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A robust optimisation framework in composite generation and transmission expansion planning considering inherent uncertainties

S.A. Mansouri, M.S. Javadi

Journal of Experimental & Theoretical Artificial Intelligence Vol. 29, nº. 4, pp. 717 - 730

Resumen:

This paper presents a robust optimisation framework for long-term composite generation and transmission expansion planning problem which considers inherent uncertainties such as load growth, fuel cost and renewable energy output uncertainties. In this paper, a bi-level robust optimisation framework is proposed to accommodate wind output uncertainty in line with the uncertain demanded loads and uncertain fuel cost. The addressed optimisation problem is modelled as a mixed-integer optimisation framework with the objective of providing a robust expansion plan while maintaining the minimum cost expansion. In order to evaluate the robustness of each plan, an off-line Lattice Monte Carlo simulation technique is adopted in this study. The validity of the proposed method is examined on a simple six-bus and modified IEEE 118-bus test system as a large-scale case study. The simulation results show that the presented method is both satisfactory and consistent with expectation.


Palabras Clave: Generation and transmission expansion planning; inherent uncertainties; off-line lattice Monte Carlo simulation; robust optimisation


Índice de impacto JCR y cuartil WoS: 1,011 - Q3 (2017); 2,200 - Q3 (2022)

Referencia DOI: DOI icon https://doi.org/10.1080/0952813X.2016.1259262

Publicado en papel: Julio 2017.

Publicado on-line: Noviembre 2016.



Cita:
S.A. Mansouri, M.S. Javadi, A robust optimisation framework in composite generation and transmission expansion planning considering inherent uncertainties. Journal of Experimental & Theoretical Artificial Intelligence. Vol. 29, nº. 4, pp. 717 - 730, Julio 2017. [Online: Noviembre 2016]


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