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Planning and Design of Distribution Networks with Integration of Smart Grid Technologies

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

About This PhD Project

Project Description

The UK Government’s target is to achieve 15% of electricity generation from renewable energy sources (RES) by 2020. The installation of large amounts of RES in distribution networks introduces several economic and technical challenges to distribution network operators (DNOs). Thus, DNOs should develop a rational operating approach considering dispatching distributed generators (DGs), interrupting loads, and purchasing power from the wholesale market subject to network constraints. DNOs play the retailers role which buy power on the wholesale market at volatile prices and sell it again at fixed tariffs to small consumers. However, active network management integration schemes, including coordinated voltage control of on load tap changers and adaptive power factor control of DGs, are advantageous for DNOs in comparison with the management of passive networks.

The aim of this project is to optimally plan, design and operate mesh and/or radial distribution networks within a market environment with high penetration of RES and energy storage systems using network reinforcement programmes and smart gird technologies. Big-Data Analytics techniques are utilised to model the uncertainties related to renewable energy sources and load demand.

The successful candidate should have (or expect to achieve) a minimum of a UK Honours degree at 2.1 or above (or equivalent) in relevant engineering discipline (e.g. Renewable Energy, Mechanical, Electrical, Power, Civil/Structural) or Applied Maths. Applicants are expected to have a good background knowledge in power system analysis, uncertainty modelling, and optimisation and Big-Data analytics techniques. Good software knowledge of MATLAB and GAMS, along with experience with power system analysis and optimisation techniques is essential.

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