Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning

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dc.contributor.authorDeng, Weisi
dc.contributor.authorJi, Yuting
dc.contributor.authorTong, Lang
dc.date.accessioned2016-12-29T01:14:34Z
dc.date.available2016-12-29T01:14:34Z
dc.date.issued2017-01-04
dc.description.abstractThe 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.extent7 pages
dc.identifier.doi10.24251/HICSS.2017.377
dc.identifier.isbn978-0-9981331-0-2
dc.identifier.urihttp://hdl.handle.net/10125/41545
dc.language.isoeng
dc.relation.ispartofProceedings of the 50th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDictionary learning
dc.subjectelectricity market
dc.subjectmachine learning in power systems
dc.subjectpower flow distributions
dc.subjectprobabilistic price forecasting.
dc.titleProbabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning
dc.typeConference Paper
dc.type.dcmiText

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