This project is a collaboration between Goldwind, Technical University of Denmark (DTU) and Delft University of Technology (TUDelft), of which focusing in developing an optimization model that could determinate the optimal operation schedule for wind turbine under the uncertainty of the electricity price and limitation in life-time equivalent fatigue loads, for the purpose of improving and maximizing the revenue or cash flow for wind farm project. The project comprises of two main parts, including the design and validation of up- and de-rated operation modes, and the model development and optimization of the revenue. The design of the different operation modes provide varies rated power without sacrificing the life-time structural integrity, as such offer extra flexibility in wind turbine operation schedule according to the fluctuation in electricity price. Life-time equivalent loads would be calculated for the different operation modes design and a weighting factor, that indicate the rate of fatigue consumption relatively to the normal operation, would be computed and used in the optimization process. An optimization model is developed based on the turbine structural constraints as well as the financial profile. The optimization model processes the electricity price data and other wind turbine data, for example the fatigue consumption, to reproduce the optimal operation schedule and operation price thresholds that would yield utmost financial benefits. The optimizer has a proven performance improvement in revenue generation of 1.5% and 5% reduction in the project related interest payment under one of the case study under electricity price uncertainty. The model is effective in scheduling the turbine operation upon the varying electricity price. This optimization model is providing game changing insights and operation strategies that could be used throughout the turbine life-time under the ever changing electricity price of the energy market. And further drive down the cost of energy in the highly competitive market. By improvising continuous input to the optimizer including bending moment sensors inputs, the optimization process could reproduce a more accurate revenue improvement.