Optimal economic operation of smart grid by fuzzy advanced quantum evolutionary method

This paper presents a fuzzy controlled intelligent economic operation of smart grid environment facilitating an advanced quantum evolutionary method. The wind generation (WG) and photo-voltaic generation (PV) are used as renewable power generation sources. Thermal generators (TG) are included in this model to provide the maximum amount of energy to meet consumers' demand. Plug-in hybrid electric vehicles (PHEVs) are capable of reducing CO₂, hence gradually becoming the integral part of smart-grid infrastructure. Such integration introduces uncertainties into the system which are addressed by fuzzy logic based formulations. Therefore, efficient dynamic economic operations are required to provide thermal unit commitment (UC), economic dispatch of thermal and renewable energy sources as well as PHEV operation. A quantum evolutionary algorithm (QEA) method is proposed and applied in this model to perform the intelligent economic operation. The method is tested on a hypothetical power system with 10 thermal units, 43,000 PHEVs, equivalent solar and wind farm.