Abstract
Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system's operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.