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

Author

Listed:
  • Seyed Hamed Jalalzad

    (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)

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.

Suggested Citation

  • Seyed Hamed Jalalzad & Hossein Yektamoghadam & Rouzbeh Haghighi & Majid Dehghani & Amirhossein Nikoofard & Mahdi Khosravy & Tomonobu Senjyu, 2022. "A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy," Energies, MDPI, vol. 15(3), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1172-:d:742505
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    References listed on IDEAS

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    Cited by:

    1. Shahenda Sarhan & Abdullah Shaheen & Ragab El-Sehiemy & Mona Gafar, 2022. "A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies," Mathematics, MDPI, vol. 10(13), pages 1-26, June.
    2. Shahenda Sarhan & Abdullah M. Shaheen & Ragab A. El-Sehiemy & Mona Gafar, 2022. "Enhanced Teaching Learning-Based Algorithm for Fuel Costs and Losses Minimization in AC-DC Systems," Mathematics, MDPI, vol. 10(13), pages 1-22, July.

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