A model predictive control method to optimize voltages for active distribution networks with electric vehicles

Duan, R.; Ding, Chaofan; Gao, C.; Yu, M.

doi:10.1049/icp.2022.1645

Cited by 1 publication

(1 citation statement)

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“…[17], [18]. When the traditional static load models are not sufficient to represent the load behavior, the alternative dynamic load models are needed [19], [20]. The study of this model allows to have a very detailed idea on the network in real time [21].…”

Section: Introductionmentioning

confidence: 99%

Advanced dynamic stability system developed for nonlinear load

Abderrazak,

Ilyas,

Sofiane

et al. 2023

IJPEDS

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<span lang="EN-US">The conventional production of electrical energy gives rise to environmental problems and involves high production costs. To cope with the increasing demand for electrical loads and to optimize power transmission on the grid, it is imperative to shift towards renewable energies and their hybrid utilization. This planning study assesses the impact of fluctuating electric loads on bus voltages and frequency, with the objective of distributing energy in a more efficient and dynamic manner. Moreover, we delve into the implementation of artificial intelligence (IAPS) to curtail operations, minimize losses (GED), optimize production plans (EMSO), and synchronize decentralized multi-machine power systems (MMSA). Our study scrutinizes power dispatching on dynamic models of purely consuming loads, which are subject to dynamic constraints, by utilizing educational software that encompasses 13 cycles for 24 hours of frequency studies for multisource production in a standardized electrical system (EPSS). The control outcomes obtained through validation on an IEEE30 bus network display a resilient model for planned productions.</span>

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