research-article

Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources

Authors:

Haibo HeAuthors Info & Claims

Neurocomputing, Volume 170, Issue C

Pages 406 - 416

https://doi.org/10.1016/j.neucom.2015.04.092

Published: 25 December 2015 Publication History

Abstract

Increasing deployment of intermittent power generation from renewable resources in the smart grid, such as photovoltaic (PV) or wind farm, will cause large system frequency fluctuation when the load-frequency control (LFC) capacity is not enough to compensate the unbalance of generation and load demand. Even worse, the system inertia will decrease when the smart grid is in island operating mode, which would degrade system damping and cause system instability. Meanwhile, electric vehicles (EVs) will be widely used by customers in the near future, where the EV station could be treated as dispersed battery energy storage. Therefore, the vehicle-to-grid (V2G) technology can be employed to compensate for inadequate LFC capacity, thus improving the island smart grid frequency stability. In this paper, an on-line reinforcement learning (RL) based method, called goal representation adaptive dynamic programming (GrADP), is employed to adaptive control of units in an island smart grid. In the controller design, adaptive supplementary control signals are provided to proportional-integral-derivative (PID) controller by GrADP in a real-time manner. Comparative simulation studies on a benchmark smart grid with micro-turbine (MT), EVs, PV array and wind power are carried out among the GrADP controller, the original PID controller and the particle swarm optimization (PSO) based fuzzy logic controller. Simulation results demonstrate competitive performance and satisfied learning ability of the GrADP based coordinate controller. Moreover, the impact of signal transmission delay on the control performance is also considered, and suggestions to address this issue are given in the paper.

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Cited By

Tong XMa DWang RXie XZhang H(2023)Dynamic Event-Triggered-Based Integral Reinforcement Learning Algorithm for Frequency Control of Microgrid With Stochastic UncertaintyIEEE Transactions on Consumer Electronics10.1109/TCE.2023.324168469:3(321-330)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TCE.2023.3241684
Qadir ZEver EBatunlu C(2021)Use of Neural Network Based Prediction Algorithms for Powering Up Smart Portable AccessoriesNeural Processing Letters10.1007/s11063-020-10397-353:1(721-756)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s11063-020-10397-3
Zhong QSun YPeng L(2018)A Novel Control Strategy on Multiple-Mode Application of Electric Vehicle in Distributed Photovoltaic SystemsComplexity10.1155/2018/16403952018Online publication date: 11-Jul-2018
https://dl.acm.org/doi/10.1155/2018/1640395
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Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources
1. Information systems
  1. Information systems applications
    1. Process control systems

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cover image Neurocomputing

Neurocomputing Volume 170, Issue C

December 2015

466 pages

ISSN:0925-2312

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 25 December 2015

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Cited By

Tong XMa DWang RXie XZhang H(2023)Dynamic Event-Triggered-Based Integral Reinforcement Learning Algorithm for Frequency Control of Microgrid With Stochastic UncertaintyIEEE Transactions on Consumer Electronics10.1109/TCE.2023.324168469:3(321-330)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TCE.2023.3241684
Qadir ZEver EBatunlu C(2021)Use of Neural Network Based Prediction Algorithms for Powering Up Smart Portable AccessoriesNeural Processing Letters10.1007/s11063-020-10397-353:1(721-756)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s11063-020-10397-3
Zhong QSun YPeng L(2018)A Novel Control Strategy on Multiple-Mode Application of Electric Vehicle in Distributed Photovoltaic SystemsComplexity10.1155/2018/16403952018Online publication date: 11-Jul-2018
https://dl.acm.org/doi/10.1155/2018/1640395
Li XYan H(2018)Fuzzy logic-based coordinated control method for multi-type battery energy storage systemsArtificial Intelligence Review10.1007/s10462-016-9523-549:2(227-243)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s10462-016-9523-5
Liu WMu CWang DLuo CRen C(2017)Neural adaptive control of microgrid frequency regulation with wind powerIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society10.1109/IECON.2017.8217305(7451-7456)Online publication date: 29-Oct-2017
https://dl.acm.org/doi/10.1109/IECON.2017.8217305
Wei YVenayagamoorthy G(2017)Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power systemNeural Networks10.1016/j.neunet.2017.03.00893:C(21-35)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.neunet.2017.03.008
Malla NShrestha DNi ZTonkoski R(undefined)Supplementary control for virtual synchronous machine based on adaptive dynamic programming2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7744033(1998-2005)
https://dl.acm.org/doi/10.1109/CEC.2016.7744033

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