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User-Aware Electricity Price Optimization for the Competitive Market

Author

Listed:
  • Allegra De Filippo

    (Department of Computer Science and Engineering, University of Bologna, Viale Risorgimento 2, 40126 Bologna, Italy)

  • Michele Lombardi

    (Department of Computer Science and Engineering, University of Bologna, Viale Risorgimento 2, 40126 Bologna, Italy)

  • Michela Milano

    (Department of Computer Science and Engineering, University of Bologna, Viale Risorgimento 2, 40126 Bologna, Italy)

Abstract

Demand response mechanisms and load control in the electricity market represent an important area of research at the international level: the trend towards competition and market liberalization has led to the development of methodologies and tools to support energy providers. Demand side management helps energy suppliers to reduce the peak demand and remodel load profiles. This work is intended to support energy suppliers and policy makers in developing strategies to act on the behavior of energy consumers, with the aim to make a more efficient use of energy. We develop a non-linear optimization model for the dynamics of the electricity market, which can be used to obtain tariff recommendations or for setting the goals of a sensibilization campaign. The model comes in two variants: a stochastic version, designed for residential electricity consumption, and a deterministic version, suitable for large electricity users (e.g., public buildings, industrial users). We have tested our model on data from the Italian energy market and performed an extensive analysis of different scenarios. We also tested the optimization model in a real setting in the context of the FP7 DAREED project (http://www.dareed.eu/), where the model has been employed to provide tariff recommendations or to help the identification of goals for local policies.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1378-:d:111536
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    References listed on IDEAS

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    1. Jacopo Torriti, 2017. "The Risk of Residential Peak Electricity Demand: A Comparison of Five European Countries," Energies, MDPI, vol. 10(3), pages 1-14, March.
    2. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    3. M.M., Eissa, 2011. "Demand side management program evaluation based on industrial and commercial field data," Energy Policy, Elsevier, vol. 39(10), pages 5961-5969, October.
    4. Torriti, Jacopo, 2012. "Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy," Energy, Elsevier, vol. 44(1), pages 576-583.
    5. Koichiro Ito, 2014. "Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing," American Economic Review, American Economic Association, vol. 104(2), pages 537-563, February.
    6. Lijesen, Mark G., 2007. "The real-time price elasticity of electricity," Energy Economics, Elsevier, vol. 29(2), pages 249-258, March.
    7. Ramos, Andres & Ventosa, Mariano & Rivier, Michel, 1999. "Modeling competition in electric energy markets by equilibrium constraints," Utilities Policy, Elsevier, vol. 7(4), pages 233-242, February.
    8. Syngjoo Choi & Raymond Fisman & Douglas Gale & Shachar Kariv, 2007. "Consistency, Heterogeneity, and Granularity of Individual Behavior under Uncertainty," Economics Working Papers 0076, Institute for Advanced Study, School of Social Science.
    9. Lujano-Rojas, Juan M. & Monteiro, Cláudio & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2012. "Optimum residential load management strategy for real time pricing (RTP) demand response programs," Energy Policy, Elsevier, vol. 45(C), pages 671-679.
    10. Syngjoo Choi & Raymond Fisman & Douglas Gale & Shachar Kariv, 2007. "Consistency and Heterogeneity of Individual Behavior under Uncertainty," American Economic Review, American Economic Association, vol. 97(5), pages 1921-1938, December.
    11. Zhe Luo & Seung-Ho Hong & Jong-Beom Kim, 2016. "A Price-Based Demand Response Scheme for Discrete Manufacturing in Smart Grids," Energies, MDPI, vol. 9(8), pages 1-18, August.
    12. Luciano C. Siebert & Adriana Sbicca & Alexandre Rasi Aoki & Germano Lambert-Torres, 2017. "A Behavioral Economics Approach to Residential Electricity Consumption," Energies, MDPI, vol. 10(6), pages 1-18, June.
    13. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
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