Maximising wind farm connections : an investigation of novel voltage management and principles of access techniques in active distribution networks

  • Daniel Danzerl

Student thesis: Doctoral Thesis

Abstract

Power production from stochastic wind generators connected at the lower voltage levels on the distribution network where weak strength of the network prevails, can make voltage management particularly challenging for network operators. Distribution network operators (DNO's) tend to manage their networks conservatively by imposing stricter operational limits when deciding whether to allow generators to connect or not. Voltage rise limitation is a major barrier and impacts significantly on the capacities of generators that can be connected at lower voltage and weak distribution networks.This thesis develops and presents novel voltage control techniques which involves a coordination of active network management (ANM) technological solutions and principles of access (PoA) arrangements specific for wind farm connections managed under voltage constraint conditions. The proposed coordinated voltage control and PoA techniques effectively mitigate the voltage rise limitation and enhance the capacity of the network to connect more wind generation. The research presented assesses the performance of the strategy and it quantifies the benefit to the wind generators by using time-series optimal power flow methods on a realistic UK 11 kV distribution network.The proposed ANM solution provides DNO's alternative control options to address the voltage rise problem to facilitate cheaper and faster DG connections, deferring the need for costly and time-consuming reinforcement. It offers wind farm owners, exible options to minimise excessive curtailment by controlling their voltage at the point of connection. It provides a guide for suitable locations for future wind farm investment. It quantifies the risks and uncertainties associated with the different commercial arrangements and proposes alternative PoA suitable for voltage constrained networks.
Date of Award1 Oct 2017
LanguageEnglish
Awarding Institution
  • University Of Strathclyde
SponsorsEPSRC (Engineering and Physical Sciences Research Council)
SupervisorOlimpo Anaya-Lara (Supervisor) & (Supervisor)

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