A Novel Real-Coded Genetic Algorithm for Dynamic Economic Dispatch Integrating Plug-In Electric Vehicles

Massive popularity of plug-in electric vehicles (PEVs) may bring considerable opportunities and challenges to the power grid. The scenario is highly dependent on whether PEVs can be effectively managed. Dynamic economic dispatch with (DED PEVs) determines optimal level online units PEVs, minimize fuel cost grid fluctuations. Considering valve-point effects transmission losses a complex constrained optimization problem non-smooth, non-linear, non-convex characteristics. High efficient DED method provides powerful tool in both system scheduling charging coordination. In this study, firstly, are integrated into problem, which carry out orderly charge discharge management improve quality To tackle this, novel real-coded genetic algorithm (RCGA), namely, dimension-by-dimension mutation based feature intervals (GADMFI), proposed enhance exploitation exploration conventional RCGAs. Thirdly, simple constraint handling for an infeasible solution DED. Finally, compared current literature six cases three scenarios, including only thermal units, disorderly PEVs. GADMFI shows outstanding advantages solving with/without obtaining effect cutting peaks filling valleys problem.