Spinning Reserve Assessment in Multiarea Systems via Monte Carlo Simulation and CE Method

Castro, Jose F. Costa; Silva, Armando M. Leite da; Rosa, Mauro; Vieira, Pedro Cesar Cordeiro

doi:10.1109/pmaps.2018.8440533

Cited by 2 publications

(2 citation statements)

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“…The cross-entropy method is a well-documented adaptive importance sampling method and has been extensively used in power system reliability studies [18][19][20][21][22]32]. It succeeds by calibrating the proposal density from an assumptive prototype in a data-driven manner [33].…”

Section: Adaptive Importance Sampling Based On Cross-entropymentioning

confidence: 99%

“…Note, the spinning reserve risk evaluation falls into the scope of rare-event evaluation of a high-dimensional system, such a special issue makes importance sampling techniques appealing to conducting the evaluation task. Indeed, adaptive importance sampling methods accounting for generating unit force outage and statistical fluctuation of wind power and load demand are proposed to deal with the spinning risk evaluation [18][19][20][21][22]. However, these methods are developed without considering implicit kinds of random variable correlation in a spinning reserve planning issue.…”

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Tracing spinning reserve inadequacy risk via hybrid importance sampling with an optimised partially collapsed Gibbs sampler

Wang

2021

IET Renewable Power Gen

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From the simulation perspective, spinning reserve risk evaluation of power system is commonly a rare-event assessing issue, for which importance sampling is an appealing solution technique. This paper proposes a hybrid importance sampling method for tracing spinning reserve inadequacy risk of power system integrating with renewables. The proposed method is oriented towards a generic high-dimensional integral calculus, which allows for continuous and discrete random variables mixed together for casting kinds of risk indices. To improve the robustness in high-dimensional applications, a partially collapsed Gibbs sampler is devised to help in exploring adaptive training samples which feature typical rare outage events. Interestingly, the originators engendering extreme spinning reserve inadequacy events can be traced using the sampling information of the proposed method. The performance of the proposed method is tested in an updated RTS-79 system vis-à-vis several existing methods. Spinning reserve inadequacy risk tracing is explained by uncovering a scenario frequency distribution concerning the short-term expected energy not supplied. INTRODUCTIONNowadays, the power systems have evolved to be an indispensable facility of great complexity to support civil life and social development. The increasing complexity primarily intended to be adapted for customer needs and electric markets and newly adopted techniques etc., commonly endows the contemporary power systems with basic defensive abilities against regional credible contingencies. However, we have already witnessed grave blackout consequences of complex power systems initiated by unpredictable rare contingencies, for example equipment decay or human-produced or natural hazards [1]. One recognised reason for spawning the observed rare initiators successful evolving into blackout essentially comes down to the lack of awareness of flexible operating reserve backing up very rare supply deficit.Operating reserve planning accounting for probabilistic factors can be traced back to 1963 [2], and since that period, the literature has documented several operating reserve planning schemes to economically hedging against outage risk, especially for renewable energy systems in recent passing years [3][4][5][6][7][8].This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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