A P-graph model for multi-period optimization of sustainable energy systems

Deployment of efficient, integrated energy systems can make a substantial contribution to reduction in greenhouse gas emissions. Such systems are suitable for the provision of distributed energy supply in remote areas, and offer the advantages of compactness and operational flexibility. Also, the si...

Full description

Saved in:
Bibliographic Details
Main Authors: Aviso, Kathleen B., Lee, Jui Yuan, Dulatre, Jonathan Carlo, Madria, Venn Royce, Okusa, James, Tan, Raymond Girard R.
Format: text
Published: Animo Repository 2017
Subjects:
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/3470
https://animorepository.dlsu.edu.ph/context/faculty_research/article/4472/type/native/viewcontent/j.jclepro.2017.06.044
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: De La Salle University
Description
Summary:Deployment of efficient, integrated energy systems can make a substantial contribution to reduction in greenhouse gas emissions. Such systems are suitable for the provision of distributed energy supply in remote areas, and offer the advantages of compactness and operational flexibility. Also, the simultaneous production of multiple products provides the opportunity for Process Integration, thus leading to improved fuel efficiency and reduced carbon emissions. Process Systems Engineering methods can be applied for the synthesis of such sustainable energy systems. For example, process graph (or P-graph) models have previously been developed for synthesizing single-period polygeneration systems based on the Process Network Synthesis framework. In this work, a P-graph model for multi-period optimization of sustainable energy systems is developed. The model is able to synthesize flexible systems that can cope with changes in the availability of raw material supply and variations in product demand. In addition, the P-graph model is also capable of generating near-optimal solutions, which provide insights that may be significant to decision-makers, such as structural features that are common to a range of good solutions. Two case studies are presented to demonstrate the approach developed in this work. © 2017 Elsevier Ltd