4.7 Article

A Sensitivity Analysis Toolkit for the Simplification of MV Distribution Network Voltage Management

Journal

IEEE TRANSACTIONS ON SMART GRID
Volume 5, Issue 2, Pages 559-568

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2014.2300146

Keywords

Load flow; power distribution; reactive power control; sensitivity analysis; smart grids; voltage control

Funding

  1. Australian Power Quality and Reliability Centre

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As distribution networks become increasingly variable and generation becomes increasingly decentralised, voltage standards are becoming more liable to violation. Accordingly, intelligent voltage management strategies are required to enable standards compliance without unduly increasing network maintenance or infrastructure costs. The relationship between network power and voltages, however, is complex, non-linear and interdependent, and thus is difficult to conceptualise and use for decision-making and control purposes. This paper introduces a software toolkit that simplifies the development of voltage management strategies by the application of sensitivity analysis. Sensitivity analysis reduces complex network P-Q-vertical bar V vertical bar relationships to simple linear equations, and thus enables easy and comprehensive conceptualisation of the effect of network modifications. Sensitivity data is obtained through a unique 'perturb-and-observe' algorithm built on top of an open-source simulation package. The toolkit enables the rapid development of sensitivity-driven network experimentation and is highly extensible. A number of applications that demonstrate the usefulness of these techniques are presented, including the development of a reactive power control algorithm for the mitigation of inverter-based, distributed generation (DG)-induced voltage rise, and the verification of voltage-support-capacitor-bank placement for a real Australian semi-rural network. Finally, this paper presents some suggestions for possible future applications of data-driven, simulator-augmented sensitivity analysis techniques.

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