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Adaptive robust optimization models for DER planning in distribution networks under long- and short-term uncertainties

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  • García-Muñoz, Fernando
  • Duran-Mateluna, Cristian

Abstract

This study introduces adaptive robust optimization (ARO) and adaptive robust stochastic optimization (ARSO) approaches to address long- and short-term uncertainties in the optimal sizing and placement of distributed energy resources in distribution networks. ARO models uncertainty using a budget of uncertainty (BoU), while ARSO distinguishes long-term (LT) demand (via BoU) and short-term (ST) photovoltaic generation (via scenarios). Adapted Benders decomposition and column-and-constraint generation algorithms are presented to tackle the tri-level optimization challenges. The experiments consider a modified version of the IEEE 33-bus system to test these two approaches and also compare them with traditional robust and stochastic optimization models. The results indicate that distinguishing between LT and ST uncertainties using a hybrid formulation, such as ARSO, yields a solution closer to the optimal solution under perfect information than ARO.

Suggested Citation

  • García-Muñoz, Fernando & Duran-Mateluna, Cristian, 2026. "Adaptive robust optimization models for DER planning in distribution networks under long- and short-term uncertainties," Applied Energy, Elsevier, vol. 412(C).
    • Handle: RePEc:eee:appene:v:412:y:2026:i:c:s0306261926002783
    DOI: 10.1016/j.apenergy.2026.127626
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