J.L. Cremer
18 records found
1
Predicting Transformer Tap-Positions using Graph Neural Networks
For Distribution Grids
Distributed energy resources challenge the situational awareness of power flows. Many distribution grid (DG) operators have not yet implemented state estimation (SE) due to the expense or privacy constraints of measurements that lead to an unobservable system, as well as inaccura
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State estimation (SE) plays a critical role as a prerequisite for grid control and operation. However, the increasing penetration of distributed energy resources (DERs) and integrated energy systems (IES) introduces new challenges—such as unreliable pseudo-measurements and time-v
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Cascading failures in power networks pose a significant threat, capable of escalating from isolated line outages to extensive blackouts with severe economic and societal impacts. The topic presents a probabilistic framework designed to assess and compute the risk of cascading fai
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Uncovering Sequential Social Dilemmas in Multi-Agent Reinforcement Learning
Challenges and Strategies for Local Energy Communities
This thesis investigates the occurrence and mitigation of Sequential Social Dilemmas (SSDs) in Local Energy Communities (LECs) managed through Multi-agent Reinforcement Learning (MARL). LECs have great potential as pivotal elements in the green energy transition, yet the inherent
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As the power system grows more complex and active, equivalent models have become a solution for modelling parts of the network that have limited observability or are confidential or too complex to simulate otherwise. In the past decade, this topic has also made its way to distrib
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The rapid shift toward renewable energy has positioned solar power as a key player in reducing carbon emissions. Yet, the inherent variability of solar irradiance, in particular abrupt fluctuations caused by local cloud movements, poses significant challenges for grid stability a
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To keep pace with increasing renewable energy penetration and consequent increase in inverter-based resources in the power grid, it is pertinent for present-day research to address the resulting drop in system inertia levels and its impact on frequency stability. With decreasing
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This work seeks to resolve an outstanding problem in the use of reinforcement-learning methods for the simulation of economically-rational agents. We discuss the problem of non-stationarity, and how this subsequently limits market simulation capabilities. After explicating and
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The growing demand for electricity, driven by widespread adoption of heat pumps, electric vehicles, and industrial electrification, strains power grids and introduces challenges for a reliable and secure supply amidst intermittent renewable energy integration. Network topology co
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In the electricity system, one barrier to the energy transition is the degradation of frequency stability due to the decrease of system inertia and frequency control ancillary services (FCAS), which is caused by the replacement of inertia-abundant and governor-based conventional
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The transition to green energy is reshaping the energy landscape, marked by increased integration of renewable energy sources, distributed resources, and the electrification of other energy sectors. These changes challenge grid security, particularly regarding the N-1 security cr
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Whereas in the past, Distribution Systems played a passive role in connecting customers to electricity, Distribution System Operators (DSOs) will have to take in the future a more active role in monitoring and regulating the network to deal with the new behaviors and dynamics of
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Detection and Classification of Faults in Residential PV Systems with a Synthetic PV Training Database
A Machine Learning-Based Approach Using the PVMD Toolbox to Generate Synthetic PV Yield Data
In this thesis, a new photovoltaic fault detection and classification method is proposed. It combines the generation of a synthetic photovoltaic training database and the use of a machine learning model to detect and classify faults in small-scale residential PV systems. The data
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With the increasing environmental concerns, the world is moving towards rapid decarbonization, and to meet the growing energy demand, more renewable energy sources like solar, Wind are getting added to the existing Distribution grid. The addition of new loads like Electric vehicl
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The growth of renewable energy technologies is leading to energy systems that are more reliant than ever on renewables such as Wind and Photovoltaic (PV) power. Despite their benefits in terms of sustainability, their ubiquity poses challenges in maintaining grid stability given
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Empirical battery degradation modelling
Similarities, differences and shortcomings of various models
Renewable energy sources, although they are quickly increasing their share in the energy mix, face a major barrier to more widespread adoption. Energy storage solutions overcome this hurdle, and lithium-ion batteries are at the forefront of this. The need for lithium-ion battery
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Short-term solar forecasting is crucial for large scale implementation of solar energy and plays an important role in grid balancing, energy trading, and power plant operation. Cloud movement is the main source of unpredictability within solar forecasting and can be recorded us
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Distribution System Operators (DSOs) are responsible preventing grid congestion, while accounting for growing demand and the intermittent nature of renewable energy resources. Incentive-based demand response programs promise real-time flexibility to relieve grid congestion. To in
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