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

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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.

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Notes

  1. For a technical overview of UNGS see Katz and Tek (1981) and Tek (2012).

  2. When gas is withdrawn (injected) from (at) the storage, the stock is emptied (filled) into an upper (lower) amount than the one actually delivered (withdrawn) to the system \({\varvec{w}}_{r,t}\) (\({\varvec{y}}_{r, t}\)), since part of the gas is consumed in the compressors (Martins 2012).

  3. Although \({\varvec{f}}^{\mathrm{TO}}_{l,t}\) and \({\varvec{f}}^{\mathrm{FROM}}_{m,t}\) have been created to simplify the notation of the model, relating all incoming and outgoing gas flow, respectively, the primary variable is \(f_{m,l,t}\).

  4. For a better understanding, the SDDP algorithm used is presented in Dowson (2018).

  5. By 2015, about 80% of the total imported LNG volume was purchased on the spot market.

  6. This price excludes ICMS and PIS/COFINS taxes, transmission and distribution margin.

  7. After June 2021, the expected expansion of the Brazilian power system comprises an increased demand of gas-based thermoelectric plants in the Northeast subsystem.

  8. OPEX are the expenses a company incurs for running their day-to-day operations.

  9. CAPEX are purchases of significant goods or services designed to improve a company’s performance in the future.

  10. Given that variable operating costs are incorporated into the model as stocking cost, OPEX took into account the fixed operating costs presented in the previous chapter.

  11. To annualize the cost of capital investment, a 30-year amortization period was considered, which is reasonable for capital intensive investments in infrastructure.

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Authors and Affiliations

  1. Industrial Engineering Department, PUC-Rio, Rio de Janeiro, Brazil

    Larissa de Oliveira Resende & Davi Valladão

  2. PSR Energy Consulting and Analytics (PSR), Rio de Janeiro, Brazil

    Bernardo Vieira Bezerra

  3. National Electrical System Operator (ONS), Rio de Janeiro, Brazil

    Yasmin Monteiro Cyrillo

Authors
  1. Larissa de Oliveira Resende
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  2. Davi Valladão
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  3. Bernardo Vieira Bezerra
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  4. Yasmin Monteiro Cyrillo
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Correspondence to Larissa de Oliveira Resende.

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Resende, L.d.O., Valladão, D., Bezerra, B.V. et al. Assessing the value of natural gas underground storage in the Brazilian system via stochastic dual dynamic programming. TOP 29, 106–124 (2021). https://doi.org/10.1007/s11750-020-00575-w

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