In the past, the economic viability of biomass cogeneration plants was often dependent on green electricity subsidies, but these are increasingly being phased out, as the example of Steyr shows. This master's thesis therefore examines the implementation and advantages of an economic optimization of a biomass cogeneration plant in order to increase efficiency and profitability in the face of volatile electricity prices and dynamic heat loads. By developing a dynamic optimization model, electricity production is better adapted to changing market conditions. To reach this goal, a detailed analysis of the theoretical principles of electricity trading and biomass cogeneration plants was first carried out. The particular focus here was on the difference between long-term and short-term electricity trading and the specific challenges of the day-ahead market and intraday trading. In the empirical part, historical temperature data from St. Pölten and Steyr were compared in order to investigate their relevance for heat load forecasts. This led to the development of an adapted heat load forecast that takes into account the specific climatic conditions in Steyr. In addition, extensive data analyses and an optimization with MATLAB® were carried out to optimize the operation of the steam turbine of the CHP plant. The results show that by implementing an optimized operating model, the cumulative contribution margin of electricity sales could be significantly increased in 2023, which underlines the importance of flexibly adapting energy generation to market conditions. The work illustrates that improved forecasting models not only offer economic advantages, but also environmental ones, if fuel use becomes more efficient and the planning of maintenance work and shutdowns can be improved at the same time.