The increasing variability in power plant load in response to a wildly uncertain electricity market and the need to to mitigate CO2
emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort and are the subjects of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model was used as a test-bed for dynamic simulations, in which the coal load was regulated to satisfy a varying power demand. Plant-level control regulated the plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant’s operation at varying loads was optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems were formulated to search for optimal time-varying input trajectories that satisfy operability and safety constraints during the transition between plant states. An improvement in time-averaged efficiency of up to 1.8% points was shown to be feasible with corresponding savings in coal consumption of 184.8 tons/day and a carbon footprint decrease of 0.035 kg/kWh.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited