Online Estimation of Plant Participation Factors for Automatic Generation Control in Power Systems with Variable Energy Resources

Online Estimation of Plant Participation Factors for Automatic Generation Control in Power Systems with Variable Energy Resources

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Parent link:IEEE Transactions on Industry Applications
Vol. ХХ, iss. Х.— 2022.— [10 p.]
Hlavní autor: Tsydenov E. A. Evgeny Aleksandrovich
Korporativní autor: Национальный исследовательский Томский политехнический университет Инженерная школа энергетики Отделение электроэнергетики и электротехники (ОЭЭ)
Další autoři: Prokhorov A. V. Anton Viktorovich, Wang Li
Shrnutí:Title screen
The paper discusses the limitations of existing automatic generation control systems that appear under the impact of variable energy resources. To overcome identified issues, the authors proposed an approach that advances the functional block responsible for computation of plant participation factors. This approach connects an optimizer with a component for power flow calculations and allows online estimation of plant participation factors to increase flexibility and selectivity of automatic generation control. The corresponding optimization models were established to perform conventional and advanced control strategies. To meet performance requirements imposed by variable energy sources, the machine learning model, namely the densely connected neural network, was designed for power flow calculations. Besides, Lasso regression method was proposed to select relevant features for the considered control tasks and improve the performance of the machine learning-based power flow model. Finally, the software tool was developed to implement the proposed approach and tested on a model of real 60 GW interconnection containing 464 nodes and 742 branches. The results of the software testing confirmed its feasibility and easy integration into existing automatic generation control systems.
Режим доступа: по договору с организацией-держателем ресурса
Jazyk:angličtina
Vydáno: 2022
Témata:
труды учёных ТПУ
электронный ресурс
automatic generation control
load flow
task analysis
optimization
power systems
adaptation models
databases
автоматическое управление
нагрузки
оптимизация
энергосистемы
адаптация
On-line přístup:https://doi.org/10.1109/TIA.2022.3174190
Médium: Elektronický zdroj Kapitola
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=668135

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