Spatiotemporal Splitting of Distribution Networks Into Self-Healing Resilient Microgrids Using an Adjustable Interval Optimization

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Farhad Samadi Gazijahani (Autor) Outra; Javad Salehi (Autor) Outra; Miadreza Shafie-khah (Autor) Outra; João P. S. Catalão (Autor) FEUP

Revista

Título: IEEE Transactions on Industrial Informatics

Vol. 17

Páginas: 5218-5229

ISSN: 1551-3203

Editora: IEEE

Indexação

ISI Web of Knowledge - 7 Citações

Scopus - 11 Citações

Outras Informações

ID Authenticus: P-00T-VY7

DOI: 10.1109/tii.2020.3033560

Abstract (EN): The distribution networks can convincingly break down into small-scale self-controllable areas, namely microgrids (mu G), to substitute mu Gs arrangements for effectively coping with perturbations. This flexible structure not only could potentially possess the strength to recover quickly, but also ensures the supply of vital loads and preserves functionalities under any contingency. To achieve these targets, this article examines a novel spatiotemporal algorithm to split the existing network into a set of self-healing mu Gs. In this endeavor, after designing the mu Gs by determining a mix of heterogeneous generation resources and allocating remotely controlled switches, the mu Gs operational scheduling is decomposed into interconnected and islanded modes. The main intention in the grid-tied state is to maximize the mu Gs profit while equilibrating load and generation at the islanded state by sectionalizing on-fault area, executing resources rescheduling, network reconfiguration and load shedding when the main grid is interrupted. The proposed problem is formulated as an exact computationally efficient mixed integer linear programming problem relying on the column & constraint generation framework and an adjustable interval optimization is envisaged to make the mu Gs less susceptible against renewables variability. Finally, the effectiveness of the proposed model is adequately assured by performing a realistic case study.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12