| Titre : | Decomposition based stochastic programming approach for polygeneration energy systems design under uncertainty (2010) |
| Auteurs : | Pei Liu, Auteur ; Efstratios N. Pistikopoulos, Auteur ; Li, Zheng, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Industrial & engineering chemistry research (Vol. 49 N° 7, Avril 2010) |
| Article en page(s) : | pp. 3295–3305 |
| Note générale : | Industrial Chemistry |
| Langues : | Anglais |
| Tags : | Stochastic Programming Polygeneration Energy Systems under Uncertainty |
| Résumé : | Polygeneration, a multi-input multioutput energy conversion process which typically involves the coproduction of electricity and liquid synthetic fuels, is a promising technology which offers real potential toward the reduction of excessive energy consumption and consequent greenhouse gas emissions. The optimal design of such a complex and nonlinear process system under inevitable and unpredictable future uncertainty poses great challenges in terms of both modeling and corresponding solution strategies. In this paper, we propose a stochastic programming framework for the optimal design under uncertainty of polygeneration energy systems. On the basis of a detailed mixed-integer nonlinear programming (MINLP) model, proposed in our previous work, a two-stage stochastic programming problem is formulated, which is then converted into a large-scale multiperiod MINLP problem by employing cubature based integration and sampling techniques. A decomposition algorithm is utilized for the efficient solution of the multiperiod problem, which involves iterations between a set of nonlinear subproblems of much smaller size and a master mixed-integer linear programming problem. A case study is then presented, where detailed computational results and comparisons between optimal designs obtained for both the stochastic and deterministic cases are shown. |
| ISSN : | 0888-5885 |
| En ligne : | http://pubs.acs.org/doi/abs/10.1021/ie901490g |
