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Assessing the value of natural gas underground storage in the Brazilian system via stochastic dual dynamic programming

Author

Listed:
  • Larissa de Oliveira Resende

    (PUC-Rio)

  • Davi Valladão

    (PUC-Rio)

  • Bernardo Vieira Bezerra

    (PSR Energy Consulting and Analytics (PSR))

  • Yasmin Monteiro Cyrillo

    (National Electrical System Operator (ONS))

Abstract

The Brazilian natural gas sector is currently characterized by low maturity and dynamism of the market. The stochastic behavior of the demand for natural gas added to its associated market price volatility motivates the usage of underground storage to provide supply flexibility and protection against price fluctuations. However, the existing literature lacks a proper analytical tool to assess the benefits of underground natural gas storage (UNGS) activity. In this work, it is proposed a stochastic dynamic programming model for long/medium-term operation planning to determine the optimal gas supply and storage policies. A markovian model characterizes the uncertainty over the thermoelectric demand and market price. The proposed model is efficiently solved using the stochastic dual dynamic programming algorithm for the Brazilian case study considering realistic data for the actual gas network and electric power system. For an exogenous but meaningful choice of underground storage location and size, it is observed the operational and economic benefits of the provided storage flexibility. Finally, our numerical simulations show that the economic benefit for the system surpasses the operational and capital expenses for the storage infrastructure in depleted fields and salt caverns.

Suggested Citation

  • Larissa de Oliveira Resende & Davi Valladão & Bernardo Vieira Bezerra & Yasmin Monteiro Cyrillo, 2021. "Assessing the value of natural gas underground storage in the Brazilian system via stochastic dual dynamic programming," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 106-124, April.
    • Handle: RePEc:spr:topjnl:v:29:y:2021:i:1:d:10.1007_s11750-020-00575-w
    DOI: 10.1007/s11750-020-00575-w
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    References listed on IDEAS

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    Cited by:

    1. Xiao, Ludi & Zhou, Peng & Bai, Yang & Zhang, Kai, 2024. "Modeling the dynamic allocation problem of multi-service storage system with strategy learning," Energy, Elsevier, vol. 302(C).

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