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

Daniel Kitamura, Leonardo Willer, Bruno Dias and Tiago Soares ()
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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

Energies, 2023, vol. 16, issue 3, 1-16

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.

Keywords: hybrid electrical energy system; stochastic programming; risk analysis; optimization; renewable energy sources (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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