DC8: Advanced control for highly energy-efficient buildings and neighborhoods

Operating energy systems with increased penetration of variable renewable generation costeffectively and securely will be increasingly challenging and, in this context, the role of buildings and neighbourhoods at the distribution level is crucial. There is a growing need of non-conventional technologies that can support flexible and efficient grid operation. System Operators worldwide are increasingly aware that the number of significant losses and their absolute size will increase over the years, hence building and demand-side participation is essential for addressing this issue and bridging the gap to net zero. It is timely and essential to provide a methodology to assess the technical capability of building- and neighbourhood- demand and storage technologies to address the challenges related to the safe aggregation of a large number of simultaneous device responses and to the close coordination of the requirements of both the transmission and distribution network operators. Building upon existing expertise (e.g., [1-3]), the focus of this project is to devise and demonstrate a highly scalable learning-based distributed control framework for transforming buildings into positive energy neighbourhoods through advanced automation, which procures flexibility to support more efficient grid operation. Physics informed data-driven approached will be adopted, with consideration of the building geographical location, of the network connections and of the associated sources of uncertainty in both network and building operation, such as renewable power generation, inflexible demand and consumers’ patterns. The DC will join the Department of Electrical and Electronic Engineering and work in close collaboration with other doctoral students and colleagues working on relevant projects, such as the Supergen Energy Network Impact Hub.

Eligibility

Please, see https://dense-dn.eu/recruitment/ for all relevant information, paperwork and application procedure. The project is the DC8.

Funding

Please, see https://dense-dn.eu/recruitment/ for all relevant information, paperwork and application procedure. 

Before you apply

We strongly recommend that you contact the supervisor(s) for this project before you apply.

How to apply

Apply online through our website: https://dense-dn.eu/recruitment/

EDI

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).