Climate change drives policymakers to reduce emissions and enhance the integration of variable renewable energy sources (VRES) into the power system. Wind power is considered among the most beneficial VRES as it can generate cost-effectively carbon-free electricity but comes with inherent intermittency. Hydropower is a proposed solution among the research community to handle VRES output volatility and ensure balanced energy output to the electricity grid. This thesis addresses the problem by investigating the integration of intermittent wind power into a hydropower system cost-effectively. The research question "How does the integration of wind power affect the hydro operations and the cost of purchased electricity?" is answered within the Design Science Research framework by optimizing a subset of the Røldal-Suldal hydropower system in Norway's NO2 region. The cost-minimization model utilizes historical data from 2018 on water inflows, hourly electricity prices, hourly wind production, and hourly energy consumption for a smelter within the NO2 zone. To reduce the 8,760-time steps and computational concerns, the kmeans clustering algorithm is implemented to obtain four representative weeks. A multiperiod linear programming model is structured to assess the joint operation of wind and hydropower while ensuring a minimum energy production to satisfy the system's power demands. A benchmark scenario with no wind capacity is formed to serve as the basis for comparisons. Ten scenarios with 100-MW incremental steps of wind capacity are implemented. The minimized cost for the benchmark scenario is €104,981,312.34, with electricity purchases covering more than 75% of the energy demand and hydropower satisfying the remaining 25%. Adding 100 MW of wind capacity reduces costs by more than €2,000,000, restricting the purchased energy’s share by 1.49%, which is the equivalent increased share of wind power during each incremental step. A wind capacity of 1,000 MW leads to a 21.24% cost reduction. Hydropower production remains unaffected by the wind integration based on terminal values of reservoir level or turbined water volume. However, the distribution of hydropower production throughout the year changes after installing wind capacity enabling hydropower to utilize stored water optimally to minimize the costs of purchasing energy. A sensitivity analysis to assess the uncertainties tied with the model coefficients shows that increasing initial reservoir levels and adding 1,000 MW of wind capacity is the most influential factor in the optimization model.