Advanced Network Management Systems for Low Carbon Smart Distribution Networks

Institution: University of Manchester

Dept/School/Faculty: EPSRC Centre for Doctoral Training in Power Networks

PhD Supervisor: Dr LN Ochoa

Application Deadline: Applications accepted all year round

Funding Availability: Funded PhD Project (European/UK Students Only)

Student background required: 

BSc degree in Electrical engineering with emphasis on Power Systems. Ideally, with an MSc (or MEng) degree in Power Systems-related areas or equivalent industrial experience. 

Benefit to / Impact on Industry: 
This project will explore the different Smart Grid control approaches that can be realistically implemented by Distribution Network Operators (DNOs) to optimally manage in real time multiple network elements and participants across voltage levels in order to allow high penetrations of low carbon technologies (LCTs: photovoltaics, wind farms, electric vehicles, etc.) considering uncertainties and limited observability. 

What novelty will the student base their PhD on? 
This work will consider sophisticated real-time control approaches to manage the complex nature of future Smart Distribution Networks and also the pre-control processing of network information (known as state estimation) to ensure a realistic consideration of monitoring errors and limited network observability.

Project overview:
The objective of this project is to develop comprehensive, realistic real-time optimal network management systems framework to allow the cost-effective integration of high penetrations of LCTs. Throughout this project sophisticated optimisation approaches and control strategies that could be used by future network manage systems to manage the complexity and uncertainties of multiple participants (generation and new loads) and as well as network devices (on-load tap changer-fitted transformers, reconfiguration switches, capacitor banks) will be investigated. Crucially, it will consider the pre-control processing of network information (known as state estimation) to ensure a realistic consideration of monitoring errors and limited network observability. 

Outline Proposed Project Plan:
Year 1: Taught courses and preparatory study 
Year 2: Year 2 will focus on the implementation of basic optimal control algorithms as well as state estimation methods. By the end of year 2, key control and state estimation approaches should be identified and tested in real HV and LV networks. 
Year 3: The interaction between the identified control and state estimation approaches will be thoroughly investigated considering different levels of monitoring and uncertainty in HV and LV networks operated (both separately and jointly). The latter will then be used to quantify the true requirements for monitoring and uncertainty as well as the corresponding benefits from such a comprehensive approach. 
Year 4: Refine potential LCT scenarios and the ‘life-span’ of Smart Grid schemes. Analyse results and deployment aspects from the final RTDS implementation. Write up thesis.

Funding Notes:

This project is funded by EPSRC, the University of Manchester and our Industry partners. Funding is available to UK candidates. EU candidates are also eligible if they have been studying or working continuously in the UK for three or more years (prior to the start date of the programme). The successful candidates will have their fees paid in full and will receive an enhanced maintenance stipend.

See here for information on how to apply and entry requirements: www.power-networks-cdt.manchester.ac.uk/study/projects-apply