Abstract
In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.
Highlights
Wind power generation modeling has been a popular research topic in recent years since the installed wind generation capacities have been increasing rapidly in numerous countries
While this paper focuses on the simulation of wind generation, the presented full vector autoregressive (VAR) model can be considered for forecast simulations, where the use of a simplified time series model can have similar drawbacks, to those demonstrated in this paper for wind generation
This paper has presented an improved VAR model based methodology to model wind power generation in multiple new wind power plants (WPPs) locations
Summary
Wind power generation modeling has been a popular research topic in recent years since the installed wind generation capacities have been increasing rapidly in numerous countries. The proposed full VAR model can capture the correct shape of the spatial correlation structure between two locations with greater accuracy, which is important for the assessment of the aggregated power generation of multiple WPPs. In addition, the proposed VAR model can be estimated using only wind speed measurement data, which is not the case with many previously published approaches, such as in References [12,16]. We show that the autocorrelation of a sum of multiple time series depends on the cross-correlations between the individual time series It is crucial to be able to model the cross-correlations between individual non-measured WPP locations as accurately as possible in order to capture the correct behavior of the ACF of the aggregated time series for the WPP locations
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