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Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems

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  • Silva, Rodolfo Rodrigues Barrionuevo
  • Martins, André Christóvão Pio
  • Soler, Edilaine Martins
  • Baptista, Edméa Cássia
  • Balbo, Antonio Roberto
  • Nepomuceno, Leonardo

Abstract

The Energy Procurement (EP) problem faced by a large consumer is concerned with planning the energy procurement in the various energy markets available, such that its short- and medium-term demands are met, and the risks involved in such trading are mitigated. Although a number of EP models have been proposed for purely thermal systems, no specific model has been addressed for solving this problem for a large consumer located in a hydro-dominated system. In this paper we discuss the main specific features and issues involving EP problems for hydro-dominated markets. A central issue is the estimation of future energy prices in the pool market. In hydro-dominated systems, uncertainties in incremental water inflows into reservoirs affect directly such prices, as well as the estimated demands, and these are difficult correlations to be captured by a price estimation model. In this paper, we propose a Price Scenario Generation (PSG) model to estimate future pool prices, which is able to capture spatial and temporal correlation among uncertain prices, water inflows and demands. The estimated prices obtained by the PSG are introduced in the proposed Energy Procurement Model for Hydrothermal Systems (EPMHS), which calculates the optimal procurement decisions, involving the portions of energy traded in the pool, bilateral and futures markets, as well as the self-produced energy. The proposed EPMHS is formulated as a sequence of mixed-integer two-stage stochastic linear programming problems, where pool prices are handled as uncertain parameters. The EPMHS represents trading risks using the Conditional Value at Risk (CVaR) metric. We also propose a strategy for including yearly estimation of water inflows into the EPMHS, since prices in hydro-dominated markets are generally driven by water inflows forecasts. The model proposed is applied to the generation system of the northeast region of Brazil, and the results reveal coherent correlations between hydro and economic variables.

Suggested Citation

  • Silva, Rodolfo Rodrigues Barrionuevo & Martins, André Christóvão Pio & Soler, Edilaine Martins & Baptista, Edméa Cássia & Balbo, Antonio Roberto & Nepomuceno, Leonardo, 2022. "Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems," Energy Economics, Elsevier, vol. 107(C).
    • Handle: RePEc:eee:eneeco:v:107:y:2022:i:c:s0140988322000275
    DOI: 10.1016/j.eneco.2022.105841
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    Cited by:

    1. Chaves, Caio Nogueira & da Silva, Tiago Forti & Gaspar, João Paulo Manarelli & Martins, André Christóvão Pio & Soler, Edilaine Martins & Balbo, Antonio Roberto & Nepomuceno, Leonardo, 2025. "Adaptive stochastic approach for solving long-term hydrothermal scheduling problems," Applied Energy, Elsevier, vol. 378(PA).
    2. Laís Domingues Leonel & Mateus Henrique Balan & Luiz Armando Steinle Camargo & Dorel Soares Ramos & Roberto Castro & Felipe Serachiani Clemente, 2024. "Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function," Energies, MDPI, vol. 17(21), pages 1-19, October.

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    More about this item

    Keywords

    Energy procurement in electricity markets; Portfolio optimization problem; Large consumers; Medium-term hydrothermal scheduling;
    All these keywords.

    JEL classification:

    • D00 - Microeconomics - - General - - - General
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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