Decarbonisation pathways for industrial clusters through multi-energy systems
Bibliographic Data
| ID | 7397283 |
|---|---|
| Authors | Ugochukwu Ngwaka (0000-0002-6536-0811), Yousaf Ali Khalid, Janie Ling‐chin (0000-0002-3512-7779), John Counsell (0000-0001-9550-7362), Ruben Pinedo‐cuenca (0000-0002-2703-6631), Huda Dawood (0000-0003-1032-7560), Andrew Smallbone (0000-0001-7843-8967), Nashwan Dawood (0000-0002-4873-7576), Anthony Paul Roskilly (0000-0002-9356-3157) |
| Year | 2025 |
| Volume | 9 |
| Pages | 100656-100656 |
| Publication date | 2025-05-09 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Futures (JOURNAL) |
| Journal identifiers | ISSN: 2666-1888 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.sftr.2025.100656 |
| OpenAlex | W4410246472 |
| Language | EN |
| References cited | 26 |
Decarbonising industrial clusters will lay the foundation for decarbonisation in non-cluster-based industries whilst aligning with carbon-neutral targets. Nonetheless, the technical and economic viability appraisal for appropriate decarbonisation technologies within these clusters demands detailed scenario planning and utilising digital tools to simulate proposed pathways. This study aims to develop a dynamic simulation tool to facilitate the planning and evaluation of net-zero decarbonisation routes for industrial clusters. Given the complex and nonlinear interconnections among systems within a multi-energy cluster, this study extends the dynamic multi-vector methodology to multi-energy system clusters, representing variables as nodes and converting them into transfer functions for system integration. A multi-energy industrial cluster case study is analysed to determine how various decarbonisation technologies impact energy efficiency, emissions, and costs in an industrial cluster using a private energy network. The scenario-based analysis revealed that electrified heating significantly influenced performance indicators, primarily because of the cluster's high heat-to-electricity demand ratio. Integrating renewables and electrified heating reduced energy costs by 87 %, emissions by 49 %, energy usage by 44 %, and increased energy efficiency by 81 % compared to the baseline case. Due to the unique characteristics of industrial clusters, individual clusters need to assess their specific requirements and choose suitable decarbonisation pathways. Moreover, the tool will assist industrial clusters in developing a well-informed decarbonisation roadmap
Distributed and Parallel Computing Systems · Environmental Science · Hybrid Renewable Energy Systems · Integrated Energy Systems Optimization
| Highly cited | No |
|---|