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A Practical Formulation for Ex-Ante Scheduling of Energy and Reserve in Renewable-Dominated Power Systems: Case Study of the Iberian Peninsula

Author

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  • Miguel Carrión

    (Department of Electrical Engineering, University of Castilla—La Mancha, 45071 Toledo, Spain)

  • Rafael Zárate-Miñano

    (Department of Electrical Engineering, University of Castilla—La Mancha, 13400 Almadén, Spain)

  • Ruth Domínguez

    (Department of Electrical Engineering, University of Castilla—La Mancha, 45071 Toledo, Spain)

Abstract

Scheduling energy and reserve in power systems with a large number of intermittent units is a challenging problem. Traditionally, the reserve requirements are assigned after clearing the day-ahead energy market using ad hoc rules or solving computationally intense mathematical programming problems to co-optimize energy and reserve. While the former approach often leads to costly oversized reserve provisions, the computational time required by the latter makes it generally incompatible with the daily power system operational practices. This paper proposes an alternative deterministic formulation for computing the energy and reserve scheduling, considering the uncertainty of the demand and the intermittent power production in such a way that the resulting problem requires a lower number of constraints and variables than stochastic programming-based formulations. The performance of the proposed formulation has been compared with respect to two standard stochastic programming formulations in a small-size power system. Finally, a realistic case study based on the Iberian Peninsula power system has been solved and discussed.

Suggested Citation

  • Miguel Carrión & Rafael Zárate-Miñano & Ruth Domínguez, 2018. "A Practical Formulation for Ex-Ante Scheduling of Energy and Reserve in Renewable-Dominated Power Systems: Case Study of the Iberian Peninsula," Energies, MDPI, vol. 11(8), pages 1-22, July.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1939-:d:159981
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    References listed on IDEAS

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    1. Geoffrey Pritchard & Golbon Zakeri & Andrew Philpott, 2010. "A Single-Settlement, Energy-Only Electric Power Market for Unpredictable and Intermittent Participants," Operations Research, INFORMS, vol. 58(4-part-2), pages 1210-1219, August.
    2. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    3. Domínguez, R. & Conejo, A.J. & Carrión, M., 2014. "Operation of a fully renewable electric energy system with CSP plants," Applied Energy, Elsevier, vol. 119(C), pages 417-430.
    4. Kyu-Hyung Jo & Mun-Kyeom Kim, 2018. "Stochastic Unit Commitment Based on Multi-Scenario Tree Method Considering Uncertainty," Energies, MDPI, vol. 11(4), pages 1-17, March.
    5. Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
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