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A Probabilistically Constrained Approach for the Energy Procurement Problem

Author

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  • Patrizia Beraldi

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, via P. Bucci 41/C, 87036 Rende (CS), Italy)

  • Antonio Violi

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, via P. Bucci 41/C, 87036 Rende (CS), Italy)

  • Maria Elena Bruni

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, via P. Bucci 41/C, 87036 Rende (CS), Italy)

  • Gianluca Carrozzino

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, via P. Bucci 41/C, 87036 Rende (CS), Italy)

Abstract

The definition of the electric energy procurement plan represents a fundamental problem that any consumer has to deal with. Bilateral contracts, electricity market and self-production are the main supply sources that should be properly combined to satisfy the energy demand over a given time horizon at the minimum cost. The problem is made more complex by the presence of uncertainty, mainly related to the energy requirements and electricity market prices. Ignoring the uncertain nature of these elements can lead to the definition of procurement plans which are infeasible or overly expensive in a real setting. In this paper, we deal with the procurement problem under uncertainty by adopting the paradigm of joint chance constraints to define reliable plans that are feasible with a high probability level. Moreover, the proposed model includes in the objective function a risk measure to control undesirable effects caused by the random variations of the electricity market prices. The proposed model is applied to a real test case. The results show the benefit deriving from the stochastic optimization approach and the effect of considering different levels of risk aversion.

Suggested Citation

  • Patrizia Beraldi & Antonio Violi & Maria Elena Bruni & Gianluca Carrozzino, 2017. "A Probabilistically Constrained Approach for the Energy Procurement Problem," Energies, MDPI, vol. 10(12), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2179-:d:123575
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    References listed on IDEAS

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    1. 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.
    2. Abebe Geletu & Michael Klöppel & Hui Zhang & Pu Li, 2013. "Advances and applications of chance-constrained approaches to systems optimisation under uncertainty," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1209-1232.
    3. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    4. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    5. Lima, Ricardo M. & Novais, Augusto Q. & Conejo, Antonio J., 2015. "Weekly self-scheduling, forward contracting, and pool involvement for an electricity producer. An adaptive robust optimization approach," European Journal of Operational Research, Elsevier, vol. 240(2), pages 457-475.
    6. del Río, Pablo & Cerdá, Emilio, 2014. "The policy implications of the different interpretations of the cost-effectiveness of renewable electricity support," Energy Policy, Elsevier, vol. 64(C), pages 364-372.
    7. Beraldi, Patrizia & Ruszczynski, Andrzej, 2005. "Beam search heuristic to solve stochastic integer problems under probabilistic constraints," European Journal of Operational Research, Elsevier, vol. 167(1), pages 35-47, November.
    8. Ferruzzi, Gabriella & Cervone, Guido & Delle Monache, Luca & Graditi, Giorgio & Jacobone, Francesca, 2016. "Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production," Energy, Elsevier, vol. 106(C), pages 194-202.
    9. Allegra De Filippo & Michele Lombardi & Michela Milano, 2017. "User-Aware Electricity Price Optimization for the Competitive Market," Energies, MDPI, vol. 10(9), pages 1-23, September.
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    Cited by:

    1. 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).
    2. Patrizia Beraldi & Maria Elena Bruni, 2022. "Enhanced indexation via chance constraints," Operational Research, Springer, vol. 22(2), pages 1553-1573, April.
    3. Xiaoliang Wang & Yong Kang & Mengda Zhang & Miao Yuan & Deng Li, 2018. "The Effects of the Downstream Contraction Ratio of Organ-Pipe Nozzle on the Pressure Oscillations of Self-Resonating Waterjets," Energies, MDPI, vol. 11(11), pages 1-12, November.
    4. Thibaut Théate & Sébastien Mathieu & Damien Ernst, 2020. "An Artificial Intelligence Solution for Electricity Procurement in Forward Markets," Energies, MDPI, vol. 13(23), pages 1-17, December.
    5. Thibaut Th'eate & S'ebastien Mathieu & Damien Ernst, 2020. "An Artificial Intelligence Solution for Electricity Procurement in Forward Markets," Papers 2006.05784, arXiv.org, revised Dec 2020.
    6. Maria Elena Bruni, 2022. "MDPI Sustainability: Special Issue: “Women’s Special Issue Series: Sustainable Energy”," Sustainability, MDPI, vol. 14(8), pages 1-2, April.

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