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Dynamic Carbon-Constrained EPEC Model for Strategic Generation Investment Incentives with the Aim of Reducing CO 2 Emissions

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  • Jaber Valinejad

    (Bradley Department of Electrical Computer Engineering, Virginia Tech, Northern Virginia Center, Greater Washington, D.C., VA 22043, USA)

  • Mousa Marzband

    (Department of Maths, Physics and Electrical Engineering, Faculty of Engineering and Environment, Northumbria University Newcastle, Newcastle upon Tyne NE1 8ST, UK)

  • Michael Elsdon

    (Department of Maths, Physics and Electrical Engineering, Faculty of Engineering and Environment, Northumbria University Newcastle, Newcastle upon Tyne NE1 8ST, UK)

  • Ameena Saad Al-Sumaiti

    (Advanced Power and Energy Center, Electrical Engineering and Computer Science, Khalifa University, P.O. Box 127788, Abu Dhabi, UAE)

  • Taghi Barforoushi

    (Department of Electrical and Computer Engineering, Babol Noshirvaini University of Technology, Babol 4714871167, Iran)

Abstract

According to the European Union Emissions Trading Scheme, energy system planners are encouraged to consider the effects of greenhouse gases such as CO 2 in their short-term and long-term planning. A decrease in the carbon emissions produced by the power plant will result in a tax decrease. In view of this, the Dynamic carbon-constrained Equilibrium programming equilibrium constraints (DCC-EPEC) Framework is suggested to explore the effects of distinct market models on generation development planning (GEP) on electricity markets over a multi-period horizon. The investment incentives included in our model are the firm contract and capacity payment. The investment issue, which is regarded as a set of dominant producers in the oligopolistic market, is developed as an EPEC optimization problem to reduce carbon emissions. In the suggested DCC-EPEC model, the sum of the carbon emission tax and true social welfare are assumed as the objective function. Investment decisions and the strategic behavior of producers are included at the first level so as to maximize the overall profit of the investor over the scheduling period. The second-level issue is market-clearing, which is resolved by an independent system operator (ISO) to maximize social welfare. A real power network, as a case study, is provided to assess the suggested carbon-constrained EPEC framework. Simulations indicate that firm contracts and capacity payments can initiate the capacity expansion of different technologies to improve the long-term stability of the electricity market.

Suggested Citation

  • Jaber Valinejad & Mousa Marzband & Michael Elsdon & Ameena Saad Al-Sumaiti & Taghi Barforoushi, 2019. "Dynamic Carbon-Constrained EPEC Model for Strategic Generation Investment Incentives with the Aim of Reducing CO 2 Emissions," Energies, MDPI, vol. 12(24), pages 1-35, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4813-:d:298980
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    References listed on IDEAS

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    Cited by:

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    3. Tumiran Tumiran & Lesnanto Multa Putranto & Roni Irnawan & Sarjiya Sarjiya & Candra Febri Nugraha & Adi Priyanto & Ira Savitri, 2022. "Power System Planning Assessment for Optimizing Renewable Energy Integration in the Maluku Electricity System," Sustainability, MDPI, vol. 14(14), pages 1-25, July.
    4. Rego, Erik Eduardo & Costa, Oswaldo L.V. & Ribeiro, Celma de Oliveira & Lima Filho, Roberto Ivo da R. & Takada, Hellinton & Stern, Julio, 2020. "The trade-off between demand growth and renewables: A multiperiod electricity planning model under CO2 emission constraints," Energy, Elsevier, vol. 213(C).
    5. Aryani, Morteza & Ahmadian, Mohammad & Sheikh-El-Eslami, Mohammad-Kazem, 2021. "Coordination of risk-based generation investments in conventional and renewable capacities in oligopolistic electricity markets: A robust regulatory tool," Energy, Elsevier, vol. 214(C).
    6. Choorikkat, Sreerag & Lee, Yu-Ching & Hsu, Hsin-Wei, 2025. "Model for subsidizing industry and power company through gray-electricity taxation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 211(C).
    7. Majid Dehghani & Mohammad Taghipour & Saleh Sadeghi Gougheri & Amirhossein Nikoofard & Gevork B. Gharehpetian & Mahdi Khosravy, 2021. "A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime," Energies, MDPI, vol. 14(23), pages 1-21, December.

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