Microgrids are small, self-controlled systems embedding distributed energy resources and loads, which can be operated in either grid-connected or islanded mode. Existing microgrids often adopt AC systems, but the growing penetration of DC loads, DC-based renewable energy sources and DC-based energy storage has recently drawn interest in DC microgrids. Although DC microgrids have become an attractive option thanks to the increasing use of DC technology, nevertheless, in a fast evolving technology and policy framework, the variety of energy applications hinders the definition of clear and reproducible standards for DC distribution. In this situation, hybrid AC/DC microgrids -aggregating distributed energy resources and loads into distinct AC and DC sub- microgrids, tied together by bidirectional interlinking converters - represent a more likely architecture. Hybrid microgrids can be a cost-effective solution to supply affordable and reliable electricity to rural and remote communities, given their unique feature of using locally available generation resources (such as solar, wind, water stream and biomass) to supply the specific demand needs.
The aim of this project is to introduce hybrid AC–DC Microgrids in the future distribution networks to utilise both benefits of alternative and direct currents.
An operational framework is proposed to minimise the operational cost by receiving maximum power from renewable distributed resources, minimising power transfer between AC and DC links, and controlling the voltage variation of both AC and DC subgrids. A mixed integer linear model is also suggested to balance the generation and load considering the interconnection of AC and DC subgrids for minimising total operational cost of the hybrid microgrid.
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. Essential: Power system analysis and optimisation techniques and a good software knowledge in GAMS and MATLAB.