1,728 research outputs found
The computational turn: thinking about the digital humanities
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Data generation and model usage for machine learning-based dynamic security assessment and control
The global effort to decarbonise, decentralise and digitise electricity grids in response to climate change and evolving electricity markets with active consumers (prosumers) is gaining traction in countries around the world. This effort introduces new challenges to electricity grid operation. For instance, the introduction of variable renewable energy generation like wind and solar energy to replace conventional power generation like oil, gas, and coal increases the uncertainty in power systems operation. Additionally, the dynamics introduced by these renewable energy sources that are interfaced through converters are much faster than those in conventional system with thermal power plants.
This thesis investigates new operating tools for the system operator that are data-driven to help manage the increased operational uncertainty in this transition. The presented work aims to an- swer some open questions regarding the implementation of these machine learning approaches in real-time operation, primarily related to the quality of training data to train accurate machine- learned models for predicting dynamic behaviour, and the use of these machine-learned models in the control room for real-time operation.
To answer the first question, this thesis presents a novel sampling approach for generating ’rare’ operating conditions that are physically feasible but have not been experienced by power systems before. In so doing, the aim is to move away from historical observations that are often limited in describing the full range of operating conditions. Then, the thesis presents a novel approach based on Wasserstein distance and entropy to efficiently combine both historical and ’rare’ operating conditions to create an enriched database capable of training a high- performance classifier. To answer the second question, this thesis presents a scalable and rigorous workflow to trade-off multiple objective criteria when choosing decision tree models for real-time operation by system operators. Then, showcases a practical implementation for using a machine-learned model to optimise power system operation cost using topological control actions. Future research directions are underscored by the crucial role of machine learning in securing low inertia systems, and this thesis identifies research gaps covering physics-informed learning, machine learning-based network planning for secure operation, and robust training datasets are outlined.Open Acces
Energy management and guidelines to digitalisation of integrated natural gas distribution systems equipped with expander technology
In a swirling dynamic interaction, digital innovation, environment and
anthropological evolution are swiftly shaping the smart grid scenario. Integration
and flexibility are the keywords in this emergent picture characterised by a low
carbon footprint. Digitalisation, within the natural limits imposed by the
thermodynamics, seems to offer excellent opportunities for these purposes. Of
course, here starts a new challenge: how digital technologies should be employed
to achieve these objectives? How would we ensure a digital retrofit does not lead to
a carbon emission increase? In author opinion, as long as it remains a generalised
question, none answer exists: the need to contextualise the issue emerges from the
variety of the characteristics of the energy systems and from their interactions with
external processes. To address these points, in the first part of this research, the
author presented a collection of his research contributions to the topic related to the
energy management in natural gas pressure reduction station equipped with turbo
expander technology. Furthermore, starting from the state of the art and the author's
previous research contributions, the guidelines for the digital retrofit for a specific
kind of distributed energy system, were outlined. Finally, a possible configuration
of the ideal ICT architecture is extracted. This aims to achieve a higher level of
coordination involving, natural gas distribution and transportation, local energy
production, thermal user integration and electric vehicles charging. Finally, the
barriers and the risks of a digitalisation process are critically analysed outlining in
this way future research needs
Distributed Governance: a Principal-Agent Approach to Data Governance -- Part 1 Background & Core Definitions
To address the need for regulating digital technologies without hampering
innovation or pre-digital transformation regulatory frameworks, we provide a
model to evolve Data governance toward Information governance and precise the
relation between these two terms. This model bridges digital and non-digital
information exchange. By considering the question of governed data usage
through the angle of the Principal-Agent problem, we build a distributed
governance model based on Autonomous Principals defined as entities capable of
choice, therefore capable of exercising a transactional sovereignty. Extending
the legal concept of the privacy sphere to a functional equivalent in the
digital space leads to the construction of a digital self to which rights and
accountability can be attached. Ecosystems, defined as communities of
autonomous principals bound by a legitimate authority, provide the basis of
interacting structures of increasing complexity endowed with a self-replicating
property that mirrors physical world governance systems. The model proposes a
governance concept for multi-stakeholder information systems operating across
jurisdictions. Using recent software engineering advances in decentralised
authentication and semantics, we provide a framework, Dynamic Data Economy to
deploy a distributed governance model embedding checks and balance between
human and technological governance. Domain specific governance models are left
for further publications. Similarly, the technical questions related to the
connection between a digital-self and its physical world controller (e.g
biometric binding) will be treated in upcoming publications.Comment: 27 pages, 20 figures, basis of presentation at University of Geneva's
lectures on Information Securit
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