​​AI-Driven Maintenance Optimization for Minigrid​

ccess to reliable and sustainable energy is essential for economic growth, especially in rural and off-grid communities. Solar-powered minigrids hold great potential to transform these areas by enabling productive activities like irrigation, cold storage, and small-scale manufacturing. However, challenges such as inefficient supply chains, high maintenance costs, and uncertain financial returns hinder their widespread adoption. ​ 

​Building on the UKRI-funded Smart SIP+ project in Bangladesh, which develops solar-powered pumps and cold storage systems to support agriculture, this PhD research aims to address key barriers to scaling solar-powered minigrids. While existing efforts have focused on technical aspects like energy production, this research will tackle critical gaps in predictive maintenance, and associated business model design. 

​The project will leverage AI and IoT to create predictive maintenance strategies, enhancing reliability and minimizing downtime. Additionally, innovative business models and alternative revenue streams will be explored to ensure financial viability and improve returns on investment. Inclusivity will be central to the framework, ensuring the benefits of minigrid systems reach marginalized and underrepresented community members. 

​Research Questions 

1. ​How can AI and IoT be used to optimize the supply chain for minigrid components, reducing costs and taking maintenance into account? 
2. ​What predictive maintenance strategies can be developed using AI to enhance the reliability and lifespan of minigrid systems? 
3. ​What business models associated to the maintenance can ensure the financial viability and ROI of solar-powered minigrids? 
4. ​How can inclusivity be integrated into the design, deployment, and operation of minigrid systems to ensure equitable benefits for all community members? 

Theoretical Contributions:  

• ​A novel AI-driven framework for integrating supply chain, maintenance optimization and business planning in minigrid systems.  
• ​New insights into the application of Industry 4.0/5.0 technologies in the renewable energy sector.  

Practical Contributions:  

• ​A scalable and cost-effective solution for improving the reliability and sustainability of minigrids.  
• ​A user-friendly digital platform for supply chain, maintenance management, business models and alternative revenue streams that enhance ROI and financial viability.  
• ​Inclusive design and deployment strategies that ensure equitable benefits for all community members. 

Entry Requirements: 

• ​To apply for our Engineering PhD Research Degree you should have, or expect to be awarded, a Master’s degree in a relevant subject area from a British or overseas university.  
• ​Exceptional candidates without a Master’s degree, but holding a first class or upper second class Bachelor’s degree in a relevant subject area, may be considered.  
• ​We also welcome enquiries from potential PhD researchers with appropriate levels of professional experience. 

​Essential Criteria:  

• ​Experience in conducting research at appropriate level, preferably in areas related to renewable energy, supply chain optimization or predictive maintenance.  
• ​Proficiency in AI and IoT technologies. Experience with digital twins and predictive maintenance strategies. Knowledge of solar energy systems and minigrids.  
• ​Excellent written and verbal communication skills. Ability to present research findings effectively to both technical and non-technical audiences.  
• ​Cultural sensitivity and the ability to work effectively in diverse cultural settings.  
• ​A strong motivation to contribute to the field of renewable energy and economic development. Commitment to completing a PhD and conducting high-quality research.