Intentional Controlled Islanding Research Articles

Intentional Controlled Islanding and Risk Assessment: A Unified Framework

Power systems are prone to cascading outages leading to large-area blackouts, and intentional controlled islanding (ICI) can mitigate these catastrophic events by splitting the system into sustainable islands. ICI schemes are used as the last resort to prevent cascading events; thus, it is critical to evaluate the corresponding system risks to ensure their correct operation. This paper proposes a unified framework to assess the risk of ICI schemes. First, a novel ICI method to create islands with minimum power imbalance is presented. Further, a risk assessment methodology is used to assess the probability and impact of the main operational modes of the ICI scheme. The unified framework provides insights on the benefits of implementing ICI, considering the uncertainties related to its reliability. The ICI scheme is demonstrated using the IEEE 9-bus system. The proposed unified framework is then fully deployed on the actual power system of Cyprus. Multiple case studies on the real network are created to demonstrate the adaptability and robustness of the proposed scheme to different system conditions. The adoption of the unified framework highlights that the system risk significantly reduces with the ICI in service, even when the reliability uncertainties associated with the scheme are considered.

Controlled Islanding with Special Consideration of Parallel Power System Restoration Constraints

Las islas controladas intencionales (ICI, siglas en inglés) pueden evitar apagones de gran extensión mediante la división del sistema eléctrico en islas después de una perturbación severa. No obstante, eventos posteriores a la creación de estas islas podrían conducir a inestabilidades que pueden resultar en apagones dentro de una o más islas. Por lo tanto, garantizar que estas se puedan restaurar en el caso de apagones locales es fundamental, sin embargo, esto no se ha abordado en la literatura. Para llenar este vacío, este artículo propone un método de ICI que considera junto a sus restricciones típicas, la restauración en paralelo del sistema de potencia (PPSR, siglas en inglés). Se considera la mínima interrupción del flujo de potencia garantizando la coherencia de generadores, pero se amplía para incluir al menos una unidad de arranque autógeno dentro de cada isla y para excluir distintas líneas de las posibles soluciones. El método cuantifica la generación de energía activa y reactiva disponible dentro de cada isla para determinar la máxima demanda que puede ser incrementada. Utilizando el método propuesto, el proceso derestauración puede facilitarse y acelerarse. Para demostrar la eficacia del método propuesto en la determinación de una solución de islas considerando PPSR, fueron realizadas simulaciones en dos sistemas de la IEEE con diferentes topologías y tamaños.

Constrained spectral clustering‐based methodology for intentional controlled islanding of large‐scale power systems

Intentional controlled islanding is an effective corrective approach to minimise the impact of cascading outages leading to large-area blackouts. This study proposes a novel methodology, based on `constrained spectral clustering', that is computationally very efficient and determines an islanding solution with minimal power flow disruption, while ensuring that each island contains only coherent generators. The proposed methodology also enables operators to constrain any branch, which must not be disconnected, to be excluded from the islanding solution. The methodology is tested using the dynamic models of the IEEE 39- and IEEE 118-bus test systems. Time-domain simulation results for different contingencies are used to demonstrate the effectiveness of the proposed methodology to minimise the impact of cascading outages leading to large-area blackouts. In addition, a realistically sized system (a reduced model of the Great Britain network with 815 buses) is used to evaluate the efficiency and accuracy of the methodology in large-scale networks. These simulations demonstrate that the author's methodology is more efficient, in a factor of approximately 10, and more accurate than another existing approach for minimal power flow disruption.