Intelligent Resource Scheduling in Green Smart Grid Considering Uncertainties

Abstract

This paper presents an intelligent economic operation on smart grid environment facilitating an advanced quantum evolutionary method. The proposed method models the wind generation (WG) and the photovoltaic generation (PV) as renewable power generation sources as measures of global warming effect. Thermal generators (TGs) are included in this model to provide the maximum amount of energy to meet consumers' demand. On the other hand, plug-in hybrid electric vehicles (PHEV) are capable of reducing CO₂ and gradually becoming an integral part of a smart-grid infrastructure. Such an integration introduces uncertainties into the system that are addressed by a fuzzy agent (FA). The demanded load, the wind speed, the solar radiation and a number of involved PHEVs are taken as fuzzy parameters to resolve uncertainties. An optimizer agent (OA), based on intelligent quantum inspired evolutionary algorithm, is deployed to carry out the economic scheduling operation concerning scheduling and dispatching with the help of FA. OA features intelligent operators such as a sophisticated rotation operator, a differential operator, etc. The method is tested on a hypothetical power system with 10 thermal units, an equivalent number of PHEVs, an equivalent solar and wind farm. The simulation results will show the effectiveness of OA-FA that provides an excellent operational resource scheduling while reducing the production cost and emission.