Efficient Design under Uncertainty of Renewable Power Generation Systems Using Partitioning and Regression in the Course of Optimization

Renewable power generation systems are significantly affected by uncertainty due to intense variability often observed in energy sources. Uncertainty should be considered during design to enable optimum performance within constantly changing conditions. However, the resulting computational complexity and effort is high, especially in view of flowsheets integrating multiple subsystems. To address this challenge, the presented work proposes the partitioning of the space representing uncertain realizations to facilitate the development and continuous update of a surrogate model in the course of optimization. A wide exploration of this strategy reveals and addresses important issues in the implementation of the partitioning and model regression layers. Formal statistical associations are examined regarding the beneficial implications of partitioning to computational efficiency and surrogate model development. The proposed strategy is presented as part of a Simulated Annealing algorithm. This is tested in terms of computational efficiency and solution robustness against an adaptation of Stochastic Annealing, which addresses computational intensity through a different approach while depending entirely on a full system model. Results are illustrated through numerical examples and case studies on a stand-alone, hybrid system using renewable energy sources for power generation and storage

Toward Optimum Working Fluid Mixtures for Organic Rankine Cycles using Molecular Design and Sensitivity Analysis

This work presents a Computer-Aided Molecular Design (CAMD) method for the synthesis and selection of binary working fluid mixtures used in Organic Rankine Cycles (ORC). The method consists of two stages, initially seeking optimum mixture performance targets by designing molecules acting as the first component of the binaries. The identified targets are subsequently approached by designing the required matching molecules and selecting the optimum mixture concentration. A multiobjective formulation of the CAMD-optimization problem enables the identification of numerous mixture candidates, evaluated using an ORC process model in the course of molecular mixture design. A nonlinear sensitivity analysis method is employed to address model-related uncertainties in the mixture selection procedure. The proposed approach remains generic and independent of the considered mixture design application. Mixtures of high performance are identified simultaneously with their sensitivity characteristics regardless of the employed property prediction method