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Risk-Averse Stochastic Programming for Planning Hybrid Electrical Energy Systems: A Brazilian Case

Author

Listed:
  • Daniel Kitamura

    (Electrical Energy Department, Federal University of Juiz de Fora, UFJF, Juiz de Fora 36036-330, Brazil)

  • Leonardo Willer

    (Electrical Energy Department, Federal University of Juiz de Fora, UFJF, Juiz de Fora 36036-330, Brazil)

  • Bruno Dias

    (Electrical Energy Department, Federal University of Juiz de Fora, UFJF, Juiz de Fora 36036-330, Brazil)

  • Tiago Soares

    (Center for Power and Energy Systems, Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal)

Abstract

This work presents a risk-averse stochastic programming model for the optimal planning of hybrid electrical energy systems (HEES), considering the regulatory policy applied to distribution systems in Brazil. Uncertainties associated with variables related to photovoltaic (PV) generation, load demand, fuel price for diesel generation and electricity tariff are considered, through the definition of scenarios. The conditional value-at-risk (CVaR) metric is used in the optimization problem to consider the consumer’s risk propensity. The model determines the number and type of PV panels, diesel generation, and battery storage capacities, in which the objective is to minimize investment and operating costs over the planning horizon. Case studies involving a large commercial consumer are carried out to evaluate the proposed model. Results showed that under normal conditions only the PV system is viable. The PV/diesel system tends to be viable in adverse hydrological conditions for risk-averse consumers. Under this condition, the PV/battery system is viable for a reduction of 87% in the battery investment cost. An important conclusion is that the risk analysis tool is essential to assist consumers in the decision-making process of investing in HEES.

Suggested Citation

  • Daniel Kitamura & Leonardo Willer & Bruno Dias & Tiago Soares, 2023. "Risk-Averse Stochastic Programming for Planning Hybrid Electrical Energy Systems: A Brazilian Case," Energies, MDPI, vol. 16(3), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1463-:d:1054926
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    References listed on IDEAS

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