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Multi-county combined heat and power dynamic economic emission dispatch incorporating electric vehicle parking lot

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  • Basu, M.

Abstract

The objective is to study multi-county combined heat and power dynamic economic emission dispatch in presence of renewable energy resources and electric vehicle parking lot with integrated demand response program. Due to slowly lessening of fossil fuel, economical use of fuel for power and heat production is a very important concern of utilities. This problem is dealt with and without fuel constraints. Nondominated sorting genetic algorithm-III and strength pareto evolutionary algorithm 2 have been applied to solve this problem and substantiated on a real world complicated three county test system. Each county consists of thermal generating units, cogeneration units, heat only units, wind turbine generators, solar PV plants, pumped storage hydro plants and a number of plug-in electric vehicles with charging and discharging facility. It is seen that the results obtained without fuel constraints are more optimal than the results obtained with fuel constraints. Multi county combined heat and power dynamic economic dispatch, cost obtained with fuel constraints is about 3.52% more than the cost obtained without fuel constraints and multi county combined heat and power dynamic emission dispatch, emission obtained with fuel constraints is about 0.56% more than the emission obtained without fuel constraints.

Suggested Citation

  • Basu, M., 2023. "Multi-county combined heat and power dynamic economic emission dispatch incorporating electric vehicle parking lot," Energy, Elsevier, vol. 275(C).
    • Handle: RePEc:eee:energy:v:275:y:2023:i:c:s0360544223009179
    DOI: 10.1016/j.energy.2023.127523
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    References listed on IDEAS

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    1. Liu, Zhi-Feng & Zhao, Shi-Xiang & Zhang, Xi-Jia & Tang, Yu & You, Guo-Dong & Li, Ji-Xiang & Zhao, Shuang-Le & Hou, Xiao-Xin, 2023. "Renewable energy utilizing and fluctuation stabilizing using optimal dynamic grid connection factor strategy and artificial intelligence-based solution method," Renewable Energy, Elsevier, vol. 219(P1).

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