Publication

Stochastic optimal power flow with joint chance constraints using generalised polynomial chaos

2027

Abstract

This paper presents a novel approach for the approximation of multiple joint chance constraints. Generalised polynomial chaos is used to model uncertainties, allowing to consider non-Gaussian probability distributions for the input random variables of the system. The effectiveness of the presented approach for the treatment of both individual and joint chance constraints is demonstrated by solving a stochastic dispatching problem on IEEE benchmark systems. Additionally, the approach is compared to state-of-the-art methods for joint chance constraint optimization and current methods for the solution of individual non-Gaussian chance constraints using generalised polynomial chaos. Finally, challenges and limitations for the application of general polynomial chaos methods in real systems and applications are presented.

Official source

https://infoscience.epfl.ch/entities/publication/ecbbf29e-ba30-433f-be26-bcac5a22e769

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

2027

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/ecbbf29e-ba30-433f-be26-bcac5a22e769

About this result

Related concepts (32)

Normal distribution

In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is its standard deviation. The variance of the distribution is . A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.

Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.

Uncertainty quantification

Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known. An example would be to predict the acceleration of a human body in a head-on crash with another car: even if the speed was exactly known, small differences in the manufacturing of individual cars, how tightly every bolt has been tightened, etc.

Related publications (3)

Stochastic optimal power flow with joint chance constraints using generalised polynomial chaos

Chat with Graph Search

Euclid preparation: LXXXV. Toward a DR1 application of higher-order weak lensing statistics

Uncertainty in Causality: A New Frontier

Towards Provably Personalized Federated Learning via Threshold-Clustering of Similar Clients