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Non-linear Optimization of Business Models in the Electricity Market

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Integration of AI and OR Techniques in Constraint Programming (CPAIOR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9676))

Abstract

Demand Response mechanisms and load control in the electricity market represent an important area of research at international level, and the market liberalization is opening new perspectives. This calls for the development of methodologies and tools that energy providers can use to define specific business models. In this work we develop an optimization model to provide recommendations on time-of-use based prices to providers, taking into account some key factors of the customer and market behavior. We have tested our model on data from the Italian energy market, merging statistical census and population information. The main advantage of the model is that it provides a tool for sensitivity analysis, namely for understanding the impact of economical and behavioral parameters on the consumption profiles.

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Notes

  1. 1.

    See http://www.iea.org/textbase/npsum/weo2014sum.pdf.

  2. 2.

    See http://www.mercatoelettrico.org/En/Default.aspx.

  3. 3.

    Available at http://dati-censimentopopolazione.istat.it.

  4. 4.

    Available at http://www.neos-server.org/neos/.

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Acknowledgments

This work is partially supported by the EU FP7 project DAREED (g.a. 609082). We thank the anonymous reviewers for their comments.

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Authors and Affiliations

  1. DISI, University of Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy

    Allegra De Filippo, Michele Lombardi & Michela Milano

Authors
  1. Allegra De Filippo
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  2. Michele Lombardi
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  3. Michela Milano
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Corresponding author

Correspondence to Allegra De Filippo .

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Editors and Affiliations

  1. Université Laval, Quebec, Québec, Canada

    Claude-Guy Quimper

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De Filippo, A., Lombardi, M., Milano, M. (2016). Non-linear Optimization of Business Models in the Electricity Market. In: Quimper, CG. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2016. Lecture Notes in Computer Science(), vol 9676. Springer, Cham. https://doi.org/10.1007/978-3-319-33954-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-33954-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33953-5

  • Online ISBN: 978-3-319-33954-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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