</sup> , Patrizia Bocchetta <sup>6</sup> and Muhammad Sufyan Javed 7,\***


**Abstract:** Recently, the expansion of energy communities has been aided by the lowering cost of storage technologies and the appearance of mechanisms for exchanging energy that is driven by economics. An amalgamation of different renewable energy sources, including solar, wind, geothermal, tidal, etc., is necessary to offer sustainable energy for smart cities. Furthermore, considering the induction of large-scale electric vehicles connected to the regional micro-grid, and causes of increase in the randomness and uncertainty of the load in a certain area, a solution that meets the community demands for electricity, heating, cooling, and transportation while using renewable energy is needed. This paper aims to define the impact of large-scale electric vehicles on the operation and management of the microgrid using a hybridized algorithm. First, with the use of the natural attributes of electric vehicles such as flexible loads, a large-scale electric vehicle response dispatch model is constructed. Second, three factors of micro-grid operation, management, and environmental pollution control costs with load fluctuation variance are discussed. Third, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) approach is proposed to confirm the method's authenticity and reliability. The constructed large-scale electric vehicle response dispatch model significantly improves the load smoothness of the micro-grid after the large-scale electric vehicles are connected and reduces the impact of the entire grid. The proposed hybridized optimization method was solved within 296.7 s, the time taken for electric vehicle users to charge from and discharge to the regional micro-grid, which improves the economy of the micro-grid, and realizes the effective management of the regional load. The weight coefficients *λ*<sup>1</sup> and *λ*<sup>2</sup> were found at 0.589 and 0.421, respectively. This study provides key findings and suggestions that can be useful to scholars and decisionmakers.

**Keywords:** microgrid; sustainable society; electric vehicles; flexible load; optimization
