A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty
This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain amore »
- Publication Date:
- OSTI Identifier:
- Report Number(s):
- DOE Contract Number:
- Resource Type:
- Resource Relation:
- Conference: IEEE Power and Energy Society General Meeting (PES 2013), July 21-25, 2013, Vancouver, BC, Canada
- IEEE, Piscataway, NJ, United States(US).
- Research Org:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
- Sponsoring Org:
- Country of Publication:
- United States
- wind integration, wind forecast error, load forecast error, power generation planning, stochastic simulation, wind statistics, load forecast statistics.