A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

Seyed Hamed Jalalzad Mahvizani, Hossein Yektamoghadam, Rouzbeh Haghighi, Majid Dehghani, Amirhossein Nikoofard, Mahdi Khosravy and Tomonobu Senjyu
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Seyed Hamed Jalalzad Mahvizani: Department of Engineering, Sardar Jangal University, Gilan 4193165-151, Iran
Hossein Yektamoghadam: Department of Electrical Engineering, K.N. Toosi University of Technology, Tehran 196976-4499, Iran
Rouzbeh Haghighi: Department of Electrical Engineering, Amirkabir University of Technology, Tehran 159163-4311, Iran
Majid Dehghani: Department of Electrical Engineering, Amirkabir University of Technology, Tehran 159163-4311, Iran
Amirhossein Nikoofard: Department of Electrical Engineering, K.N. Toosi University of Technology, Tehran 196976-4499, Iran
Mahdi Khosravy: Cross Labs, Cross-Compass Ltd., Tokyo 104-0045, Japan
Tomonobu Senjyu: Faculty of Engineering, University of the Ryukyus, Okinawa 903-0213, Japan

Energies, 2022, vol. 15, issue 3, 1-16

Abstract: In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching–learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.

Keywords: generation expansion planning; teaching–learning based optimization; game theory; carbon emission (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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