Abstract
Integration of renewable energy sources at the distribution side can boost the benefits of a smart grid. Due to a growing awareness of the environmental effect of electric power production and current technology utilized in many generations that are more environmentally friendly than traditional plants, distributed generation (DG) is gaining traction. The growing demand for power in emerging nations necessitates the use of DG to generate additional energy. Because of advancements such as greater competition, real-time pricing, and spot pricing, precise energy loss assessment is critical for obtaining more technical and economic advantages.The modeling of load is very important while finding location and size of DG. The improper modeling of load may give rise to poor benefits. This paper examines the impact of load models on the estimated energy loss in DG planning using Genetic Algorithm (GA) and Ant Lion Optimization (ALO). With the provided information, the suggested technique investigates the impact of load models on the estimation of energy loss based on the year's seasonal pattern. Comparative tests of the traditional static load model and the Voltage Dependent Load Model (VDLM) for energy loss at various load levels are conducted using one of the substations delivering energy from a state utility and a 13-bus system.
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