(767a) Multi-Objective and Dynamic Real-Time Optimization of Supercritical Coal-Fired Power Plant Cycling | AIChE

(767a) Multi-Objective and Dynamic Real-Time Optimization of Supercritical Coal-Fired Power Plant Cycling

Authors 

Kim, R. - Presenter, West Virginia University
Lima, F. V., West Virginia University
As the penetration of intermittent renewable energy sources, such as solar and wind, into the power grid increases[1], there is a growing need for baseload fossil-fueled power plants to cycle their load[2]. Cycling baseload power plants may introduce unexpected inefficiencies into the system that potentially incur increase in costs, emissions, and wear&tear, as the power plants are no longer operating at their optimal design point. To address these challenges, baseload power plant cycling became a significant topic of research[3-8] in process systems engineering. The reported contributions are focused mainly on single-objective[4-5], often economic, or small-scale systems[6-8].

A multi-objective and dynamic real-time optimization (MOO-DRTO) framework was successfully implemented[7-8] on a reduced-order model of a methylethylamine (MEA)-based carbon capture subsystem (CCS) from a supercritical coal-fired (SCPC) power plant[9]. The framework was focused on economic and environmental objectives and considered the cycling profile, electricity price market and cap&trade as forcing functions to determine the optimal output trajectories for different carbon capture policies. The multi-objective component was based on a Tchebycheff weighted metric method, which guarantees a Pareto-front optimal compromise. Such component also maintains the optimization independent of the hourly input of a decision maker and within a satisfactory calculating time for a load-following scenario of about 120 seconds/calculated hour. Moreover, the DRTO approach was based on a hybrid of a Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) for improved computational performance. Thus, the MOO-DRTO framework showed promising results for extension to a plant-wide optimization application.

Based on the previous implementation results for the CCS[7-8], a plant-wide SCPC dynamic optimization is performed considering an extension of the MOO-DRTO framework. The system dimensionality and the imposed load-following time constraint are the main challenges for the implementation of the optimization framework on the SCPC. In the proposed framework, the optimization algorithm is required to determine both the optimal trajectories for each objective by the DRTO and the optimal compromise between objectives by the MOO. Different optimization algorithms are assessed to improve computational efficiency, such as SQP in Matlab®, a modified SQP[10], and a customized interior point-based method. The first-principles Aspen® Dynamics model of the SCPC boiler and turbines are reduced for online optimization purposes using system identification techniques. The optimization is focused on the economic performance and environmental objectives and consider the overall plant efficiency and generated net power output. Considerations are also made regarding potential increase of wear&tear under the studied scenarios. Electricity price market and renewable intermittency profiles are included as forcing functions/constraints to determine the optimal output trajectories. Such optimal dynamic trajectories can ultimately be sent to lower-level advanced model-based controllers[3,6,10].

References

[1] REN21 Renewable Energy Policy Network for 21st Century. Available at: http://www.ren21.net/status-of-renewables/global-status-report/. Accessed on April 16, 2018.

[2] BENTEK ENERGY LLC. (2010). How Less Became More: Wind, Power and Unintended Consequences in the Colorado Energy Market. Prepared for Independent Petroleum Association of Mountain States.

[3] HE, X. & LIMA, F. V. (2019). Development and Implementation of Advanced Control Strategies for Power Plant Cycling with Carbon Capture. Computers & Chemical Engineering, 121, pp. 497–509.

[4] CHEN, C., & BOLLAS, G. M., (2018). Dynamic Optimization of Subcritical Steam Power Plant Under Time-Varying Power Load. Processes, 6(8), pp. 114–114. doi: 10.3390/pr6080114.

[5] BANKOLE, T., JONES, D., BHATTACHARYYA, D., TURTON, R. & ZITNEY, S. E. (2018). Optimal Scheduling and Its Lyapunov Stability for Advanced Load-Following Energy Plants with CO2 Capture. Computers and Chemical Engineering, 109, pp. 30–47. doi: 10.1016/j.compchemeng.2017.10.025

[6] HE, X, WANG, Y, BHATTACHARYYA, D, LIMA, F.V, & TURTON, R. (2018). Dynamic Modeling and Advanced Control of Post-Combustion CO2 Capture Plants. Chemical Engineering Research and Design. doi: 10.1016/j.cherd.2017.12.020.

[7] KIM, R., LIMA, F. V., (2018). Multi-objective and Dynamic Real-time Optimization of Postcombustion Carbon Capture Processes for Cycling Applications. AIChE Annual Meeting, Pittsburgh, PA.

[8] KIM, R., LIMA, F. V., (2019). Multi-objective and Dynamic Real-time Optimization for Postcombustion MEA-based CO2 Processes under Cycling Conditions. In preparation for publication.

[9] ZHANG, Q., TURTON, R., & BHATTACHARYYA, D. (2016). Development of Model and Model-Predictive Control of an MEA-Based Postcombustion CO2 Capture Process. Industrial and Engineering Chemistry Research. 55, 1292-1308.

[10] HE, X. & LIMA, F. V. (2019). A Modified SQP-based Model Predictive Control Algorithm: Application to Supercritical Coal-fired Power Plant Cycling. Submitted for publication.