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

- Article in a journal -

Area
Energy

Author(s)
Rebecca Kim , Fernando V. Lima

Published in
Optimal Control Applications and Methods

Year
2023

Abstract
Abstract 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. 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 \mathrmCO_2 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 \mathrmCO_2 captured under favorable market conditions.

AD Tools
ADiMat

BibTeX
@ARTICLE{
         Kim2023Nma,
       author = "Kim, Rebecca and Lima, Fernando V.",
       title = "Nonlinear multiobjective and dynamic real-time predictive optimization for optimal
         operation of baseload power plants under variable renewable energy",
       journal = "Optimal Control Applications and Methods",
       volume = "44",
       number = "2",
       pages = "798--829",
       year = "2023",
       keywords = "dynamic real-time optimization, energy systems, multi-objective optimization,
         nonlinear systems, optimization algorithms, variable renewable energy",
       doi = "10.1002/oca.2852",
       url = "https://doi.org/10.1002/oca.2852",
       eprint = "https://onlinelibrary.wiley.com/doi/pdf/10.1002/oca.2852",
       abstract = "Abstract 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. 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
         $\varepsilon$-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 $\mathrm{CO}_2$ 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 $\mathrm{CO}_2$
         captured under favorable market conditions."
,
       ad_area = "Energy",
       ad_tools = "ADiMat"}

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