Equipment size selection for optimizing polygeneration systems with reliability aspects

Charlette Cabral, Viknesh Andiappan, Kathleen Aviso and Raymond Tan

Energy, 2021, vol. 234, issue C

Abstract: Polygeneration systems produce multiple energy streams and other useful outputs at high efficiency and lower environmental impact than stand-alone systems. The interdependency among process units can however lead to cascading failures when equipment capacity fails to meet its load requirement. Since process units are commercially available in discrete sizes, the overall reliability of a polygeneration system is dependent on capacity-load ratio of the selected equipment. The conventional definition of reliability, which is based on whether equipment are operational or not, fails to account for the capacity-load ratio. To address this gap, this paper presents a mixed-integer nonlinear programming (MINLP) model for the synthesis of an optimal energy system that meets reliability specifications. The system reliability is optimized through selection of equipment capacity considering each process unit's intrinsic reliability profile relative to its capacity-load ratio. The rated capacity of the selected equipment size should exceed the expected load of each process unit by a margin that gives the required reliability. The model is applied to a polygeneration system case study to demonstrate its capabilities. The results emphasize the tradeoff between the total annualized cost and the system reliability.

Keywords: Polygeneration; Equipment size selection; Energy system; System reliability; Capacity-load ratio; Input-output model (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221015504
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:234:y:2021:i:c:s0360544221015504

DOI: 10.1016/j.energy.2021.121302

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().