2016
DOI: 10.1016/j.egypro.2015.12.350
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Joint Modelling of Wind Power and Hydro Inflow for Power System Scheduling

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Cited by 9 publications
(5 citation statements)
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“…To account for correlations in time and between the variables in the multivariate time series, a vector auto-regressive model of order one (VAR(1)) was fitted to the data, assuming the seasonally adjusted data to be weekly stationary. VAR models have been found to give improved descriptions of inflow in systems with correlations between inflow and wind [48]. A general VAR(p) model is given in Eq.…”
Section: Markov Model and Scenarios Samplingmentioning
confidence: 99%
“…To account for correlations in time and between the variables in the multivariate time series, a vector auto-regressive model of order one (VAR(1)) was fitted to the data, assuming the seasonally adjusted data to be weekly stationary. VAR models have been found to give improved descriptions of inflow in systems with correlations between inflow and wind [48]. A general VAR(p) model is given in Eq.…”
Section: Markov Model and Scenarios Samplingmentioning
confidence: 99%
“…Uncertainty is considered for inflow, wind power generation and electricity demand from households (temperature dependent). Serial-and cross-correlations in the stochastic variables are accounted for by the use of a vector auto-regressive model of order one (VAR(1)) to draw scenarios [18]. Each scenario consist of 52 weekly values for each of the stochastic variables.…”
Section: A Representation Of Uncertaintymentioning
confidence: 99%
“…A limited range of additional services aimed at the hydropower sector are offered by different semi-commercial vendors. For example, SINTEF Energy Research have developed a market simulation model, to support hydro scheduling in competitive electricity markets [56], where information/forecasts/projections of, e.g., wind and hydropower, are drawn from time series data. A similar tool and associated services are offered by vendor Thomson Reuters Point Carbon, who have developed a system based on 23 regional hydrological models [57].…”
Section: Current Climate Service Suppliersmentioning
confidence: 99%