(687b) Large-Scale Dynamic and Multi-Objective Optimization for Power Plant Cycling
AIChE Annual Meeting
2020 Virtual AIChE Annual Meeting
Computing and Systems Technology Division
Optimization-based Estimation and Control
Wednesday, November 18, 2020 - 8:15am to 8:30am
In particular, a framework comprised of a Tchebycheff-based multi-objective and a hybrid of a Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) dynamic real-time optimization was previously introduced for a carbon capture subsystem from a supercritical coal-fired (SCPC) power plant. Due to the promising performance results for a low-dimensional case, the extension of the framework to plant-wide optimization was initiated. Such plant-wide optimization had 10 decision variables per time step, including the slack variables, which leaded to a total number of decision variables up to 300 per time horizon. An additional customized Interior-Point-based (IP) step was designed to assess trajectory optimality of the plant-wide optimization. With that additional step, the PSO-SQP algorithm was used to generate an initial feasible trajectory, while the IP step was employed to search for trajectory optimality. Results indicated that the PSO-SQP component performed slower than real-time, while the IP component performed faster than real-time.
Based on the previous implementation results, in this presentation, the developed optimization framework is extended to address large-scale dynamic multi-objective optimization challenges for power plant cycling. The system dimensionality, the imposed load-following demand, and the computational time for real-time implementation are the main challenges for this framework. Assessments of subsections of the optimization algorithm are conducted to analyze large-scale suitability of the current methods as well as other IP algorithms to improve computational efficiency. Parallelization of the PSO component is also performed considering 4 and 64 cores clusters. The optimization is focused on economic and environmental objectives, considering overall efficiency and demand load as constraints. Electricity price market, renewable intermittency profiles, and policies are included as forcing functions/constraints to determine the optimal output trajectories. The obtained optimal dynamic trajectories for power generation can be ultimately sent to lower-level advanced model-based controllers.
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