Nonlinear multiobjective and dynamic real-time predictive optimization for optimal operation of baseload power plants under variable renewable energy

Journal Article · 18 January 2022 · Optimal Control Applications and Methods

DOI: https://doi.org/10.1002/oca.2852 · OSTI ID:2418475

Kim, Rebecca  ^[1]; Lima, Fernando V.  ^[2]

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1. West Virginia Univ., Morgantown, WV (United States); OSTI
2. West Virginia Univ., Morgantown, WV (United States)

Considering the increase of disruptive variable renewable energy penetration into the power grid, this article focuses on the investigation of a multiobjective and dynamic real-time optimization framework to address the cycling of large-scale power plants under renewable penetration. In this framework, a parallelized particle swarm optimization step is first performed to generate feasible initial points. Then, a multiobjective and dynamic real-time optimization formulation generates optimal trajectories. Further, the benefit of predictive capability is investigated for the dynamic component, which introduces the novel nonlinear multiobjective and dynamic real-time predictive optimization approach. Two multiobjective formulations to obtain Pareto front optimal in real time are explored: the modified Tchebycheff-based weighted metric and ϵ-constraint methods. Economic and environmental objectives are considered in this study. A novel topical discussion on the intersection of dynamic real-time optimization with model predictive control is also presented. The developed framework is successfully applied to a baseload coal-fired power plant with postcombustion CO₂ capture. Results indicate that the approach can be deployed for a large-scale system if automatic differentiation, model reduction, and parallelization are adopted to improve computational tractability, with computational improvement up to 120-folds after performing these steps. Finally, market and carbon policies showed an impact on the optimal compromise between the objectives with an additional 63 ton of CO₂ captured under favorable market conditions.

• Research Organization: West Virginia Univ., Morgantown, WV (United States)
• Sponsoring Organization: USDOE Office of Policy (OP); USDOE Office of International Affairs (IA)
• Grant/Contract Number: PI0000017
• OSTI ID: 2418475
• Journal Information: Optimal Control Applications and Methods, Journal Name: Optimal Control Applications and Methods Journal Issue: 2 Vol. 44; ISSN 0143-2087
• Publisher: WileyCopyright Statement
• Country of Publication: United States
• Language: English