466928 Real-Time Dynamic Efficiency Optimization of Coal-Fired Steam Power Plants

Monday, November 14, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Chen Chen and George M. Bollas, Chemical & Biomolecular Engineering, University of Connecticut, Storrs Mansfield, CT

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.


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


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3. Chen, C., Han, L. & Bollas, G. M. Dynamic simulation of fixed bed chemical looping combustion reactors integrated in combined cycle power plants. Energy Technol. (accepted) (2016).

4. Åkesson, J., Årzén, K.-E., Gäfvert, M., Bergdahl, T. & Tummescheit, H. Modeling and optimization with Optimica and JModelica.org—Languages and tools for solving large-scale dynamic optimization problems. Comput. Chem. Eng. 34, 1737–1749 (2010).

5. Lind, A., Sällberga, E. & Velutb, S. Start-up Optimization of a Combined Cycle Power Plant. Model. 2012 Conf. Proc. (2012). at <http://www.control.lth.se/documents/2012/5900_pop.pdf>

6. Franke, R., Babji, B. S., Antoine, M. & Isaksson, A. Model-based online applications in the ABB Dynamic Optimization framework. in Model. 2008 279–285 (2008).

7. Johnsson, A. Nonlinear Model Predictive Control for Combined Cycle Power Plants. (2013).

8. Rubin, E. S., Chen, C. & Rao, A. B. Cost and performance of fossil fuel power plants with CO2 capture and storage. Energy Policy 35, 4444–4454 (2007).

9. Singer, J. G. Combustion Fossil Power: A Reference Book on Fuel Burning and Steam Generation. (Combustion Engineering, 1991).

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See more of this Session: Interactive Session: Systems and Process Design
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