Integrated risk assessment for robustness evaluation and resilience optimisation of power systems after cascading failures
Resumen: Power systems face failures, attacks and natural disasters on a daily basis, making robustness and resilience an important topic. In an electrical network, robustness is a network’s ability to withstand and fully operate under the effects of failures, while resilience is the ability to rapidly recover from such disruptive events and adapt its structure to mitigate the impact of similar events in the future. This paper presents an integrated framework for jointly assessing these concepts using two complementary algorithms. The robustness model, which is based on a cascading failure algorithm, quantifies the degradation of the power network due to a cascading event, incorporating the circuit breaker protection mechanisms of the power lines. The resilience model is posed as a mixed-integer optimisation problem and uses the previous disintegration state to determine both the optimal dispatch and topology at each restoration stage. To demonstrate the applicability of the proposed framework, the IEEE 118-bus test network is used as a case study. Analyses of the impact of variations in both generation and load are provided for 10 simulation scenarios to illustrate different network operating conditions. The results indicate that a network’s recovery could be related to the overload capacity of the power lines. In other words, a power system with high overload capacity can withstand higher operational stresses, which is related to increased robustness and a faster recovery process.
Idioma: Inglés
DOI: 10.3390/en14072028
Año: 2021
Publicado en: Energies 14, 7 (2021), 2028 [18 pp.]
ISSN: 1996-1073

Factor impacto JCR: 3.252 (2021)
Categ. JCR: ENERGY & FUELS rank: 80 / 119 = 0.672 (2021) - Q3 - T3
Factor impacto CITESCORE: 5.0 - Engineering (Q1) - Mathematics (Q1) - Energy (Q2)

Factor impacto SCIMAGO: 0.653 - Energy (miscellaneous) (Q1) - Energy Engineering and Power Technology (Q1) - Fuel Technology (Q1) - Control and Optimization (Q1) - Engineering (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2019-104711RB-100
Tipo y forma: Article (Published version)
Área (Departamento): Área Ingeniería Eléctrica (Dpto. Ingeniería Eléctrica)

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