Examining Essential Requirements for a Smart Grid Evaluation Framework Tailored for low Resource Settings
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Significant research has been done regarding frameworks for evaluation of smart grids in a bid to assess their efficiency, reliability, and impact. However, the frameworks are mainly targeting developed grids, adapting these frameworks to low-resource settings present significant challenges due to limited infrastructure, constrained budgets, inadequate technical expertise, regulatory gaps, and unreliable power supply. Albait the automation of the said evaluation frameworks has also not been given enough attension. This study employs a combined User-Centered Design (UCD) and Quality Function Deployment (QFD) approach to examine in detail and identify, prioritize, and translate user requirements into technical specifications for designing an automated tailor-made smart evaluation framework tailored for resource-constrained environments. A mixed-method research design, including literature reviews, interviews, and surveys, was used to gather responses from smart grid users in Uganda and Kenya. According to the findings ease of integration scored (15.2%), localization scored (15.0%), and environmental adaptability returned (14.2%) and these turned out to be the most important technical requirements. This meant that there is need for automated smart grid evaluation frameworks that seamlessly integrate into existing grid and at the same time adapt to the local contexts. Additionally, modularity scored 9.2%, offline capabilities returned 8.8%, and resource efficiency scored 8.4%. Users emphasized these requirements to ensure ease of growth and resilience in environments with poor connectivity and power stability. User requirements such as reliable data acquisition, resilience to intermittent internet connectivity, power outage readiness, and big data management were notably preffered. The study further indicated that the users focus was mainly on energy efficiency metrics, resource conservation, and grid infrastructure robustness. On the other hand, regulatory compliance was recognized but rather ranked lower in importance. This study provides a data-driven foundation for developing automated scalable, adaptive, and locally relevant smart grid evaluation frameworks for low-resource settings, ensuring sustainability, accessibility, and operational resilience. Future work in this series will look at development of a prototype, its validation, and real-world implementation to enhance the practical impact of these findings.
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