This thesis explores how integrating solar power and battery storage into an existing wind farm can increase profitability and operational flexibility, effectively transforming it into a hybrid park. An optimization-based model was developed to evaluate the techno-economic performance of various hybrid configurations over the years 2023 and 2024. The model consists of three compo- nents: a power prediction module using Quantile Regression Forests (QRF) trained on historical weather forecasts and production data, a market participation optimization that places bids on the day-ahead electricity market and the mFRR down ancillary service market, and finally a financial evaluation module that calculates key investment metrics, including internal rate of return (IRR).

Over 8,000 hybrid park configurations were simulated, varying in solar capacity, battery size, grid capacity, and market participation. The results show that solar power can lead to an IRR of around 7%, small batteries are generally favored, and larger batteries can be viable when combined with high solar capacity. Participation in the mFRR down market consistently improves profitability. Grid expansion beyond the existing connection is rarely economically justified due to high costs and limited gains from reduced curtailment.

The study concludes that hybrid parks are a promising solution for enhancing the value of re- newable energy assets. Investment outcomes are most sensitive to capital expenditure (CAPEX) and day-ahead electricity prices. While the cannibalization effect poses a challenge to renewable investments, hybrid parks offer resilience by combining complementary technologies. Future re- search should investigate additional ancillary service markets and how varying levels of installed wind capacity affect the overall profitability of hybrid parks.