Optimal Energy Scheduling in Smart Electrical Systems Considering Economic and Emission Objective Functions

Y. Romani (), Rustem Shichiyakh, Zokir Mamadiyarov, Bobur Mirzayev, Shokhjakhon Akhmedov, Saidislom Rashidov, Nurbek Matyakubov, I. B. Sapaev, Islambek Norbotaev, Elyor Saitov and Sobir Parmanov
Additional contact information
Y. Romani: Universidad Central de Venezuela, Sierra Maestra, Facultad de Ciencias
Rustem Shichiyakh: Kuban State Agrarian University Named After I.T. Trubilin
Zokir Mamadiyarov: Termez University of Economics and Service
Bobur Mirzayev: Alfraganus University
Shokhjakhon Akhmedov: Urgench State University
Saidislom Rashidov: Mamun University
Nurbek Matyakubov: Urgench Innovation University
I. B. Sapaev: ” “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University
Islambek Norbotaev: Denov Institute of Entrepreneurship and Pedagogy
Elyor Saitov: University of Tashkent for Applied Sciences
Sobir Parmanov: National University of Uzbekistan

SN Operations Research Forum, 2025, vol. 6, issue 4, 1-23

Abstract: Abstract The relationship between energy and social sustainability, along with economic and environmental factors, has prompted energy operators to conduct various investigations into energy systems. The energy system represents a contemporary approach to energy generation, utilizing different types of energy including plug-in electric vehicles and renewable energy sources in smart electrical distribution networks. This paper focused on optimal participation of the plug-in electric vehicles and renewable energy sources in the smart electrical distribution networks for optimal energy generation considering economic and environmental factors. The participation of the plug-in electric vehicles and renewable energy sources in the electrical distribution networks is modelled for minimization of emission polluting and energy generation costs in power plants. The emission polluting and energy generation costs are modelled as multi-objective functions optimization. The particle swarm optimization algorithm is used for solving proposed approach. To demonstrate the practicality and efficiency of the suggested method, several case studies are examined. The method is evaluated on a 33-bus distribution system. The analysis of the outcomes shows that the recommended smart grid framework optimizes the utilization of renewable energy sources while effectively lowering fuel costs and emissions in power plants. The participation of the PEVs and renewable energies alongside power plant leads to minimization of the costs and emission pollution of the units by 2.91% and 7.36%, respectively.

Keywords: Economic and environmental factors; Plug-in electric vehicles; Smart electrical distribution networks; Renewable energy sources; Multi-objective functions optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00547-5

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