Analysis and Forecasting of Utility-Scale Hybrid Wind and PV Power Parks

Abstract: The increasing share of wind and photovoltaic (PV) power in the electricity generation mix pose challenges in power system management due to their non-dispatchable and intermittent nature. Co-locating wind and PV parks, forming utility-scale hybrid power parks (HPPs), means that the sources can share grid connection, land, permitting procedures as well as operation and maintenance work. On top of this, the combined power output is generally smoothened due to the anti-correlated characteristics of the respective resources.This licentiate thesis contributes to the state-of-the-art and progress the knowledge of co-located wind and PV parks, where the total number of studies on HPPs are limited compared to the respective fields. According to the results, the power output of co-located wind and PV parks are generally anti-correlated for all studied time scales (seasonal, mid-term, synoptic, diurnal and hourly resolution). The useful anti-correlation is found on the seasonal and diurnal time scale where wind turbine sites are likely to be more anti-correlated than any randomly chosen site. On the synoptic time scale, the useful anti-correlation is consistent, although to a lesser extent.The smoothing effect as a result of co-location is also studied in terms of probabilistic forecasting, which corresponds to estimating the uncertainty of power production predictions by means of a probabilistic distribution. By forecasting aggregated time series of co-located wind and PV, the probabilistic forecasts can be improved which is explained by the aggregated time series being smoother and therefore more straightforward to predict. The value of improved forecasts could also be realized in the day-ahead market, where sharper and more reliable forecasts lower the regulation costs.

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