Optimal Fuel Scheduling of Thermal Units Considering Fuel Delivery and Storage Constraints

Economic utilization of available fossil fuels for power generation has become an important issue for electric utilities. The optimal short-term fuel scheduling of the thermal units has been carried out by the particle swarm optimization, group search algorithm and the evolutionary programming method. In this paper, a nonlinear fuel scheduling problem is optimized under the convex over and under envelopes for nonlinear estimation (COUENNE) solver of the generalized algebraic mathematical modeling system (GAMS) to minimize the daily fuel cost and emission footprint of the thermal units considering the power balance constraint and the fuel delivery and storage limits. The lower and upper levels of the fuel delivered to each power plant, minimum and maximum volumes of fuel storage, power generation capacity, and the initial fuel storage of the thermal units are considered in simulations. Numerical results obtained from solving the optimal fuel scheduling strategy indicates that the COUENNE algorithm converges to more accurate and better operating scenarios for thermal power plants than other metaheuristic algorithms.

Keywords:

Economic fuel scheduling , convex over and under envelopes for nonlinear estimation (COUENNE) , generalized algebraic mathematical modeling system (GAMS).