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An optimization model for natural gas supply portfolios of a power generation company

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  • Jirutitijaroen, Panida
  • Kim, Sujin
  • Kittithreerapronchai, Oran
  • Prina, José

Abstract

This paper considers a deregulated electricity market environment where a natural gas-fired generation company can engage in different types of contracts to manage its natural gas supply as well as trade on the electricity market. If the contracts are properly designed, they can protect the company from fluctuations in electricity price and demand, at some cost to the company’s expected profit. This reduction in profit can be mitigated by trading on the natural gas and electricity spot markets, but this trading activity may also sometimes result in losses. A stochastic programming model is formulated to capture the hedging decisions made by the company, as well as the interactions between the natural gas and electricity markets. The benefits offered by this approach for profit maximization in a variety of business scenarios, such as the case where the company can hold some amount of gas in storage are studied and presented. It is found that the stochastic model enables the company to optimize the electricity generation schedule and the natural gas consumption, including spot price transactions and gas storage management. Several managerial insights into the natural gas market, natural gas storage, and distribution profit are given.

Suggested Citation

  • Jirutitijaroen, Panida & Kim, Sujin & Kittithreerapronchai, Oran & Prina, José, 2013. "An optimization model for natural gas supply portfolios of a power generation company," Applied Energy, Elsevier, vol. 107(C), pages 1-9.
    • Handle: RePEc:eee:appene:v:107:y:2013:i:c:p:1-9
    DOI: 10.1016/j.apenergy.2013.02.020
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    References listed on IDEAS

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    Cited by:

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    2. Messagie, Maarten & Mertens, Jan & Oliveira, Luis & Rangaraju, Surendraprabu & Sanfelix, Javier & Coosemans, Thierry & Van Mierlo, Joeri & Macharis, Cathy, 2014. "The hourly life cycle carbon footprint of electricity generation in Belgium, bringing a temporal resolution in life cycle assessment," Applied Energy, Elsevier, vol. 134(C), pages 469-476.
    3. Li, Guoqing & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Bai, Linquan & Li, Xiaojing, 2017. "Security-constrained bi-level economic dispatch model for integrated natural gas and electricity systems considering wind power and power-to-gas process," Applied Energy, Elsevier, vol. 194(C), pages 696-704.
    4. Hernán Gómez-Villarreal & Miguel Carrión & Ruth Domínguez, 2019. "Optimal Management of Combined-Cycle Gas Units with Gas Storage under Uncertainty," Energies, MDPI, vol. 13(1), pages 1-29, December.
    5. Huang, Zhouchun & Zheng, Qipeng Phil, 2020. "A multistage stochastic programming approach for preventive maintenance scheduling of GENCOs with natural gas contract," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1036-1051.
    6. Cui, Hantao & Li, Fangxing & Hu, Qinran & Bai, Linquan & Fang, Xin, 2016. "Day-ahead coordinated operation of utility-scale electricity and natural gas networks considering demand response based virtual power plants," Applied Energy, Elsevier, vol. 176(C), pages 183-195.
    7. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    8. Qiao, Zheng & Guo, Qinglai & Sun, Hongbin & Sheng, Tongtian, 2018. "Multi-time period optimized configuration and scheduling of gas storage in gas-fired power plants," Applied Energy, Elsevier, vol. 226(C), pages 924-934.
    9. Wu, Jung-Hua & Huang, Yun-Hsun, 2014. "Electricity portfolio planning model incorporating renewable energy characteristics," Applied Energy, Elsevier, vol. 119(C), pages 278-287.
    10. Vithayasrichareon, Peerapat & MacGill, Iain F., 2014. "Incorporating short-term operational plant constraints into assessments of future electricity generation portfolios," Applied Energy, Elsevier, vol. 128(C), pages 144-155.

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