Today’s spread of power distribution networks, with the installation of a significant number of renewable generators that depend on environmental conditions and on users’ consumption profiles, requires sophisticated models for monitoring the power flow, regulating the electricity market, and assessing the reliability of power grids. Such models cannot avoid taking into account the variability that is inherent to the electrical system and users’ behavior. In this paper, we present a solution for the generation of a compressed surrogate model of the electrical state of a realistic power network that is subject to a large number (on the order of a few hundreds) of uncertain parameters representing the power injected by distributed renewable s...
This paper presents a comprehensive approach to the probabilistic analysis of residential distributi...
This paper introduces the basis-adaptive sparse polynomial chaos (BASPC) expansion to perform the pr...
Infrastructure systems are complex networks with inherent sources of uncertainty. Optimal operation ...
This paper deals with the application of the support vector machine (SVM) and the least-squares SVM ...
This paper aims to present a general-purpose Surrogate Model for the probabilistic analysis of power...
As new services and business models are being associated with the power distribution network, it bec...
This paper presents an innovative modeling strategy for the construction of efficient and compact su...
The simulation of uncertainties due to renewable and load forecasts is becoming more and more import...
This paper investigates residential distribution networks with uncertain loads and photovoltaic dist...
In this paper, the authors apply a surrogate model-based method for probabilistic power flow (PPF) i...
In power systems modelling, optimization methods based on certain objective function(s) are widely u...
The integration of distributed energy resources and increasing adoption of electric vehicles continu...
Some applications of deep learning require not only to provide accurate results but also to quantify...
Uncertainties in load and renewable generations impose new challenges on the operation of distributi...
This paper presents a comprehensive approach to the probabilistic analysis of residential distributi...
This paper introduces the basis-adaptive sparse polynomial chaos (BASPC) expansion to perform the pr...
Infrastructure systems are complex networks with inherent sources of uncertainty. Optimal operation ...
This paper deals with the application of the support vector machine (SVM) and the least-squares SVM ...
This paper aims to present a general-purpose Surrogate Model for the probabilistic analysis of power...
As new services and business models are being associated with the power distribution network, it bec...
This paper presents an innovative modeling strategy for the construction of efficient and compact su...
The simulation of uncertainties due to renewable and load forecasts is becoming more and more import...
This paper investigates residential distribution networks with uncertain loads and photovoltaic dist...
In this paper, the authors apply a surrogate model-based method for probabilistic power flow (PPF) i...
In power systems modelling, optimization methods based on certain objective function(s) are widely u...
The integration of distributed energy resources and increasing adoption of electric vehicles continu...
Some applications of deep learning require not only to provide accurate results but also to quantify...
Uncertainties in load and renewable generations impose new challenges on the operation of distributi...
This paper presents a comprehensive approach to the probabilistic analysis of residential distributi...
This paper introduces the basis-adaptive sparse polynomial chaos (BASPC) expansion to perform the pr...
Infrastructure systems are complex networks with inherent sources of uncertainty. Optimal operation ...