000102230 001__ 102230
000102230 005__ 20230519145342.0
000102230 0247_ $$2doi$$a10.3390/en14072028
000102230 0248_ $$2sideral$$a124413
000102230 037__ $$aART-2021-124413
000102230 041__ $$aeng
000102230 100__ $$0(orcid)0000-0003-3992-4393$$aBeyza, Jesus$$uUniversidad de Zaragoza
000102230 245__ $$aIntegrated risk assessment for robustness evaluation and resilience optimisation of power systems after cascading failures
000102230 260__ $$c2021
000102230 5060_ $$aAccess copy available to the general public$$fUnrestricted
000102230 5203_ $$aPower 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.
000102230 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-104711RB-100
000102230 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000102230 590__ $$a3.252$$b2021
000102230 592__ $$a0.653$$b2021
000102230 594__ $$a5.0$$b2021
000102230 591__ $$aENERGY & FUELS$$b80 / 119 = 0.672$$c2021$$dQ3$$eT3
000102230 593__ $$aEnergy (miscellaneous)$$c2021$$dQ1
000102230 593__ $$aEnergy Engineering and Power Technology$$c2021$$dQ1
000102230 593__ $$aFuel Technology$$c2021$$dQ1
000102230 593__ $$aControl and Optimization$$c2021$$dQ1
000102230 593__ $$aEngineering (miscellaneous)$$c2021$$dQ1
000102230 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000102230 700__ $$0(orcid)0000-0003-3174-9703$$aYusta, Jose M.$$uUniversidad de Zaragoza
000102230 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000102230 773__ $$g14, 7 (2021), 2028 [18 pp.]$$pENERGIES$$tEnergies$$x1996-1073
000102230 8564_ $$s3093235$$uhttps://zaguan.unizar.es/record/102230/files/texto_completo.pdf$$yVersión publicada
000102230 8564_ $$s2714516$$uhttps://zaguan.unizar.es/record/102230/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000102230 909CO $$ooai:zaguan.unizar.es:102230$$particulos$$pdriver
000102230 951__ $$a2023-05-18-13:16:37
000102230 980__ $$aARTICLE