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