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

Amir Abbas Safaie, Mohsen Alizadeh Bidgoli and Saeid Javadi

Energy, 2022, vol. 239, issue PC

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.

Keywords: Integrated electricity and natural gas networks; Smart homes; Multi-objective optimization; Emissions; User comfort; Uncertainties (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221024622

DOI: 10.1016/j.energy.2021.122214

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