Class topper optimizer for cost-efficient smart grid operation under renewable energy uncertainties

Demand Side Management (DSM) has emerged as a key strategy in smart grids due to its flexibility and cost-saving potential, helping consumers manage and reduce their electricity expenses. Within the energy market, stakeholders such as consumers, demand response aggregators, and utility providers aim to enhance their respective profits. However, aligning these interests simultaneously poses significant challenges. To address this, the present work integrates the concepts of DSM and Dynamic Economic Dispatch (DED) into a unified tri-objective optimization framework that accounts for the variability inherent in solar and wind power generation. The proposed DSM-DED model is tackled using the Class Topper Optimization (CTO) algorithm. The objective is to efficiently schedule both demand and generation over a 24-hour horizon to minimize peak loads, improve the load factor, cut operational costs, reduce consumer bills, and ensure equitable profit distribution among all market participants. Prior to integration with the smart grid model, wind speed and solar irradiance are forecasted using the Weibull and Lognormal probability distribution functions, respectively. Simulation results underscore the importance of effective DSM strategies and renewable energy integration in enhancing the overall economic and operational performance of smart grids.