(635f) A Multistage Stochastic Optimization Approach to Power Plant Scheduling with Flexible Carbon Capture

Authors: 
Zantye, M. S., Texas A&M University
Arora, A., Texas A&M University
Hasan, M. M. F., Artie McFerrin Department of Chemical Engineering, Texas A&M University
CO2 emissions from fossil-fueled power plants can be effectively reduced by carbon capture using solvent-based absorption. However, the high energy intensity of solvent regeneration and CO2 compression stages reduces the net power output of the plant by 25-40% [1]. The dynamic variation of electricity prices offers an opportunity to reduce energy consumption in CO2 capture, thereby making the technology attractive for widespread deployment. The capture unit can be scheduled to operate flexibly, wherein more CO2 capture occurs during low electricity price periods and less CO2 is captured when the price is high. Majority of the previous literature in flexible carbon capture scheduling of power plants assumes perfect foreknowledge of electricity prices [2-4]. The competitive electricity markets exhibit high uncertainty owing to several market factors, which necessitates the incorporation of price uncertainty in optimal decision-making.

To this end, we develop a multi-stage stochastic programming algorithm based on reinforcement learning principles to determine an optimal hourly schedule of power production and carbon capture operations in uncertain electricity markets [5]. We consider a pulverized coal-fired power plant retrofitted with a carbon capture unit, which varies its load with dynamic price variation in the day-ahead market. A deterministic optimization formulation for maximizing profit with perfect foreknowledge of electricity prices is extended to a stochastic model to incorporate price uncertainty. Moreover, hourly electricity prices can assume a range of values, resulting in a large number of price scenarios. To reduce the computational complexity in the optimization framework, we develop low-complexity surrogate models for optimal action policy at each stage through data-driven modeling. The results represent the optimal hourly action policy as continuous functions of electricity price enabling power plants to take cost-effective decisions under uncertainty. These models are then used to determine total optimal profit for different real-time scenarios of electricity price. The mean profit obtained under uncertainty is within 25% of the benchmark, maximum profit with CO2 emissions being sufficiently below the threshold limit.

References:

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[2] Q. Chen, C. Kang, Q. Xia, and D. S. Kirschen, “Optimal flexible operation of a CO2 capture power plant in a combined energy and carbon emission market,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1602–1609, 2012.

[3] R. Khalilpour, “Multi-level investment planning and scheduling under electricity and carbon market dynamics: retrofit of a power plant with PCC (post-combustion carbon capture) processes,” Energy, vol. 64, pp. 172-186, 2014.

[4] S. M. Cohen, G. T. Rochelle, and M. E. Webber, “Optimizing post-combustion CO2 capture in response to volatile electricity prices,” Int. J. Greenh. Gas Control, vol. 8, pp. 180–195, 2012.

[5] M. S. Zantye, A. Arora, and M. M. F. Hasan, “Operational Scheduling of Power Plants with Flexible Carbon Capture under Uncertain Electricity Price: A Multistage Stochastic Optimization Approach,” Submitted, 2019.