Simulation and Optimization of Wind Farm Operations under Stochastic Conditions

This dissertation develops a new methodology and associated solution tools toachieve optimal operations and maintenance strategies for wind turbines, helpingreduce operational costs and enhance the marketability of wind generation. Theintegrated framework proposed includes two optimization models for enabling decisionsupport capability, and one discrete event-based simulation model that characterizesthe dynamic operations of wind power systems. The problems in the optimizationmodels are formulated as a partially observed Markov decision process to determinean optimal action based on a wind turbine's health status and the stochastic weatherconditions.The rst optimization model uses homogeneous parameters with an assumptionof stationary weather characteristics over the decision horizon. We derive a set ofclosed-form expressions for the optimal policy and explore the policy's monotonicity.The second model allows time-varying weather conditions and other practical aspects.Consequently, the resulting strategy are season-dependent. The model is solved usinga backward dynamic programming method. The bene ts of the optimal policy arehighlighted via a case study that is based upon eld data from the literature andindustry. We nd that the optimal policy provides options for cost-e ective actions,because it can be adapted to a variety of operating conditions.Our discrete event-based simulation model incorporates critical components, suchas a wind turbine degradation model, power generation model, wind speed model,and maintenance model. We provide practical insights gained by examining di erentmaintenance strategies. To the best of our knowledge, our simulation model is the rst discrete-event simulation model for wind farm operations.Last, we present the integration framework, which incorporates the optimizationresults in the simulation model. Preliminary results reveal that the integrated modelhas the potential to provide practical guidelines that can reduce the operation costsas well as enhance the marketability of wind energy.