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The increasing share of intermittent and non-dispatchable power sources such as wind and solar photovoltaic (PV) power in the electrical energy generation mix pose operational challenges in the electric power system and corresponding markets. Co-locating wind and PV power parks, forming utility-scale hybrid power parks (HPPs), means that the power sources can share grid connection, land, permitting procedures as well as operation and maintenance work. According to the results of the thesis, the power output of co-located wind and PV power parks are generally negatively correlated, which results in a smoothed aggregated power output. The seasonal and diurnal time scales contribute the most to the negative correlation, where wind power parks are likely to be more negatively correlated than any randomly chosen site. The smoothing effect as a result of aggregation 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 co-located wind and PV power production, 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 is also realized in the day-ahead market, where sharper and more reliable probabilistic forecasts improve decision making by lowering imbalance costs. Furthermore, when trading energy from HPPs with storage, probabilistic forecasts reduce the energy throughput of the battery and is preferable over a deterministic model when the regulating prices are more difficult to forecast than the spot-prices, and when the battery energy capacity is low. Finally, a techno-economic simulation model to assess and forecast the potential to retrofit existing wind power parks with PV power parks was developed. Retrofitting means that a PV power park is connected behind the same point of interconnection to the electricity grid as an existing wind power park. Results show that the curtailment losses from retrofitting are small (max. 3.5% of PV power generation with over 100% added capacity) due to the complementary characteristics of the power sources. On top of this, the most influential resource-related site characteristics for a profitable investment from retrofitting are, in their order of importance; high PV power capacity factor, low wind power capacity factor, and strong negative correlation between PV and wind power production. By estimating these three variables, a forecast of the expected income from retrofitting at any given site can be estimated using a simple regression model.