Advanced Network Management Systems for Low Carbon Smart Distribution Networks (FULLY FUNDED)

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. This is key for the industry as it will allow defining the most suitable approaches and infrastructure needed to avoid costly reinforcements.

What novelty will the student base their PhD on?
This work will be unique in that it will not only consider sophisticated real-time control approaches to manage the complex nature of future Smart Distribution Networks but will also consider the pre-control processing of network information (known as state estimation) to ensure a realistic consideration of monitoring errors and limited network observability. Furthermore, to ensure a realistic environment for the actual deployment of the investigated control approaches, this work will implement and test them on the state-of-the-art Hardware-In-the-Loop laboratory at Manchester, particularly using the large-scale Real-Time Digital Simulator (RTDS).

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. Understanding the corresponding interactions between observability and control will help identifying the true requirements for monitoring and uncertainty. The large-scale Real-Time Digital Simulator (RTDS), part of the state-of-the-art Hardware-In-the-Loop laboratory at Manchester, will also be used to implement and test the different control approaches so as to ensure a realistic, industry-level environment for their potential deployment. Finally, this project will draw guidelines and recommendations on the most suitable approaches and infrastructure needed to avoid costly reinforcements.

Outline Proposed Project Plan:
Year 1: Taught courses and preparatory study
Year 2: Based on the literature review produced in Year 1 and the initial familiarisation with the modelling software (AIMMS and OpenDSS) and the work carried out by Dr Ochoa’s research team, 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. Familiarisation with the RTDS and initial implementation of the approaches will also be carried out.
Year 4: This year will be used to refine the results from year 3 in terms of potential LCT scenarios and the ‘life-span’ of Smart Grid schemes as opposed to traditional reinforcements. Results and deployment aspects from the final RTDS implementation will also be analysed. This year also considers the writing of the PhD thesis.