Transparent Management of Dynamically Evolving Data in Smart Local Energy Systems

Abstract

Smart Energy Systems represent a shift from vertically oriented approaches within the limits of sub-sectors / infrastructures in the area of generation, transmission and distribution of electricity into an integrated approach that leads to coherent sustainable energy systems cite{LUND2017556}. As a result of this transition, new subsystems such as buildings that both generate and consume energy, a transport and mobility system incorporating electric vehicles, community or individual energy generation from renewable sources and engagement also become part of the energy system. The enhanced scope resulted in the emergence of a diversified way of describing data pertaining to the system elements. When the structure of data and system elements within such an energy system exhibit dynamic behaviour (e.g., by adding new elements, new or changed data formats, etc.) maintaining a consistent functionality of dependant services becomes a challenge. This is particularly relevant for functionality delivered through distributed ledger technologies, where any recorded data (including its structure and format) must remain unchanged to be processable. The elements of the distributed system that handle data collection and processing can utilise a semantic layer to help them decouple the consistent service delivery from the evolving, dynamic state of the underlying data sources. In this thesis, the design and development aspects of such a layer - a distributed middleware system called Prototype-B - which supports consistent functionality of services in distributed system applications, despite the presence of ongoing evolutionary change and heterogeneously described data, are presented. Prototype-B does this by transparently maintaining a dynamic ontology, that hierarchically arranges semantic structures abstractly describing elements of the system, oversees their evolution, and validates datasets against them. When a change occurs, Prototype-B validates previously used and newly suggested datasets against the agreed semantic structures. Finally, when requested, it helps services and applications identify required data sources and obtain data from them

Date of Award	3 Oct 2023
Original language	English
Awarding Institution	University of Bristol
Supervisor	Ruzanna Chitchyan (Supervisor)