A multi-objective optimization framework for integrated electricity and natural gas networks considering smart homes in downward under uncertainties

Abstract

This paper presents a multi-objective optimization framework for day-ahead scheduling of integrated electricity and natural gas networks in the presence of five sets of smart homes. The study system includes a 69-bus electricity distribution network and a 14-node natural gas network, equipped with gas turbines, wind turbines, photovoltaic (PV) panels, electrical energy storage (EES) systems and power-to-gas (P2G) technologies. The scheduling problem is modeled as a two-objective optimization problem and its objectives include minimizing the operation cost and CO2 emissions. In order to model the two-objective optimization problem, the epsilon-constraint method has been adopted. Finally, the proposed model has been solved in the form of 3 case studies by CPLEX solver in general algebraic modeling system (GAMS) software. The simulation results demonstrate that the two-objective modeling of the scheduling problem leads to a 2.87% reduction in CO2 emissions despite a 0.75% increase in operating costs. The results also illustrate that a 21.93% increase in the customer's comfort index leads to a 41% increase in annual operating costs. Finally, the results substantiate that the installation of P2G technologies along with wind turbines prevents wind power curtailment in some hours.