Power Plant Operation Optimization Economic dispatch of combined cycle power plants

Abstract: As electricity production from renewable sources increases, higher flexibility is required by fossil fuel generation to cope with the inherent fluctuations of solar and wind power. This results in shorter operating cycles and steeper ramps for the turbines, and more uncertainty for the operators. This thesis work applies mathematical optimization and statistical learning to improve the economic dispatch of a combined cycle power plant composed by two separate blocks of two gas turbines and one steam turbine. The goal is to minimize the input fuel to the gas turbines while respecting a series of constraints related to the demand the plant faces, power generation limits etc. This is achieved through the creation of a mathematical model of the plant that regulates how the plant can operate. The model is then optimized to reduce fuel consumption at a minimum. Machine learning techniques have been applied to sensor data from the plant itself to realistically simulate the behavior of the turbines. Input-Output curves have been obtained for power and exhaust heat generation of all the turbines using ordinary least squares on monthly data with a ten minutes sampling rate. The model is cross-validated and proven statistically valid. The optimization problem is formulated through generalized disjunctive programming in the form of a mixed-integer linear problem (MILP) and solved using a branch-and-bound algorithm. The output of the model is a one-week dispatch, in fifteen minutes intervals, carried out for two months in total. Lower fuel consumption is achieved using the optimization model, with a weekly reduction of fuel consumed in the range of 2-4%. A sensitivity analysis and a correlation matrix are used to highlights the demand and the maximum available capacity as critical parameters. Results show that the most efficient machines (alternatively, the ones with highest available capacity) should be operated at maximum load while still striving for an efficient utilization of the exhaust gas.