Electrification Planning for Healthcare Facilities in Low-Income Countries, Application of a Portfolio-Level, Multi Criteria Decision-Making Approach
This study presents a multi-platform analysis for accelerating the deployment of distributed renewable energy (DRE) systems for the electrification of healthcare facilities (HCFs) in low-income regions. While existing tools capture national and regional scale planning for DRE deployment in HCFs, the...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/a8f996584262482e850812787ca22e7b |
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| Sumario: | This study presents a multi-platform analysis for accelerating the deployment of distributed renewable energy (DRE) systems for the electrification of healthcare facilities (HCFs) in low-income regions. While existing tools capture national and regional scale planning for DRE deployment in HCFs, there are limited tools for facility level energy needs and no existing data-driven approach for systematic decision-making and resource allocation across a portfolio of HCFs. We address this gap by utilizing decentralized data collection, and multi-criteria decision-making to evaluate each HCF against a set of weighted decision criteria. We applied the approach presented in this research in a case study across 56 HCF in Uganda. Results present current and future energy needs for each individual clinic and the prioritization of HCFs for allocation of resources for DRE deployment. Additionally, results provide insight for best practices for reliability of services that are specific to each HCF. For example, failures in the existing solar photovoltaic (PV) systems are approximately up to 60% due to a lack of proper operation and management (O&M) strategy, and 40% is attributable to improper system design and installation. Thus, this study enables decision-makers to better understand the electrification needs of different HCFs, prioritize DRE deployment, financial investments, cost-effective procurement, and long-term O&M. |
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