A P-graph model for multi-period optimization of sustainable energy systems. (10th September 2017)

• Record Type: Journal Article
• Authors: Aviso, Kathleen B.; Lee, Jui-Yuan; Dulatre, Jonathan Carlo; Madria, Venn Royce; Okusa, James; Tan, Raymond R.
• Is Part Of: Journal of cleaner production. Volume 161(2017)
• Page Start: 1338
• Page End: 1351
• Publication Date: 2017-09-10
• Subjects: Sustainable energy system -- Process integration -- Process network synthesis -- P-graph -- Hybrid renewable energy system -- Polygeneration system; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2017.06.044
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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.