Intelligent Economic Operation of Smart-Grid Facilitating Fuzzy Advanced Quantum Evolutionary Method

Abstract

<?Pub Dtl=""?> This paper presents an intelligent economic operation of smart grid environment facilitating an advanced quantum evolutionary method. The proposed method models the wind generation (WG) and photovoltaic (PV) generation as renewable power generation sources as a measure 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 (PHEVs) are capable of reducing CO <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$_2$</tex></formula> , NO <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$_x$</tex></formula> , and gradually becoming an integral part of smart-grid infrastructure. Such integration introduces uncertainties into the system that are addressed by fuzzy-logic-based formulations. Demanded load, wind speed, solar radiation, and number of involved PHEVs are taken under fuzzy formulations. An intelligent quantum inspired evolutionary algorithm (IQEA) is proposed and applied in this model to perform the intelligent economic scheduling operation concerning scheduling and dispatching TG, WG, PV, and PHEV. IQEA features intelligent operators such as sophisticated rotation operator, differential operator, etc. The method is tested on a hypothetical power system with 10 thermal units, equivalent number of PHEVs, equivalent solar and wind farm. The simulation results will show the effectiveness of IQEA that provides excellent operational resource scheduling while reducing the production cost and emission.