A robust multi-objective statistical improvement approach to electric power portfolio selection

Engineering design;Robust design;Expected improvement;Multi objective optimization;Bayesian models;Electric power portfolios;Design of experiments

Murphy, Jonathan Rodgers ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Engineering design;    Robust design;    Expected improvement;    Multi objective optimization;    Bayesian models;    Electric power portfolios;    Design of experiments;   

Others  :  https://smartech.gatech.edu/bitstream/1853/45946/1/murphy_jonathan_r_201212_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Motivated by an electric power portfolio selection problem, a sampling method is developed for simulation-based robust design that builds on existing multi-objective statistical improvement methods. It uses a Bayesian surrogate model regressed on both design and noise variables, and makes use of methods for estimating epistemic model uncertainty in environmental uncertainty metrics. Regions of the design space are sequentially sampled in a manner that balances exploration of unknown designs and exploitation of designs thought to be Pareto optimal, while regions of the noise space are sampled to improve knowledge of the environmental uncertainty. A scalable test problem is used to compare the method with design of experiments (DoE) and crossed array methods, and the method is found to be more efficient for restrictive sample budgets. Experiments with the same test problem are used to study the sensitivity of the methods to numbers of design and noise variables. Lastly, the method is demonstrated on an electric power portfolio simulation code.