The power dispatching of a solid oxide fuel cell (SOFC)/gas turbine (GT)-based cogeneration system, which is a parallel combination of natural gas-to-power and coal-to-power units, is investigated. To address the economic dispatch (ED) problem, the AI-based forecasting model such as a Gaussian process regression (GPR) and an integration of GPR and neural networks (NN) models is employed to predict natural gas/coal prices and load demand. The economic dispatch (ED) optimization algorithm for minimizing total operating costs of the cogeneration system is employed to determine the monthly inlet flowrates of natural gas and coal. Since the forecasting errors for purchasing natural gas/coal are inevitable and the high carbon tax is needed to meet the 2015 Paris Agreement, carbon tax with US$60/ton CO2 and ±10% forecasting errors of purchasing monthly fuel prices are taken into consideration. The flexible fuel purchasing strategies (FFPS) with 1024 approaches are aided to improve the power dispatch performance. It is validated that the worst approach of FFPS ensures the total profit improvement (TPI) with 0.30% at least and the best approach of FFPS increases the TPI to 7.33%.
|Journal||Journal of the Taiwan Institute of Chemical Engineers|
|Publication status||Published - 2022 Jan|
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)