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Electricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study

Author

Listed:
  • Emanuel Canelas

    (Department of Engineering and Management, CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

  • Tânia Pinto-Varela

    (Department of Engineering and Management, CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal)

  • Bartosz Sawik

    (Department of Applied Computer Science, School of Management, AGH University of Science and Technology, 30059 Krakow, Poland
    Haas School of Business, University of California at Berkeley, Berkeley, CA 94720, USA)

Abstract

Electricity markets are nowadays flooded with uncertainties that rise from renewable energy applications, technological development, and fossil fuel prices fluctuation, among others. These aspects result in a lumpy electricity prices for consumers, making it necessary to come up with risk management tools to help them hedge this associated risk. In this work a portfolio optimization applied to electricity sector, is proposed. A mixed integer programming model is presented to characterize the electricity portfolio of large consumers. The energy sources available for the portfolio characterization are the day-ahead spot market, forward contracts, and self-generation. The study novelty highlights the energy portfolio characterization for players denoted as large consumers, which has been overlooked by the scientific community and, focuses on the Iberian electricity market as a real case study. A multi-objective methodology is explored, using a weighted-sum approach. The expected cost and the conditional value-at-risk (CVaR) minimization are used as objective function. Three case studies illustrate the model applicability through the characterization of how the portfolio evolves with different demand profiles and how to take advantage from seasonality characteristic in the spot market. A scenario analysis is explored to reflect the uncertainty on the price of the spot market. The expected cost and CVaR are optimized for each case study and the portfolio analysis for each risk posture is characterized. The results illustrate the advantage to reduce costs and risk if the prices seasonality is considered, triggering to an adaptive seasonal behavior, which support the decision-maker decision towards its goals.

Suggested Citation

  • Emanuel Canelas & Tânia Pinto-Varela & Bartosz Sawik, 2020. "Electricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study," Energies, MDPI, vol. 13(9), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2249-:d:353782
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    Cited by:

    1. Jorge Antunes & Luis Alberiko Gil-Alana & Rossana Riccardi & Yong Tan & Peter Wanke, 2022. "Unveiling endogeneity and temporal dependence in energy prices and demand in Iberian countries: a stochastic hidden Markov model approach," Annals of Operations Research, Springer, vol. 313(1), pages 191-229, June.
    2. Carlo Andrea Bollino & Philipp Galkin, 2021. "Energy Security and Portfolio Diversification: Conventional and Novel Perspectives," Energies, MDPI, vol. 14(14), pages 1-24, July.
    3. Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
    4. Štefan Bojnec & Alan Križaj, 2021. "Electricity Markets during the Liberalization: The Case of a European Union Country," Energies, MDPI, vol. 14(14), pages 1-21, July.
    5. Bartosz Sawik, 2023. "Space Mission Risk, Sustainability and Supply Chain: Review, Multi-Objective Optimization Model and Practical Approach," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    6. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    7. Narajewski, Michał & Ziel, Florian, 2022. "Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs," Energy Economics, Elsevier, vol. 110(C).
    8. Minhui Qian & Ning Chen & Yuge Chen & Changming Chen & Weiqiang Qiu & Dawei Zhao & Zhenzhi Lin, 2021. "Optimal Coordinated Dispatching Strategy of Multi-Sources Power System with Wind, Hydro and Thermal Power Based on CVaR in Typhoon Environment," Energies, MDPI, vol. 14(13), pages 1-35, June.

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