With a given power demand pattern supplied by a set of pulverized fuel coal-fired (PC) power generation units at various locations, different power dispatching solutions will result in different fuel consumptions, CO2 emissions, and power generation costs. This is due to the performance differences of their shut-down and start-up processes as well as those under the operational conditions, and to fuel prices differences between different power stations. In this paper, a methodology characterized with a multi-objective optimization approach based on a fast evolutionary algorithm is employed to optimize the daily total power generation operating cost and the daily total CO2 emissions. The shut down and start-up processes are divided into 7 sub-operations: load-decreasing, shutdown, boiler ignition preparation, ignition-warming up, connecting to grid, load-increasing and stabilization process, according to their characteristics in order to calculate the fuel consumptions, the CO2 emissions and the cost. Available data have been used to derive the models that characterize the emission and cost performances of typical PC units as well as their rating and partial load performances [1]. From the results of the multi-objective optimization, the so called Pareto Optimal Frontiers (POFs) are used to evaluate the effect of CO2 tax on the optimal power dispatching solutions. The influence of SO2 tax on the CO2 abatement marginal cost is also analyzed.

You do not currently have access to this content.