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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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    1. Rodilla, Pablo & García-González, Javier & Baíllo, Álvaro & Cerisola, Santiago & Batlle, Carlos, 2015. "Hydro resource management, risk aversion and equilibrium in an incomplete electricity market setting," Energy Economics, Elsevier, vol. 51(C), pages 365-382.
    2. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
    3. Zare, Kazem & Moghaddam, Mohsen Parsa & Sheikh El Eslami, Mohammad Kazem, 2010. "Electricity procurement for large consumers based on Information Gap Decision Theory," Energy Policy, Elsevier, vol. 38(1), pages 234-242, January.
    4. Boroumand, Raphaël Homayoun & Goutte, Stéphane & Porcher, Simon & Porcher, Thomas, 2015. "Hedging strategies in energy markets: The case of electricity retailers," Energy Economics, Elsevier, vol. 51(C), pages 503-509.
    5. Bierbrauer, Michael & Menn, Christian & Rachev, Svetlozar T. & Truck, Stefan, 2007. "Spot and derivative pricing in the EEX power market," Journal of Banking & Finance, Elsevier, vol. 31(11), pages 3462-3485, November.
    6. Botterud, Audun & Kristiansen, Tarjei & Ilic, Marija D., 2010. "The relationship between spot and futures prices in the Nord Pool electricity market," Energy Economics, Elsevier, vol. 32(5), pages 967-978, September.
    7. Y Shi & F Wu & L K Chu & D Sculli & Y H Xu, 2011. "A portfolio approach to managing procurement risk using multi-stage stochastic programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(11), pages 1958-1970, November.
    8. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.
    9. Zhou, Fan & Page, Lionel & Perrons, Robert K. & Zheng, Zuduo & Washington, Simon, 2019. "Long-term forecasts for energy commodities price: What the experts think," Energy Economics, Elsevier, vol. 84(C).
    10. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
    11. Parlane, Sarah & Ryan, Lisa, 2020. "Optimal contracts for renewable electricity," Energy Economics, Elsevier, vol. 91(C).
    12. Y Shi & F Wu & L K Chu & D Sculli & Y H Xu, 2011. "A portfolio approach to managing procurement risk using multi-stage stochastic programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(11), pages 1958-1970, November.
    13. Contreras, Javier & Rodríguez, Yeny E. & Sosa, Aníbal, 2017. "Construction of an efficient portfolio of power purchase decisions based on risk-diversification tradeoff," Energy Economics, Elsevier, vol. 64(C), pages 286-297.
    14. Kostrzewski, Maciej & Kostrzewska, Jadwiga, 2019. "Probabilistic electricity price forecasting with Bayesian stochastic volatility models," Energy Economics, Elsevier, vol. 80(C), pages 610-620.
    15. de Oliveira, Francisco Alexandre & de Paiva, Anderson Paulo & Lima, José Wanderley Marangon & Balestrassi, Pedro Paulo & Mendes, Ronã Rinston Amaury, 2011. "Portfolio optimization using Mixture Design of Experiments: Scheduling trades within electricity markets," Energy Economics, Elsevier, vol. 33(1), pages 24-32, January.
    16. Ferreira, Pedro Guilherme Costa & Oliveira, Fernando Luiz Cyrino & Souza, Reinaldo Castro, 2015. "The stochastic effects on the Brazilian Electrical Sector," Energy Economics, Elsevier, vol. 49(C), pages 328-335.
    17. Mahmood Akbari & Peter Overloop & Abbas Afshar, 2011. "Clustered K Nearest Neighbor Algorithm for Daily Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1341-1357, March.
    18. Cerisola, Santiago & Latorre, Jesus M. & Ramos, Andres, 2012. "Stochastic dual dynamic programming applied to nonconvex hydrothermal models," European Journal of Operational Research, Elsevier, vol. 218(3), pages 687-697.
    19. Ferrara, Massimiliano & Violi, Antonio & Beraldi, Patrizia & Carrozzino, Gianluca & Ciano, Tiziana, 2021. "An integrated decision approach for energy procurement and tariff definition for prosumers aggregations," Energy Economics, Elsevier, vol. 97(C).
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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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