Adaptive Supervisory Control Scheme for Voltage Controlled Demand Response in Power Systems

  • Etimbuk Abraham

Student thesis: Phd


Radical changes to present day power systems will lead to power systems with a significant penetration of renewable energy sources and smartness, expressed in an extensive utilization of novel sensors and cyber secure Information and Communication Technology. Although these renewable energy sources prove to contribute to the reduction of CO2 emissions into the environment, its high penetration affects power system dynamic performance as a result of reduced power system inertia as well as less flexibility with regards to dispatching generation to balance future demand. These pose a threat both to the security and stability of future power systems. It is therefore very important to develop new methods through which power system security and stability can be maintained. This research investigated the development of methods through which the contributions of on-load tap changing transformers/transformer clusters could be assessed with the intent of developing real time adaptive voltage controlled demand response schemes for power systems. The development of such a scheme enables more active system components to be involved in the provision of frequency control as an ancillary service and deploys a new frequency control service with low infrastructural investment, bearing in mind that OLTC transformers are already very prevalent in power systems. In this thesis, a novel online adaptive supervisory controller for ensuring optimal dispatch of voltage-controlled demand response resources is developed. This novel controller is designed using the assessment results of OLTC transformer impacts on steady-state frequency and was tested for a variety of scenarios. To achieve the effective performance of the adaptive supervisory controller, the extensive use of statistical techniques for assessing OLTC transformer contributions to voltage controlled demand response is presented. This thesis also includes the use of unsupervised machine learning techniques for power system partitioning and the further use of statistical methods for assessing the contributions of OLTC transformer aggregates.
Date of Award1 Aug 2018
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorJoseph Mutale (Supervisor) & Vladimir Terzija (Supervisor)


  • principal component analysis
  • Frequency control
  • voltage-controlled demand response
  • model predictive control
  • structural equation modeling
  • clustering

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