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Portfolio analysis and geographical allocation of renewable sources: A stochastic approach

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  • Scala, Antonio
  • Facchini, Angelo
  • Perna, Umberto
  • Basosi, Riccardo

Abstract

We take inspiration from the Modern Portfolio Theory introduced by Markowitz to propose a simplified strategy for the portfolio management of renewable energy sources based on Gaussian fluctuations with tunable correlations. By analyzing the impact of production fluctuations, we show how – depending on the sources' temporal correlation patterns – a careful geographical allocation of different types of renewal energy sources can reduce both the energy needs for balancing the power system and its uncertainty. The proposed strategy can be easily integrated in a decision support system for the planning of renewable energy sources. Therefore, providing policy/decision makers with an additional tool. We test our strategy on a set of case studies including a real-case based on literature data for solar and wind sources, and discuss how to extend the computation to non-Gaussian sources. The paper shows that in the Markowitz framework an efficient trade-off between production and fluctuations can be easily achieved, and that such framework also leads to important considerations on energy security. In perspective, analysis of time series together with such enriched frameworks would allow for the analysis of multiple realistic renewable generation scenarios helping decisions on the optimal size and spatial allocation of future energy storage facilities.

Suggested Citation

  • Scala, Antonio & Facchini, Angelo & Perna, Umberto & Basosi, Riccardo, 2019. "Portfolio analysis and geographical allocation of renewable sources: A stochastic approach," Energy Policy, Elsevier, vol. 125(C), pages 154-159.
    • Handle: RePEc:eee:enepol:v:125:y:2019:i:c:p:154-159
    DOI: 10.1016/j.enpol.2018.10.034
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    References listed on IDEAS

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    2. Castro, Gabriel Malta & Klöckl, Claude & Regner, Peter & Schmidt, Johannes & Pereira, Amaro Olimpio, 2022. "Improvements to Modern Portfolio Theory based models applied to electricity systems," Energy Economics, Elsevier, vol. 111(C).
    3. Gabriel Malta Castro & Claude Klockl & Peter Regner & Johannes Schmidt & Amaro Olimpio Pereira Jr, 2021. "Improvements to Modern Portfolio Theory based models applied to electricity systems," Papers 2105.08182, arXiv.org.
    4. Michaela Makešová & Michaela Valentová, 2021. "The Concept of Multiple Impacts of Renewable Energy Sources: A Critical Review," Energies, MDPI, vol. 14(11), pages 1-21, May.
    5. Kaiqiang An & Guiyu Zhao & Jinjun Li & Jingsong Tian & Lihua Wang & Liang Xian & Chen Chen, 2023. "Best-Case Scenario Robust Portfolio: Evidence from China Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(2), pages 297-322, June.
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