Risk-averse stochastic dual dynamic programming approach for the operation of a hydro-dominated power system in the presence of wind uncertainty

José L. Morillo, Luckny Zéphyr, Juan Fernando Pérez Bernal, C. Lindsay Anderson, Ángela Cadena

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    15 Scopus citations


    Operation planning models for hydro-dominated power systems usually use low temporal resolutions due to the excessive computational burden, thus ignoring short-term characteristics of such systems. As a result, in systems coupled with wind energy, such models may fail to accurately capture wind variability, and may not appropriately take into account potential consequences of uncertainty on the system operation. This paper addresses this drawback by (i) “controlling” the cost associated with the operation of a hydro-dominated power system equipped with wind power and batteries via a risk-measure and (ii) formulating the short-term load balance as probabilistic constraints in order to hedge against potential extreme wind power scenarios. The risk-averse scheme is embedded in the stochastic dual dynamic programming framework. Simulation results for a case study on a real industrial setting show that hedging the system against the short-term volatility of wind power contributes to mitigating the risk of excessive operations costs or load curtailments, and that the consideration of the decision maker risk profile contributes to decreasing the variability of the solutions. In addition, the results of the application also illustrate the potential of the scheme to assess the energy situation of a country or a region under the penetration of wind energy and batteries deployment.

    Translated title of the contributionEnfoque de programación dinámico dual estocástico aversivo al riesgo para la operación de un sistema de energía dominado por hidroelectricidad en presencia de incertidumbre del viento
    Original languageEnglish (US)
    Article number105469
    JournalInternational Journal of Electrical Power and Energy Systems
    StatePublished - Feb 1 2020

    All Science Journal Classification (ASJC) codes

    • Energy Engineering and Power Technology
    • Electrical and Electronic Engineering


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