Modelling cost-effective of electric vehicles and demand response in smart electrical microgrids

Abstract

The intermittent nature of renewable energy sources such as solar and wind power can lead to fluctuations in the supply of electricity within a microgrid, making it difficult to maintain a consistent and reliable power supply. This can result in disruptions to critical operations and services that rely on a stable source of energy. Additionally, the integration of electric vehicles into a microgrid introduces another layer of complexity, as the charging and discharging of these vehicles can create additional demand and strain on the grid. This can lead to imbalances in the supply and demand of electricity, further impacting the stability and efficiency of the microgrid. This paper presents an approach for the optimal behaviour of electric vehicles and demand side for an electrical microgrid. The proposed approaches are multi-domain attention-dependent conditional generative adversarial network (MDACGAN) and seahorse optimization (SHO) techniques. The primary goal of the suggested method is to reduce the operational cost of the system, maximize the utilization of solar power and reduce electricity fluctuations. The economic dispatch model manages the fluctuation of renewable energy sources through the implementation of suggested techniques to handle unpredictability. The effectiveness of this approach is evaluated using the MATLAB platform and compared against other methods. The suggested technique demonstrates superior outcomes across all methodologies. Based on the findings, it can be inferred that the suggested technique boasts a lower cost in comparison to other methods.