(248aa) Real-Time Dynamic Efficiency Optimization of Coal-Fired Steam Power Plants

Authors: 
Bollas, G. M., University of Connecticut
Chen, C., University of Connecticut
Physics-based dynamic models of power plants play a substantial role in the design of system configuration, development of control strategies, and optimization of system operation. Due to the seasonal and daily fluctuations of the electricity demand, the plants are subject to frequent load changes and partial shutdowns.1 Extensive research has been performed on dynamic power plant models, with focus on dynamic simulation,2,3 real time optimization,4,5 start-up optimization,5 and model predictive control.6,7 In this work, coal-fired power plant configurations are studied, because coal is a more reliable energy source with attractive pricing than other resources.8

This work presents a dynamic model of a 600 MW subcritical coal-fired steam power plant and the real-time optimization for power plant efficiency. In this presentation, the steps for the model development and steady-state validation of an integrated steam cycle-boiler model are discussed. The model is validated against steady-state data from a reference subcritical-pressure power plant.9 Conventional control designs are successfully incorporated in the system model. Transient analyses of the response of the power plant to varying the coal loads show that the model provides a robust test-bed for dynamically changing power demand. The optimization capabilities of the complementary tool chain are demonstrated in case studies of nominal efficiency optimization of the integrated plant with respect to admissible plant inputs. The objective of real-time power plant optimization is to maximize the efficiency of power plants, operating in a transient fashion. This is done by calculating optimum time-varying input trajectories, which satisfy operability and safety constraints during the transition between steady states.

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. 1054718.

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