Multi-objectives approach for economic optimization in the smart electrical grid with demand response and electric vehicle

Abstract

This paper presents an introduction to a multi-objective optimization framework that has been specifically designed to enhance the short-term operational scheduling of energy systems within smart parking lots. The innovative framework integrates the Improved Seagull Algorithm (ISA) with an Adaptive approach, which is crucial for effectively balancing the dual aspects of global exploration and local exploitation, particularly in the context of dynamic and uncertain environments that characterize modern energy systems. To address the complexities involved, a multi-objective formulation has been meticulously developed to take into account the stochastic behavior associated with wind generation, the fluctuations in real-time electricity prices, and the varying demands of electric vehicles (EVs). The primary goal of this model is to jointly optimize several critical factors, including operation costs, voltage deviations, and the dependency on power drawn from the grid. Through extensive simulation studies conducted across a variety of case studies and test systems, it has been demonstrated that the proposed algorithm significantly outperforms established benchmark techniques as well as other competing algorithms in the field. Notably, this method achieves an impressive 18.1% reduction in total operational costs, alongside a remarkable 23.8% decrease in dependency on the grid for power. These compelling findings highlight the proposed framework as not only a practical solution but also a computationally efficient approach for managing uncertainty-aware, multi-objective energy scheduling, paving the way for advancements in the next generation of smart grids.