Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning

dc.contributor.author Deng, Weisi
dc.contributor.author Ji, Yuting
dc.contributor.author Tong, Lang
dc.date.accessioned 2016-12-29T01:14:34Z
dc.date.available 2016-12-29T01:14:34Z
dc.date.issued 2017-01-04
dc.description.abstract The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators.
dc.format.extent 7 pages
dc.identifier.doi 10.24251/HICSS.2017.377
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41545
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Dictionary learning
dc.subject electricity market
dc.subject machine learning in power systems
dc.subject power flow distributions
dc.subject probabilistic price forecasting.
dc.title Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning
dc.type Conference Paper
dc.type.dcmi Text
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