Electrical model-free voltage calculations using neural networks and smart meter data
The proliferation of residential technologies such as photovoltaic (PV) systems and electric
vehicles can cause voltage issues in low voltage (LV) networks. During operation, voltage …
vehicles can cause voltage issues in low voltage (LV) networks. During operation, voltage …
A data-driven global sensitivity analysis framework for three-phase distribution system with PVs
Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and
load variations plays an important role in designing optimal voltage control schemes. This …
load variations plays an important role in designing optimal voltage control schemes. This …
Predicting Voltage Changes in Low-Voltage Secondary Networks using Deep Neural Networks
High penetrations of residential solar PV can cause voltage issues on low-voltage (LV)
secondary networks. Distribution utility planners often utilize model-based power flow …
secondary networks. Distribution utility planners often utilize model-based power flow …
Voltage Calculations in Secondary Distribution Networks via Physics-Inspired Neural Network Using Smart Meter Data
The increasing penetration of distributed energy resources (DERs) leads to voltage issues
across distribution networks, necessitating voltage calculations by utilities. Electric model …
across distribution networks, necessitating voltage calculations by utilities. Electric model …
Co-Simulation of a cellular energy system
The concept of cellular energy systems of the German Association for Electrical, Electronic &
Information Technologies (VDE) proposes sector coupled energy networks for energy …
Information Technologies (VDE) proposes sector coupled energy networks for energy …
Towards a universally applicable neural state estimation through transfer learning
With the expansion of renewable energies, more grid transparency is necessary in order to
continue to guarantee a stable grid operation. In transmission grids, state estimation has …
continue to guarantee a stable grid operation. In transmission grids, state estimation has …
Accelerating energy-economic simulation models via machine learning-based emulation and time series aggregation
AJ Bogensperger, Y Fabel, J Ferstl - Energies, 2022 - mdpi.com
Energy-economic simulation models with high levels of detail, high time resolutions, or large
populations (eg, distribution networks, households, electric vehicles, energy communities) …
populations (eg, distribution networks, households, electric vehicles, energy communities) …
Sampling strategies for static powergrid models
Machine learning and computational intelligence technologies gain more and more
popularity as possible solution for issues related to the power grid. One of these issues, the …
popularity as possible solution for issues related to the power grid. One of these issues, the …
Sample-Efficient Learning for a Surrogate Model of Three-Phase Distribution System
A surrogate model that accurately predicts distribution system voltages is crucial for reliable
smart grid planning and operation. This letter proposes a fixed-point data-driven surrogate …
smart grid planning and operation. This letter proposes a fixed-point data-driven surrogate …
[PDF][PDF] Deliverable 0: Concept, Smart Meter Data, and Initial Findings
This report provides the foundations of the electrical model-free approach to calculate
voltages proposed by the University of Melbourne within the context of the Project “Model …
voltages proposed by the University of Melbourne within the context of the Project “Model …