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Optimal management of reactive power sources in far-offshore wind power plants

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conference contribution
posted on 13.07.2017 by A.M. Theologi, M. Ndreko, Jose L. Rueda, M.A.M.M. Van Der Meijden, Francisco Gonzalez-Longatt
This paper introduces a new approach for the optimal management of reactive power sources, which follows a predictive optimization scheme (i.e. day-ahead, intraday application). Predictive optimization is based to the principle of minimizing the real power losses, as well the number of On-load Tap Changer (OLTC) operations for 24 time steps ahead. The mixed-integer nature of the problem and the restricted computing budget is tackled by using an emerging metaheuristic algorithm called Mean-Variance Mapping Optimization (MVMO). The evolutionary mechanism of MVMO is enhanced by introducing a new mapping function, which improves its global search capability. The effectiveness of MVMO (i.e. fast convergence and robustness against randomness in initialization and factors used in evolutionary operations) and the achievement of optimal grid code compliance are demonstrated by investigating the case of a far-offshore wind power plant, interconnected with HVDC link.

Funding

This work was supported by Delft University of Technology.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Powertech 2017

Citation

THEOLOGI, A.M. ... et al, 2017. Optimal management of reactive power sources in far-offshore wind power plants. IEEE PowerTech 2017, Manchester, UK, 18th-22nd June 2017.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Acceptance date

01/05/2017

Publication date

2017

Notes

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Language

en

Location

Manchester UK

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