Developing an Artificial Hummingbird Algorithm for Probabilistic Energy Management of Microgrids Considering Demand Response

This study proposes an artificial hummingbird algorithm (AHA) for energy management (EM) optimal operation of a microgrid (MG), including conventional sources and renewable (RES), with incentive-based demand response (DR). Due to the stochastic nature solar wind output power uncertainty prices load, probabilistic EM hybrid AHA point estimation method (PEM) is proposed model this by utilizing normal Weibull distribution functions. The PEM considered good tool handling problems. It achieves results using same procedures used deterministic problems while maintaining low computational efforts. technique employed solve incentive DR program, goal reducing overall cost, which includes cost generator fuel transaction main grid taking into account load demand. Two different case studies are tested. simulation compared well-known metaheuristic algorithms demonstrate its efficacy. According AHA’s results, total reduction consumption 104 KWh first 2677 MWh second achieved achieving lowest operating cost. that adequate tackling problem. Then, examine effect on MG state, problem solved AHA-PEM.