Using multi-vector demonstrators of energy modernisation
Lead Research Organisation:
Newcastle University
Department Name: Sch of Engineering
Abstract
It is not possible to fully understand complex evolving multi-vector energy systems through high-level simulation only, hence detailed high temporal resolution control system models are required alongside the real physical and engineering systems. It is yet unclear to what extent these models and the dynamic interactions between them can be trusted without backing them up with experimental emulation and real world demonstration. This project will investigate how much and in which ways can facilities improve modelling approaches or even help develop new ones (e.g. empirical models of battery degradation). It will couple existing models of sub-components and systems, develop new ones and demonstrate their value in novel future energy systems with particular focus on the co-evolution of supply and demand using experimental demonstrators. This can inform both modelling approaches of such systems but also processes like diagnoses and prognoses of their operation. It will also develop roadmaps of how to design such facilities in order to maximise their benefits and usefulness.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509528/1 | 01/10/2016 | 31/03/2022 | |||
| 1948735 | Studentship | EP/N509528/1 | 01/10/2017 | 30/06/2021 | Natalia Maria Zografou Barredo |
| Description | This award is still in progress. However, there are two key findings to this point. Both are associated with the techno-economic impact of modelling methods for energy network operation. More specifically, this research has created modelling methods for the resilient optimal scheduling of a type of networks called microgrids (these are small-scale energy networks), where: firstly, there is an improved accuracy regarding the incorporation of network losses, which we find has economic as well as operational benefits; and secondly, uncertainty is incorporated (e.g. in electrical demand) in a way with which the network can be operated prepared against any possible scenario of the uncertain data, while preserving a low cost and at the same time meet the customers energy needs. The benefits of these methods are currently being assessed beyond the optimal scheduling of microgrids. |
| Exploitation Route | This award is still in progress. However, the method developed up to this point, can be used for the operation of a smart grids, modernised energy networks or microgrids, and for future research. Therefore, the outcomes can be useful to a variety of applications and disciplines, some of which include: microgrid operators, distribution network/system operators, energy system designers, policy makers, market modellers, and scientists beyond the discipline of electrical engineering, such as mathematicians. |
| Sectors | Energy |